http://compneurosci.com/wiki/api.php?action=feedcontributions&feedformat=atom&user=Gunnar 2026-05-09T13:05:53Z User contributions MediaWiki 1.41.5 http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1863 2026-04-09T16:20:44Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> ----<br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> ----<br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [https://github.com GitHub] and create a new repository:<br /> <br /> * Initialize it with a README and a ''.gitignore'' (select the Python template).<br /> * Choose a licence. For open science, we recommend ''MIT'' for code-heavy projects or ''CC-BY 4.0'' for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash &lt;br&gt;<br /> cd ~/projects &lt;br&gt;<br /> git clone https://github.com/YOUR-USERNAME/project-name.git &lt;br&gt;<br /> cd project-name<br /> &lt;/code&gt;&lt;/blockquote&gt;<br /> <br /> ----<br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> project-name/&lt;br&gt;<br /> ├── code/ # reusable Python modules (your installable package)&lt;br&gt;<br /> │ └── __init__.py&lt;br&gt;<br /> ├── data/&lt;br&gt;<br /> │ ├── raw/ # raw, untouched data (never modify these files)&lt;br&gt;<br /> │ └── processed/ # cleaned or transformed data&lt;br&gt;<br /> ├── docs/&lt;br&gt;<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)&lt;br&gt;<br /> │ └── protocols/ # experimental protocols and SOPs&lt;br&gt;<br /> ├── results/&lt;br&gt;<br /> │ ├── figures/ # publication-quality figures&lt;br&gt;<br /> │ └── intermediate/ # checkpoints, intermediate outputs&lt;br&gt;<br /> ├── scripts/ # analysis scripts and notebooks&lt;br&gt;<br /> ├── outputs/&lt;br&gt;<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)&lt;br&gt;<br /> │ └── posters/ # poster source files&lt;br&gt;<br /> ├── tests/ # unit tests for your code&lt;br&gt;<br /> ├── environment.yml # conda environment specification&lt;br&gt;<br /> ├── setup.py # makes your code pip-installable&lt;br&gt;<br /> └── README.md # project overview and instructions<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> You can create this in one command:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests&lt;br&gt;<br /> touch code/__init__.py<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> A few things to note about this structure. The ''code/'' directory holds reusable Python modules that you import (equivalent to ''src/'' in the Good Research Code Handbook). The ''scripts/'' directory holds analysis scripts and Jupyter notebooks that call functions from ''code/''. The ''data/raw/'' directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in ''data/processed/''.<br /> <br /> ----<br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda create --name project-name python=3.11&lt;br&gt;<br /> conda activate project-name&lt;br&gt;<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Export the environment specification and commit it:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda env export &gt; environment.yml&lt;br&gt;<br /> git add environment.yml&lt;br&gt;<br /> git commit -m &quot;Add conda environment specification&quot;<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Keep ''environment.yml'' up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda env create --file environment.yml<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [https://goodresearch.dev/setup Good Research Code Handbook: Setup].<br /> <br /> ----<br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of ''sys.path'' hacks and makes your modules importable from anywhere in the project. Create a minimal ''setup.py'' in the project root:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> python&lt;br&gt;<br /> from setuptools import find_packages, setup&lt;br&gt;<br /> &lt;br&gt;<br /> setup(&lt;br&gt;<br /> name='project-name',&lt;br&gt;<br /> packages=find_packages(),&lt;br&gt;<br /> )<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Then install your package in editable mode:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> pip install -e .<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Now you can ''import code.my_module' from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [https://goodresearch.dev/setup#install-a-project-package Good Research Code Handbook: Setup] for a more detailed walkthrough.<br /> <br /> ----<br /> == Step 5: Configure ''.gitignore'' and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your ''.gitignore'':<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Data (tracked separately, see below)&lt;br&gt;<br /> data/&lt;br&gt;<br /> results/intermediate/&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Python&lt;br&gt;<br /> &lt;nowiki&gt;*&lt;/nowiki&gt;.egg-info/&lt;br&gt;<br /> __pycache__/&lt;br&gt;<br /> &lt;nowiki&gt;*&lt;/nowiki&gt;.pyc/&lt;br&gt;<br /> .ipynb_checkpoints/&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; OS files&lt;br&gt;<br /> .DS_Store&lt;br&gt;<br /> Thumbs.db&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Environment&lt;br&gt;<br /> .env<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> '''For large or sensitive data''', Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [https://www.datalad.org/ DataLad] (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [https://handbook.datalad.org/ DataLad Handbook] for a thorough tutorial.<br /> * [https://dvc.org/ DVC (Data Version Control)]: a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove ''data/'' from ''.gitignore''.<br /> <br /> ----<br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [https://bids.neuroimaging.io/ Brain Imaging Data Structure (BIDS)]. For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv&lt;br&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a ''data/README.md'' that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> ----<br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the ''docs/labnotebook/'' directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md&lt;br&gt;<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [https://obsidian.md/ Obsidian] or [https://logseq.com/ Logseq] for richer note-taking and link them to your repository.<br /> <br /> ----<br /> == Step 8: Write a Good README ==<br /> <br /> Your ''README.md'' is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * '''Project title and one-paragraph summary''' of the scientific question.<br /> * '''How to set up the environment''' (''conda env create --file environment.yml'').<br /> * '''How to reproduce the results''' (which scripts to run, in what order).<br /> * '''Directory structure''' (copy and paste the tree from Step 2 and annotate it).<br /> * '''Data availability''' (where the data live if not in the repository).<br /> * '''Authors and contact information'''.<br /> * '''Licence'''.<br /> <br /> ----<br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> git add scripts/01_preprocess.py&lt;br&gt;<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;&lt;br&gt;<br /> git push<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> If you are not comfortable with the Git command line, the [https://code.visualstudio.com/docs/sourcecontrol/overview Git panel in VS Code] is an excellent GUI alternative.<br /> <br /> ----<br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. '''Make the GitHub repository public''' (or archive it on [https://zenodo.org/ Zenodo] for a citable DOI).<br /> 2. '''Deposit the data''' on a public repository such as [https://openneuro.org/ OpenNeuro] (for BIDS neuroimaging data), [https://osf.io/ OSF], [https://figshare.com/ Figshare], or [https://datadryad.org/ Dryad].<br /> 3. '''Link everything in the paper''': point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> ----<br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and ''.gitignore''<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create ''setup.py'' and ''pip install -e .''<br /> * [ ] Configure ''.gitignore'' (and DataLad/DVC if needed for large data)<br /> * [ ] Write a ''data/README.md'' documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft ''README.md'' with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> ----<br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [https://goodresearch.dev/ The Good Research Code Handbook]. The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [https://doi.org/10.1371/journal.pcbi.1005510 Good enough practices in scientific computing]. PLOS Computational Biology.<br /> * Gorgolewski, K. J. et al. (2016). [https://doi.org/10.1038/sdata201644 The Brain Imaging Data Structure]. Scientific Data.<br /> * Halchenko, Y. O. et al. (2021). [https://doi.org/10.21105/joss.03262 DataLad: distributed system for joint management of code, data, and their relationship]. Journal of Open Source Software.<br /> * The [https://handbook.datalad.org/ DataLad Handbook] for a comprehensive tutorial on data version control.<br /> * The [https://software-carpentry.org/lessons/ Software Carpentry] lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1862 2026-04-09T16:19:39Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [https://github.com GitHub] and create a new repository:<br /> <br /> * Initialize it with a README and a ''.gitignore'' (select the Python template).<br /> * Choose a licence. For open science, we recommend ''MIT'' for code-heavy projects or ''CC-BY 4.0'' for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash &lt;br&gt;<br /> cd ~/projects &lt;br&gt;<br /> git clone https://github.com/YOUR-USERNAME/project-name.git &lt;br&gt;<br /> cd project-name<br /> &lt;/code&gt;&lt;/blockquote&gt;<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> project-name/&lt;br&gt;<br /> ├── code/ # reusable Python modules (your installable package)&lt;br&gt;<br /> │ └── __init__.py&lt;br&gt;<br /> ├── data/&lt;br&gt;<br /> │ ├── raw/ # raw, untouched data (never modify these files)&lt;br&gt;<br /> │ └── processed/ # cleaned or transformed data&lt;br&gt;<br /> ├── docs/&lt;br&gt;<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)&lt;br&gt;<br /> │ └── protocols/ # experimental protocols and SOPs&lt;br&gt;<br /> ├── results/&lt;br&gt;<br /> │ ├── figures/ # publication-quality figures&lt;br&gt;<br /> │ └── intermediate/ # checkpoints, intermediate outputs&lt;br&gt;<br /> ├── scripts/ # analysis scripts and notebooks&lt;br&gt;<br /> ├── outputs/&lt;br&gt;<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)&lt;br&gt;<br /> │ └── posters/ # poster source files&lt;br&gt;<br /> ├── tests/ # unit tests for your code&lt;br&gt;<br /> ├── environment.yml # conda environment specification&lt;br&gt;<br /> ├── setup.py # makes your code pip-installable&lt;br&gt;<br /> └── README.md # project overview and instructions<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> You can create this in one command:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests&lt;br&gt;<br /> touch code/__init__.py<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> A few things to note about this structure. The ''code/'' directory holds reusable Python modules that you import (equivalent to ''src/'' in the Good Research Code Handbook). The ''scripts/'' directory holds analysis scripts and Jupyter notebooks that call functions from ''code/''. The ''data/raw/'' directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in ''data/processed/''.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda create --name project-name python=3.11&lt;br&gt;<br /> conda activate project-name&lt;br&gt;<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Export the environment specification and commit it:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda env export &gt; environment.yml&lt;br&gt;<br /> git add environment.yml&lt;br&gt;<br /> git commit -m &quot;Add conda environment specification&quot;<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Keep ''environment.yml'' up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda env create --file environment.yml<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [https://goodresearch.dev/setup Good Research Code Handbook: Setup].<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of ''sys.path'' hacks and makes your modules importable from anywhere in the project. Create a minimal ''setup.py'' in the project root:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> python&lt;br&gt;<br /> from setuptools import find_packages, setup&lt;br&gt;<br /> &lt;br&gt;<br /> setup(&lt;br&gt;<br /> name='project-name',&lt;br&gt;<br /> packages=find_packages(),&lt;br&gt;<br /> )<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Then install your package in editable mode:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> pip install -e .<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Now you can ''import code.my_module' from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [https://goodresearch.dev/setup#install-a-project-package Good Research Code Handbook: Setup] for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure ''.gitignore'' and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your ''.gitignore'':<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Data (tracked separately, see below)&lt;br&gt;<br /> data/&lt;br&gt;<br /> results/intermediate/&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Python&lt;br&gt;<br /> &lt;nowiki&gt;*&lt;/nowiki&gt;.egg-info/&lt;br&gt;<br /> __pycache__/&lt;br&gt;<br /> &lt;nowiki&gt;*&lt;/nowiki&gt;.pyc/&lt;br&gt;<br /> .ipynb_checkpoints/&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; OS files&lt;br&gt;<br /> .DS_Store&lt;br&gt;<br /> Thumbs.db&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Environment&lt;br&gt;<br /> .env<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> '''For large or sensitive data''', Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [https://www.datalad.org/ DataLad] (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [https://handbook.datalad.org/ DataLad Handbook] for a thorough tutorial.<br /> * [https://dvc.org/ DVC (Data Version Control)]: a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove ''data/'' from ''.gitignore''.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [https://bids.neuroimaging.io/ Brain Imaging Data Structure (BIDS)]. For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv&lt;br&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a ''data/README.md'' that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the ''docs/labnotebook/'' directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md&lt;br&gt;<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [https://obsidian.md/ Obsidian] or [https://logseq.com/ Logseq] for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your ''README.md'' is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * '''Project title and one-paragraph summary''' of the scientific question.<br /> * '''How to set up the environment''' (''conda env create --file environment.yml'').<br /> * '''How to reproduce the results''' (which scripts to run, in what order).<br /> * '''Directory structure''' (copy and paste the tree from Step 2 and annotate it).<br /> * '''Data availability''' (where the data live if not in the repository).<br /> * '''Authors and contact information'''.<br /> * '''Licence'''.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> git add scripts/01_preprocess.py&lt;br&gt;<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;&lt;br&gt;<br /> git push<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> If you are not comfortable with the Git command line, the [https://code.visualstudio.com/docs/sourcecontrol/overview Git panel in VS Code] is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. '''Make the GitHub repository public''' (or archive it on [https://zenodo.org/ Zenodo] for a citable DOI).<br /> 2. '''Deposit the data''' on a public repository such as [https://openneuro.org/ OpenNeuro] (for BIDS neuroimaging data), [https://osf.io/ OSF], [https://figshare.com/ Figshare], or [https://datadryad.org/ Dryad].<br /> 3. '''Link everything in the paper''': point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and ''.gitignore''<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create ''setup.py'' and ''pip install -e .''<br /> * [ ] Configure ''.gitignore'' (and DataLad/DVC if needed for large data)<br /> * [ ] Write a ''data/README.md'' documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft ''README.md'' with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [https://goodresearch.dev/ The Good Research Code Handbook]. The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [https://doi.org/10.1371/journal.pcbi.1005510 Good enough practices in scientific computing]. PLOS Computational Biology.<br /> * Gorgolewski, K. J. et al. (2016). [https://doi.org/10.1038/sdata201644 The Brain Imaging Data Structure]. Scientific Data.<br /> * Halchenko, Y. O. et al. (2021). [https://doi.org/10.21105/joss.03262 DataLad: distributed system for joint management of code, data, and their relationship]. Journal of Open Source Software.<br /> * The [https://handbook.datalad.org/ DataLad Handbook] for a comprehensive tutorial on data version control.<br /> * The [https://software-carpentry.org/lessons/ Software Carpentry] lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1861 2026-04-09T16:12:55Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [https://github.com GitHub] and create a new repository:<br /> <br /> * Initialize it with a README and a ''.gitignore'' (select the Python template).<br /> * Choose a licence. For open science, we recommend ''MIT'' for code-heavy projects or ''CC-BY 4.0'' for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash &lt;br&gt;<br /> cd ~/projects &lt;br&gt;<br /> git clone https://github.com/YOUR-USERNAME/project-name.git &lt;br&gt;<br /> cd project-name<br /> &lt;/code&gt;&lt;/blockquote&gt;<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> project-name/&lt;br&gt;<br /> ├── code/ # reusable Python modules (your installable package)&lt;br&gt;<br /> │ └── __init__.py&lt;br&gt;<br /> ├── data/&lt;br&gt;<br /> │ ├── raw/ # raw, untouched data (never modify these files)&lt;br&gt;<br /> │ └── processed/ # cleaned or transformed data&lt;br&gt;<br /> ├── docs/&lt;br&gt;<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)&lt;br&gt;<br /> │ └── protocols/ # experimental protocols and SOPs&lt;br&gt;<br /> ├── results/&lt;br&gt;<br /> │ ├── figures/ # publication-quality figures&lt;br&gt;<br /> │ └── intermediate/ # checkpoints, intermediate outputs&lt;br&gt;<br /> ├── scripts/ # analysis scripts and notebooks&lt;br&gt;<br /> ├── outputs/&lt;br&gt;<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)&lt;br&gt;<br /> │ └── posters/ # poster source files&lt;br&gt;<br /> ├── tests/ # unit tests for your code&lt;br&gt;<br /> ├── environment.yml # conda environment specification&lt;br&gt;<br /> ├── setup.py # makes your code pip-installable&lt;br&gt;<br /> └── README.md # project overview and instructions<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> You can create this in one command:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests&lt;br&gt;<br /> touch code/__init__.py<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> A few things to note about this structure. The ''code/'' directory holds reusable Python modules that you import (equivalent to ''src/'' in the Good Research Code Handbook). The ''scripts/'' directory holds analysis scripts and Jupyter notebooks that call functions from ''code/''. The ''data/raw/'' directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in ''data/processed/''.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda create --name project-name python=3.11&lt;br&gt;<br /> conda activate project-name&lt;br&gt;<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Export the environment specification and commit it:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda env export &gt; environment.yml&lt;br&gt;<br /> git add environment.yml&lt;br&gt;<br /> git commit -m &quot;Add conda environment specification&quot;<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Keep ''environment.yml'' up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda env create --file environment.yml<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [https://goodresearch.dev/setup Good Research Code Handbook: Setup].<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of ''sys.path'' hacks and makes your modules importable from anywhere in the project. Create a minimal ''setup.py'' in the project root:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> python&lt;br&gt;<br /> from setuptools import find_packages, setup&lt;br&gt;<br /> &lt;br&gt;<br /> setup(&lt;br&gt;<br /> name='project-name',&lt;br&gt;<br /> packages=find_packages(),&lt;br&gt;<br /> )<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Then install your package in editable mode:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> pip install -e .<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Now you can ''import code.my_module' from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [https://goodresearch.dev/setup#install-a-project-package Good Research Code Handbook: Setup] for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure ''.gitignore'' and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your ''.gitignore'':<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Data (tracked separately, see below)&lt;br&gt;<br /> data/&lt;br&gt;<br /> results/intermediate/&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Python&lt;br&gt;<br /> &lt;nowiki&gt;*&lt;/nowiki&gt;.egg-info/&lt;br&gt;<br /> __pycache__/&lt;br&gt;<br /> &lt;nowiki&gt;*&lt;/nowiki&gt;.pyc&lt;br&gt;<br /> .ipynb_checkpoints/&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; OS files&lt;br&gt;<br /> .DS_Store&lt;br&gt;<br /> Thumbs.db&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Environment&lt;br&gt;<br /> .env<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> '''For large or sensitive data''', Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [https://www.datalad.org/ DataLad] (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [https://handbook.datalad.org/ DataLad Handbook] for a thorough tutorial.<br /> * [https://dvc.org/ DVC (Data Version Control)]: a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove ''data/'' from ''.gitignore''.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [https://bids.neuroimaging.io/ Brain Imaging Data Structure (BIDS)]. For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv&lt;br&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md&lt;br&gt;<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> git add scripts/01_preprocess.py&lt;br&gt;<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;&lt;br&gt;<br /> git push<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1860 2026-04-09T16:11:41Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [https://github.com GitHub] and create a new repository:<br /> <br /> * Initialize it with a README and a ''.gitignore'' (select the Python template).<br /> * Choose a licence. For open science, we recommend ''MIT'' for code-heavy projects or ''CC-BY 4.0'' for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash &lt;br&gt;<br /> cd ~/projects &lt;br&gt;<br /> git clone https://github.com/YOUR-USERNAME/project-name.git &lt;br&gt;<br /> cd project-name<br /> &lt;/code&gt;&lt;/blockquote&gt;<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> project-name/&lt;br&gt;<br /> ├── code/ # reusable Python modules (your installable package)&lt;br&gt;<br /> │ └── __init__.py&lt;br&gt;<br /> ├── data/&lt;br&gt;<br /> │ ├── raw/ # raw, untouched data (never modify these files)&lt;br&gt;<br /> │ └── processed/ # cleaned or transformed data&lt;br&gt;<br /> ├── docs/&lt;br&gt;<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)&lt;br&gt;<br /> │ └── protocols/ # experimental protocols and SOPs&lt;br&gt;<br /> ├── results/&lt;br&gt;<br /> │ ├── figures/ # publication-quality figures&lt;br&gt;<br /> │ └── intermediate/ # checkpoints, intermediate outputs&lt;br&gt;<br /> ├── scripts/ # analysis scripts and notebooks&lt;br&gt;<br /> ├── outputs/&lt;br&gt;<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)&lt;br&gt;<br /> │ └── posters/ # poster source files&lt;br&gt;<br /> ├── tests/ # unit tests for your code&lt;br&gt;<br /> ├── environment.yml # conda environment specification&lt;br&gt;<br /> ├── setup.py # makes your code pip-installable&lt;br&gt;<br /> └── README.md # project overview and instructions<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> You can create this in one command:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests&lt;br&gt;<br /> touch code/__init__.py<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> A few things to note about this structure. The ''code/'' directory holds reusable Python modules that you import (equivalent to ''src/'' in the Good Research Code Handbook). The ''scripts/'' directory holds analysis scripts and Jupyter notebooks that call functions from ''code/''. The ''data/raw/'' directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in ''data/processed/''.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda create --name project-name python=3.11&lt;br&gt;<br /> conda activate project-name&lt;br&gt;<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Export the environment specification and commit it:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda env export &gt; environment.yml&lt;br&gt;<br /> git add environment.yml&lt;br&gt;<br /> git commit -m &quot;Add conda environment specification&quot;<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Keep ''environment.yml'' up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> conda env create --file environment.yml<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [https://goodresearch.dev/setup Good Research Code Handbook: Setup].<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of ''sys.path'' hacks and makes your modules importable from anywhere in the project. Create a minimal ''setup.py'' in the project root:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> python&lt;br&gt;<br /> from setuptools import find_packages, setup&lt;br&gt;<br /> &lt;br&gt;<br /> setup(&lt;br&gt;<br /> name='project-name',&lt;br&gt;<br /> packages=find_packages(),&lt;br&gt;<br /> )<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Then install your package in editable mode:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> pip install -e .<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Now you can ''import code.my_module' from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [https://goodresearch.dev/setup#install-a-project-package Good Research Code Handbook: Setup] for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure ''.gitignore'' and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your ''.gitignore'':<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Data (tracked separately, see below)&lt;br&gt;<br /> data/&lt;br&gt;<br /> results/intermediate/&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Python&lt;br&gt;<br /> *.egg-info/&lt;br&gt;<br /> __pycache__/&lt;br&gt;<br /> *.pyc&lt;br&gt;<br /> .ipynb_checkpoints/&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; OS files&lt;br&gt;<br /> .DS_Store&lt;br&gt;<br /> Thumbs.db&lt;br&gt;<br /> &lt;br&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Environment&lt;br&gt;<br /> .env<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> '''For large or sensitive data''', Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [https://www.datalad.org/ DataLad] (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [https://handbook.datalad.org/ DataLad Handbook] for a thorough tutorial.<br /> * [https://dvc.org/ DVC (Data Version Control)]: a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove ''data/'' from ''.gitignore''.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [https://bids.neuroimaging.io/ Brain Imaging Data Structure (BIDS)]. For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv&lt;br&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md&lt;br&gt;<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash&lt;br&gt;<br /> git add scripts/01_preprocess.py&lt;br&gt;<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;&lt;br&gt;<br /> git push<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1859 2026-04-09T16:09:23Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [https://github.com GitHub] and create a new repository:<br /> <br /> * Initialize it with a README and a ''.gitignore'' (select the Python template).<br /> * Choose a licence. For open science, we recommend ''MIT'' for code-heavy projects or ''CC-BY 4.0'' for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash &lt;br&gt;<br /> cd ~/projects &lt;br&gt;<br /> git clone https://github.com/YOUR-USERNAME/project-name.git &lt;br&gt;<br /> cd project-name<br /> &lt;/code&gt;&lt;/blockquote&gt;<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> You can create this in one command:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> A few things to note about this structure. The ''code/'' directory holds reusable Python modules that you import (equivalent to ''src/'' in the Good Research Code Handbook). The ''scripts/'' directory holds analysis scripts and Jupyter notebooks that call functions from ''code/''. The ''data/raw/'' directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in ''data/processed/''.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Export the environment specification and commit it:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Keep ''environment.yml'' up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> conda env create --file environment.yml<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [https://goodresearch.dev/setup Good Research Code Handbook: Setup].<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of ''sys.path'' hacks and makes your modules importable from anywhere in the project. Create a minimal ''setup.py'' in the project root:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Then install your package in editable mode:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> pip install -e .<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Now you can ''import code.my_module' from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [https://goodresearch.dev/setup#install-a-project-package Good Research Code Handbook: Setup] for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure ''.gitignore'' and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your ''.gitignore'':<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> &lt;nowiki&gt;#&lt;/nowiki&gt; OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> &lt;nowiki&gt;#&lt;/nowiki&gt; Environment<br /> .env<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> '''For large or sensitive data''', Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [https://www.datalad.org/ DataLad] (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [https://handbook.datalad.org/ DataLad Handbook] for a thorough tutorial.<br /> * [https://dvc.org/ DVC (Data Version Control)]: a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove ''data/'' from ''.gitignore''.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [https://bids.neuroimaging.io/ Brain Imaging Data Structure (BIDS)]. For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1858 2026-04-09T14:59:10Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [https://github.com GitHub] and create a new repository:<br /> <br /> * Initialize it with a README and a ''.gitignore'' (select the Python template).<br /> * Choose a licence. For open science, we recommend ''MIT'' for code-heavy projects or ''CC-BY 4.0'' for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash &lt;br&gt;<br /> cd ~/projects &lt;br&gt;<br /> git clone https://github.com/YOUR-USERNAME/project-name.git &lt;br&gt;<br /> cd project-name<br /> &lt;/code&gt;&lt;/blockquote&gt;<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> You can create this in one command:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Export the environment specification and commit it:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> conda env create --file environment.yml<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Then install your package in editable mode:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> pip install -e .<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure `.gitignore` and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> * [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> &lt;blockquote&gt;&lt;code&gt;<br /> bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> &lt;/blockquote&gt;&lt;/code&gt;<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1857 2026-04-09T14:55:42Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [https://github.com GitHub] and create a new repository:<br /> <br /> * Initialize it with a README and a ''.gitignore'' (select the Python template).<br /> * Choose a licence. For open science, we recommend ''MIT'' for code-heavy projects or ''CC-BY 4.0'' for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;bash &lt;br&gt;<br /> cd ~/projects &lt;br&gt;<br /> git clone https://github.com/YOUR-USERNAME/project-name.git &lt;br&gt;<br /> cd project-name<br /> &lt;/code&gt;&lt;/blockquote&gt;<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> ```<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> ```<br /> <br /> You can create this in one command:<br /> <br /> ```bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> ```<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> ```bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> ```<br /> <br /> Export the environment specification and commit it:<br /> <br /> ```bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> ```<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> ```bash<br /> conda env create --file environment.yml<br /> ```<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> ```python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> ```<br /> <br /> Then install your package in editable mode:<br /> <br /> ```bash<br /> pip install -e .<br /> ```<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure `.gitignore` and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> ```<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> ```<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> * [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> ```<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> ```<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> ```<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> ```<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> ```bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> ```<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1856 2026-04-09T14:54:07Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [https://github.com GitHub] and create a new repository:<br /> <br /> * Initialize it with a README and a ''.gitignore'' (select the Python template).<br /> * Choose a licence. For open science, we recommend ''MIT'' for code-heavy projects or ''CC-BY 4.0'' for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> &lt;blockquote&gt;&lt;code&gt;bash<br /> cd ~/projects<br /> git clone https://github.com/YOUR-USERNAME/project-name.git<br /> cd project-name<br /> &lt;/code&gt;&lt;/blockquote&gt;<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> ```<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> ```<br /> <br /> You can create this in one command:<br /> <br /> ```bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> ```<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> ```bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> ```<br /> <br /> Export the environment specification and commit it:<br /> <br /> ```bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> ```<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> ```bash<br /> conda env create --file environment.yml<br /> ```<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> ```python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> ```<br /> <br /> Then install your package in editable mode:<br /> <br /> ```bash<br /> pip install -e .<br /> ```<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure `.gitignore` and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> ```<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> ```<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> * [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> ```<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> ```<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> ```<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> ```<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> ```bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> ```<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1855 2026-04-09T14:52:10Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [https://github.com GitHub] and create a new repository:<br /> <br /> * Initialize it with a README and a ''.gitignore'' (select the Python template).<br /> * Choose a licence. For open science, we recommend ''MIT'' for code-heavy projects or ''CC-BY 4.0'' for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> &lt;code&gt;bash<br /> cd ~/projects<br /> git clone https://github.com/YOUR-USERNAME/project-name.git<br /> cd project-name<br /> &lt;/code&gt;<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> ```<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> ```<br /> <br /> You can create this in one command:<br /> <br /> ```bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> ```<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> ```bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> ```<br /> <br /> Export the environment specification and commit it:<br /> <br /> ```bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> ```<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> ```bash<br /> conda env create --file environment.yml<br /> ```<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> ```python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> ```<br /> <br /> Then install your package in editable mode:<br /> <br /> ```bash<br /> pip install -e .<br /> ```<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure `.gitignore` and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> ```<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> ```<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> * [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> ```<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> ```<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> ```<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> ```<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> ```bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> ```<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1854 2026-04-09T14:48:17Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: '''one project = one paper = one repository'''. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> '''Git and GitHub''' are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> '''The command line''' is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [http://swcarpentry.github.io/shell-novice/ Software Carpentry: The Unix Shell]<br /> <br /> '''Python packaging and environments''' are essential for making your code portable and reproducible.<br /> <br /> * [https://goodresearch.dev/ The Good Research Code Handbook] (read the whole thing, it's short and very good)<br /> * [https://docs.conda.io/en/latest/ Conda documentation]<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [GitHub](https://github.com) and create a new repository:<br /> <br /> * Initialize it with a README and a `.gitignore` (select the Python template).<br /> * Choose a licence. For open science, we recommend **MIT** for code-heavy projects or **CC-BY 4.0** for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> ```bash<br /> cd ~/projects<br /> git clone https://github.com/YOUR-USERNAME/project-name.git<br /> cd project-name<br /> ```<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> ```<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> ```<br /> <br /> You can create this in one command:<br /> <br /> ```bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> ```<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> ```bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> ```<br /> <br /> Export the environment specification and commit it:<br /> <br /> ```bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> ```<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> ```bash<br /> conda env create --file environment.yml<br /> ```<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> ```python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> ```<br /> <br /> Then install your package in editable mode:<br /> <br /> ```bash<br /> pip install -e .<br /> ```<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure `.gitignore` and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> ```<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> ```<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> * [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> ```<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> ```<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> ```<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> ```<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> ```bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> ```<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1853 2026-04-09T14:45:42Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [https://goodresearch.dev/ Good Research Code Handbook] by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: **one project = one paper = one repository**. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> **Git and GitHub** are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [https://swcarpentry.github.io/git-novice/ Software Carpentry: Version Control with Git]<br /> * [https://skills.github.com/ GitHub Skills] (interactive, browser-based tutorials)<br /> * [https://git-scm.com/book/en/v2 Pro Git Book] (comprehensive reference)<br /> <br /> **The command line** is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [Software Carpentry: The Unix Shell](http://swcarpentry.github.io/shell-novice/)<br /> <br /> **Python packaging and environments** are essential for making your code portable and reproducible.<br /> <br /> * [The Good Research Code Handbook](https://goodresearch.dev/) (read the whole thing, it's short and very good)<br /> * [Conda documentation](https://docs.conda.io/en/latest/)<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [GitHub](https://github.com) and create a new repository:<br /> <br /> * Initialize it with a README and a `.gitignore` (select the Python template).<br /> * Choose a licence. For open science, we recommend **MIT** for code-heavy projects or **CC-BY 4.0** for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> ```bash<br /> cd ~/projects<br /> git clone https://github.com/YOUR-USERNAME/project-name.git<br /> cd project-name<br /> ```<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> ```<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> ```<br /> <br /> You can create this in one command:<br /> <br /> ```bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> ```<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> ```bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> ```<br /> <br /> Export the environment specification and commit it:<br /> <br /> ```bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> ```<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> ```bash<br /> conda env create --file environment.yml<br /> ```<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> ```python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> ```<br /> <br /> Then install your package in editable mode:<br /> <br /> ```bash<br /> pip install -e .<br /> ```<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure `.gitignore` and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> ```<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> ```<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> * [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> ```<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> ```<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> ```<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> ```<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> ```bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> ```<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1852 2026-04-09T14:42:51Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [Good Research Code Handbook](https://goodresearch.dev/) by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: **one project = one paper = one repository**. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> **Git and GitHub** are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> * [Software Carpentry: Version Control with Git](https://swcarpentry.github.io/git-novice/) (start here if you're new)<br /> * [GitHub Skills](https://skills.github.com/) (interactive, browser-based tutorials)<br /> * [Pro Git Book](https://git-scm.com/book/en/v2) (comprehensive reference)<br /> <br /> **The command line** is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> * [Software Carpentry: The Unix Shell](http://swcarpentry.github.io/shell-novice/)<br /> <br /> **Python packaging and environments** are essential for making your code portable and reproducible.<br /> <br /> * [The Good Research Code Handbook](https://goodresearch.dev/) (read the whole thing, it's short and very good)<br /> * [Conda documentation](https://docs.conda.io/en/latest/)<br /> <br /> <br /> == Step 1: Name Your Project and Create the Repository ==<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [GitHub](https://github.com) and create a new repository:<br /> <br /> * Initialize it with a README and a `.gitignore` (select the Python template).<br /> * Choose a licence. For open science, we recommend **MIT** for code-heavy projects or **CC-BY 4.0** for data/content-heavy projects.<br /> * Clone the repository to your local machine:<br /> <br /> ```bash<br /> cd ~/projects<br /> git clone https://github.com/YOUR-USERNAME/project-name.git<br /> cd project-name<br /> ```<br /> <br /> <br /> == Step 2: Set Up the Directory Structure ==<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> ```<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> ```<br /> <br /> You can create this in one command:<br /> <br /> ```bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> ```<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> == Step 3: Set Up a Virtual Environment ==<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> ```bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> ```<br /> <br /> Export the environment specification and commit it:<br /> <br /> ```bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> ```<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> ```bash<br /> conda env create --file environment.yml<br /> ```<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> == Step 4: Make Your Code Pip-Installable ==<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> ```python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> ```<br /> <br /> Then install your package in editable mode:<br /> <br /> ```bash<br /> pip install -e .<br /> ```<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> == Step 5: Configure `.gitignore` and Data Tracking ==<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> ```<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> ```<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> * [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> * [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> == Step 6: Organize Your Data ==<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> ```<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> ```<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> == Step 7: Keep an Electronic Lab Notebook ==<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> ```<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> ```<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> == Step 8: Write a Good README ==<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> * **Project title and one-paragraph summary** of the scientific question.<br /> * **How to set up the environment** (`conda env create --file environment.yml`).<br /> * **How to reproduce the results** (which scripts to run, in what order).<br /> * **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> * **Data availability** (where the data live if not in the repository).<br /> * **Authors and contact information**.<br /> * **Licence**.<br /> <br /> <br /> == Step 9: Commit Early, Commit Often ==<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> ```bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> ```<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> == Step 10: Prepare for Publication from Day One ==<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> == Quick-Reference Checklist ==<br /> <br /> When starting a new project, work through the following:<br /> <br /> * [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> * [ ] Clone it locally and set up the directory structure<br /> * [ ] Create and export a conda environment<br /> * [ ] Create `setup.py` and `pip install -e .`<br /> * [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> * [ ] Write a `data/README.md` documenting your data conventions<br /> * [ ] Start your lab notebook with a first entry<br /> * [ ] Write a draft `README.md` with setup and reproduction instructions<br /> * [ ] Make your first commit and push<br /> <br /> <br /> == Further Reading ==<br /> <br /> * Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> * Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> * Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> * Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> * The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> * The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1851 2026-04-09T14:38:57Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [Good Research Code Handbook](https://goodresearch.dev/) by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: **one project = one paper = one repository**. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> == Prerequisites: Learn the Tools ==<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> **Git and GitHub** are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> - [Software Carpentry: Version Control with Git](https://swcarpentry.github.io/git-novice/) (start here if you're new)<br /> - [GitHub Skills](https://skills.github.com/) (interactive, browser-based tutorials)<br /> - [Pro Git Book](https://git-scm.com/book/en/v2) (comprehensive reference)<br /> <br /> **The command line** is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> - [Software Carpentry: The Unix Shell](http://swcarpentry.github.io/shell-novice/)<br /> <br /> **Python packaging and environments** are essential for making your code portable and reproducible.<br /> <br /> - [The Good Research Code Handbook](https://goodresearch.dev/) (read the whole thing, it's short and very good)<br /> - [Conda documentation](https://docs.conda.io/en/latest/)<br /> <br /> <br /> # Step 1: Name Your Project and Create the Repository<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [GitHub](https://github.com) and create a new repository:<br /> <br /> - Initialize it with a README and a `.gitignore` (select the Python template).<br /> - Choose a licence. For open science, we recommend **MIT** for code-heavy projects or **CC-BY 4.0** for data/content-heavy projects.<br /> - Clone the repository to your local machine:<br /> <br /> ```bash<br /> cd ~/projects<br /> git clone https://github.com/YOUR-USERNAME/project-name.git<br /> cd project-name<br /> ```<br /> <br /> <br /> # Step 2: Set Up the Directory Structure<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> ```<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> ```<br /> <br /> You can create this in one command:<br /> <br /> ```bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> ```<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> # Step 3: Set Up a Virtual Environment<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> ```bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> ```<br /> <br /> Export the environment specification and commit it:<br /> <br /> ```bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> ```<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> ```bash<br /> conda env create --file environment.yml<br /> ```<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> # Step 4: Make Your Code Pip-Installable<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> ```python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> ```<br /> <br /> Then install your package in editable mode:<br /> <br /> ```bash<br /> pip install -e .<br /> ```<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> # Step 5: Configure `.gitignore` and Data Tracking<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> ```<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> ```<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> - [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> - [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> # Step 6: Organize Your Data<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> ```<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> ```<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> # Step 7: Keep an Electronic Lab Notebook<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> ```<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> ```<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> # Step 8: Write a Good README<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> - **Project title and one-paragraph summary** of the scientific question.<br /> - **How to set up the environment** (`conda env create --file environment.yml`).<br /> - **How to reproduce the results** (which scripts to run, in what order).<br /> - **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> - **Data availability** (where the data live if not in the repository).<br /> - **Authors and contact information**.<br /> - **Licence**.<br /> <br /> <br /> # Step 9: Commit Early, Commit Often<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> ```bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> ```<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> # Step 10: Prepare for Publication from Day One<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> # Quick-Reference Checklist<br /> <br /> When starting a new project, work through the following:<br /> <br /> - [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> - [ ] Clone it locally and set up the directory structure<br /> - [ ] Create and export a conda environment<br /> - [ ] Create `setup.py` and `pip install -e .`<br /> - [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> - [ ] Write a `data/README.md` documenting your data conventions<br /> - [ ] Start your lab notebook with a first entry<br /> - [ ] Write a draft `README.md` with setup and reproduction instructions<br /> - [ ] Make your first commit and push<br /> <br /> <br /> # Further Reading<br /> <br /> - Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> - Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> - Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> - Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> - The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> - The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1850 2026-04-09T14:37:45Z

<p>Gunnar: </p> <hr /> <div>The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [Good Research Code Handbook](https://goodresearch.dev/) by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: **one project = one paper = one repository**. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> # Prerequisites: Learn the Tools<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> **Git and GitHub** are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> - [Software Carpentry: Version Control with Git](https://swcarpentry.github.io/git-novice/) (start here if you're new)<br /> - [GitHub Skills](https://skills.github.com/) (interactive, browser-based tutorials)<br /> - [Pro Git Book](https://git-scm.com/book/en/v2) (comprehensive reference)<br /> <br /> **The command line** is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> - [Software Carpentry: The Unix Shell](http://swcarpentry.github.io/shell-novice/)<br /> <br /> **Python packaging and environments** are essential for making your code portable and reproducible.<br /> <br /> - [The Good Research Code Handbook](https://goodresearch.dev/) (read the whole thing, it's short and very good)<br /> - [Conda documentation](https://docs.conda.io/en/latest/)<br /> <br /> <br /> # Step 1: Name Your Project and Create the Repository<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [GitHub](https://github.com) and create a new repository:<br /> <br /> - Initialize it with a README and a `.gitignore` (select the Python template).<br /> - Choose a licence. For open science, we recommend **MIT** for code-heavy projects or **CC-BY 4.0** for data/content-heavy projects.<br /> - Clone the repository to your local machine:<br /> <br /> ```bash<br /> cd ~/projects<br /> git clone https://github.com/YOUR-USERNAME/project-name.git<br /> cd project-name<br /> ```<br /> <br /> <br /> # Step 2: Set Up the Directory Structure<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> ```<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> ```<br /> <br /> You can create this in one command:<br /> <br /> ```bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> ```<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> # Step 3: Set Up a Virtual Environment<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> ```bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> ```<br /> <br /> Export the environment specification and commit it:<br /> <br /> ```bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> ```<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> ```bash<br /> conda env create --file environment.yml<br /> ```<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> # Step 4: Make Your Code Pip-Installable<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> ```python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> ```<br /> <br /> Then install your package in editable mode:<br /> <br /> ```bash<br /> pip install -e .<br /> ```<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> # Step 5: Configure `.gitignore` and Data Tracking<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> ```<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> ```<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> - [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> - [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> # Step 6: Organize Your Data<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> ```<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> ```<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> # Step 7: Keep an Electronic Lab Notebook<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> ```<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> ```<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> # Step 8: Write a Good README<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> - **Project title and one-paragraph summary** of the scientific question.<br /> - **How to set up the environment** (`conda env create --file environment.yml`).<br /> - **How to reproduce the results** (which scripts to run, in what order).<br /> - **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> - **Data availability** (where the data live if not in the repository).<br /> - **Authors and contact information**.<br /> - **Licence**.<br /> <br /> <br /> # Step 9: Commit Early, Commit Often<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> ```bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> ```<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> # Step 10: Prepare for Publication from Day One<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> # Quick-Reference Checklist<br /> <br /> When starting a new project, work through the following:<br /> <br /> - [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> - [ ] Clone it locally and set up the directory structure<br /> - [ ] Create and export a conda environment<br /> - [ ] Create `setup.py` and `pip install -e .`<br /> - [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> - [ ] Write a `data/README.md` documenting your data conventions<br /> - [ ] Start your lab notebook with a first entry<br /> - [ ] Write a draft `README.md` with setup and reproduction instructions<br /> - [ ] Make your first commit and push<br /> <br /> <br /> # Further Reading<br /> <br /> - Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> - Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> - Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> - Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> - The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> - The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=New_Projects&diff=1849 2026-04-09T14:33:28Z

<p>Gunnar: Created page with &quot;# Getting Started with a New Project The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [Good Research Code Handbook](https://goodresearch.dev/) by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers. The core principle i...&quot;</p> <hr /> <div># Getting Started with a New Project<br /> <br /> The goal of this guide is to help you set up every new project so that it is well-organized from day one, fully reproducible, and ready to be made public at publication. We follow the philosophy of the [Good Research Code Handbook](https://goodresearch.dev/) by Patrick Mineault, but extend it to cover the full scope of a research project: experimental data, analysis code, lab notes, figures, posters, and papers.<br /> <br /> The core principle is simple: **one project = one paper = one repository**. Everything related to a project lives together, is version-controlled, and is structured so that anyone (including future you) can understand and reproduce the work.<br /> <br /> <br /> ## Prerequisites: Learn the Tools<br /> <br /> Before you begin, make sure you have a working knowledge of the following. If you don't, work through the linked tutorials first.<br /> <br /> **Git and GitHub** are the foundation of everything below. Git tracks changes to your files over time; GitHub hosts your repository online and enables collaboration.<br /> <br /> - [Software Carpentry: Version Control with Git](https://swcarpentry.github.io/git-novice/) (start here if you're new)<br /> - [GitHub Skills](https://skills.github.com/) (interactive, browser-based tutorials)<br /> - [Pro Git Book](https://git-scm.com/book/en/v2) (comprehensive reference)<br /> <br /> **The command line** is needed for most of the setup steps below. You don't need to be an expert, but you should be comfortable navigating directories and running commands.<br /> <br /> - [Software Carpentry: The Unix Shell](http://swcarpentry.github.io/shell-novice/)<br /> <br /> **Python packaging and environments** are essential for making your code portable and reproducible.<br /> <br /> - [The Good Research Code Handbook](https://goodresearch.dev/) (read the whole thing, it's short and very good)<br /> - [Conda documentation](https://docs.conda.io/en/latest/)<br /> <br /> <br /> ## Step 1: Name Your Project and Create the Repository<br /> <br /> Pick a short, descriptive name. This name will be used for the folder, the GitHub repository, and the installable Python package, so keep it lowercase with no spaces (use hyphens or underscores if needed). For example: `pupil-dynamics`, `pursuit-acceleration`, `meg-connectivity`.<br /> <br /> Go to [GitHub](https://github.com) and create a new repository:<br /> <br /> - Initialize it with a README and a `.gitignore` (select the Python template).<br /> - Choose a licence. For open science, we recommend **MIT** for code-heavy projects or **CC-BY 4.0** for data/content-heavy projects.<br /> - Clone the repository to your local machine:<br /> <br /> ```bash<br /> cd ~/projects<br /> git clone https://github.com/YOUR-USERNAME/project-name.git<br /> cd project-name<br /> ```<br /> <br /> <br /> ## Step 2: Set Up the Directory Structure<br /> <br /> Create the following folder structure inside your repository:<br /> <br /> ```<br /> project-name/<br /> ├── code/ # reusable Python modules (your installable package)<br /> │ └── __init__.py<br /> ├── data/<br /> │ ├── raw/ # raw, untouched data (never modify these files)<br /> │ └── processed/ # cleaned or transformed data<br /> ├── docs/<br /> │ ├── labnotebook/ # electronic lab notes (Markdown files, dated)<br /> │ └── protocols/ # experimental protocols and SOPs<br /> ├── results/<br /> │ ├── figures/ # publication-quality figures<br /> │ └── intermediate/ # checkpoints, intermediate outputs<br /> ├── scripts/ # analysis scripts and notebooks<br /> ├── outputs/<br /> │ ├── papers/ # manuscript drafts (LaTeX or Markdown source)<br /> │ └── posters/ # poster source files<br /> ├── tests/ # unit tests for your code<br /> ├── environment.yml # conda environment specification<br /> ├── setup.py # makes your code pip-installable<br /> └── README.md # project overview and instructions<br /> ```<br /> <br /> You can create this in one command:<br /> <br /> ```bash<br /> mkdir -p code data/{raw,processed} docs/{labnotebook,protocols} \<br /> results/{figures,intermediate} scripts outputs/{papers,posters} tests<br /> touch code/__init__.py<br /> ```<br /> <br /> A few things to note about this structure. The `code/` directory holds reusable Python modules that you import (equivalent to `src/` in the Good Research Code Handbook). The `scripts/` directory holds analysis scripts and Jupyter notebooks that call functions from `code/`. The `data/raw/` directory is sacred: raw data go in, but nothing ever comes back out modified. All transformations produce new files in `data/processed/`.<br /> <br /> <br /> ## Step 3: Set Up a Virtual Environment<br /> <br /> Every project gets its own conda environment. This ensures that your dependencies are documented and that the project can be reproduced on any machine.<br /> <br /> ```bash<br /> conda create --name project-name python=3.11<br /> conda activate project-name<br /> conda install numpy scipy matplotlib pandas seaborn jupyter<br /> ```<br /> <br /> Export the environment specification and commit it:<br /> <br /> ```bash<br /> conda env export &gt; environment.yml<br /> git add environment.yml<br /> git commit -m &quot;Add conda environment specification&quot;<br /> ```<br /> <br /> Keep `environment.yml` up to date as you add packages. Anyone can recreate your environment with:<br /> <br /> ```bash<br /> conda env create --file environment.yml<br /> ```<br /> <br /> For more details on managing conda environments (including mixing pip and conda packages), see the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup).<br /> <br /> <br /> ## Step 4: Make Your Code Pip-Installable<br /> <br /> This step avoids the mess of `sys.path` hacks and makes your modules importable from anywhere in the project. Create a minimal `setup.py` in the project root:<br /> <br /> ```python<br /> from setuptools import find_packages, setup<br /> <br /> setup(<br /> name='project-name',<br /> packages=find_packages(),<br /> )<br /> ```<br /> <br /> Then install your package in editable mode:<br /> <br /> ```bash<br /> pip install -e .<br /> ```<br /> <br /> Now you can `import code.my_module` from any script or notebook in the project without worrying about paths. If you change the code, the changes are picked up automatically. See the [Good Research Code Handbook: Setup](https://goodresearch.dev/setup#install-a-project-package) for a more detailed walkthrough.<br /> <br /> <br /> ## Step 5: Configure `.gitignore` and Data Tracking<br /> <br /> Not everything belongs in Git. Add the following to your `.gitignore`:<br /> <br /> ```<br /> # Data (tracked separately, see below)<br /> data/<br /> results/intermediate/<br /> <br /> # Python<br /> *.egg-info/<br /> __pycache__/<br /> *.pyc<br /> .ipynb_checkpoints/<br /> <br /> # OS files<br /> .DS_Store<br /> Thumbs.db<br /> <br /> # Environment<br /> .env<br /> ```<br /> <br /> **For large or sensitive data**, Git is not the right tool. You have two main options depending on your situation:<br /> <br /> - [DataLad](https://www.datalad.org/) (recommended for neuroscience): built on Git and git-annex, it version-controls arbitrarily large files and integrates with BIDS and OpenNeuro. See the [DataLad Handbook](https://handbook.datalad.org/) for a thorough tutorial.<br /> - [DVC (Data Version Control)](https://dvc.org/): a lightweight alternative that works well with remote storage backends (S3, Google Drive, etc.).<br /> <br /> If your data are small enough to share directly (&lt; 50 MB of text-based files, e.g., behavioural CSVs), you can keep them in the Git repository and remove `data/` from `.gitignore`.<br /> <br /> <br /> ## Step 6: Organize Your Data<br /> <br /> Follow community standards for data organization wherever possible. For neuroimaging data (MRI, EEG, MEG), use the [Brain Imaging Data Structure (BIDS)](https://bids.neuroimaging.io/). For behavioural and psychophysics data, adopt a consistent naming convention:<br /> <br /> ```<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_beh.csv<br /> data/raw/sub-01/ses-01/sub-01_ses-01_task-pursuit_eyetrack.edf<br /> ```<br /> <br /> The key principles are: use subject and session identifiers consistently, include the task name, separate metadata from data, and never modify raw files. Write a `data/README.md` that documents the naming convention, variable definitions, and any relevant acquisition parameters.<br /> <br /> <br /> ## Step 7: Keep an Electronic Lab Notebook<br /> <br /> Use the `docs/labnotebook/` directory for dated Markdown entries. A simple naming convention works well:<br /> <br /> ```<br /> docs/labnotebook/2026-03-30_pilot-data-collection.md<br /> docs/labnotebook/2026-04-02_initial-analysis.md<br /> ```<br /> <br /> Each entry should briefly note what you did, what you observed, any decisions you made, and links to relevant scripts or results. These notes are version-controlled along with everything else and provide a timestamped record of the project's evolution.<br /> <br /> You can also use tools like [Obsidian](https://obsidian.md/) or [Logseq](https://logseq.com/) for richer note-taking and link them to your repository.<br /> <br /> <br /> ## Step 8: Write a Good README<br /> <br /> Your `README.md` is the front door to the project. Write it early and update it as the project evolves. It should include:<br /> <br /> - **Project title and one-paragraph summary** of the scientific question.<br /> - **How to set up the environment** (`conda env create --file environment.yml`).<br /> - **How to reproduce the results** (which scripts to run, in what order).<br /> - **Directory structure** (copy and paste the tree from Step 2 and annotate it).<br /> - **Data availability** (where the data live if not in the repository).<br /> - **Authors and contact information**.<br /> - **Licence**.<br /> <br /> <br /> ## Step 9: Commit Early, Commit Often<br /> <br /> A good rule of thumb is to commit every meaningful unit of work: a new analysis function, a cleaned dataset, a draft of a figure. Each commit should have a short, informative message. Aim for several commits per day when you are actively working.<br /> <br /> ```bash<br /> git add scripts/01_preprocess.py<br /> git commit -m &quot;Add preprocessing pipeline for eye-tracking data&quot;<br /> git push<br /> ```<br /> <br /> If you are not comfortable with the Git command line, the [Git panel in VS Code](https://code.visualstudio.com/docs/sourcecontrol/overview) is an excellent GUI alternative.<br /> <br /> <br /> ## Step 10: Prepare for Publication from Day One<br /> <br /> The reason we set all of this up at the start is so that sharing is effortless when the paper is ready. At publication time, you should be able to:<br /> <br /> 1. **Make the GitHub repository public** (or archive it on [Zenodo](https://zenodo.org/) for a citable DOI).<br /> 2. **Deposit the data** on a public repository such as [OpenNeuro](https://openneuro.org/) (for BIDS neuroimaging data), [OSF](https://osf.io/), [Figshare](https://figshare.com/), or [Dryad](https://datadryad.org/).<br /> 3. **Link everything in the paper**: point readers to the code repository, the data repository, and specify the exact environment needed to reproduce the results.<br /> <br /> If you have followed this guide, all three steps should take minutes rather than days.<br /> <br /> <br /> ## Quick-Reference Checklist<br /> <br /> When starting a new project, work through the following:<br /> <br /> - [ ] Create a GitHub repository with README, licence, and `.gitignore`<br /> - [ ] Clone it locally and set up the directory structure<br /> - [ ] Create and export a conda environment<br /> - [ ] Create `setup.py` and `pip install -e .`<br /> - [ ] Configure `.gitignore` (and DataLad/DVC if needed for large data)<br /> - [ ] Write a `data/README.md` documenting your data conventions<br /> - [ ] Start your lab notebook with a first entry<br /> - [ ] Write a draft `README.md` with setup and reproduction instructions<br /> - [ ] Make your first commit and push<br /> <br /> <br /> ## Further Reading<br /> <br /> - Mineault, P. J. (2021). [The Good Research Code Handbook](https://goodresearch.dev/). The essential guide to writing clean, maintainable research code.<br /> - Wilson, G. et al. (2017). [Good enough practices in scientific computing](https://doi.org/10.1371/journal.pcbi.1005510). *PLOS Computational Biology*.<br /> - Gorgolewski, K. J. et al. (2016). [The Brain Imaging Data Structure](https://doi.org/10.1038/sdata201644). *Scientific Data*.<br /> - Halchenko, Y. O. et al. (2021). [DataLad: distributed system for joint management of code, data, and their relationship](https://doi.org/10.21105/joss.03262). *Journal of Open Source Software*.<br /> - The [DataLad Handbook](https://handbook.datalad.org/) for a comprehensive tutorial on data version control.<br /> - The [Software Carpentry](https://software-carpentry.org/lessons/) lessons for foundational skills in the Unix shell, Git, and Python.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=MediaWiki:Sidebar&diff=1848 2026-04-08T20:09:42Z

<p>Gunnar: </p> <hr /> <div><br /> * navigation<br /> ** mainpage|mainpage-description<br /> ** Lab_Info|Lab Info<br /> ** New_Projects|New Project Setup <br /> ** Lab_Ethos|Lab Ethos<br /> ** Learning_Resources|Learning Resources<br /> ** software|Software<br /> ** Other_Resources|Other Resources<br /> ** CoSMo | CoSMo<br /> ** Neuromatch|Neuromatch<br /> ** Journal_Club|Journal Club<br /> ** IRTG|IRTG<br /> ** EDI|EDI<br /> * SEARCH<br /> * TOOLBOX<br /> * LANGUAGES</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Other_Resources&diff=1847 2026-01-14T13:07:54Z

<p>Gunnar: /* Work-life balance &amp; mental health */</p> <hr /> <div>== Funding Opportunities == <br /> *[http://queensu.ca/sgs/sites/webpublish.queensu.ca.sgswww/files/files/Students/Queen's%20Presentation%20Graduate%20Students%20Morning%20April%2010th.pdf Mitacs graduate and post-doc funding (for private sector /NGO internships, or academic travel scholarships)]<br /> * [https://anneurai.net/2016/10/16/postdoctoral-fellowship-programmes/ Anne Urai's list of postdoctoral fellowship programs] <br /> * [https://research.jhu.edu/rdt/funding-opportunities/postdoctoral/?s=03 List of postdoctoral fellowships] from Johns Hopkins<br /> * [https://research.jhu.edu/rdt/funding-opportunities/early-career/ List of early career funding opportunities] by Johns Hopkins<br /> <br /> == How to do science ==<br /> * [http://compneurosci.com/wiki/index.php/Paper_Writing_101 Konrad's paper writing 101]<br /> * Konrad's [http://biorxiv.org/content/early/2016/12/14/088278?utm_content=buffer8d04a&amp;utm_medium=social&amp;utm_source=facebook.com&amp;utm_campaign=buffer 10 simple rules for structuring papers] advice: read this before you start writing!!!<br /> * [http://collections.plos.org/ten-simple-rules PLoS CB - 10 simple rules collection]: a must read for everyone!<br /> * [http://colorbrewer2.org Colorbrewer]: great tool for selecting colour schemes on publications<br /> * [http://neuronline.sfn.org/Articles/Professional-Development/2016/Tricks-of-the-Trade-How-to-Peer-Review-a-Manuscript How to peer review a manuscript]: Webinar and resources from SfN Neuronline. <br /> * [http://neuronline.sfn.org/Articles/Career-Advice/2017/Tricks-of-the-Trade-Modelling-Papers-Resources How To Review Modelling Papers]: A webinar + resources from SfN Neuronline. The webinar date has passed, but if you 'register' there is an email link that will lead you to the archived video. <br /> * [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4284499/ The pleasure of publishing (Malhotra &amp; Marder, 2015)]: Elife editors' opinions on what makes an effective manuscript. <br /> * [[Media:OpenScience.pdf | How to do Open Science slides]]<br /> * [https://docs.google.com/document/d/1r6nDcF43esu3xBjmk3ERAmaEHKEB75_HflSkk3zZhBk/edit Write this instead of that] - tips for better writing<br /> * [https://youtu.be/Ss8fNOvVozo Gunnar's MAIN 2020 &quot;Introduction to writing scientific papers&quot; lecture] and [[Media:MAIN2020.pdf|accompanying lecture slides]]<br /> * [https://research.lib.buffalo.edu/poster-presentations Designing effective posters] - compilation of resources for scientific poster design<br /> * [https://carpentries-incubator.github.io/managing-computational-projects/ Managing Open and Reproducible Computational Projects] - a user guide<br /> <br /> == How to be successful ==<br /> * [https://www.technologyreview.com/s/409043/how-to-think/?utm_content=buffer9ea8e&amp;utm_medium=social&amp;utm_source=facebook.com&amp;utm_campaign=buffer Ed Boyden's advice on how to think]<br /> * [http://www.wikihow.com/Think Developing better thought processes]<br /> * [http://faculty.georgetown.edu/kingch/How_to_Think.htm How to argue well]<br /> * [http://www.lifehack.org/articles/productivity/12-weekend-habits-highly-successful-people.html Habits for success]<br /> * [http://www.cell.com/neuron/pdf/S0896-6273(15)00331-1.pdf Hitchhikers guide to a Career in Neuroscience]<br /> * [http://www.programmerfu.com/2017/04/20/fast-is-slow-slow-is-smooth-smooth-is-fast.html Slow is smooth and smooth is fast]<br /> * [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.8391&amp;rep=rep1&amp;type=pdf Survival Skills for Graduate School and Beyond] (Fischer and Zigmond, 1998; still very applicable)<br /> * [https://www.cs.utexas.edu/users/EWD/transcriptions/EWD06xx/EWD637.html 3 golden rules of scientific success] by Dijkstra<br /> * [https://www.nature.com/articles/d41586-019-03914-5 Secrets to writing a winning grant]<br /> ----<br /> <br /> == Career development ==<br /> It is never to early to think about what you'd like to do in the future! This is true at any stage in your training or career. Critical (re-)evaluation of skills, goals and gaps allows for better planning and ultimately better job and happiness prospects. Take it seriously!!!<br /> * [http://www.cihr-irsc.gc.ca/e/50516.html CIHR individual career development plans]<br /> * your professional digital presence on the web is crucial for future employment! Future employers look at it! Use LinkedIn, make and maintain a web site (e.g. Google Sites), use Twitter professionally, etc<br /> * [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007163 how to give a job talk]<br /> * [https://www.cca-reports.ca/reports/the-labour-market-transition-of-phd-graduates/ Expert Panel on the Labour Market Transition of PhD Graduates]<br /> * [https://www.sciencedirect.com/science/article/pii/S0166223621000709 5 remediation strategies to overcome the gender gap]<br /> <br /> ----<br /> <br /> == Work-life balance &amp; mental health ==<br /> As an undergraduate, graduate student or postdoc, as exciting a time as this is, you will face stress, frustration and deception. Your passion for science can suck up all your time and wack your physical and mental health out of balance. This can lead to a downward spiral out of which comes no good. Yes, grad school / postdoc work is hard, don't let it destroy you. So here are a few tips to avoid that in the first place. For Queen's students, please consider contacting [https://www.queensu.ca/studentwellness/ Student Wellness Services] if you feel you could use some help...<br /> * Get enough exercise and sleep!<br /> * [http://www.nextscientist.com/work-life-balance-in-academia/ The Happy PhD zone]: how to maintain a work-life balance in academia<br /> * [http://thegradstudentway.com/blog/?p=76#.WRZWlGfSkvc 6 Ways To Survive Grad School and Achieve Work-Life Balance]<br /> * [https://www.mcgill.ca/gradsupervision/supervisees/work-life Official McGill University guidelines] on work-life balance<br /> * [https://www.bustle.com/articles/87409-graduate-school-is-hard-so-here-are-8-simple-ways-to-maintain-your-sanity How to maintain your sanity] during grad school<br /> * [https://psychcentral.com/lib/12-tips-for-surviving-and-thriving-in-grad-school/ Grad school survival tips]<br /> * [http://www.sciencemag.org/careers/2018/08/three-reminders-help-you-thrive-not-merely-survive-grad-school Three reminders to help you thrive—not merely survive—in grad school]<br /> * [http://www.nature.com/nature/journal/v545/n7654/full/nj7654-375a.html?WT.mc_id=FBK_NatureNews&amp;sf79769979=1 Break or burn out]: a nice article in Nature<br /> * [https://www.psychologytoday.com/files/attachments/1035/arts-foster-scientific-success.pdf Creative outlets and Scientific Success] Scientists are not more likely to have creative outlets, but the most successful ones are<br /> * [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006914 Ten simple rules towards healthier research labs]<br /> * [https://elifesciences.org/articles/108853 The SAFE Labs Handbook as a tool for improving lab culture]<br /> <br /> == Resources for organizing a scientific project ==<br /> * [https://towardsdatascience.com/how-to-keep-your-research-projects-organized-part-1-folder-structure-10bd56034d3a How to keep your research projects organized, part 1: folder structure]<br /> * [http://nikola.me/folder_structure.html Setting up an Organised Folder Structure for Research Projects]<br /> * [https://www.data.cam.ac.uk/data-management-guide/organising-your-data Organising your data]<br /> * [http://help.osf.io/m/bestpractices/l/611391-organizing-files OSF Guidelines]<br /> * [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424 A Quick Guide to Organizing Computational Biology Projects]<br /> * [https://evernote.com/ Evernote: Note taking app]<br /> * [https://trello.com/ Trello: Collaborate and share notes]<br /> * [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510 Good enough practices for scientific computing]<br /> * [https://www.hhmi.org/science-education/programs/making-right-moves?s=03 Making the Right Moves: A Practical Guide to Scientific Management for Postdocs and New Faculty] - AMAZING free online book<br /> * [https://online.ucpress.edu/collabra/article/7/1/18684/115927/Easing-Into-Open-Science-A-Guide-for-Graduate?s=03 How to ease into Open Science] - a tutorial guide<br /> <br /> == Teaching resources ==<br /> * [https://eddl.tru.ca/eddl-5141-online-teaching-and-learning/ Online Teaching and Learning] course - a how-to from course design to student motivation<br /> * [https://theprofessorisin.com/2017/10/27/looking-presentable-on-webcam-a-guest-post/ Looking presentable on webcam] (&quot;professor is in&quot; guest post)<br /> * [https://ropensci.org/commcalls/2018-10-16/?s=03 Code review in the lab...]</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Learning_Resources&diff=1846 2026-01-09T13:10:06Z

<p>Gunnar: /* General Computational Neuroscience */</p> <hr /> <div>== Bayesian Nonparametrics ==<br /> * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]<br /> * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&amp;acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).<br /> * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.<br /> * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&amp;hl=en&amp;oi=ao Any of the papers] of Radford Neal.<br /> * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].<br /> <br /> == General Computational Neuroscience ==<br /> * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.<br /> * [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.<br /> * [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control &amp; learning]<br /> * [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]<br /> * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli<br /> * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience<br /> * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]<br /> * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2<br /> * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij<br /> * [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF)<br /> * [https://direct.mit.edu/books/book/3159/Computational-Modeling-Methods-for-Neuroscientists Computational Modeling Methods for Neuroscientists] - (free PDF) by Erik De Schutter<br /> * [https://gershmanlab.com/textbook.html Computational Foundations of Cognitive Neuroscience] textbook by Sam Gershman<br /> <br /> == Information Theory ==<br /> * [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] <br /> * [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.<br /> * [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]<br /> <br /> == Machine Learning ==<br /> ==== Books ====<br /> * [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control] <br /> * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]<br /> * [http://alex.smola.org/drafts/thebook.pdf Smola &amp; Vishwanathan's book]<br /> * [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]<br /> * [https://probml.github.io/pml-book/ Murphy's book]<br /> * [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz &amp; Ben-David's book]<br /> * [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]<br /> * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]<br /> * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]<br /> * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).<br /> * [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.<br /> * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''<br /> * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]<br /> * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt &amp; Recht<br /> * [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts &amp; Yaida<br /> <br /> ==== Online Resources ====<br /> * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --&gt; free enrollment)]<br /> * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]<br /> * [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]<br /> * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop<br /> * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.<br /> * Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)<br /> <br /> ==== Journal Club Tutorials ====<br /> See [[Journal Club#Tutorials]].<br /> <br /> == General Math ==<br /> * [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]<br /> * [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. <br /> * [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]<br /> * [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]<br /> * [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]<br /> *[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&amp;token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]<br /> * [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]<br /> * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.<br /> * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler<br /> * [https://www.biodyn.ro/course/literatura/Nonlinear_Dynamics_and_Chaos_2018_Steven_H._Strogatz.pdf Nonlinear Dynamics And Chaos] book by Steven H. Strogatz<br /> <br /> == MATLAB ==<br /> * [https://www.coursera.org/learn/matlab Intro to programming] with Matlab<br /> * [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]<br /> * [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] (&quot;The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.&quot;)<br /> * [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]<br /> * [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]<br /> **[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]<br /> * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts<br /> * [https://www.matlabcoding.com/2023/12/matlab-for-neuroscientists-introduction.html Matlab for Neuroscientists] (free PDF) 2nd edition, by Pascal Wallish et al.<br /> <br /> == Python ==<br /> * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch<br /> * [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python<br /> * [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]<br /> * [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]<br /> * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]<br /> * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault<br /> * [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal<br /> * [https://virati.medium.com/make-your-code-last-forever-18e5bd3e4842 How to use containers for code] - by Vineet Tiruvadi<br /> <br /> == Statistics ==<br /> * [http://onlinestatbook.com/2/index.html Rice University online Stats book]<br /> * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book<br /> * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods &amp; significance estimation]<br /> * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)<br /> * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]<br /> * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]<br /> * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]<br /> * [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]<br /> * [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]<br /> * [https://statsthinking21.github.io/statsthinking21-core-site/ &quot;Statistical Thinking for the 21st Century&quot;] free online book by Russell A. Poldrack<br /> * [http://www.stat.columbia.edu/~gelman/book/ &quot;Bayesian Data Analysis&quot;] book by Andrew Gelman et al. with examples in Python and R<br /> * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]<br /> * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free<br /> * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free<br /> * [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free<br /> * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!<br /> * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code<br /> * [https://bruno.nicenboim.me/bayescogsci/ An Introduction to Bayesian Data Analysis for Cognitive Science] free online book by Bruno Nicenboim, Daniel J. Schad, and Shravan Vasishth<br /> <br /> == Neuroimaging analyses ==<br /> * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]<br /> * [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Ethos&diff=1844 2025-02-14T18:31:32Z

<p>Gunnar: /* Lab Ethos */</p> <hr /> <div>Our lab is committed to '''fostering a culture of respect, honesty, integrity, and curiosity''', where all individuals are valued for their contributions and empowered to explore new ideas without fear of judgment. We uphold the highest ethical standards in our research and interactions, ensuring that honesty and integrity guide our work. We embrace curiosity as the driving force behind discovery, cultivating an environment where questions are encouraged, and learning is continuous. Harassment, discrimination, and exclusion have no place in our lab—we are dedicated to inclusivity, collaboration, and mutual support. Through these principles, we seek to push the boundaries of knowledge while maintaining a community built on trust, openness, and shared purpose.<br /> <br /> We believe that '''diversity in backgrounds, perspectives, and experiences''' is the foundation of innovation, creativity, and transformative scientific progress. Our lab thrives on the strength that comes from bringing together individuals with different viewpoints, cultures, and expertise, as we recognize that the best ideas emerge from inclusive collaboration. We actively foster an environment where everyone feels empowered to contribute, knowing that diverse voices enrich our research and propel new discoveries. By embracing and celebrating our differences, we build a stronger, more dynamic community that challenges assumptions, expands possibilities, and ensures our work has a broad and lasting impact.<br /> <br /> '''Science is for everyone''', not just a select few, and we are committed to making research accessible, inclusive, and welcoming to all who are curious and passionate about discovery. We reject the notion that science belongs only to an elite group and instead champion the idea that diverse perspectives and backgrounds strengthen scientific inquiry. Knowledge should not be a privilege but a shared resource that benefits all of humanity. We strive to break down barriers—whether social, economic, or institutional—that limit participation. Through mentorship, outreach, and open collaboration, we create opportunities for anyone who wishes to engage with science, ensuring that the pursuit of knowledge remains open, equitable, and driven by collective curiosity rather than exclusivity.<br /> <br /> The '''health and well-being''' of all lab members are fundamental to our success, and we are committed to fostering an environment that prioritizes both physical and mental well-being. We recognize that research is demanding, but we believe that productivity should never come at the expense of personal health. We prioritize open conversations about workload and stress. We support one another and ensure resources are available. Mutual respect, kindness, and a culture of care guide our interactions, and we actively promote policies and practices that create a sustainable and supportive work environment. Whether through flexible work arrangements, shared wisdom, or simply fostering a community where individuals feel valued and heard, we are dedicated to ensuring that every member of our lab thrives both professionally and personally.<br /> <br /> '''Science is inherently political''' because it shapes and is shaped by the societies in which it operates. The questions we ask, the funding we receive, the policies that govern our research, and the ways scientific knowledge is applied all exist within political contexts. Science influences public policy on issues like AI, public health, and (neuro-)technology, and in turn, political systems determine who has access to education, research opportunities, and resources. We recognize that science is not neutral—it has the power to challenge injustices, inform decision-making, and drive societal progress. As researchers, we have a responsibility to engage critically with the political dimensions of our work, advocate for evidence-based policies, and ensure that science serves the common good rather than reinforcing systems of inequality or exclusion.<br /> <br /> '''Open science''' is a fundamental principle that ensures research is transparent, accessible, and reproducible. As most scientific research is publicly funded, we believe that the knowledge generated should be openly available to the public, rather than locked behind paywalls or restricted access. Open science practices, including open-access publishing, data/code sharing, and reproducible methodologies, strengthen scientific integrity, accelerate discovery, and promote collaboration across disciplines and communities. Science is a global effort, and open science fosters a spirit of collaboration and community by enabling researchers from around the world to share knowledge, build on each other’s work, and solve complex problems together. By embracing openness, we increase accountability, reduce barriers to participation, and maximize the impact of our research. We are committed to fostering a culture of transparency, where scientific progress benefits all and where knowledge is a shared resource for the advancement of society as a whole.<br /> <br /> '''Together''', we can harness the power of science and research to illuminate a path forward, even in the face of political, social, and environmental challenges. While it is easy to feel discouraged by uncertainty or division, we believe that discovery, innovation, and knowledge provide a foundation for optimism. Science is a testament to human curiosity and resilience—it has the power to solve complex problems, improve lives, and reveal new possibilities for the future. Through socially responsible research, we gain not only a deeper understanding of the world but also the tools to shape it for the better. By working collaboratively, asking bold questions, and committing to truth and progress, we can confront the challenges of our time with confidence. Science reminds us that the future is not predetermined—it is something we can build together, with courage, creativity, and a shared commitment to a better world.<br /> <br /> == Land Acknowledgment ==<br /> <br /> We are grateful to live and work as uninvited guests upon the traditional territories of the Haudenosaunee Confederacy and the Anishinabek Nation. To acknowledge this land is to recognize its deep and enduring history, one that predates the establishment of European settlements. It is also to recognize the significance of this territory for the Indigenous Peoples who have lived, and continue to live, upon it, whose cultural practices, languages, and spiritualities are deeply tied to the land and its ecosystems.<br /> <br /> Our research is shaped by the land we inhabit, and we strive to conduct our work with respect for Indigenous knowledge systems and environmental stewardship. We acknowledge that scientific inquiry does not exist in isolation from history, and we commit to learning from and collaborating with Indigenous communities to ensure that our research is ethical, responsible, and inclusive. By engaging in meaningful dialogue and action, we seek to honour Indigenous contributions to knowledge and to work toward a more equitable and sustainable future.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Ethos&diff=1843 2025-02-14T18:30:50Z

<p>Gunnar: /* Lab Ethos */</p> <hr /> <div>= Lab Ethos =<br /> <br /> <br /> Our lab is committed to '''fostering a culture of respect, honesty, integrity, and curiosity''', where all individuals are valued for their contributions and empowered to explore new ideas without fear of judgment. We uphold the highest ethical standards in our research and interactions, ensuring that honesty and integrity guide our work. We embrace curiosity as the driving force behind discovery, cultivating an environment where questions are encouraged, and learning is continuous. Harassment, discrimination, and exclusion have no place in our lab—we are dedicated to inclusivity, collaboration, and mutual support. Through these principles, we seek to push the boundaries of knowledge while maintaining a community built on trust, openness, and shared purpose.<br /> <br /> We believe that '''diversity in backgrounds, perspectives, and experiences''' is the foundation of innovation, creativity, and transformative scientific progress. Our lab thrives on the strength that comes from bringing together individuals with different viewpoints, cultures, and expertise, as we recognize that the best ideas emerge from inclusive collaboration. We actively foster an environment where everyone feels empowered to contribute, knowing that diverse voices enrich our research and propel new discoveries. By embracing and celebrating our differences, we build a stronger, more dynamic community that challenges assumptions, expands possibilities, and ensures our work has a broad and lasting impact.<br /> <br /> '''Science is for everyone''', not just a select few, and we are committed to making research accessible, inclusive, and welcoming to all who are curious and passionate about discovery. We reject the notion that science belongs only to an elite group and instead champion the idea that diverse perspectives and backgrounds strengthen scientific inquiry. Knowledge should not be a privilege but a shared resource that benefits all of humanity. We strive to break down barriers—whether social, economic, or institutional—that limit participation. Through mentorship, outreach, and open collaboration, we create opportunities for anyone who wishes to engage with science, ensuring that the pursuit of knowledge remains open, equitable, and driven by collective curiosity rather than exclusivity.<br /> <br /> The '''health and well-being''' of all lab members are fundamental to our success, and we are committed to fostering an environment that prioritizes both physical and mental well-being. We recognize that research is demanding, but we believe that productivity should never come at the expense of personal health. We prioritize open conversations about workload and stress. We support one another and ensure resources are available. Mutual respect, kindness, and a culture of care guide our interactions, and we actively promote policies and practices that create a sustainable and supportive work environment. Whether through flexible work arrangements, shared wisdom, or simply fostering a community where individuals feel valued and heard, we are dedicated to ensuring that every member of our lab thrives both professionally and personally.<br /> <br /> '''Science is inherently political''' because it shapes and is shaped by the societies in which it operates. The questions we ask, the funding we receive, the policies that govern our research, and the ways scientific knowledge is applied all exist within political contexts. Science influences public policy on issues like AI, public health, and (neuro-)technology, and in turn, political systems determine who has access to education, research opportunities, and resources. We recognize that science is not neutral—it has the power to challenge injustices, inform decision-making, and drive societal progress. As researchers, we have a responsibility to engage critically with the political dimensions of our work, advocate for evidence-based policies, and ensure that science serves the common good rather than reinforcing systems of inequality or exclusion.<br /> <br /> '''Open science''' is a fundamental principle that ensures research is transparent, accessible, and reproducible. As most scientific research is publicly funded, we believe that the knowledge generated should be openly available to the public, rather than locked behind paywalls or restricted access. Open science practices, including open-access publishing, data/code sharing, and reproducible methodologies, strengthen scientific integrity, accelerate discovery, and promote collaboration across disciplines and communities. Science is a global effort, and open science fosters a spirit of collaboration and community by enabling researchers from around the world to share knowledge, build on each other’s work, and solve complex problems together. By embracing openness, we increase accountability, reduce barriers to participation, and maximize the impact of our research. We are committed to fostering a culture of transparency, where scientific progress benefits all and where knowledge is a shared resource for the advancement of society as a whole.<br /> <br /> '''Together''', we can harness the power of science and research to illuminate a path forward, even in the face of political, social, and environmental challenges. While it is easy to feel discouraged by uncertainty or division, we believe that discovery, innovation, and knowledge provide a foundation for optimism. Science is a testament to human curiosity and resilience—it has the power to solve complex problems, improve lives, and reveal new possibilities for the future. Through socially responsible research, we gain not only a deeper understanding of the world but also the tools to shape it for the better. By working collaboratively, asking bold questions, and committing to truth and progress, we can confront the challenges of our time with confidence. Science reminds us that the future is not predetermined—it is something we can build together, with courage, creativity, and a shared commitment to a better world.<br /> <br /> === Land Acknowledgment ===<br /> We are grateful to live and work as uninvited guests upon the traditional territories of the Haudenosaunee Confederacy and the Anishinabek Nation. To acknowledge this land is to recognize its deep and enduring history, one that predates the establishment of European settlements. It is also to recognize the significance of this territory for the Indigenous Peoples who have lived, and continue to live, upon it, whose cultural practices, languages, and spiritualities are deeply tied to the land and its ecosystems.<br /> <br /> Our research is shaped by the land we inhabit, and we strive to conduct our work with respect for Indigenous knowledge systems and environmental stewardship. We acknowledge that scientific inquiry does not exist in isolation from history, and we commit to learning from and collaborating with Indigenous communities to ensure that our research is ethical, responsible, and inclusive. By engaging in meaningful dialogue and action, we seek to honour Indigenous contributions to knowledge and to work toward a more equitable and sustainable future.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Ethos&diff=1842 2025-02-14T18:29:13Z

<p>Gunnar: Created page with &quot;= Lab Ethos = Our lab is committed to **fostering a culture of respect, honesty, integrity, and curiosity**, where all individuals are valued for their contributions and empowered to explore new ideas without fear of judgment. We uphold the highest ethical standards in our research and interactions, ensuring that honesty and integrity guide our work. We embrace curiosity as the driving force behind discovery, cultivating an environment where questions are encouraged, an...&quot;</p> <hr /> <div>= Lab Ethos =<br /> <br /> Our lab is committed to **fostering a culture of respect, honesty, integrity, and curiosity**, where all individuals are valued for their contributions and empowered to explore new ideas without fear of judgment. We uphold the highest ethical standards in our research and interactions, ensuring that honesty and integrity guide our work. We embrace curiosity as the driving force behind discovery, cultivating an environment where questions are encouraged, and learning is continuous. Harassment, discrimination, and exclusion have no place in our lab—we are dedicated to inclusivity, collaboration, and mutual support. Through these principles, we seek to push the boundaries of knowledge while maintaining a community built on trust, openness, and shared purpose.<br /> <br /> We believe that **diversity in backgrounds, perspectives, and experiences** is the foundation of innovation, creativity, and transformative scientific progress. Our lab thrives on the strength that comes from bringing together individuals with different viewpoints, cultures, and expertise, as we recognize that the best ideas emerge from inclusive collaboration. We actively foster an environment where everyone feels empowered to contribute, knowing that diverse voices enrich our research and propel new discoveries. By embracing and celebrating our differences, we build a stronger, more dynamic community that challenges assumptions, expands possibilities, and ensures our work has a broad and lasting impact.<br /> <br /> **Science is for everyone**, not just a select few, and we are committed to making research accessible, inclusive, and welcoming to all who are curious and passionate about discovery. We reject the notion that science belongs only to an elite group and instead champion the idea that diverse perspectives and backgrounds strengthen scientific inquiry. Knowledge should not be a privilege but a shared resource that benefits all of humanity. We strive to break down barriers—whether social, economic, or institutional—that limit participation. Through mentorship, outreach, and open collaboration, we create opportunities for anyone who wishes to engage with science, ensuring that the pursuit of knowledge remains open, equitable, and driven by collective curiosity rather than exclusivity.<br /> <br /> The **health and well-being** of all lab members are fundamental to our success, and we are committed to fostering an environment that prioritizes both physical and mental well-being. We recognize that research is demanding, but we believe that productivity should never come at the expense of personal health. We prioritize open conversations about workload and stress. We support one another and ensure resources are available. Mutual respect, kindness, and a culture of care guide our interactions, and we actively promote policies and practices that create a sustainable and supportive work environment. Whether through flexible work arrangements, shared wisdom, or simply fostering a community where individuals feel valued and heard, we are dedicated to ensuring that every member of our lab thrives both professionally and personally.<br /> <br /> **Science is inherently political** because it shapes and is shaped by the societies in which it operates. The questions we ask, the funding we receive, the policies that govern our research, and the ways scientific knowledge is applied all exist within political contexts. Science influences public policy on issues like AI, public health, and (neuro-)technology, and in turn, political systems determine who has access to education, research opportunities, and resources. We recognize that science is not neutral—it has the power to challenge injustices, inform decision-making, and drive societal progress. As researchers, we have a responsibility to engage critically with the political dimensions of our work, advocate for evidence-based policies, and ensure that science serves the common good rather than reinforcing systems of inequality or exclusion.<br /> <br /> **Open science** is a fundamental principle that ensures research is transparent, accessible, and reproducible. As most scientific research is publicly funded, we believe that the knowledge generated should be openly available to the public, rather than locked behind paywalls or restricted access. Open science practices, including open-access publishing, data/code sharing, and reproducible methodologies, strengthen scientific integrity, accelerate discovery, and promote collaboration across disciplines and communities. Science is a global effort, and open science fosters a spirit of collaboration and community by enabling researchers from around the world to share knowledge, build on each other’s work, and solve complex problems together. By embracing openness, we increase accountability, reduce barriers to participation, and maximize the impact of our research. We are committed to fostering a culture of transparency, where scientific progress benefits all and where knowledge is a shared resource for the advancement of society as a whole.<br /> <br /> **Together**, we can harness the power of science and research to illuminate a path forward, even in the face of political, social, and environmental challenges. While it is easy to feel discouraged by uncertainty or division, we believe that discovery, innovation, and knowledge provide a foundation for optimism. Science is a testament to human curiosity and resilience—it has the power to solve complex problems, improve lives, and reveal new possibilities for the future. Through socially responsible research, we gain not only a deeper understanding of the world but also the tools to shape it for the better. By working collaboratively, asking bold questions, and committing to truth and progress, we can confront the challenges of our time with confidence. Science reminds us that the future is not predetermined—it is something we can build together, with courage, creativity, and a shared commitment to a better world.<br /> <br /> === Land Acknowledgment ===<br /> We are grateful to live and work as uninvited guests upon the traditional territories of the Haudenosaunee Confederacy and the Anishinabek Nation. To acknowledge this land is to recognize its deep and enduring history, one that predates the establishment of European settlements. It is also to recognize the significance of this territory for the Indigenous Peoples who have lived, and continue to live, upon it, whose cultural practices, languages, and spiritualities are deeply tied to the land and its ecosystems.<br /> <br /> Our research is shaped by the land we inhabit, and we strive to conduct our work with respect for Indigenous knowledge systems and environmental stewardship. We acknowledge that scientific inquiry does not exist in isolation from history, and we commit to learning from and collaborating with Indigenous communities to ensure that our research is ethical, responsible, and inclusive. By engaging in meaningful dialogue and action, we seek to honour Indigenous contributions to knowledge and to work toward a more equitable and sustainable future.</div>

Gunnar http://compneurosci.com/wiki/index.php?title=MediaWiki:Sidebar&diff=1841 2025-02-14T18:25:07Z

<p>Gunnar: </p> <hr /> <div><br /> * navigation<br /> ** mainpage|mainpage-description<br /> ** Lab_Info|Lab Info<br /> ** Lab_Ethos|Lab Ethos<br /> ** Learning_Resources|Learning Resources<br /> ** software|Software<br /> ** Other_Resources|Other Resources<br /> ** CoSMo | CoSMo<br /> ** Neuromatch|Neuromatch<br /> ** Journal_Club|Journal Club<br /> ** IRTG|IRTG<br /> ** EDI|EDI<br /> * SEARCH<br /> * TOOLBOX<br /> * LANGUAGES</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1838 2024-12-06T18:22:02Z

<p>Gunnar: </p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> == On-boarding: welcome to the lab==<br /> Once you join Queen's University, you will be issued an email address and credentials for accessing university-internal resources. <br /> Here is a list of things you will need to ask for / do:<br /> * get keys (and security code) for office and lab spaces<br /> * get a lab tour<br /> * meet administrative, IT and teaching staff<br /> * request an invite to the lab's Slack channel<br /> * ask your supervisor for desk space and a computer...<br /> ----<br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity<br /> * make sure your work hours overlap with your supervisor<br /> * communicate with your supervisor frequently<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully<br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions<br /> * no form of harassment or discrimination will be tolerated<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected<br /> * please do NOT come to work if you feel unwell, especially if you have upper respiratory symptoms<br /> <br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor<br /> * Please book experimental sessions on the lab Google calendar<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out<br /> * Do not handle food / beverages around lab equipment other than the computers<br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research<br /> * Back up your data frequently. Always imagine you could lose all of your data / work today<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research<br /> * Communicate often with your supervisor<br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor<br /> * NEW: once the first draft is written, we will finalize it together, i.e. we will sit together in front of a computer (or shared Zoom screen) and work on it together to maximize learning<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work<br /> * The goal is for everyone to attend at least 1 conference / year<br /> * The condition to attend a conference is that you must have NEW material to present<br /> * Conference abstracts are NOT written in the 11th hour... Your supervisor will pull the plug if that happens and you don’t get to go<br /> * Keep conference costs at a minimum. The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded<br /> * Conferences are an amazing learning experience. Take fully advantage of them</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Software&diff=1837 2024-10-30T13:30:45Z

<p>Gunnar: /* (Vector) Graphics */</p> <hr /> <div>== General ==<br /> * [https://git-scm.com/ Git] (free) - version control software and [[Media:Canada-git-presentation_Dmytro.pdf | Dmytro's tutorial]]<br /> * [http://www.ghisler.com/ Total Commander] (free) - file manager<br /> * [https://winscp.net/eng/index.php WinSCP] (free) - file transfer tool<br /> * [https://www.mendeley.com/ Mendeley reference manager] (AVOID! Use Zotero instead!!!) - manage references &amp; automatic bibliography generator for MSWord, LibreOffice and BibTeX<br /> * [https://www.zotero.org/ Zotero] (free) article and biography manager for Word, LibreOffice, and Google Docs. [https://gdalle.github.io/phd-resources/tutorials/zotero/?s=03 Here is a tutorial to get started...]<br /> * [http://www.seamonkey-project.org/ SeaMonkey] (free) - internet application suite including web editor<br /> * [https://pandoc.org/ Pandoc] (free) - the ultimate document converter (supporting all kinds of formats, including LaTeX, Markdown, DocX, etc)<br /> * [https://miro.com/ Brainstorming virtual whiteboard] (free)<br /> * [https://marktext.app/ Mark Text desktop Markdown editor] (free)<br /> <br /> == Online teaching / collaborations / broadcasting ==<br /> * [https://obsproject.com/ Open Broadcaster Software] (free) for recording / editing / creating videos and live streaming<br /> * [https://meet.jit.si/ Jitsi] (free) secure fully featured video conferencing<br /> * [https://zoom.us/ Zoom] video communications platform<br /> * [https://lifehacker.com/the-best-alternatives-to-zoom-for-remote-meetings-1842431075 Article about Zoom alternatives...]<br /> * [https://whereby.com/ Whereby] - free basic version for video meetings<br /> <br /> == Text Editors ==<br /> * [http://www.texniccenter.org/ TeXnicCenter] (free) - LateX editor<br /> * [https://www.lyx.org/ LyX] (free) - graphical document processing interface using LaTeX<br /> * [https://atom.io/ Atom] - Customizable text editor by GitHub. Allows for real-time collaboration (!) and has not-too-clumsy Git integration.<br /> * [http://spacemacs.org/ Spacemacs] - A distribution of Emacs, at the deep end of customizable text editors. Emacs has many &quot;modes&quot;; the most useful is probably [https://orgmode.org/ Org mode], which is a kind of outliner+agenda that can be used to rapidly create LaTeX/Beamer files, and for [https://hal.archives-ouvertes.fr/hal-00591455 literate programming] (think Jupyter), among [http://ehneilsen.net/notebook/orgExamples/org-examples.html other uses]. Compared to plain Emacs, Spacemacs starts with a better configuration that is more modular and easier to customize, and allows for the (optional) use of Vim keybindings.<br /> * [https://typora.io/ Typora] (free) - an awesome minimalist MarkDown editor<br /> <br /> == Imaging Analysis ==<br /> * [http://www.fil.ion.ucl.ac.uk/spm/ SPM] (free) - general purpose (fMRI oriented)<br /> * [http://www.lcs.poli.usp.br/~baccala/pdc/CRCBrainConnectivity/AsympPDC/ PDC] (free) - partially directed coherence analysis<br /> * [http://www.fieldtriptoolbox.org/ FieldTrip] (free) - general purpose (EEG/MEG oriented)<br /> * [http://cheynelab.utoronto.ca/brainwave Brainwave] (free) and [http://cheynelab.utoronto.ca/httpdoc/Brainwave_v3.1_Documentation_6October_2015_final.pdf documentation] - MEG analysis toolbox<br /> * [http://brainvis.wustl.edu/wiki/index.php/Caret:Download CARET] (free) - anatomical reconstruction toolkit for structural and anatomical data ([http://prefrontal.org/blog/2009/04/using-caret-for-fmri-visualization/ simple tutorial])<br /> * [http://neurosynth.org/ Atlas for identifying brain regions / function] based on fMRI meta-analyses<br /> <br /> == Machine Learning ==<br /> * [http://www.cs.ubc.ca/~murphyk/Software/Kalman/kalman.html Kevin Murphy's Kalman filter toolbox] (free)<br /> *[http://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles/index-1.0.html libSVM] - Support Vector Machine Library<br /> *[https://github.com/merwan/ml-class Andrew Ng's ML Programming Exercises]<br /> <br /> == (Vector) Graphics ==<br /> * [http://www.coreldraw.com/ca/ CorelDraw] - ask Gunnar for installation CD<br /> * [https://inkscape.org/en/ Inkscape] (free)<br /> * [https://biorender.com/ BioRender] (free) graphics for life sciences<br /> * [http://betterposters.blogspot.com/ Better Posters] blog with lots of great tips!<br /> * [https://scidraw.io/ Scientific drawings] (free) premade graphics<br /> * [https://www.nature.com/articles/s41467-020-19160-7 Better use of color], paper with good suggestions on colors in figures<br /> * [https://www.photopea.com/ Photopea] - free online photo editor<br /> * [https://glimpse-editor.org/ Glimpse] - free image editor<br /> * [https://bioicons.com/ Bioicons] - another free database of scientific graphics<br /> * [https://bioart.niaid.nih.gov/ NIH bioart] (free)<br /> <br /> == Statistics == <br /> * [http://statcheck.io/ statcheck] - Use this to check papers for errors in statistical reporting!<br /> * SPSS - general purpose<br /> * [https://www.r-project.org/ R] (free) - general purpose with [http://r4ds.had.co.nz/ great online book]!<br /> * [http://psignifit.sourceforge.net/ psignifit] (free) - psychometric function fitting<br /> * [https://jasp-stats.org/ JASP] (free) is especially useful for Bayesian stats! And [https://www.youtube.com/watch?v=YIA3x6rp7cE&amp;t=59s this is a great YouTube video] to understand Bayesian ANOVAs with JASP...<br /> * [https://www.jamovi.org/ jamovi] - another great open statistics program (with R integration)<br /> <br /> == Experiments ==<br /> * [https://peterscarfe.com/ptbtutorials.html?s=03 Psychtoolbox Demos] by Peter Scarfe lab</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Learning_Resources&diff=1836 2024-10-22T19:45:23Z

<p>Gunnar: /* Statistics */</p> <hr /> <div>== Bayesian Nonparametrics ==<br /> * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]<br /> * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&amp;acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).<br /> * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.<br /> * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&amp;hl=en&amp;oi=ao Any of the papers] of Radford Neal.<br /> * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].<br /> <br /> == General Computational Neuroscience ==<br /> * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.<br /> * [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.<br /> * [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control &amp; learning]<br /> * [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]<br /> * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli<br /> * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience<br /> * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]<br /> * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2<br /> * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij<br /> * [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF)<br /> * [https://direct.mit.edu/books/book/3159/Computational-Modeling-Methods-for-Neuroscientists Computational Modeling Methods for Neuroscientists] - (free PDF) by Erik De Schutter<br /> <br /> == Information Theory ==<br /> * [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] <br /> * [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.<br /> * [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]<br /> <br /> == Machine Learning ==<br /> ==== Books ====<br /> * [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control] <br /> * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]<br /> * [http://alex.smola.org/drafts/thebook.pdf Smola &amp; Vishwanathan's book]<br /> * [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]<br /> * [https://probml.github.io/pml-book/ Murphy's book]<br /> * [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz &amp; Ben-David's book]<br /> * [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]<br /> * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]<br /> * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]<br /> * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).<br /> * [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.<br /> * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''<br /> * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]<br /> * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt &amp; Recht<br /> * [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts &amp; Yaida<br /> <br /> ==== Online Resources ====<br /> * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --&gt; free enrollment)]<br /> * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]<br /> * [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]<br /> * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop<br /> * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.<br /> * Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)<br /> <br /> ==== Journal Club Tutorials ====<br /> See [[Journal Club#Tutorials]].<br /> <br /> == General Math ==<br /> * [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]<br /> * [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. <br /> * [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]<br /> * [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]<br /> * [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]<br /> *[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&amp;token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]<br /> * [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]<br /> * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.<br /> * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler<br /> * [https://www.biodyn.ro/course/literatura/Nonlinear_Dynamics_and_Chaos_2018_Steven_H._Strogatz.pdf Nonlinear Dynamics And Chaos] book by Steven H. Strogatz<br /> <br /> == MATLAB ==<br /> * [https://www.coursera.org/learn/matlab Intro to programming] with Matlab<br /> * [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]<br /> * [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] (&quot;The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.&quot;)<br /> * [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]<br /> * [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]<br /> **[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]<br /> * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts<br /> * [https://www.matlabcoding.com/2023/12/matlab-for-neuroscientists-introduction.html Matlab for Neuroscientists] (free PDF) 2nd edition, by Pascal Wallish et al.<br /> <br /> == Python ==<br /> * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch<br /> * [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python<br /> * [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]<br /> * [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]<br /> * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]<br /> * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault<br /> * [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal<br /> * [https://virati.medium.com/make-your-code-last-forever-18e5bd3e4842 How to use containers for code] - by Vineet Tiruvadi<br /> <br /> == Statistics ==<br /> * [http://onlinestatbook.com/2/index.html Rice University online Stats book]<br /> * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book<br /> * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods &amp; significance estimation]<br /> * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)<br /> * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]<br /> * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]<br /> * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]<br /> * [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]<br /> * [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]<br /> * [https://statsthinking21.github.io/statsthinking21-core-site/ &quot;Statistical Thinking for the 21st Century&quot;] free online book by Russell A. Poldrack<br /> * [http://www.stat.columbia.edu/~gelman/book/ &quot;Bayesian Data Analysis&quot;] book by Andrew Gelman et al. with examples in Python and R<br /> * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]<br /> * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free<br /> * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free<br /> * [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free<br /> * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!<br /> * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code<br /> * [https://bruno.nicenboim.me/bayescogsci/ An Introduction to Bayesian Data Analysis for Cognitive Science] free online book by Bruno Nicenboim, Daniel J. Schad, and Shravan Vasishth<br /> <br /> == Neuroimaging analyses ==<br /> * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]<br /> * [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Learning_Resources&diff=1835 2024-03-08T14:43:35Z

<p>Gunnar: /* General Math */</p> <hr /> <div>== Bayesian Nonparametrics ==<br /> * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]<br /> * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&amp;acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).<br /> * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.<br /> * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&amp;hl=en&amp;oi=ao Any of the papers] of Radford Neal.<br /> * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].<br /> <br /> == General Computational Neuroscience ==<br /> * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.<br /> * [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.<br /> * [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control &amp; learning]<br /> * [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]<br /> * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli<br /> * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience<br /> * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]<br /> * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2<br /> * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij<br /> * [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF)<br /> * [https://direct.mit.edu/books/book/3159/Computational-Modeling-Methods-for-Neuroscientists Computational Modeling Methods for Neuroscientists] - (free PDF) by Erik De Schutter<br /> <br /> == Information Theory ==<br /> * [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] <br /> * [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.<br /> * [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]<br /> <br /> == Machine Learning ==<br /> ==== Books ====<br /> * [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control] <br /> * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]<br /> * [http://alex.smola.org/drafts/thebook.pdf Smola &amp; Vishwanathan's book]<br /> * [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]<br /> * [https://probml.github.io/pml-book/ Murphy's book]<br /> * [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz &amp; Ben-David's book]<br /> * [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]<br /> * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]<br /> * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]<br /> * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).<br /> * [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.<br /> * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''<br /> * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]<br /> * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt &amp; Recht<br /> * [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts &amp; Yaida<br /> <br /> ==== Online Resources ====<br /> * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --&gt; free enrollment)]<br /> * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]<br /> * [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]<br /> * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop<br /> * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.<br /> * Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)<br /> <br /> ==== Journal Club Tutorials ====<br /> See [[Journal Club#Tutorials]].<br /> <br /> == General Math ==<br /> * [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]<br /> * [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. <br /> * [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]<br /> * [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]<br /> * [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]<br /> *[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&amp;token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]<br /> * [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]<br /> * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.<br /> * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler<br /> * [https://www.biodyn.ro/course/literatura/Nonlinear_Dynamics_and_Chaos_2018_Steven_H._Strogatz.pdf Nonlinear Dynamics And Chaos] book by Steven H. Strogatz<br /> <br /> == MATLAB ==<br /> * [https://www.coursera.org/learn/matlab Intro to programming] with Matlab<br /> * [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]<br /> * [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] (&quot;The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.&quot;)<br /> * [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]<br /> * [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]<br /> **[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]<br /> * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts<br /> * [https://www.matlabcoding.com/2023/12/matlab-for-neuroscientists-introduction.html Matlab for Neuroscientists] (free PDF) 2nd edition, by Pascal Wallish et al.<br /> <br /> == Python ==<br /> * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch<br /> * [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python<br /> * [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]<br /> * [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]<br /> * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]<br /> * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault<br /> * [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal<br /> * [https://virati.medium.com/make-your-code-last-forever-18e5bd3e4842 How to use containers for code] - by Vineet Tiruvadi<br /> <br /> == Statistics ==<br /> * [http://onlinestatbook.com/2/index.html Rice University online Stats book]<br /> * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book<br /> * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods &amp; significance estimation]<br /> * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)<br /> * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]<br /> * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]<br /> * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]<br /> * [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]<br /> * [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]<br /> * [https://statsthinking21.github.io/statsthinking21-core-site/ &quot;Statistical Thinking for the 21st Century&quot;] free online book by Russell A. Poldrack<br /> * [http://www.stat.columbia.edu/~gelman/book/ &quot;Bayesian Data Analysis&quot;] book by Andrew Gelman et al. with examples in Python and R<br /> * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]<br /> * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free<br /> * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free<br /> * [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free<br /> * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!<br /> * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code<br /> <br /> == Neuroimaging analyses ==<br /> * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]<br /> * [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Learning_Resources&diff=1834 2024-02-02T13:17:37Z

<p>Gunnar: /* MATLAB */</p> <hr /> <div>== Bayesian Nonparametrics ==<br /> * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]<br /> * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&amp;acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).<br /> * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.<br /> * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&amp;hl=en&amp;oi=ao Any of the papers] of Radford Neal.<br /> * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].<br /> <br /> == General Computational Neuroscience ==<br /> * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.<br /> * [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.<br /> * [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control &amp; learning]<br /> * [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]<br /> * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli<br /> * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience<br /> * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]<br /> * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2<br /> * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij<br /> * [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF)<br /> * [https://direct.mit.edu/books/book/3159/Computational-Modeling-Methods-for-Neuroscientists Computational Modeling Methods for Neuroscientists] - (free PDF) by Erik De Schutter<br /> <br /> == Information Theory ==<br /> * [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] <br /> * [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.<br /> * [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]<br /> <br /> == Machine Learning ==<br /> ==== Books ====<br /> * [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control] <br /> * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]<br /> * [http://alex.smola.org/drafts/thebook.pdf Smola &amp; Vishwanathan's book]<br /> * [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]<br /> * [https://probml.github.io/pml-book/ Murphy's book]<br /> * [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz &amp; Ben-David's book]<br /> * [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]<br /> * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]<br /> * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]<br /> * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).<br /> * [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.<br /> * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''<br /> * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]<br /> * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt &amp; Recht<br /> * [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts &amp; Yaida<br /> <br /> ==== Online Resources ====<br /> * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --&gt; free enrollment)]<br /> * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]<br /> * [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]<br /> * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop<br /> * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.<br /> * Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)<br /> <br /> ==== Journal Club Tutorials ====<br /> See [[Journal Club#Tutorials]].<br /> <br /> == General Math ==<br /> * [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]<br /> * [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. <br /> * [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]<br /> * [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]<br /> * [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]<br /> *[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&amp;token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]<br /> * [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]<br /> * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.<br /> * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler<br /> <br /> == MATLAB ==<br /> * [https://www.coursera.org/learn/matlab Intro to programming] with Matlab<br /> * [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]<br /> * [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] (&quot;The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.&quot;)<br /> * [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]<br /> * [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]<br /> **[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]<br /> * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts<br /> * [https://www.matlabcoding.com/2023/12/matlab-for-neuroscientists-introduction.html Matlab for Neuroscientists] (free PDF) 2nd edition, by Pascal Wallish et al.<br /> <br /> == Python ==<br /> * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch<br /> * [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python<br /> * [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]<br /> * [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]<br /> * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]<br /> * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault<br /> * [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal<br /> * [https://virati.medium.com/make-your-code-last-forever-18e5bd3e4842 How to use containers for code] - by Vineet Tiruvadi<br /> <br /> == Statistics ==<br /> * [http://onlinestatbook.com/2/index.html Rice University online Stats book]<br /> * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book<br /> * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods &amp; significance estimation]<br /> * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)<br /> * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]<br /> * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]<br /> * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]<br /> * [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]<br /> * [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]<br /> * [https://statsthinking21.github.io/statsthinking21-core-site/ &quot;Statistical Thinking for the 21st Century&quot;] free online book by Russell A. Poldrack<br /> * [http://www.stat.columbia.edu/~gelman/book/ &quot;Bayesian Data Analysis&quot;] book by Andrew Gelman et al. with examples in Python and R<br /> * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]<br /> * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free<br /> * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free<br /> * [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free<br /> * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!<br /> * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code<br /> <br /> == Neuroimaging analyses ==<br /> * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]<br /> * [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Learning_Resources&diff=1833 2024-02-02T13:17:16Z

<p>Gunnar: /* MATLAB */</p> <hr /> <div>== Bayesian Nonparametrics ==<br /> * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]<br /> * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&amp;acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).<br /> * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.<br /> * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&amp;hl=en&amp;oi=ao Any of the papers] of Radford Neal.<br /> * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].<br /> <br /> == General Computational Neuroscience ==<br /> * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.<br /> * [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.<br /> * [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control &amp; learning]<br /> * [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]<br /> * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli<br /> * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience<br /> * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]<br /> * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2<br /> * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij<br /> * [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF)<br /> * [https://direct.mit.edu/books/book/3159/Computational-Modeling-Methods-for-Neuroscientists Computational Modeling Methods for Neuroscientists] - (free PDF) by Erik De Schutter<br /> <br /> == Information Theory ==<br /> * [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] <br /> * [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.<br /> * [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]<br /> <br /> == Machine Learning ==<br /> ==== Books ====<br /> * [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control] <br /> * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]<br /> * [http://alex.smola.org/drafts/thebook.pdf Smola &amp; Vishwanathan's book]<br /> * [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]<br /> * [https://probml.github.io/pml-book/ Murphy's book]<br /> * [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz &amp; Ben-David's book]<br /> * [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]<br /> * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]<br /> * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]<br /> * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).<br /> * [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.<br /> * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''<br /> * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]<br /> * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt &amp; Recht<br /> * [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts &amp; Yaida<br /> <br /> ==== Online Resources ====<br /> * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --&gt; free enrollment)]<br /> * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]<br /> * [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]<br /> * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop<br /> * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.<br /> * Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)<br /> <br /> ==== Journal Club Tutorials ====<br /> See [[Journal Club#Tutorials]].<br /> <br /> == General Math ==<br /> * [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]<br /> * [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. <br /> * [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]<br /> * [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]<br /> * [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]<br /> *[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&amp;token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]<br /> * [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]<br /> * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.<br /> * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler<br /> <br /> == MATLAB ==<br /> * [https://www.coursera.org/learn/matlab Intro to programming] with Matlab<br /> * [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]<br /> * [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] (&quot;The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.&quot;)<br /> * [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]<br /> * [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]<br /> **[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]<br /> * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts<br /> * [Matlab for Neuroscientists https://www.matlabcoding.com/2023/12/matlab-for-neuroscientists-introduction.html] (free PDF) 2nd edition, by Pascal Wallish et al.<br /> <br /> == Python ==<br /> * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch<br /> * [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python<br /> * [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]<br /> * [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]<br /> * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]<br /> * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault<br /> * [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal<br /> * [https://virati.medium.com/make-your-code-last-forever-18e5bd3e4842 How to use containers for code] - by Vineet Tiruvadi<br /> <br /> == Statistics ==<br /> * [http://onlinestatbook.com/2/index.html Rice University online Stats book]<br /> * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book<br /> * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods &amp; significance estimation]<br /> * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)<br /> * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]<br /> * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]<br /> * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]<br /> * [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]<br /> * [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]<br /> * [https://statsthinking21.github.io/statsthinking21-core-site/ &quot;Statistical Thinking for the 21st Century&quot;] free online book by Russell A. Poldrack<br /> * [http://www.stat.columbia.edu/~gelman/book/ &quot;Bayesian Data Analysis&quot;] book by Andrew Gelman et al. with examples in Python and R<br /> * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]<br /> * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free<br /> * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free<br /> * [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free<br /> * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!<br /> * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code<br /> <br /> == Neuroimaging analyses ==<br /> * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]<br /> * [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Main_Page&diff=1832 2023-09-06T12:58:33Z

<p>Gunnar: /* Lab Philosophy */</p> <hr /> <div>Welcome to the [http://www.compneurosci.com/ Blohm lab] wiki. We're the computational Vision and Kinematics Group (ViKinG) at Queen's University. This is where we will post useful information about lab activities, links to software, tutorials etc.<br /> <br /> == Lab Philosophy ==<br /> <br /> The number 1 priority of the lab is the well-being of all students and personnel. We encourage communication, work-life balance, and personal growth. The goal is to be happy and stay happy; because a happy scientist is a productive scientist. &lt;br&gt;<br /> <br /> As academics, we thrive to grow personally and professionally. Thus, learning, debating and being open minded is at the core of who we are and what we do. &lt;br&gt;<br /> <br /> The lab carries out Open Science research. We embrace Open Science principles and all new projects follow [https://cos.io/ Open Science methods].<br /> <br /> == Lab pictures ==<br /> <br /> [[File:GunnarNMA3.jpg|400px|thumb|left|October 2020: Gunnar MCing at Neuromatch 3.0 conference]]<br /> <br /> [[File:2020VikingLabRetreat.jpg|400px|thumb|left|March 2020 lab retreat to Mont Saint Marie, QC.&lt;br&gt; With special guest: Dominik Endres]]<br /> <br /> [[File:IMG_20190117_185243.jpg|400px|thumb|left|January 2019 lab retreat to Le Massif, QC.&lt;br&gt; With guests: Joe DeSouza and Philippe Lefevre]]<br /> <br /> [[File:2018-08-16_15.22.00.jpg|400px|thumb|left|Lab meeting with Philippe Lefevre (August 2018)]]<br /> <br /> [[File:retreat-2018.jpg|400px|thumb|left|At our January 2018 retreat to Sutton, QC.]]<br /> <br /> [[File:TDCSlab.jpg|400px|thumb|left|tDCS lab picture taken during May 2016 CBC interview]]</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1831 2023-09-06T12:57:15Z

<p>Gunnar: /* Paper writing */</p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> <br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity<br /> * make sure your work hours overlap with your supervisor<br /> * communicate with your supervisor frequently<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully<br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions<br /> * no form of harassment or discrimination will be tolerated<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected<br /> * please do NOT come to work if you feel unwell, especially if you have upper respiratory symptoms<br /> <br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor<br /> * Please book experimental sessions on the lab Google calendar<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out<br /> * Do not handle food / beverages around lab equipment other than the computers<br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research<br /> * Back up your data frequently. Always imagine you could lose all of your data / work today<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research<br /> * Communicate often with your supervisor<br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor<br /> * NEW: once the first draft is written, we will finalize it together, i.e. we will sit together in front of a computer (or shared Zoom screen) and work on it together to maximize learning<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work<br /> * The goal is for everyone to attend at least 1 conference / year<br /> * The condition to attend a conference is that you must have NEW material to present<br /> * Conference abstracts are NOT written in the 11th hour... Your supervisor will pull the plug if that happens and you don’t get to go<br /> * Keep conference costs at a minimum. The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded<br /> * Conferences are an amazing learning experience. Take fully advantage of them</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1830 2023-09-06T12:56:32Z

<p>Gunnar: /* Conferences */</p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> <br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity<br /> * make sure your work hours overlap with your supervisor<br /> * communicate with your supervisor frequently<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully<br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions<br /> * no form of harassment or discrimination will be tolerated<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected<br /> * please do NOT come to work if you feel unwell, especially if you have upper respiratory symptoms<br /> <br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor<br /> * Please book experimental sessions on the lab Google calendar<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out<br /> * Do not handle food / beverages around lab equipment other than the computers<br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research<br /> * Back up your data frequently. Always imagine you could lose all of your data / work today<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research<br /> * Communicate often with your supervisor<br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor<br /> * NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work<br /> * The goal is for everyone to attend at least 1 conference / year<br /> * The condition to attend a conference is that you must have NEW material to present<br /> * Conference abstracts are NOT written in the 11th hour... Your supervisor will pull the plug if that happens and you don’t get to go<br /> * Keep conference costs at a minimum. The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded<br /> * Conferences are an amazing learning experience. Take fully advantage of them</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1829 2023-09-06T12:56:12Z

<p>Gunnar: /* Conferences */</p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> <br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity<br /> * make sure your work hours overlap with your supervisor<br /> * communicate with your supervisor frequently<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully<br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions<br /> * no form of harassment or discrimination will be tolerated<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected<br /> * please do NOT come to work if you feel unwell, especially if you have upper respiratory symptoms<br /> <br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor<br /> * Please book experimental sessions on the lab Google calendar<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out<br /> * Do not handle food / beverages around lab equipment other than the computers<br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research<br /> * Back up your data frequently. Always imagine you could lose all of your data / work today<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research<br /> * Communicate often with your supervisor<br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor<br /> * NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work<br /> * The goal is for everyone to attend at least 1 conference / year<br /> * The condition to attend a conference is that you must have NEW material to present<br /> * Conference abstracts are NOT written in the 11th hour... Your supervisor will pull the plug if that happens and you don’t get to go<br /> * Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded<br /> * Conferences are an amazing learning experience! Take fully advantage of them</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1828 2023-09-06T12:55:51Z

<p>Gunnar: /* Paper writing */</p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> <br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity<br /> * make sure your work hours overlap with your supervisor<br /> * communicate with your supervisor frequently<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully<br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions<br /> * no form of harassment or discrimination will be tolerated<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected<br /> * please do NOT come to work if you feel unwell, especially if you have upper respiratory symptoms<br /> <br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor<br /> * Please book experimental sessions on the lab Google calendar<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out<br /> * Do not handle food / beverages around lab equipment other than the computers<br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research<br /> * Back up your data frequently. Always imagine you could lose all of your data / work today<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research<br /> * Communicate often with your supervisor<br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor<br /> * NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work!<br /> * The goal is for everyone to attend at least 1 conference / year.<br /> * The condition to attend a conference is that you must have NEW material to present!<br /> * Conference abstracts are NOT written in the 11th hour! Your supervisor will pull the plug if that happens and you don’t get to go!<br /> * Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded. <br /> * Conferences are an amazing learning experience! Take fully advantage of them!</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1827 2023-09-06T12:55:36Z

<p>Gunnar: /* Research */</p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> <br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity<br /> * make sure your work hours overlap with your supervisor<br /> * communicate with your supervisor frequently<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully<br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions<br /> * no form of harassment or discrimination will be tolerated<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected<br /> * please do NOT come to work if you feel unwell, especially if you have upper respiratory symptoms<br /> <br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor<br /> * Please book experimental sessions on the lab Google calendar<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out<br /> * Do not handle food / beverages around lab equipment other than the computers<br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research<br /> * Back up your data frequently. Always imagine you could lose all of your data / work today<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research<br /> * Communicate often with your supervisor<br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first!<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor!<br /> * NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning!<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced.<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work!<br /> * The goal is for everyone to attend at least 1 conference / year.<br /> * The condition to attend a conference is that you must have NEW material to present!<br /> * Conference abstracts are NOT written in the 11th hour! Your supervisor will pull the plug if that happens and you don’t get to go!<br /> * Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded. <br /> * Conferences are an amazing learning experience! Take fully advantage of them!</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1826 2023-09-06T12:55:07Z

<p>Gunnar: /* Experimental labs */</p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> <br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity<br /> * make sure your work hours overlap with your supervisor<br /> * communicate with your supervisor frequently<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully<br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions<br /> * no form of harassment or discrimination will be tolerated<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected<br /> * please do NOT come to work if you feel unwell, especially if you have upper respiratory symptoms<br /> <br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor<br /> * Please book experimental sessions on the lab Google calendar<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out<br /> * Do not handle food / beverages around lab equipment other than the computers<br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project!<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies!<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research!<br /> * Back up your data frequently! Always imagine you could lose all of your data / work today!<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research! <br /> * Communicate often with your supervisor! <br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first!<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor!<br /> * NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning!<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced.<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work!<br /> * The goal is for everyone to attend at least 1 conference / year.<br /> * The condition to attend a conference is that you must have NEW material to present!<br /> * Conference abstracts are NOT written in the 11th hour! Your supervisor will pull the plug if that happens and you don’t get to go!<br /> * Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded. <br /> * Conferences are an amazing learning experience! Take fully advantage of them!</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1825 2023-09-06T12:54:41Z

<p>Gunnar: /* Life in the lab */</p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> <br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity<br /> * make sure your work hours overlap with your supervisor<br /> * communicate with your supervisor frequently<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully<br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions<br /> * no form of harassment or discrimination will be tolerated<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected<br /> * please do NOT come to work if you feel unwell, especially if you have upper respiratory symptoms<br /> <br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab! <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor! <br /> * Please book experimental sessions on the lab Google calendar!<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors!<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards!<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out. <br /> * Do not handle food / beverages around lab equipment other than the computers. <br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project!<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies!<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research!<br /> * Back up your data frequently! Always imagine you could lose all of your data / work today!<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research! <br /> * Communicate often with your supervisor! <br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first!<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor!<br /> * NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning!<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced.<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work!<br /> * The goal is for everyone to attend at least 1 conference / year.<br /> * The condition to attend a conference is that you must have NEW material to present!<br /> * Conference abstracts are NOT written in the 11th hour! Your supervisor will pull the plug if that happens and you don’t get to go!<br /> * Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded. <br /> * Conferences are an amazing learning experience! Take fully advantage of them!</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1824 2023-09-06T12:53:42Z

<p>Gunnar: /* Life in the lab */</p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> <br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity!<br /> * make sure your work hours overlap with your supervisor!<br /> * communicate with your supervisor frequently!<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise!<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully. <br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions!<br /> * no form of harassment or discrimination will be tolerated!<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected!<br /> * please do NOT come to work if you feel unwell, especially if you have upper respiratory symptoms!<br /> <br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab! <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor! <br /> * Please book experimental sessions on the lab Google calendar!<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors!<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards!<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out. <br /> * Do not handle food / beverages around lab equipment other than the computers. <br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project!<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies!<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research!<br /> * Back up your data frequently! Always imagine you could lose all of your data / work today!<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research! <br /> * Communicate often with your supervisor! <br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first!<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor!<br /> * NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning!<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced.<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work!<br /> * The goal is for everyone to attend at least 1 conference / year.<br /> * The condition to attend a conference is that you must have NEW material to present!<br /> * Conference abstracts are NOT written in the 11th hour! Your supervisor will pull the plug if that happens and you don’t get to go!<br /> * Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded. <br /> * Conferences are an amazing learning experience! Take fully advantage of them!</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Lab_Info&diff=1823 2023-09-06T12:52:19Z

<p>Gunnar: /* Work environment regulations */</p> <hr /> <div>== Mission statement ==<br /> The long-term goal of the ViKinG lab is to uncover fundamental principles of brain function by investigating sensory-motor control. Our research program exploits the fact that virtually all processes in the brain start with sensation (e.g. vision, proprioception or touch) and ultimately culminate in an action (e.g. an eye, head or arm movement) to influence the outside world or gather new information. Thus, by studying the various sensory-motor processes underlying ecologically valid behavioural outcomes, we hope to gain insights into the computational building blocks of the brain. To do so, we take a multi-disciplinary approach that taps into the strengths of complementary experimental and computational methods to answer specific questions from as many perspectives as possible, ultimately solidifying (or falsifying) proposed concepts. We thus work towards advancing our understanding of the computational principles underlying brain function and dysfunction and hope that the cumulative body of work from our lab will make an impactful contribution. <br /> ----<br /> == Work environment regulations ==<br /> Please make sure you read the following documents to understand roles and responsibilities (SGS handbook) as well as timelines and expectations for graduate studies (CNS handbook). <br /> * [https://www.queensu.ca/grad-postdoc/sites/sgswww/files/uploaded_files/Graduate%20Supervision%20Handbook%202022.pdf SGS graduate supervision handbook]<br /> * [https://neuroscience.queensu.ca/sites/default/files/2022-12/Student%20Handbook_2022_23.pdf Centre for Neuroscience Graduate Student Handbook]<br /> ----<br /> <br /> == Equipment ==<br /> Visit [http://www.compneurosci.com/equipment.html here] for a list of available equipment.<br /> ----<br /> == Rules and Practices ==<br /> ==== Life in the lab ====<br /> * there are no fixed office hours. Chose your work times to maximize your productivity!<br /> * make sure your work hours overlap with your supervisor!<br /> * communicate with your supervisor frequently!<br /> * inform your supervisor about absences (sickness, vacation, conferences, etc)<br /> * make sure you have a healthy work-life balance and get enough exercise!<br /> * for the respect of others, the lab is a scent-free environment. There is no official dress code but please dress respectfully. <br /> * we expect honesty and scientific integrity. But everyone makes mistakes - communicate them to your supervisor to find solutions!<br /> * no form of harassment or discrimination will be tolerated!<br /> * if you have a problem that you feel uncomfortable talking about to your supervisor, please use available university services to resolve the issues (e.g. talk to graduate program assistant - Lucy; make use of student health services; uses SGS resources)<br /> * be a good citizen in the lab and at the CNS! Active engagement and participation in lab / program activities is expected!<br /> ==== Experimental labs ====<br /> * Handle equipment with care! There is about $1M worth of equipment in the lab! <br /> * Every experimenter must have read the relevant equipment manuals prior to starting a project to ensure proper handling and usage of all devices.<br /> * Experiments are only to be carried out after explicit approval of your supervisor! <br /> * Please book experimental sessions on the lab Google calendar!<br /> * After experiments, the lab should be left the way you would like to find it, i.e. clean and in order. Even if you are the only one running experiments! Also, please lock all windows / doors!<br /> * Make sure there are no cables, electronics, computers or power bars on the floor! We have had floods in the past and we should always minimize all tripping, electrocution, and other hazards!<br /> * Please make sure that there are always enough lab supplies available (e.g. disposable electrodes, bite bar cement, etc). Orders can take a while to arrive. Notify your supervisor when you can anticipate running out. <br /> * Do not handle food / beverages around lab equipment other than the computers. <br /> <br /> ==== Research ====<br /> * Write an abstract BEFORE even starting a project!<br /> * [http://help.osf.io/m/registrations Pre-register] all experimental studies!<br /> * Always adhere to the highest ethical and research standards in the field. You are responsible for your research!<br /> * Back up your data frequently! Always imagine you could lose all of your data / work today!<br /> * Communicate with your peers: if you have a question, it’s likely someone else in the lab has already solved it<br /> * Collaborate! Collaborate with your peers, with lab friends and experts in the field. Collaboration is fun and elevates everyone's research! <br /> * Communicate often with your supervisor! <br /> <br /> ==== Data / recording / analysis organization ====<br /> * Always back up all data!<br /> * Visualize newly recorded data immediately to ensure all signals have been recorded, all devices / software is working properly and all required information is present. Cables and hardware can fail at any time without resulting in data acquisition errors...<br /> * keep detailed lab notes regarding experimental procedures, participant information and particular situations arising.<br /> * Create one folder for each project / experiment. This folder should have appropriate sub-folders for all the data (raw and analyzed), all required Matlab (and/or other software) scripts for analysis / marking / plotting, all software to run the experiment, and the manuscript resulting from the study.<br /> * write clean analysis code! You will have to publish the data set along with the complete analysis pipeline! (anyone should be able to understand and execute your code and obtain the exact figures from your paper)<br /> <br /> ==== Paper writing ====<br /> * (Re-) write the abstract first!<br /> * Make use of the resources on this wiki! (e.g. CCC, Konrad’s writing advice)<br /> * Make figures and an outline with the logic and chain of arguments first and get feedback from your supervisor!<br /> * NEW: once the first draft is written, we will finalize it together! I.e. we will sit together in front of a computer and work on it together to maximize learning!<br /> * Open Science: all papers should include a link to the analysis/model code used and potential data that were used/produced.<br /> <br /> === Conferences ===<br /> * Conferences are a reward of good work!<br /> * The goal is for everyone to attend at least 1 conference / year.<br /> * The condition to attend a conference is that you must have NEW material to present!<br /> * Conference abstracts are NOT written in the 11th hour! Your supervisor will pull the plug if that happens and you don’t get to go!<br /> * Keep conference costs at a minimum! The less it costs, the more money is available for research / other conferences. Your supervisor will give you a budget for each conference that is not to be exceeded. <br /> * Conferences are an amazing learning experience! Take fully advantage of them!</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Other_Resources&diff=1822 2023-09-06T12:37:01Z

<p>Gunnar: /* How to do science */</p> <hr /> <div>== Funding Opportunities == <br /> *[http://queensu.ca/sgs/sites/webpublish.queensu.ca.sgswww/files/files/Students/Queen's%20Presentation%20Graduate%20Students%20Morning%20April%2010th.pdf Mitacs graduate and post-doc funding (for private sector /NGO internships, or academic travel scholarships)]<br /> * [https://anneurai.net/2016/10/16/postdoctoral-fellowship-programmes/ Anne Urai's list of postdoctoral fellowship programs] <br /> * [https://research.jhu.edu/rdt/funding-opportunities/postdoctoral/?s=03 List of postdoctoral fellowships] from Johns Hopkins<br /> * [https://research.jhu.edu/rdt/funding-opportunities/early-career/ List of early career funding opportunities] by Johns Hopkins<br /> <br /> == How to do science ==<br /> * [http://compneurosci.com/wiki/index.php/Paper_Writing_101 Konrad's paper writing 101]<br /> * Konrad's [http://biorxiv.org/content/early/2016/12/14/088278?utm_content=buffer8d04a&amp;utm_medium=social&amp;utm_source=facebook.com&amp;utm_campaign=buffer 10 simple rules for structuring papers] advice: read this before you start writing!!!<br /> * [http://collections.plos.org/ten-simple-rules PLoS CB - 10 simple rules collection]: a must read for everyone!<br /> * [http://colorbrewer2.org Colorbrewer]: great tool for selecting colour schemes on publications<br /> * [http://neuronline.sfn.org/Articles/Professional-Development/2016/Tricks-of-the-Trade-How-to-Peer-Review-a-Manuscript How to peer review a manuscript]: Webinar and resources from SfN Neuronline. <br /> * [http://neuronline.sfn.org/Articles/Career-Advice/2017/Tricks-of-the-Trade-Modelling-Papers-Resources How To Review Modelling Papers]: A webinar + resources from SfN Neuronline. The webinar date has passed, but if you 'register' there is an email link that will lead you to the archived video. <br /> * [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4284499/ The pleasure of publishing (Malhotra &amp; Marder, 2015)]: Elife editors' opinions on what makes an effective manuscript. <br /> * [[Media:OpenScience.pdf | How to do Open Science slides]]<br /> * [https://docs.google.com/document/d/1r6nDcF43esu3xBjmk3ERAmaEHKEB75_HflSkk3zZhBk/edit Write this instead of that] - tips for better writing<br /> * [https://youtu.be/Ss8fNOvVozo Gunnar's MAIN 2020 &quot;Introduction to writing scientific papers&quot; lecture] and [[Media:MAIN2020.pdf|accompanying lecture slides]]<br /> * [https://research.lib.buffalo.edu/poster-presentations Designing effective posters] - compilation of resources for scientific poster design<br /> * [https://carpentries-incubator.github.io/managing-computational-projects/ Managing Open and Reproducible Computational Projects] - a user guide<br /> <br /> == How to be successful ==<br /> * [https://www.technologyreview.com/s/409043/how-to-think/?utm_content=buffer9ea8e&amp;utm_medium=social&amp;utm_source=facebook.com&amp;utm_campaign=buffer Ed Boyden's advice on how to think]<br /> * [http://www.wikihow.com/Think Developing better thought processes]<br /> * [http://faculty.georgetown.edu/kingch/How_to_Think.htm How to argue well]<br /> * [http://www.lifehack.org/articles/productivity/12-weekend-habits-highly-successful-people.html Habits for success]<br /> * [http://www.cell.com/neuron/pdf/S0896-6273(15)00331-1.pdf Hitchhikers guide to a Career in Neuroscience]<br /> * [http://www.programmerfu.com/2017/04/20/fast-is-slow-slow-is-smooth-smooth-is-fast.html Slow is smooth and smooth is fast]<br /> * [http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.8391&amp;rep=rep1&amp;type=pdf Survival Skills for Graduate School and Beyond] (Fischer and Zigmond, 1998; still very applicable)<br /> * [https://www.cs.utexas.edu/users/EWD/transcriptions/EWD06xx/EWD637.html 3 golden rules of scientific success] by Dijkstra<br /> * [https://www.nature.com/articles/d41586-019-03914-5 Secrets to writing a winning grant]<br /> ----<br /> <br /> == Career development ==<br /> It is never to early to think about what you'd like to do in the future! This is true at any stage in your training or career. Critical (re-)evaluation of skills, goals and gaps allows for better planning and ultimately better job and happiness prospects. Take it seriously!!!<br /> * [http://www.cihr-irsc.gc.ca/e/50516.html CIHR individual career development plans]<br /> * your professional digital presence on the web is crucial for future employment! Future employers look at it! Use LinkedIn, make and maintain a web site (e.g. Google Sites), use Twitter professionally, etc<br /> * [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007163 how to give a job talk]<br /> * [https://www.cca-reports.ca/reports/the-labour-market-transition-of-phd-graduates/ Expert Panel on the Labour Market Transition of PhD Graduates]<br /> * [https://www.sciencedirect.com/science/article/pii/S0166223621000709 5 remediation strategies to overcome the gender gap]<br /> <br /> ----<br /> <br /> == Work-life balance &amp; mental health ==<br /> As an undergraduate, graduate student or postdoc, as exciting a time as this is, you will face stress, frustration and deception. Your passion for science can suck up all your time and wack your physical and mental health out of balance. This can lead to a downward spiral out of which comes no good. Yes, grad school / postdoc work is hard, don't let it destroy you. So here are a few tips to avoid that in the first place. For Queen's students, please consider contacting [https://www.queensu.ca/studentwellness/ Student Wellness Services] if you feel you could use some help...<br /> * Get enough exercise and sleep!<br /> * [http://www.nextscientist.com/work-life-balance-in-academia/ The Happy PhD zone]: how to maintain a work-life balance in academia<br /> * [http://thegradstudentway.com/blog/?p=76#.WRZWlGfSkvc 6 Ways To Survive Grad School and Achieve Work-Life Balance]<br /> * [https://www.mcgill.ca/gradsupervision/supervisees/work-life Official McGill University guidelines] on work-life balance<br /> * [https://www.bustle.com/articles/87409-graduate-school-is-hard-so-here-are-8-simple-ways-to-maintain-your-sanity How to maintain your sanity] during grad school<br /> * [https://psychcentral.com/lib/12-tips-for-surviving-and-thriving-in-grad-school/ Grad school survival tips]<br /> * [http://www.sciencemag.org/careers/2018/08/three-reminders-help-you-thrive-not-merely-survive-grad-school Three reminders to help you thrive—not merely survive—in grad school]<br /> * [http://www.nature.com/nature/journal/v545/n7654/full/nj7654-375a.html?WT.mc_id=FBK_NatureNews&amp;sf79769979=1 Break or burn out]: a nice article in Nature<br /> * [https://www.psychologytoday.com/files/attachments/1035/arts-foster-scientific-success.pdf Creative outlets and Scientific Success] Scientists are not more likely to have creative outlets, but the most successful ones are<br /> * [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006914 Ten simple rules towards healthier research labs]<br /> <br /> == Resources for organizing a scientific project ==<br /> * [https://towardsdatascience.com/how-to-keep-your-research-projects-organized-part-1-folder-structure-10bd56034d3a How to keep your research projects organized, part 1: folder structure]<br /> * [http://nikola.me/folder_structure.html Setting up an Organised Folder Structure for Research Projects]<br /> * [https://www.data.cam.ac.uk/data-management-guide/organising-your-data Organising your data]<br /> * [http://help.osf.io/m/bestpractices/l/611391-organizing-files OSF Guidelines]<br /> * [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1000424 A Quick Guide to Organizing Computational Biology Projects]<br /> * [https://evernote.com/ Evernote: Note taking app]<br /> * [https://trello.com/ Trello: Collaborate and share notes]<br /> * [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510 Good enough practices for scientific computing]<br /> * [https://www.hhmi.org/science-education/programs/making-right-moves?s=03 Making the Right Moves: A Practical Guide to Scientific Management for Postdocs and New Faculty] - AMAZING free online book<br /> * [https://online.ucpress.edu/collabra/article/7/1/18684/115927/Easing-Into-Open-Science-A-Guide-for-Graduate?s=03 How to ease into Open Science] - a tutorial guide<br /> <br /> == Teaching resources ==<br /> * [https://eddl.tru.ca/eddl-5141-online-teaching-and-learning/ Online Teaching and Learning] course - a how-to from course design to student motivation<br /> * [https://theprofessorisin.com/2017/10/27/looking-presentable-on-webcam-a-guest-post/ Looking presentable on webcam] (&quot;professor is in&quot; guest post)<br /> * [https://ropensci.org/commcalls/2018-10-16/?s=03 Code review in the lab...]</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Main_Page&diff=1821 2023-04-06T14:35:36Z

<p>Gunnar: /* Lab Philosophy */</p> <hr /> <div>Welcome to the [http://www.compneurosci.com/ Blohm lab] wiki. We're the computational Vision and Kinematics Group (ViKinG) at Queen's University. This is where we will post useful information about lab activities, links to software, tutorials etc.<br /> <br /> == Lab Philosophy ==<br /> <br /> The number 1 priority of the lab is the well-being of all students and personnel! We encourage communication, work-life balance, and personal growth. The goal is to be happy and stay happy; because a happy scientist is a productive scientist! &lt;br&gt;<br /> <br /> As academics, we thrive to grow personally and professionally. Thus, learning, debating and being open minded is at the core of who we are and what we do. &lt;br&gt;<br /> <br /> The lab carries out Open Science research. We embrace Open Science principles and all new projects follow [https://cos.io/ Open Science methods].<br /> <br /> == Lab pictures ==<br /> <br /> [[File:GunnarNMA3.jpg|400px|thumb|left|October 2020: Gunnar MCing at Neuromatch 3.0 conference]]<br /> <br /> [[File:2020VikingLabRetreat.jpg|400px|thumb|left|March 2020 lab retreat to Mont Saint Marie, QC.&lt;br&gt; With special guest: Dominik Endres]]<br /> <br /> [[File:IMG_20190117_185243.jpg|400px|thumb|left|January 2019 lab retreat to Le Massif, QC.&lt;br&gt; With guests: Joe DeSouza and Philippe Lefevre]]<br /> <br /> [[File:2018-08-16_15.22.00.jpg|400px|thumb|left|Lab meeting with Philippe Lefevre (August 2018)]]<br /> <br /> [[File:retreat-2018.jpg|400px|thumb|left|At our January 2018 retreat to Sutton, QC.]]<br /> <br /> [[File:TDCSlab.jpg|400px|thumb|left|tDCS lab picture taken during May 2016 CBC interview]]</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Main_Page&diff=1820 2023-04-06T14:33:54Z

<p>Gunnar: </p> <hr /> <div>Welcome to the [http://www.compneurosci.com/ Blohm lab] wiki. We're the computational Vision and Kinematics Group (ViKinG) at Queen's University. This is where we will post useful information about lab activities, links to software, tutorials etc.<br /> <br /> == Lab Philosophy ==<br /> <br /> The number 1 priority of the lab is the well-being of all students and personnel! We encourage communication, work-life balance, and personal growth. The goal is to be happy and stay happy; because a happy scientist is a productive scientist! &lt;br&gt;<br /> <br /> The lab carries out Open Science research. We embrace Open Science principles and all new projects follow [https://cos.io/ Open Science methods].<br /> <br /> == Lab pictures ==<br /> <br /> [[File:GunnarNMA3.jpg|400px|thumb|left|October 2020: Gunnar MCing at Neuromatch 3.0 conference]]<br /> <br /> [[File:2020VikingLabRetreat.jpg|400px|thumb|left|March 2020 lab retreat to Mont Saint Marie, QC.&lt;br&gt; With special guest: Dominik Endres]]<br /> <br /> [[File:IMG_20190117_185243.jpg|400px|thumb|left|January 2019 lab retreat to Le Massif, QC.&lt;br&gt; With guests: Joe DeSouza and Philippe Lefevre]]<br /> <br /> [[File:2018-08-16_15.22.00.jpg|400px|thumb|left|Lab meeting with Philippe Lefevre (August 2018)]]<br /> <br /> [[File:retreat-2018.jpg|400px|thumb|left|At our January 2018 retreat to Sutton, QC.]]<br /> <br /> [[File:TDCSlab.jpg|400px|thumb|left|tDCS lab picture taken during May 2016 CBC interview]]</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Neuromatch&diff=1819 2023-01-05T14:19:34Z

<p>Gunnar: /* Press and other materials */</p> <hr /> <div>In Spring 2020, due to the Covid-related cancellation of summer schools, we created Neuromatch Academy to teach computational neuroscience worldwide. Links and resources are collected here.<br /> <br /> == Course materials ==<br /> * [https://academy.neuromatch.io/ Neuromatch Academy web site]<br /> * [https://github.com/NeuromatchAcademy/precourse NMA prerequisites] - a list of pre-course self-directed learning resources<br /> * [https://www.youtube.com/channel/UC4LoD4yNBuLKQwDOV6t-KPw NMA Youtube channel]<br /> * [https://osf.io/8a4mt/ NMA data repository on OSF]<br /> <br /> == Press and other materials ==<br /> * [https://incf.org/blog/neuromatch-academy-online-computational-neuroscience-training-event INCF hosting of NMA]<br /> * [https://twitter.com/neuromatch?lang=en Neuromatch Twitter account]<br /> * [https://www.nature.com/articles/s42256-020-0182-5.pdf?origin=ppub Nature Machine Intelligence editorial]<br /> * [https://www.nature.com/articles/d41586-020-02347-9 Nature story about NMA overcoming Iran sanctions]<br /> * [https://medium.com/@yeditepe.cogsci/neuromatch-academy-experience-837e8a1c153f Neuromatch Academy Experience] - a Medium article written by interactive track students<br /> * [https://www.simonsfoundation.org/2020/09/17/the-self-organized-movement-to-create-an-inclusive-computational-neuroscience-school/?fbclid=IwAR0dUTOhhCRjZtItO2t0Mu83QpQWojQfeX3bmw7RAmSXTdDavS_CdWzDnh4 Simon's Foundation write-up] of NMA by Ashley Juavinett<br /> * [http://www.rdgao.com/blog/2020/09/16/ Richard Gao's blog post]<br /> * [https://knowingneurons.com/2020/10/13/neuroscience-education-in-the-time-of-covid-19-an-interview-with-dr-megan-peters-about-neuromatch-academy/ Knowing Neurons] interview with [https://faculty.sites.uci.edu/cnclab/ Dr. Megan Peters] about NMA<br /> * [https://biaswatchneuro.com/bwn-award/ NMA receives honorable mentions from BiasWatchNeuro]<br /> * [https://arxiv.org/abs/2012.08973 NMA Executive Summary paper]<br /> * [https://medium.com/the-spike/2020-a-moderately-bemused-review-of-the-year-in-neuroscience-99ca8744d03c Mark Humphries' 2020 review] - mentioning Neuromatch as a positive thing<br /> * [https://www.thelancet.com/journals/laneur/article/PIIS1474-4422(21)00074-0/fulltext Lancet article: &quot;Computational neuroscience with global accessibility&quot;]<br /> * [https://www.superdatascience.com/podcast/learning-deep-learning-together SuperDataScience interview of Konrad] for NMA 2021<br /> * [https://xcorr.net/2021/03/25/building-neuromatch-academy/ Building NMA] by Patrick Mineault<br /> * [https://gunnarblohm.medium.com/neuromatch-academy-the-story-cdaec59c430e NMA The story] - by Gunnar<br /> * [https://healthsci.queensu.ca/stories/feature/neuromatch-academy-changing-landscape-computational-neuroscience-education Queen's FHS feature story about NMA]<br /> * [https://braininspired.co/podcast/nma-1/ Brain Inspired podcast 1] - Machine Learning<br /> * [https://braininspired.co/podcast/nma-2/ Brain Inspired podcast 2] - Dynamical Systems<br /> * [https://braininspired.co/podcast/nma-3/ Brain Inspired podcast 3] - Normative models of brain &amp; mind<br /> * [https://www.sciencedirect.com/science/article/pii/S0896627321005420 For love of neuroscience: The Neuromatch movement] - a love letter by Konrad Kording<br /> * [https://www.linkedin.com/pulse/neuromatch-academy-reflections-week-jo%C3%A3o-ara%C3%BAjo/ NMA student reflections] by Joao Araujo<br /> * [https://www.mcgill.ca/neuro/article/open-science/rewarding-excellence-open-science?s=03 Neuromatch wins the 2022 Neuro - Irv and Helga Cooper Foundation Open Science Prize (international)]<br /> * [https://simpleneuro.org/democratisation-of-neuroscience-education/ Democratisation of neuroscience education] by Sabina Nowakowska (a NMA participant experience)<br /> * [https://www.socsci.uci.edu/newsevents/news/2023/2023-01-04-neuromatch-academy.php Networking for the next generation of neuroscientists] - a UCI story on NMA</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Neuromatch&diff=1818 2023-01-03T15:21:07Z

<p>Gunnar: /* Press and other materials */</p> <hr /> <div>In Spring 2020, due to the Covid-related cancellation of summer schools, we created Neuromatch Academy to teach computational neuroscience worldwide. Links and resources are collected here.<br /> <br /> == Course materials ==<br /> * [https://academy.neuromatch.io/ Neuromatch Academy web site]<br /> * [https://github.com/NeuromatchAcademy/precourse NMA prerequisites] - a list of pre-course self-directed learning resources<br /> * [https://www.youtube.com/channel/UC4LoD4yNBuLKQwDOV6t-KPw NMA Youtube channel]<br /> * [https://osf.io/8a4mt/ NMA data repository on OSF]<br /> <br /> == Press and other materials ==<br /> * [https://incf.org/blog/neuromatch-academy-online-computational-neuroscience-training-event INCF hosting of NMA]<br /> * [https://twitter.com/neuromatch?lang=en Neuromatch Twitter account]<br /> * [https://www.nature.com/articles/s42256-020-0182-5.pdf?origin=ppub Nature Machine Intelligence editorial]<br /> * [https://www.nature.com/articles/d41586-020-02347-9 Nature story about NMA overcoming Iran sanctions]<br /> * [https://medium.com/@yeditepe.cogsci/neuromatch-academy-experience-837e8a1c153f Neuromatch Academy Experience] - a Medium article written by interactive track students<br /> * [https://www.simonsfoundation.org/2020/09/17/the-self-organized-movement-to-create-an-inclusive-computational-neuroscience-school/?fbclid=IwAR0dUTOhhCRjZtItO2t0Mu83QpQWojQfeX3bmw7RAmSXTdDavS_CdWzDnh4 Simon's Foundation write-up] of NMA by Ashley Juavinett<br /> * [http://www.rdgao.com/blog/2020/09/16/ Richard Gao's blog post]<br /> * [https://knowingneurons.com/2020/10/13/neuroscience-education-in-the-time-of-covid-19-an-interview-with-dr-megan-peters-about-neuromatch-academy/ Knowing Neurons] interview with [https://faculty.sites.uci.edu/cnclab/ Dr. Megan Peters] about NMA<br /> * [https://biaswatchneuro.com/bwn-award/ NMA receives honorable mentions from BiasWatchNeuro]<br /> * [https://arxiv.org/abs/2012.08973 NMA Executive Summary paper]<br /> * [https://medium.com/the-spike/2020-a-moderately-bemused-review-of-the-year-in-neuroscience-99ca8744d03c Mark Humphries' 2020 review] - mentioning Neuromatch as a positive thing<br /> * [https://www.thelancet.com/journals/laneur/article/PIIS1474-4422(21)00074-0/fulltext Lancet article: &quot;Computational neuroscience with global accessibility&quot;]<br /> * [https://www.superdatascience.com/podcast/learning-deep-learning-together SuperDataScience interview of Konrad] for NMA 2021<br /> * [https://xcorr.net/2021/03/25/building-neuromatch-academy/ Building NMA] by Patrick Mineault<br /> * [https://gunnarblohm.medium.com/neuromatch-academy-the-story-cdaec59c430e NMA The story] - by Gunnar<br /> * [https://healthsci.queensu.ca/stories/feature/neuromatch-academy-changing-landscape-computational-neuroscience-education Queen's FHS feature story about NMA]<br /> * [https://braininspired.co/podcast/nma-1/ Brain Inspired podcast 1] - Machine Learning<br /> * [https://braininspired.co/podcast/nma-2/ Brain Inspired podcast 2] - Dynamical Systems<br /> * [https://braininspired.co/podcast/nma-3/ Brain Inspired podcast 3] - Normative models of brain &amp; mind<br /> * [https://www.sciencedirect.com/science/article/pii/S0896627321005420 For love of neuroscience: The Neuromatch movement] - a love letter by Konrad Kording<br /> * [https://www.linkedin.com/pulse/neuromatch-academy-reflections-week-jo%C3%A3o-ara%C3%BAjo/ NMA student reflections] by Joao Araujo<br /> * [https://www.mcgill.ca/neuro/article/open-science/rewarding-excellence-open-science?s=03 Neuromatch wins the 2022 Neuro - Irv and Helga Cooper Foundation Open Science Prize (international)]<br /> * [https://simpleneuro.org/democratisation-of-neuroscience-education/ Democratisation of neuroscience education] by Sabina Nowakowska (a NMA participant experience)</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Neuromatch&diff=1817 2022-10-28T13:24:31Z

<p>Gunnar: /* Press and other materials */</p> <hr /> <div>In Spring 2020, due to the Covid-related cancellation of summer schools, we created Neuromatch Academy to teach computational neuroscience worldwide. Links and resources are collected here.<br /> <br /> == Course materials ==<br /> * [https://academy.neuromatch.io/ Neuromatch Academy web site]<br /> * [https://github.com/NeuromatchAcademy/precourse NMA prerequisites] - a list of pre-course self-directed learning resources<br /> * [https://www.youtube.com/channel/UC4LoD4yNBuLKQwDOV6t-KPw NMA Youtube channel]<br /> * [https://osf.io/8a4mt/ NMA data repository on OSF]<br /> <br /> == Press and other materials ==<br /> * [https://incf.org/blog/neuromatch-academy-online-computational-neuroscience-training-event INCF hosting of NMA]<br /> * [https://twitter.com/neuromatch?lang=en Neuromatch Twitter account]<br /> * [https://www.nature.com/articles/s42256-020-0182-5.pdf?origin=ppub Nature Machine Intelligence editorial]<br /> * [https://www.nature.com/articles/d41586-020-02347-9 Nature story about NMA overcoming Iran sanctions]<br /> * [https://medium.com/@yeditepe.cogsci/neuromatch-academy-experience-837e8a1c153f Neuromatch Academy Experience] - a Medium article written by interactive track students<br /> * [https://www.simonsfoundation.org/2020/09/17/the-self-organized-movement-to-create-an-inclusive-computational-neuroscience-school/?fbclid=IwAR0dUTOhhCRjZtItO2t0Mu83QpQWojQfeX3bmw7RAmSXTdDavS_CdWzDnh4 Simon's Foundation write-up] of NMA by Ashley Juavinett<br /> * [http://www.rdgao.com/blog/2020/09/16/ Richard Gao's blog post]<br /> * [https://knowingneurons.com/2020/10/13/neuroscience-education-in-the-time-of-covid-19-an-interview-with-dr-megan-peters-about-neuromatch-academy/ Knowing Neurons] interview with [https://faculty.sites.uci.edu/cnclab/ Dr. Megan Peters] about NMA<br /> * [https://biaswatchneuro.com/bwn-award/ NMA receives honorable mentions from BiasWatchNeuro]<br /> * [https://arxiv.org/abs/2012.08973 NMA Executive Summary paper]<br /> * [https://medium.com/the-spike/2020-a-moderately-bemused-review-of-the-year-in-neuroscience-99ca8744d03c Mark Humphries' 2020 review] - mentioning Neuromatch as a positive thing<br /> * [https://www.thelancet.com/journals/laneur/article/PIIS1474-4422(21)00074-0/fulltext Lancet article: &quot;Computational neuroscience with global accessibility&quot;]<br /> * [https://www.superdatascience.com/podcast/learning-deep-learning-together SuperDataScience interview of Konrad] for NMA 2021<br /> * [https://xcorr.net/2021/03/25/building-neuromatch-academy/ Building NMA] by Patrick Mineault<br /> * [https://gunnarblohm.medium.com/neuromatch-academy-the-story-cdaec59c430e NMA The story] - by Gunnar<br /> * [https://healthsci.queensu.ca/stories/feature/neuromatch-academy-changing-landscape-computational-neuroscience-education Queen's FHS feature story about NMA]<br /> * [https://braininspired.co/podcast/nma-1/ Brain Inspired podcast 1] - Machine Learning<br /> * [https://braininspired.co/podcast/nma-2/ Brain Inspired podcast 2] - Dynamical Systems<br /> * [https://braininspired.co/podcast/nma-3/ Brain Inspired podcast 3] - Normative models of brain &amp; mind<br /> * [https://www.sciencedirect.com/science/article/pii/S0896627321005420 For love of neuroscience: The Neuromatch movement] - a love letter by Konrad Kording<br /> * [https://www.linkedin.com/pulse/neuromatch-academy-reflections-week-jo%C3%A3o-ara%C3%BAjo/ NMA student reflections] by Joao Araujo<br /> * [https://www.mcgill.ca/neuro/article/open-science/rewarding-excellence-open-science?s=03 Neuromatch wins the 2022 Neuro - Irv and Helga Cooper Foundation Open Science Prize (international)]</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Neuromatch&diff=1816 2022-10-28T13:24:16Z

<p>Gunnar: /* Press and other materials */</p> <hr /> <div>In Spring 2020, due to the Covid-related cancellation of summer schools, we created Neuromatch Academy to teach computational neuroscience worldwide. Links and resources are collected here.<br /> <br /> == Course materials ==<br /> * [https://academy.neuromatch.io/ Neuromatch Academy web site]<br /> * [https://github.com/NeuromatchAcademy/precourse NMA prerequisites] - a list of pre-course self-directed learning resources<br /> * [https://www.youtube.com/channel/UC4LoD4yNBuLKQwDOV6t-KPw NMA Youtube channel]<br /> * [https://osf.io/8a4mt/ NMA data repository on OSF]<br /> <br /> == Press and other materials ==<br /> * [https://incf.org/blog/neuromatch-academy-online-computational-neuroscience-training-event INCF hosting of NMA]<br /> * [https://twitter.com/neuromatch?lang=en Neuromatch Twitter account]<br /> * [https://www.nature.com/articles/s42256-020-0182-5.pdf?origin=ppub Nature Machine Intelligence editorial]<br /> * [https://www.nature.com/articles/d41586-020-02347-9 Nature story about NMA overcoming Iran sanctions]<br /> * [https://medium.com/@yeditepe.cogsci/neuromatch-academy-experience-837e8a1c153f Neuromatch Academy Experience] - a Medium article written by interactive track students<br /> * [https://www.simonsfoundation.org/2020/09/17/the-self-organized-movement-to-create-an-inclusive-computational-neuroscience-school/?fbclid=IwAR0dUTOhhCRjZtItO2t0Mu83QpQWojQfeX3bmw7RAmSXTdDavS_CdWzDnh4 Simon's Foundation write-up] of NMA by Ashley Juavinett<br /> * [http://www.rdgao.com/blog/2020/09/16/ Richard Gao's blog post]<br /> * [https://knowingneurons.com/2020/10/13/neuroscience-education-in-the-time-of-covid-19-an-interview-with-dr-megan-peters-about-neuromatch-academy/ Knowing Neurons] interview with [https://faculty.sites.uci.edu/cnclab/ Dr. Megan Peters] about NMA<br /> * [https://biaswatchneuro.com/bwn-award/ NMA receives honorable mentions from BiasWatchNeuro]<br /> * [https://arxiv.org/abs/2012.08973 NMA Executive Summary paper]<br /> * [https://medium.com/the-spike/2020-a-moderately-bemused-review-of-the-year-in-neuroscience-99ca8744d03c Mark Humphries' 2020 review] - mentioning Neuromatch as a positive thing<br /> * [https://www.thelancet.com/journals/laneur/article/PIIS1474-4422(21)00074-0/fulltext Lancet article: &quot;Computational neuroscience with global accessibility&quot;]<br /> * [https://www.superdatascience.com/podcast/learning-deep-learning-together SuperDataScience interview of Konrad] for NMA 2021<br /> * [https://xcorr.net/2021/03/25/building-neuromatch-academy/ Building NMA] by Patrick Mineault<br /> * [https://gunnarblohm.medium.com/neuromatch-academy-the-story-cdaec59c430e NMA The story] - by Gunnar<br /> * [https://healthsci.queensu.ca/stories/feature/neuromatch-academy-changing-landscape-computational-neuroscience-education Queen's FHS feature story about NMA]<br /> * [https://braininspired.co/podcast/nma-1/ Brain Inspired podcast 1] - Machine Learning<br /> * [https://braininspired.co/podcast/nma-2/ Brain Inspired podcast 2] - Dynamical Systems<br /> * [https://braininspired.co/podcast/nma-3/ Brain Inspired podcast 3] - Normative models of brain &amp; mind<br /> * [https://www.sciencedirect.com/science/article/pii/S0896627321005420 For love of neuroscience: The Neuromatch movement] - a love letter by Konrad Kording<br /> * [https://www.linkedin.com/pulse/neuromatch-academy-reflections-week-jo%C3%A3o-ara%C3%BAjo/ NMA student reflections] by Joao Araujo<br /> * [https://www.mcgill.ca/neuro/article/open-science/rewarding-excellence-open-science?s=03 Neuromatch wins the Neuro - Irv and Helga Cooper Foundation Open Science Prize (international)]</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Learning_Resources&diff=1815 2022-07-21T14:09:10Z

<p>Gunnar: /* Python */</p> <hr /> <div>== Bayesian Nonparametrics ==<br /> * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]<br /> * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&amp;acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).<br /> * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.<br /> * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&amp;hl=en&amp;oi=ao Any of the papers] of Radford Neal.<br /> * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].<br /> <br /> == General Computational Neuroscience ==<br /> * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.<br /> * [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.<br /> * [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control &amp; learning]<br /> * [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]<br /> * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli<br /> * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience<br /> * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]<br /> * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2<br /> * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij<br /> * [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF)<br /> * [https://direct.mit.edu/books/book/3159/Computational-Modeling-Methods-for-Neuroscientists Computational Modeling Methods for Neuroscientists] - (free PDF) by Erik De Schutter<br /> <br /> == Information Theory ==<br /> * [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] <br /> * [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.<br /> * [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]<br /> <br /> == Machine Learning ==<br /> ==== Books ====<br /> * [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control] <br /> * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]<br /> * [http://alex.smola.org/drafts/thebook.pdf Smola &amp; Vishwanathan's book]<br /> * [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]<br /> * [https://probml.github.io/pml-book/ Murphy's book]<br /> * [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz &amp; Ben-David's book]<br /> * [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]<br /> * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]<br /> * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]<br /> * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).<br /> * [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.<br /> * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''<br /> * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]<br /> * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt &amp; Recht<br /> * [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts &amp; Yaida<br /> <br /> ==== Online Resources ====<br /> * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --&gt; free enrollment)]<br /> * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]<br /> * [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]<br /> * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop<br /> * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.<br /> * Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)<br /> <br /> ==== Journal Club Tutorials ====<br /> See [[Journal Club#Tutorials]].<br /> <br /> == General Math ==<br /> * [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]<br /> * [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. <br /> * [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]<br /> * [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]<br /> * [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]<br /> *[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&amp;token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]<br /> * [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]<br /> * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.<br /> * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler<br /> <br /> == MATLAB ==<br /> * [https://www.coursera.org/learn/matlab Intro to programming] with Matlab<br /> * [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]<br /> * [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] (&quot;The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.&quot;)<br /> * [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]<br /> * [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]<br /> **[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]<br /> * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts<br /> <br /> == Python ==<br /> * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch<br /> * [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python<br /> * [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]<br /> * [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]<br /> * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]<br /> * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault<br /> * [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal<br /> * [https://virati.medium.com/make-your-code-last-forever-18e5bd3e4842 How to use containers for code] - by Vineet Tiruvadi<br /> <br /> == Statistics ==<br /> * [http://onlinestatbook.com/2/index.html Rice University online Stats book]<br /> * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book<br /> * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods &amp; significance estimation]<br /> * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)<br /> * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]<br /> * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]<br /> * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]<br /> * [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]<br /> * [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]<br /> * [https://statsthinking21.github.io/statsthinking21-core-site/ &quot;Statistical Thinking for the 21st Century&quot;] free online book by Russell A. Poldrack<br /> * [http://www.stat.columbia.edu/~gelman/book/ &quot;Bayesian Data Analysis&quot;] book by Andrew Gelman et al. with examples in Python and R<br /> * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]<br /> * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free<br /> * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free<br /> * [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free<br /> * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!<br /> * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code<br /> <br /> == Neuroimaging analyses ==<br /> * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]<br /> * [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Learning_Resources&diff=1814 2022-07-20T13:25:15Z

<p>Gunnar: /* General Computational Neuroscience */</p> <hr /> <div>== Bayesian Nonparametrics ==<br /> * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]<br /> * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&amp;acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).<br /> * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.<br /> * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&amp;hl=en&amp;oi=ao Any of the papers] of Radford Neal.<br /> * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].<br /> <br /> == General Computational Neuroscience ==<br /> * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.<br /> * [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.<br /> * [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control &amp; learning]<br /> * [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]<br /> * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli<br /> * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience<br /> * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]<br /> * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2<br /> * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij<br /> * [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF)<br /> * [https://direct.mit.edu/books/book/3159/Computational-Modeling-Methods-for-Neuroscientists Computational Modeling Methods for Neuroscientists] - (free PDF) by Erik De Schutter<br /> <br /> == Information Theory ==<br /> * [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] <br /> * [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.<br /> * [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]<br /> <br /> == Machine Learning ==<br /> ==== Books ====<br /> * [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control] <br /> * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]<br /> * [http://alex.smola.org/drafts/thebook.pdf Smola &amp; Vishwanathan's book]<br /> * [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]<br /> * [https://probml.github.io/pml-book/ Murphy's book]<br /> * [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz &amp; Ben-David's book]<br /> * [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]<br /> * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]<br /> * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]<br /> * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).<br /> * [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.<br /> * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''<br /> * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]<br /> * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt &amp; Recht<br /> * [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts &amp; Yaida<br /> <br /> ==== Online Resources ====<br /> * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --&gt; free enrollment)]<br /> * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]<br /> * [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]<br /> * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop<br /> * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.<br /> * Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)<br /> <br /> ==== Journal Club Tutorials ====<br /> See [[Journal Club#Tutorials]].<br /> <br /> == General Math ==<br /> * [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]<br /> * [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. <br /> * [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]<br /> * [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]<br /> * [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]<br /> *[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&amp;token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]<br /> * [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]<br /> * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.<br /> * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler<br /> <br /> == MATLAB ==<br /> * [https://www.coursera.org/learn/matlab Intro to programming] with Matlab<br /> * [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]<br /> * [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] (&quot;The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.&quot;)<br /> * [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]<br /> * [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]<br /> **[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]<br /> * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts<br /> <br /> == Python ==<br /> * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch<br /> * [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python<br /> * [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]<br /> * [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]<br /> * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]<br /> * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault<br /> * [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal<br /> <br /> == Statistics ==<br /> * [http://onlinestatbook.com/2/index.html Rice University online Stats book]<br /> * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book<br /> * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods &amp; significance estimation]<br /> * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)<br /> * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]<br /> * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]<br /> * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]<br /> * [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]<br /> * [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]<br /> * [https://statsthinking21.github.io/statsthinking21-core-site/ &quot;Statistical Thinking for the 21st Century&quot;] free online book by Russell A. Poldrack<br /> * [http://www.stat.columbia.edu/~gelman/book/ &quot;Bayesian Data Analysis&quot;] book by Andrew Gelman et al. with examples in Python and R<br /> * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]<br /> * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free<br /> * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free<br /> * [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free<br /> * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!<br /> * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code<br /> <br /> == Neuroimaging analyses ==<br /> * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]<br /> * [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Neuromatch&diff=1813 2022-07-11T12:54:21Z

<p>Gunnar: /* Press and other materials */</p> <hr /> <div>In Spring 2020, due to the Covid-related cancellation of summer schools, we created Neuromatch Academy to teach computational neuroscience worldwide. Links and resources are collected here.<br /> <br /> == Course materials ==<br /> * [https://academy.neuromatch.io/ Neuromatch Academy web site]<br /> * [https://github.com/NeuromatchAcademy/precourse NMA prerequisites] - a list of pre-course self-directed learning resources<br /> * [https://www.youtube.com/channel/UC4LoD4yNBuLKQwDOV6t-KPw NMA Youtube channel]<br /> * [https://osf.io/8a4mt/ NMA data repository on OSF]<br /> <br /> == Press and other materials ==<br /> * [https://incf.org/blog/neuromatch-academy-online-computational-neuroscience-training-event INCF hosting of NMA]<br /> * [https://twitter.com/neuromatch?lang=en Neuromatch Twitter account]<br /> * [https://www.nature.com/articles/s42256-020-0182-5.pdf?origin=ppub Nature Machine Intelligence editorial]<br /> * [https://www.nature.com/articles/d41586-020-02347-9 Nature story about NMA overcoming Iran sanctions]<br /> * [https://medium.com/@yeditepe.cogsci/neuromatch-academy-experience-837e8a1c153f Neuromatch Academy Experience] - a Medium article written by interactive track students<br /> * [https://www.simonsfoundation.org/2020/09/17/the-self-organized-movement-to-create-an-inclusive-computational-neuroscience-school/?fbclid=IwAR0dUTOhhCRjZtItO2t0Mu83QpQWojQfeX3bmw7RAmSXTdDavS_CdWzDnh4 Simon's Foundation write-up] of NMA by Ashley Juavinett<br /> * [http://www.rdgao.com/blog/2020/09/16/ Richard Gao's blog post]<br /> * [https://knowingneurons.com/2020/10/13/neuroscience-education-in-the-time-of-covid-19-an-interview-with-dr-megan-peters-about-neuromatch-academy/ Knowing Neurons] interview with [https://faculty.sites.uci.edu/cnclab/ Dr. Megan Peters] about NMA<br /> * [https://biaswatchneuro.com/bwn-award/ NMA receives honorable mentions from BiasWatchNeuro]<br /> * [https://arxiv.org/abs/2012.08973 NMA Executive Summary paper]<br /> * [https://medium.com/the-spike/2020-a-moderately-bemused-review-of-the-year-in-neuroscience-99ca8744d03c Mark Humphries' 2020 review] - mentioning Neuromatch as a positive thing<br /> * [https://www.thelancet.com/journals/laneur/article/PIIS1474-4422(21)00074-0/fulltext Lancet article: &quot;Computational neuroscience with global accessibility&quot;]<br /> * [https://www.superdatascience.com/podcast/learning-deep-learning-together SuperDataScience interview of Konrad] for NMA 2021<br /> * [https://xcorr.net/2021/03/25/building-neuromatch-academy/ Building NMA] by Patrick Mineault<br /> * [https://gunnarblohm.medium.com/neuromatch-academy-the-story-cdaec59c430e NMA The story] - by Gunnar<br /> * [https://healthsci.queensu.ca/stories/feature/neuromatch-academy-changing-landscape-computational-neuroscience-education Queen's FHS feature story about NMA]<br /> * [https://braininspired.co/podcast/nma-1/ Brain Inspired podcast 1] - Machine Learning<br /> * [https://braininspired.co/podcast/nma-2/ Brain Inspired podcast 2] - Dynamical Systems<br /> * [https://braininspired.co/podcast/nma-3/ Brain Inspired podcast 3] - Normative models of brain &amp; mind<br /> * [https://www.sciencedirect.com/science/article/pii/S0896627321005420 For love of neuroscience: The Neuromatch movement] - a love letter by Konrad Kording<br /> * [https://www.linkedin.com/pulse/neuromatch-academy-reflections-week-jo%C3%A3o-ara%C3%BAjo/ NMA student reflections] by Joao Araujo</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Learning_Resources&diff=1812 2022-07-11T12:52:53Z

<p>Gunnar: /* General Computational Neuroscience */</p> <hr /> <div>== Bayesian Nonparametrics ==<br /> * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]<br /> * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&amp;acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).<br /> * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.<br /> * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&amp;hl=en&amp;oi=ao Any of the papers] of Radford Neal.<br /> * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].<br /> <br /> == General Computational Neuroscience ==<br /> * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.<br /> * [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.<br /> * [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control &amp; learning]<br /> * [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]<br /> * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli<br /> * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience<br /> * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]<br /> * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2<br /> * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij<br /> * [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF)<br /> <br /> == Information Theory ==<br /> * [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] <br /> * [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.<br /> * [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]<br /> <br /> == Machine Learning ==<br /> ==== Books ====<br /> * [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control] <br /> * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]<br /> * [http://alex.smola.org/drafts/thebook.pdf Smola &amp; Vishwanathan's book]<br /> * [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]<br /> * [https://probml.github.io/pml-book/ Murphy's book]<br /> * [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz &amp; Ben-David's book]<br /> * [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]<br /> * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]<br /> * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]<br /> * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).<br /> * [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.<br /> * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''<br /> * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]<br /> * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt &amp; Recht<br /> * [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts &amp; Yaida<br /> <br /> ==== Online Resources ====<br /> * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --&gt; free enrollment)]<br /> * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]<br /> * [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]<br /> * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop<br /> * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.<br /> * Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)<br /> <br /> ==== Journal Club Tutorials ====<br /> See [[Journal Club#Tutorials]].<br /> <br /> == General Math ==<br /> * [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]<br /> * [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. <br /> * [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]<br /> * [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]<br /> * [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]<br /> *[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&amp;token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]<br /> * [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]<br /> * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.<br /> * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler<br /> <br /> == MATLAB ==<br /> * [https://www.coursera.org/learn/matlab Intro to programming] with Matlab<br /> * [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]<br /> * [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] (&quot;The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.&quot;)<br /> * [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]<br /> * [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]<br /> **[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]<br /> * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts<br /> <br /> == Python ==<br /> * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch<br /> * [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python<br /> * [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]<br /> * [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]<br /> * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]<br /> * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault<br /> * [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal<br /> <br /> == Statistics ==<br /> * [http://onlinestatbook.com/2/index.html Rice University online Stats book]<br /> * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book<br /> * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods &amp; significance estimation]<br /> * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)<br /> * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]<br /> * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]<br /> * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]<br /> * [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]<br /> * [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]<br /> * [https://statsthinking21.github.io/statsthinking21-core-site/ &quot;Statistical Thinking for the 21st Century&quot;] free online book by Russell A. Poldrack<br /> * [http://www.stat.columbia.edu/~gelman/book/ &quot;Bayesian Data Analysis&quot;] book by Andrew Gelman et al. with examples in Python and R<br /> * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]<br /> * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free<br /> * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free<br /> * [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free<br /> * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!<br /> * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code<br /> <br /> == Neuroimaging analyses ==<br /> * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]<br /> * [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni</div>

Gunnar http://compneurosci.com/wiki/index.php?title=Learning_Resources&diff=1811 2022-06-13T12:54:03Z

<p>Gunnar: /* Statistics */</p> <hr /> <div>== Bayesian Nonparametrics ==<br /> * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]<br /> * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&amp;acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).<br /> * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences.<br /> * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&amp;hl=en&amp;oi=ao Any of the papers] of Radford Neal.<br /> * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe].<br /> <br /> == General Computational Neuroscience ==<br /> * [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.<br /> * [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.<br /> * [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control &amp; learning]<br /> * [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]<br /> * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli<br /> * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience<br /> * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]<br /> * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2<br /> * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij<br /> <br /> == Information Theory ==<br /> * [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] <br /> * [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.<br /> * [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]<br /> <br /> == Machine Learning ==<br /> ==== Books ====<br /> * [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control] <br /> * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book]<br /> * [http://alex.smola.org/drafts/thebook.pdf Smola &amp; Vishwanathan's book]<br /> * [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]<br /> * [https://probml.github.io/pml-book/ Murphy's book]<br /> * [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz &amp; Ben-David's book]<br /> * [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]<br /> * [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]<br /> * [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]<br /> * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R).<br /> * [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.<br /> * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''<br /> * [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]<br /> * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt &amp; Recht<br /> * [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts &amp; Yaida<br /> <br /> ==== Online Resources ====<br /> * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --&gt; free enrollment)]<br /> * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python]<br /> * [http://setosa.io/ev/markov-chains/ Markov Chains explained visually]<br /> * [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop<br /> * [http://www.arxiv-sanity.com arXiv Sanity Preserver], an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.<br /> * Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)<br /> <br /> ==== Journal Club Tutorials ====<br /> See [[Journal Club#Tutorials]].<br /> <br /> == General Math ==<br /> * [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]<br /> * [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. <br /> * [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]<br /> * [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]<br /> * [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]<br /> *[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&amp;token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]<br /> * [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]<br /> * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.<br /> * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler<br /> <br /> == MATLAB ==<br /> * [https://www.coursera.org/learn/matlab Intro to programming] with Matlab<br /> * [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]<br /> * [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] (&quot;The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.&quot;)<br /> * [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]<br /> * [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]<br /> **[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]<br /> * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts<br /> <br /> == Python ==<br /> * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch<br /> * [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python<br /> * [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]<br /> * [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]<br /> * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]<br /> * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault<br /> * [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal<br /> <br /> == Statistics ==<br /> * [http://onlinestatbook.com/2/index.html Rice University online Stats book]<br /> * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book<br /> * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods &amp; significance estimation]<br /> * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa)<br /> * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice]<br /> * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]<br /> * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]<br /> * [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]<br /> * [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]<br /> * [https://statsthinking21.github.io/statsthinking21-core-site/ &quot;Statistical Thinking for the 21st Century&quot;] free online book by Russell A. Poldrack<br /> * [http://www.stat.columbia.edu/~gelman/book/ &quot;Bayesian Data Analysis&quot;] book by Andrew Gelman et al. with examples in Python and R<br /> * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]<br /> * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free<br /> * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free<br /> * [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free<br /> * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!<br /> * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code<br /> <br /> == Neuroimaging analyses ==<br /> * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]<br /> * [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni</div>

Gunnar