Abstract
In disciplines as varied as medicine, social sciences, and economics, data and their analyses are essential parts of researchers’ contributions to their respective fields. While sharing research data for review and analysis presents new opportunities for furthering research, capturing these data in digital forms and providing the digital infrastructure for sharing data and metadata pose several challenges. This paper reviews the motivations behind and design of the Data Staging Repository (DataStaR) platform that targets specific portions of the research data curation lifecycle: data and metadata capture and sharing prior to publication, and publication to permanent archival repositories. The goal of DataStaR is to support both the sharing and publishing of data while at the same time enabling metadata creation without imposing additional overheads for researchers and librarians. Furthermore, DataStaR is intended to provide cross-disciplinary support by being able to integrate different domain-specific metadata schemas according to researchers’ needs. DataStaR’s strategy of a usable interface coupled with metadata flexibility allows for a more scaleable solution for data sharing, publication, and metadata reuse.
Highlights
In disciplines as varied as medicine, social sciences, and economics, data and its analysis are an essential part of researchers’ contributions to their respective fields
Librarians with metadata and/or subject area expertise are in a good position to assist researchers with metadata creation, but, as Steinhart and Lowe (2007) found in their efforts to support research data curation at Cornell University’s Albert R
This approach enables the reuse of statements for other data sets, potentially decreasing the effort involved in creating metadata, as a researcher’s “collection” of metadata statements in DataStaR grows
Summary
Semantic Web technologies aim to define and interconnect data in a way similar to how traditional web technologies define and interconnect web pages. DataStaR’s use of semantic web technologies attempts to support more efficient creation of metadata by treating the metadata associated with a particular data set as a collection of statements about that data set, rather than a single, static document This approach enables the reuse of statements for other data sets, potentially decreasing the effort involved in creating metadata, as a researcher’s “collection” of metadata statements in DataStaR grows. Researchers can use DataStaR to create, share, and publish data sets described by different schemas as required. Sara continues to edit and share these data sets with colleages or research groups When her colleagues download the data set, DataStaR returns a zipped file containing the files uploaded as well as separate XML files corresponding to the different schemas with which the data set was associated. DataStaR uses a similar process to create an EML record when Sara wishes to publish the data set to the KNB repository
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