The Data Science and AI for senior Biomedical Researchers project is funded by The Alan Turing Institute's AI for Science and Government Research Programme (ASG). This workshop gives an introduction to data science and Artificial Intelligence (AI). Providing context and examples from biomedical research, the workshop content includes AI for automation, the process of unsupervised and supervised machine learning, their practical applications, and common pitfalls that researchers should be aware of in order to maintain scientific rigour and research ethics.
This workshop builds on training resources and practices developed by the following communities: The Turing Way, The Carpentries, and Open Life Science. Hosted by the The Alan Turing Institute's Tools, practices and systems (TPS) research team</a>, all materials are shared under CC-BY 4.0 License. Although the workshop is tailored to experimental biologists and biomedical researchers, materials will be generally transferable and directly relevant for data science projects across different research disciplines. Anyone interested in collaboration and improvements of this workshop is welcome to connect with the development team on GitHub (see the repository).
Prerequisites
This resource is designed for experimental biologists and biomedical researchers, with a focus on two key professional groups:
- Group leaders and lab managers without prior experience with Data Science or management of computational projects.
- Postdoc and lab scientists (next-generation senior leaders) interested in integrating computational science with research projects in the field of bioscience.
In defining the scope of this project, we make the following assumptions about our target audience:
- They have a computational project in mind for which funding and research ethics approval have been received.
- They have a good understanding of designing and contributing to a scientific project throughout its lifecycle.
- The research team is either partially or fully established; consisting of 3 or more team members.
This workshop is developed alongside the Managing Open and Reproducible Computational Projects workshop; which includes selected practices and tools for senior researchers to manage and supervise data science and AI/ML projects life science domains.