The Turing Way is an open-source, community-led and collaboratively developed “book project” on making data research accessible for a wider research community (https://the-turing-way.netlify.com). We bring together individuals from diverse fields and expertise to develop practices and learning resources that can make data research accessible and easy to understand. Our community members are researchers, engineers, data librarians, industry professionals, and experts in various domains, at all levels of seniority, from all around the world. They collaborate in the project to develop chapters by compiling best practices, tools and recommendations used by the researchers and data science communities worldwide.
Technical details: All questions, comments, recommendations and discussions are facilitated through an online GitHub repository (https://github.com/alan-turing-institute/the-turing-way). The online book with multiple guides is hosted as a Jupyter Book (https://github.com/jupyter/jupyter-book/) at https://the-turing-way.netlify.com. Jupyter Book formats markdown files and Jupyter notebooks as static HTML making them easy to read. When a notebook is included in the book, the static page includes a link to an interactive version of the notebook via Binder (https://mybinder.readthedocs.io). Additional styling of the front end is possible by providing a CSS file that handles it across the entire book.
Background: Since the project’s launch in 2019, more than 300 contributors have so far co-authored more than 200 subchapters and community documents on reproducible research, communication, collaboration, project design and ethics. As the number of chapters continues to increase, it becomes important for us to offer appropriate ways for our readers to discover relevant and desired content in the book based on their topics of interest and skill levels. Over the last 3 months, software engineers at the Turing have enhanced the user interface (UI) of the book (developed a modular Python package) that made it possible for us to create multiple entry points for different user groups, who can start reading the book by exploring a curated set of chapters, rather than browsing the entire book (See details: https://github.com/alan-turing-institute/bio-Turing-Way/blob/malvikasharan-readme/pathways/documentation/README.md).
GSoC project plan and expected outcome: A GSoC contributor will help us integrate this newly developed package to The Turing Way book and further enhance this feature through user experience (UX) design. They will be supported in setting up community/user feedback processes and conducting interviews/focus groups to understand how our readers and contributors use the book and how this UX/UI enhancement adds to their experience. Based on their interest and availability, they will have the possibility to contribute to the development of Python scripts and GitHub actions to improve the project workflow, chapter development, community engagement and the overall interactivity in the book. They will be provided with appropriate guidance and the opportunity to work in a positive working environment. They will be fairly acknowledged for their contributions to the project.
Required skills: 1) Python programming, 2) basic web-development skill required to work with Jupyter Book, 3) experience working in distributed communities, using git and GitHub.
Optional skills: Experience collaborating on data science or quantitative research projects at any level, JavaScript skills (front end development), interactive visualisation of small datasets.
Possible mentors:
- Lead mentor: Malvika Sharan @malvikasharan (msharan@turing.ac.uk)
- Co-mentors: Turing Way community
Tech keywords: Python, Jupyter, git, JavaScript