Searchable Index of Metadata Aggregators

Searchable Index of Metadata Aggregators is a database that stores general information of metadata aggregators. This database is accompanied with the “A WDS guide to Metadata Aggregators for Repository Managers”. The Searchable Index of Metadata Aggregators is an up-to-date catalogue of Dataset Metadata Aggregators (DMAs), implemented as an access database. It was designed to fill in a gap found by the Harvestable Metadata Services Working Group (HMetS-WG) members of the World Data System’s International Technology Office (WDS-ITO). These include up-to-date resources giving an overview of current infrastructures used to syndicate dataset metadata. The database contains information on DMA's supported metadata standards and software interfaces, as well as documentation on how to be aggregated by each.

The WDS Guide to Metadata Aggregators is a guidance document for the associated Searchable Index of Metadata Aggregators. We have defined DMAs as federated service infrastructures that foster the findability and accessibility of data products by enabling access to multiple, distributed metadata records via a single search interface. This guide gives a description of this catalogue and general guidance on how to use it. In the sections that follow, we give a short background to the Harvestable Metadata Services-Working Group project. Then, we outline the project's research methodology and the properties of the searchable index. Finally, we discuss this project's limitations, as well as its future development. Providing metadata to aggregators can significantly improve the findability of research data products.

Together, this guidance document and dataset package are designed to provide research data repository managers with options for participation in federated research data systems, and support institutional repositories' harvestable metadata service implementation strategies. In addition, as developers in the global research data management community seek to create pathways and workflows across data, software and compute resources, we anticipate that they're likely to prioritize connecting sites, organizations and services that have already done a lot of work harmonizing content from disparate providers. In this context, this resource will be helpful for creating roadmaps and implementation plans for integration across science clouds.