A Strategic Community #Roadmap for an #Australian#FAIR#Vocabulary Ecosystem

https://doi.org/10.25911/N6K8-F540

Three years ago, I participated in a very engaged workshop at #ANU on #vocabularies for FAIR #data management. It sharpened how I think about vocabularies. I now see them primarily as a #KnowledgeTransfer tool for representing domain expertise in an actionable form. And I think we do a terrible job both at highlighting how critical they are (particularly in an age where trusted expertise is harder to find) and also at making them easier for others to find and reuse.

I picture this scenario. A student is about to start collecting data for their thesis. They need to make choices about what variables to observe or what questions to ask participants, and they need to think about how they want to represent the results to support their analysis. In the ideal case, the actual data collecting effort is about populating an imagined but initially empty data matrix. If they could be assisted to find the best structured and most widely used (in their domain) vocabularies for any categorical values in their data, it would be possible to generate that template matrix with in-built validation tools, etc. The data they finally collect would have most of its metadata already defined and would be properly interoperable with data collected by others in their domain. Meta-analysis would be much simpler.

I am interested in why tools like this don't really exist, or at least why they are not mainstream. I think it's because vocabularies are seen as such an ultra-nerdy subset of the nerdy topic of #metadata rather than presented as an opportunity to stand on the shoulders of others. What can be done to make them more friendly and intuitive for such purposes?

Finally, after way too many struggles, we have a report and recommendations from from that meeting in 2022. I tried to add some of these ideas to the final product as best I could.