Affiliations and identifiers are critical for building a connected research community across the world. The contribution made to that connected resource for University datasets in DataCite is limited by the paucity of the required organizational identifiers.
I use the database version of the ROR data to explore data characteristics that might affect my results, usually by making it harder to find the correct RORs in a big pile of affiliations.
DataCite has a long history of registering DOIs for a variety of resource types.
Need facet data in a machine-readable format? The new option —facetdata can help!
I will describe how the DataCite metadata facets help me improve my understanding of DataCite metadata usage in this blog and introduce some software that can help you answer your questions about DataCite metadata usage.
The idea that DataCite exists only to provide DOIs is deeply embedded in repository thought processes and this idea needs to evolve. The research community needs to think about DataCite (and other elements of the global research infrastructure) as powerful resources for describing and connecting the myriad of resources that make up the modern research world.
Improving the completeness and the machine-readability of funder metadata in the global research infrastructure, i.e. DataCite and Crossref, is a critical step along the path of using that infrastructure to identify and characterize research results supported by funders all over the world. A set of 854 funder descriptions from the DRUM repository were processed into 1482 affiliation strings.
Identifying diverse contributions made to research objects is the first step in acknowledging those contributions. DataCite includes the contributorType metadata element and a list of twenty types to support this step. A type of Other is included to allow recognition of contributions not included in the list. Understanding how Other is used might help evaluate possible extensions to the contributorType list.
We are in the early days of documenting output management plans using DataCite metadata so it is an important time to identify and adopt common practices that help realize the exciting goals of machine-actionable DMPs. Common practices will facilitate the creation of interoperable DMPs and the development of tools across the research community that help us all reap the on-going benefits of these plans throughout the research life-cycle.
“Existing metadata are an exciting learning set for discerning patterns that can be used to streamline the metadata creation process. We want to ensure that data are being submitted to the best fit repository with the right metadata.