Community Driven Accessible Mini-Courses and Workshops to Further the Uptake of DRA DRI Resources

Project Quick Start

TODO when more content is added

Vision and Mission

• Vision: Making AIs more understandable, accessible, and equitable.
• Mission: Making accessible educational material to enhance the research options for equity deserving groups.

About this Repository

This repository holds the material for Anemily Machina's proposed project for the Digital Research Alliance of Canada's (DRA's) EDIA Champion Pilot Project (link)

This includes: - a survey to gague knowledge and interest in the DRA, research data management, high performance computing, and general AI - the ethics documention for the survey - high level summaries of the results of the survey - English and French language educational materials - Videos related to the eduational materials

Roadmap & Milestones

• Goals: To gague knowledge of DRA services, what people want to learn, and create educational materials based on the survey.
• Outcomes: Release a survey to gague knowledge and interest in DRA services. Create approximately 6 hours of educational materials. Create two videos/hold two workshops on approximetly 2 hours of said eduational materials.

The Team

• Members: Anemily Machina, Dr. Robert Mercer (supervisor)

Contributing

• Guidelines: Contribution Guidelines for contributors.
• Code of Conduct: Code of Conduct ensures a respectful project environment.

Licensing

This project is licensed under the CC BY-SA 4.0 License - see the LICENSE.md file for details.

You can find the licence Deed at the following link.

Citing & Acknowledgement

• Citation Instructions: We prefer if you cite individual items, but if you want to cite the whole repository: @software{Machina_Machina-Mercer_EDIA_Project_2025, author = {Machina, Anemily and Mercer, Robert}, month = jan, title = title of video/slide, url = {https://github.com/anemily-machina/DRA_EDIA_Project}, version = {1.0.0}, year = {2025} }

Machina, A., & Mercer, R. (2025). Machina-Mercer EDIA Project 2024-2025 (Version 1.0.0) [Computer software]. https://github.com/anemily-machina/DRA_EDIA_Project

• Acknowledgment:

This repository uses the template created and maintained by The Turing Way team members and shared under CC-BY 4.0 for reuse: https://github.com/alan-turing-institute/reproducible-project-template.

Link to CC-BY 4.0 Licence

The template has been modifiyed to fit the repository.

Contact

• Reach Out: Anemily Machina: anemily.machina@uwo.ca

Repo Structure

Default repo structure from template. Work inprogress and subject to change. Inspired by Cookie Cutter Data Science.

├── LICENSE ├── README.md <- The top-level README for users of this project. ├── CODE_OF_CONDUCT.md <- Guidelines for users and contributors of the project. ├── CONTRIBUTING.md <- Information on how to contribute to the project. ├── data │ ├── processed <- The final, canonical data sets for modeling. │ └── raw <- The original, immutable data dump. │ ├── docs <- A default Sphinx project; see sphinx-doc.org for details │ ├── models <- Trained and serialized models, model predictions, or model summaries │ ├── notebooks <- Jupyter notebooks. The naming convention is a number (for ordering), │ the creator's initials, and a short `-` delimited description, e.g. │ `1.0-jqp-initial-data-exploration`. │ ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. │ └── figures <- Generated graphics and figures to be used in reporting │ ├── project_management <- Meeting notes and other project planning resources │ ├── src <- Source code for use in this project. │ │ │ ├── data <- Scripts to download or generate data │ │ └── make_dataset.py │ │ │ ├── models <- Scripts to train models and then use trained models to make │ │ │ predictions │ │ ├── predict_model.py │ │ └── train_model.py │ │ │ └── visualisation <- Scripts to create exploratory and results-oriented visualisations │ └── visualise.py └──