Tools and Practices for FAIR Research Software

Licence: Creative Commons Attribution 4.0 International

Contact: a.nenadic@software.ac.uk

Keywords: FAIR, FAIR Research, Software

Target audience: Research scientists, Researchers, Post Docs, PhD Scholars, Graduates and Post Graduates, Professors, Associate Professors, Assistant Professors, Bio instruments Professionals, Bio-informatics Professionals, Directors, CEO’s of Organizations, Supply Chain companies, Manufacturing Companies, Software development companies, Research Institutes and members, PhD Students, PhD Students or young researchers in molecular biology and/or genetics with little or no background in bioinformatics.

Resource type: online course, Online material

Status: Active

Prerequisites:

Foundational knowledge of Python or another programming language used to write scientific code, Git and a command line (shell) tool.

Learning objectives:

List challenges typically faced by researchers developing software and managing data for modern computational research, including requirements commensurate with the FAIR (Findable, Accessible, Interoperable, Reusable) principles
List some tools and practices that can help make your research, data and software open and FAIR
Automate your research and enable replication of your research results by writing software to implement the research methodology
Share and version control your software using Git and GitHub
List best practices for developing and sharing open and sustainable software (including writing readable code, code documentation, licencing and citation)
Assess if the code does what it intends to do
List tools and techniques for collaborative and sustainable software development and maintenance