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Spatial Discovery and the Research Library: Linking Research Datasets and Documents


Academic libraries have always supported research across disciplines by integrating access to diverse contents and resources. They now have the opportunity to reinvent their role in facilitating interdisciplinary work by offering researchers new ways of sharing, curating, discovering, and linking research data. Spatial data and metadata support this process because location often integrates disciplinary perspectives, enabling researchers to make their own research data more discoverable, to discover data of other researchers, and to integrate data from multiple sources.

The Center for Spatial Studies at the University of California, Santa Barbara (UCSB) and the UCSB Library are undertaking joint research to better enable the discovery of research data and publications. The research addresses the question of how to spatially enable data discovery in a setting that allows for mapping and analysis in a GIS while connecting the data to publications about them. It suggests a framework for an integrated data discovery mechanism and shows how publications may be linked to associated data sets exposed either directly or through metadata on Esri’s Open Data platform. The results demonstrate a simple form of linking data to publications through spatially referenced metadata and persistent identifiers. This linking adds value to research products and increases their discoverability across disciplinary boundaries.

Current data publishing practices in academia result in datasets that are not easily discovered, hard to integrate across domains, and typically not linked to publications about them. For example, discovering that two datasets, such as archaeological observations and specimen data collections, share a spatial extent in Mesoamerica, is not currently supported, nor is it easy to get from those data sets to relevant publications or other documents. In our previous work, we had developed a basic linked metadata model relating spatially referenced datasets to documents. The research reported here applies the model to a collection of spatially referenced researcher datasets, capturing metadata and encoding them as linked open data. We use existing RDF vocabularies to triplify the metadata, to make them spatially explicit, and to link them thematically. Our latest research has produced a simple and extensible method for exposing metadata of research objects as a library service and for spatially integrating collections across repositories.

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