Where are the vocabularies that will make environmental datasets FAIR?

At a minimum, standardised, community accepted vocabularies are necessary for effective data discovery. Further, they can assist with large scale and reproducible analyses. There are some attempts to define the characteristics of a vocabulary which (a) make it most useful to a particular scientific domain, and (b) what characteristics will make it FAIR, i.e., that the vocabulary can be located ('Findability'), 'Accessibility, 'Interoperable' (or Interpretable), and 'Reusable' (e.g. Laporte et al., 2021, David et al., 2021). What do we need to do in order to make these vocabularies appealing to scientists, i.e. to the creators of research data? What do we (semantic repository curators, data managers, developers and so on) need to do to improve the level of data FAIRness, and ensure that datasets are tagged with appropriate, sufficient and community-approved controlled vocabularies? What mechanisms and tools best capture essential metadata in the physical or digital lab before they reach the data repositories?

This session at SciDataCon2022 organised by the PARSEC team of Specht, David, O'Brien and Stall, focussed on how the environmental and earth science researcher might better be able to apply vocabularies that are controlled, standardised, and persistent (sustained for at least 5 years into the future) to help maintain the integrity of their datasets. Our keynote speaker, Dr Gwenaëlle Moncoiffé introduced the challenges and potential solutions to making vocabularies more FAIR and also more accessible to researchers, followed by contributions from members of the wider community (Brownlee, Horsburgh and Sparkes), solicited specifically to help us explore the options available.

References

David et al. (2021) https://doi.org/10.5281/zenodo.5749695

Laporte et al. (2021) https://doi.org/10.5281/zenodo.5594693