The stage is set. Items are ready to be described by metadata, or have some metadata to be augmented or used. But who are the cast of players that interact with metadata to ensure its usefulness? Our project, Incentives for Improving Metadata Quality, led by Fiona Counsell, has been focused on highlighting the applications and value of metadata for all parts of the community. In order to tell these stories, the project team considered the key metadata players and how to best describe them.
Today we continue our communication of Metadata 2020 outputs as outlined in an earlier post. Since 2018, our work has been primarily divided into six project groups; as co-chairs of the Researcher Communications project, we are happy to share an update on our work. This project group is been charged with increasing our understanding of the attitudes and values that individuals have about metadata in scholarly outputs in order to help inform how we talk about metadata to this audience.
Many of you are probably aware that Metadata 2020 has a project group working on Best Practices and Principles. After many months of collaboration, we are happy to share a draft of the Principles for community input. These aspirational Metadata 2020 Principles were designed to encompass the needs of our entire community while ensuring thoughtful, purposeful, and reusable metadata resources. They advocate for all of us to be good metadata citizens.
One of the interesting ingredients for success in several current metadata projects is agreement across communities about what metadata are important for various use cases. In an earlier blog, I introduced the idea that metadata recommendations provide descriptions of which documentation concepts communities or organizations believe are important. These recommendations provide an opportunity to identify similarities and differences between community beliefs. We have collected recommendations from 10-20 organizations and communities as part of an NSF Project aimed at evaluating metadata collections in various dialects with respect to these recommendations.
no·men·cla·ture The devising or choosing of names for things, the body or system of names in a field. For me, it started with earthquake prediction – trying to find signals – real signals. I needed consistent long-term datasets. I needed to find changes and then figure out if they were real… Turned out that most signals were changes in how things were measured… I needed good documentation.