Research Data Management with DataLad

Register: https://www.zoom.com/

Zoom is a video conferencing software that allows for virtual meetings and webinars. It is essential for attending our online workshop sessions and provides the interactive features needed for effective learning.

Why Zoom?

• Breakout Rooms: Essential for our small-group exercises
• Screen Sharing: Share your screen to get help or demonstrate solutions
• Stable & Reliable: Handles large groups with consistent quality
• Recording: Sessions can be recorded for later review (where permitted)

Installation

Download and install the Zoom Desktop Client from the official website. We require the desktop client rather than the web version for full feature support.

Before Your First Session

1. Test Your Setup: Join a test meeting to check audio/video
2. Update Zoom: Make sure you have the latest version
3. Check Your Internet: Ensure you have a stable connection
4. Find a Quiet Space: Minimize background noise during sessions

Workshop Etiquette

• Keep your microphone muted when not speaking
• Use video when possible to help build community
• Use reactions (👍, ✋) to provide feedback
• Ask questions in chat or unmute to speak
• Be ready to join breakout rooms for exercises

Tips

• Familiarize yourself with screen sharing features before the workshop
• Keep your Zoom name consistent with your registration
• Use virtual backgrounds if needed for privacy
• Enable “dual monitor mode” if you have two screens

Register: https://code.visualstudio.com/download

Visual Studio Code is a powerful, lightweight code editor used for developing software. It supports various programming languages through extensions and provides an excellent environment for Python development and data science work.

Why VS Code?

• Free & Open Source: Completely free with active community development
• Extensible: Thousands of extensions for any language or tool
• Integrated Tools: Built-in terminal, debugger, and Git integration
• Jupyter Support: Work with notebooks directly in the editor
• Remote Development: Edit files on remote servers or in containers

Installation

Download and install Visual Studio Code from the official website. Choose the appropriate version for your operating system (Windows, macOS, or Linux).

Essential Extensions for Research

Python Development

• Python - IntelliSense, debugging, code navigation
• Jupyter - Run and edit Jupyter notebooks
• Pylance - Fast, feature-rich Python language support

Collaboration & Version Control

• GitLens - Supercharge Git integration
• Live Share - Real-time collaborative editing

Data & Visualization

• Data Wrangler - Explore and clean data visually
• Rainbow CSV - Colorize CSV files for easier reading

Tips

• Learn keyboard shortcuts to improve efficiency (Ctrl+Shift+P / Cmd+Shift+P for command palette)
• Customize your theme and settings
• Use the integrated terminal for running commands
• Enable autosave to never lose work
• Use Zen Mode (Ctrl+K Z) for distraction-free coding

Getting Started with Python

1. Install the Python extension
2. Select your Python interpreter (Ctrl+Shift+P → “Python: Select Interpreter”)
3. Open a .py file or create a new one
4. Run code using the play button or Ctrl+Alt+N

Register: https://pixi.sh/latest/installation/

Pixi is a modern package manager that simplifies the installation of scientific software. It’s built on top of the conda-forge ecosystem but is significantly faster and provides better dependency resolution.

Why Pixi?

• Fast: 5-10x faster than conda for most operations
• Reproducible: Uses lock files to ensure exact environment reproduction
• Task Runner: Built-in task management (like npm scripts)
• Modern Design: Clean CLI with better error messages
• Conda Compatible: Uses the conda-forge repository

Installation

Follow the installation instructions on the official Pixi website. The installer will set up Pixi and configure your PATH automatically.

Getting Started

Initialize a new project:

pixi init
pixi add python
pixi shell

Add packages:

pixi add numpy pandas matplotlib

Define and run tasks in pixi.toml:

[tasks]
dev = "python main.py"
test = "pytest tests/"

Run tasks:

pixi run dev
pixi run test

Advantages Over Conda

• Much faster package resolution and installation
• Lock files ensure reproducibility by default
• Better support for managing multiple projects
• Built-in task runner eliminates need for separate tools

Tips

• Use pixi shell to activate the environment
• Define common tasks in pixi.toml for easy project workflows
• Lock files (pixi.lock) should be committed to version control
• Use pixi global install for system-wide tools

Register: https://git-scm.com/downloads

Git is a version control system that tracks changes in source code. It allows multiple people to work on a project simultaneously and maintains a complete history of all changes.

Why Git?

• Distributed: Every developer has a complete copy of the project history
• Branching: Experiment with new features without affecting the main codebase
• Collaboration: Work with others seamlessly through platforms like GitHub
• Reproducibility: Track exactly which version of code produced which results

Installation

Download and install Git from the official website. Choose the appropriate installer for your operating system.

Windows

Use Git for Windows installer with recommended defaults.

macOS

Git comes pre-installed on most macOS systems. Update with Homebrew: brew install git

Linux

Install using your package manager: sudo apt-get install git (Ubuntu/Debian)

Configuration

After installation, configure your identity:

git config --global user.name "Your Name"
git config --global user.email "your.email@example.com"

Essential Commands

• git clone - Copy a repository to your local machine
• git add - Stage changes for commit
• git commit - Save changes with a message
• git push - Upload changes to remote repository
• git pull - Download changes from remote repository

Tips

• Use meaningful commit messages that explain why you made changes
• Commit frequently to create detailed checkpoints
• Create branches for new features or experiments
• Use .gitignore to exclude data files and generated content

Register: https://github.com/

GitHub is a web-based platform built around Git that provides hosting for software development and version control. It’s the world’s largest code hosting platform and essential for modern collaborative research.

Why GitHub?

• Collaboration: Work with researchers worldwide on shared projects
• Visibility: Make your research code discoverable and citable
• Integration: Connect with CI/CD, documentation, and project management tools
• Community: Access to millions of open-source projects and libraries
• Free for Research: Unlimited public and private repositories

Getting Started

1. Create a free account at github.com
2. Set up Git on your local machine
3. Configure Git with your GitHub credentials
4. Create your first repository or clone an existing one

Essential Features

Repositories

• Host your code with full version history
• README files for documentation
• Issues for tracking bugs and features
• Pull requests for code review

Collaboration

• Fork projects to contribute
• Star repositories to bookmark them
• Follow researchers working in your field
• Use GitHub Pages for project websites

Tips for Researchers

• Include a LICENSE file to clarify how others can use your code
• Write a clear README explaining what your code does
• Create a CITATION.cff file for proper attribution
• Use releases to mark versions associated with publications
• Add topics to make your repository discoverable

Best Practices

• Commit often with meaningful messages
• Use branches for new features
• Write clear documentation
• Add a DOI through Zenodo integration for permanent archiving

Register: https://osf.io/

The Open Science Framework (OSF) is a free, open-source platform for managing research projects. It supports the entire research lifecycle from project planning through publication, emphasizing openness and reproducibility.

Why OSF?

• Free & Open: Completely free for researchers worldwide
• Integrated Workflow: Connects with GitHub, Dropbox, Google Drive, and more
• Permanent Storage: Long-term preservation of research materials
• Preregistration: Register study plans before data collection
• DOIs: Create citable, permanent identifiers for your work

Key Features

Project Management

• Organize research materials in hierarchical projects
• Add collaborators with granular permissions
• Track changes and maintain version history
• Add wiki pages for documentation

Open Sharing

• Make projects public or keep them private
• Generate DOIs for permanent citation
• Set embargo periods for timed release
• License your work appropriately

Integrations

• Connect GitHub repositories
• Link cloud storage (Google Drive, Dropbox, Sciebo)
• Use add-ons for specialized tools
• Export to data repositories

Getting Started

1. Create a free account at osf.io
2. Create a new project for your research
3. Add components for different parts (data, code, materials)
4. Connect external services (GitHub, etc.)
5. Share with collaborators or make public

Use Cases

• Preregistration: Document your hypotheses and analysis plan before collecting data
• Data Sharing: Make datasets available with permanent DOIs
• Supplementary Materials: Host materials that don’t fit in paper supplements
• Collaboration: Central hub for multi-institution projects

Tips

• Use clear, descriptive names for projects and components
• Add detailed README files to explain your materials
• Use tags to make projects discoverable
• Consider making projects public after publication
• Link related projects together

Register: https://gin.g-node.org/

GIN (G-Node Infrastructure) is a free data management system designed for comprehensive and reproducible management of scientific data. It’s optimized for neuroscience research but suitable for any field with large datasets.

Why GIN?

• Version Control for Data: Git-like workflow for datasets
• Large File Support: Efficiently handles files of any size
• Free Storage: Generous storage quotas for researchers
• Neuroscience Focus: Designed with neuroscience workflows in mind
• DOI Integration: Publish datasets with permanent identifiers

Key Features

• Web interface for browsing and managing data
• Git integration for command-line workflows
• Support for large files through git-annex
• Issue tracking and wikis for collaboration
• Public and private repositories

Getting Started

1. Create account at gin.g-node.org
2. Install the GIN client or use Git with git-annex
3. Create a repository for your dataset
4. Push data using Git commands
5. Share with collaborators or make public

GIN vs. GitHub

• GIN: Optimized for large data files, neuroscience community
• GitHub: Optimized for code, broader software community
• Use Both: Store code on GitHub, data on GIN, link them together

Best Practices

• Use clear naming conventions for data files
• Document data structure in README files
• Use Git tags to mark dataset versions
• Archive final versions with DOIs before publication
• Link GIN datasets to GitHub code repositories

Tips

• Use the GIN client for easier large file management
• Add metadata files to describe your datasets
• Make repositories public after paper acceptance
• Use organizations for lab or project group data