Data management and scoring tools for the M2C2 project
Project description
m2c2_datakit
🚀 A set of R, Python, and NPM packages for scoring M2C2kit Data! 🚀
This is the Python package 🐍
Installation for End Users
pip install m2c2-datakitpip3 install m2c2-datakit
Developers:
- Dr. Nelson Roque | ORCID: https://orcid.org/0000-0003-1184-202X
- Dr. Scott Yabiku | ORCID: [Coming soon!]
Changelog
Source: https://github.com/nelsonroque/m2c2kit-data
See CHANGELOG.md
Features
Current Features
- Load a JSON file of a query export from MongoDB
- Load a folder of JSON files, one folder per participant, with subfolders for sessions, from Metricwire
- Interoperable Data exports (csv, tsv, pkl)
Feature Roadmap
- In General: Feature and Data parity from R, Python, and NPM scoring tools (same data + different tool = same scores)
- CLI in Python for simplifying scoring with one-liners (e.g., m2data --score --summarise --file data.json)
- Load a folder of JSON files, one folder per participant, with subfolders for sessions, from REDCAP (Coming Soon)
- Load a file export, containing columns for each trial, for each task, from Qualtrics (Coming Soon)
🚀 Getting Started
Installation for Scoring Developers
- Python 3
- Visual Studio Code with Jupyter Notebook Extension or Jupyter Lab, or Anaconda.
uv venv
# 1. Create the virtual environment
pip install uv # or pip3 install uv
uv venv .venv
# 2. Activate the virtual environment
source .venv/bin/activate # macOS/Linux
# .venv\Scripts\Activate # Windows (PowerShell)
# 3. Install dependencies
uv pip install -e .
# 4. Run formatting, linting, and type checking
make install
💡 Contributions Welcome!
📌 Have ideas? Found a bug? Want to improve the package? Open an issue!.
📜 Code of Conduct - Please be respectful and follow community guidelines.
Acknowledgements
The development of m2c2_datakit was made possible with support from NIA (1U2CAG060408-01).
🌎 More Resources:
📌 M2C2 Official Website
📌 M2C2kit Official Documentation Website
📌 Pushing to PyPI
🚀 Let's go study some brains!
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