Collaborative Research: Cross-Cutting Improvements: Infrastructure for Creating and Sharing FAIR Scientific Research Metadata via MEDFORD

About This Grant

Many researchers across a myriad of scientific domains generate and analyze data. In order to make this data reproducible and reusable, it is important to also include metadata describing the context for the data. However, most current schemas for reading and writing metadata are optimized for machine use rather than directly accessible to citizens or scientists who are not expert programmers or data technicians. This project involves developing a toolset and a language, MEDFORD, that provides an easy and accessible structured approach for researchers who are not expert programmers to create metadata in a form that is easily human readable and writable. This metadata is then structured enough to be easily translated into popular metadata standards and included in databases that are FAIR (Findable, Accessible, Interoperable, and Reusable). The MEDFORD language and supporting toolbox will be tested for usability with scientists, students, and members of the general public across several scientific domains, including marine biologists studying coral reefs, and biologists studying the animal hosts of tick-borne diseases. The involvement of scientific experts in the collection and analysis of the metadata than accompanies the complex scientific data is crucial; however, many of the recommended practices and processes focused on making these data FAIR (findable, accessible, interoperable, and reusable), as well as replicable and reproducible, can be cumbersome and difficult to implement, particularly for users that are not experts in computer science. This project posits that increasing the widespread community adoption of processes around efficient, robust, trustworthy, and FAIR data and metadata will require a new focus on making these data easily human-readable, writable, and correctable, in addition to all the valuable past effort that continues to go into making them easy for machines and database systems to ingest, validate, and parse. Thus it is focusing on a critical but under-served piece of the problem of frictionless FAIR data and metadata collection for science. The proposed solution involves inserting an intermediate layer between unstructured human annotation and existing machine-parsable metadata standards - the MEDFORD language and parser. Further development of the MEDFORD language will be informed by principled user studies in scientific communities with different needs. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.