Building a terminology for the skills necessary to make data FAIR and to keep it FAIR.
The terms4FAIRskills project aims to create a formalised terminology that describes the competencies, skills and knowledge associated with making and keeping data FAIR. When mature, this terminology will apply to a variety of use cases, including:
- To assist with the creation and assessment of stewardship curricula;
- To facilitate the annotation, discovery and evaluation of FAIR-enabling materials (e.g. training) and resources;
- To enable the formalisation of job descriptions and CVs with recognised, structured competencies.
The completed terminology will be of use to trainers who teach FAIR data skills, researchers who wish to identify skill gaps in their teams and managers who need to recruit individuals to relevant roles.
This repository will be used to store the OWL version of the terminology, plus related files. Many of these files will be a 'work in progress'.
We will use this repository as a space to work on the pipeline from the terms4FAIRskills google spreadsheet (used to generate and collate the terms and their definitions) to a true hierarchial terminology, using Robot and hand-crafted artisanal csv files.
For more information on the technical aspects of converting the google spreadsheet to the OWL file, please contact either Peter McQuilton and/or Allyson Lister.
- ROBOT - R.C. Jackson, J.P. Balhoff, E. Douglass, N.L. Harris, C.J. Mungall, and J.A. Overton. ROBOT: A tool for automating ontology workflows. BMC Bioinformatics, vol. 20, July 2019. https://doi.org/10.1186/s12859-019-3002-3
- Protege - Musen, M.A. The Protégé project: A look back and a look forward. AI Matters. Association of Computing Machinery Specific Interest Group in Artificial Intelligence, 1(4), June 2015. DOI: 10.1145/2557001.25757003.