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ATTENTION This is an experimental test of The Carpentries Workbench lesson infrastructure. It was automatically converted from the source lesson via the lesson transition script.

If anything seems off, please contact Zhian Kamvar zkamvar@carpentries.org

The Managing Open and Reproducible Computational Projects training material covers best practices for managing and supervising computational projects in biology and related fields through data science methods, analysis, interpretation, and reporting processes. Through lessons learned in this training, researchers will enhance their understanding and guide the integration of rigorous and reproducible scientific methods for designing reproducible, transparent and collaborative computational projects. Furthermore, the guidance provided for managing and supervising early career researchers in conducting computational (data-driven/informed) research will help ensure transparency and research integrity throughout the project design, methodology, analysis, interpretation and reporting process.

This training material is developed under the Data Science for Biomedical Scientists project. It massively reuses The Turing Way chapters and builds on The Carpentries and Open Life Science practices. Hosted by the Tools, practices and systems (TPS) research team, all materials are shared under CC-BY 4.0 License. Although the training course is tailored to the biomedical sciences community, materials will be generally transferable and directly relevant for data science projects across different domains. Anyone interested in collaboration and improvements of this material is welcome to connect with the development team on GitHub (see the repository).

Funding Acknowledgement: The first iteration of this project was funded by The Alan Turing Institute - AI for Science and Government (ASG) Research Programme from October 2021 to March 2022.

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Prerequisites

This resource is designed for experimental biologists, biomedical researchers and adjacent communities, with a focus on two key professional/career groups:

  • Group leaders or lab managers without prior experience with Data Science or management of computational projects
  • Postdoc and lab scientists (next-generation senior leaders) interested in enabling the integration of computational science into biosciences

In defining the scope of this project for our target audience, we make some assumptions about the learner groups:

  • Our learners have a good understanding of designing or contributing to a scientific project throughout its lifecycle.
  • They have a computational project in mind for which funding and research ethics approval have been received.
  • We also assume that the research team of any size is (either partially or fully) established.

This lesson is developed alongside the Introduction to Data Science and AI for senior researchers lesson. Our learners are encouraged to go through Introduction to Data Science and AI for senior researchers lesson to learn about data science and AI/ML practices that could be relevant to life science domains, where the best practices for Managing Open and Reproducible Computational Projects can be practically applied.

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