A project management repository for The Turing Way Practitioners Hub
The Turing Way is a flagship project of The Alan Turing Institute. Shared as an open source community-driven handbook on data science, the project promotes best practices for reproducible, ethical, inclusive and collaborative research. To date, 400+ diverse contributors from international organisations have collaborated to develop 300+ chapters and resources, which are available across guides on reproducibility, project design, communication, collaboration, research ethics and community. Hosted under the Tools, Practices and Systems Research Programme, the project draws expertise from the institute’s Community Management, Research Application Management, Academic Skills and Research Engineering Teams. To embed data science best practices and community expertise into different sectors, we are launching The Turing Way Practitioners Hub this year.
The Practitioners Hub will provide a forum for cross-sector engagement, knowledge exchange and strategic collaboration with organisations across academia, research, engineering systems, government and healthcare -- leading data science initiatives. Through the involvement of domain experts from these sectors, the Practitioners Hub will enable systematic approaches for building a shared understanding of open science, reproducibility, accessibility and research ethics to enhance quality, rigour and integrity in data science and AI.
Project Members
- Dr Malvika Sharan, TPS Senior Researcher - Open Research, Co-Lead of The Turing Way
- Alexandra Araujo Alvarez, Research Project Manager - The Turing Way
- Jennifer Ding, Research Application Manager - Acting Senior Researcher
- Shane Conneely, Partnership Development Lead
TPS Contributors
- Dr Kirstie Whitaker, TPS Programme Director
- Arielle Bennett, TPS Programme Manager
Please create an issue to share references or ideas related to the development of this project.
For any organisation-related queries or concerns, you can directly reach out to Malvika Sharan by emailing msharan@turing.ac.uk or Alexandra Araujo Alvarez (aaraujoalvarez@turing.ac.uk)
- Proposal sharing in Zenodo: https://zenodo.org/record/7427274
- Cite as: Sharan, Malvika, & Bennett, Arielle. (2022). The Turing Way Practitioners Hub - Turing Funded Proposal 2022. Zenodo. https://doi.org/10.5281/zenodo.7427274
- Recruitment
- Research Project Manager
- Senior Research Associate
- Create a primer document to share with invited organisations
- Invite five organisations from different sectors
- Align the timeline with the BridgeAI project
- Work with the Skills and Digital Catapult to put together a timeline
- Create a full 6 months timeline for the cohort members
- Identify internal stakeholders and where they will engage
- Get 2-3 alumni org from Digital Catapult to work in the first cohort
- Hold 1:1 planning meetings with each org
- Build agreement/MoU with each org, identify experts in residence
- Onboard the Senior Research Associate
- Build and centralise resources for the entire cohort
- Launch the hub's first cohort with residents and timeline with activities mapped
- Hold an onboarding and intro call for each org
- Host an in-person resident workshop
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.
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
├── images <- Generated graphics and figures to be used in reporting
├── project_management <- Meeting notes and other project planning resources
└──
This work is licensed under the MIT license (code) and Creative Commons Attribution 4.0 International license (for documentation). You are free to share and adapt the material for any purpose, even commercially, as long as you provide attribution (give appropriate credit, provide a link to the license, and indicate if changes were made) in any reasonable manner, but not in any way that suggests the licensor endorses you or your use, and with no additional restrictions.
Thanks goes to these wonderful people (emoji key):
 Malvika Sharan 🤔 🖋 🎨 🔍 🚧 📢 |
 Arielle-Bennett 🖋 🎨 🔍 |
 Alexandra Araujo Alvarez 📆 🖋 |
 Kirstie Whitaker 📢 |
 Jennifer Ding 🤔 |
This project follows the all-contributors specification. Contributions of any kind welcome!