Making data and workflows findable for machines

In this brief journal article published in Data Intelligence, Weigel et al. make their case for improving data object findability in order to benefit data and workflow management procedures for researchers. They argue that even with findable, accessible, interoperable, and reusable (FAIR) principles getting more attention, findability is the the starting point before anything else can be done, and that “support at the data infrastructure level for better automation of the processes dealing with data and workflows” is required. They succinctly describe the essential requirements for automating those processes, and then provide the basic building blocks that make up a potential solution, including—in the long-term—the application of machine learning. They conclude that adding automation to improve findability means that “researchers producing data can spend less time on data management and documentation, researchers reusing data and workflows will have access to metadata on a wider range of objects, and research administrators and funders may benefit from deeper insight into the impact of data-generating workflows.”

Please to read the entire article.