term4FAIRskills is an ontology to describe the necessary skills to make data FAIR and keep FAIR. FAIR itself is a set of principles for ensuring that data is shared in as efficient and effective a manner as possible. In the field of Data Science there are colloquial estimates that perhaps 80% of the time spent on a project is on getting the data organised for analysis. By ensuring that data is FAIR then this workload can be minimised not only for one project, but for any project that works on a FAIR data set.
terms4FAIRskills was designed to a) facilitate the annotation, discovery and evaluation of FAIR-enabling materials (e.g. training) and resources and b) assist with the creation and assessment of FAIR-related curricula. It is stored in an OWL format and a number of training materials have been annotated with it. This project is about building a tool so that an expert can look at a set of teaching materials and generate a set of annotations from terms4FAIRskills for the materials.
The developer would develop a front end to query the OWL file for terms4FAIRskills (using key word searches) and then store the resulting terms in a separate file. It should be developed in an open fashion using, for example Python Flask and designed to be updateable as updated versions of the ontology come online. If time allows it should have an API for programmatic access.