Journal:A metadata-driven approach to data repository design
|Full article title||A metadata-driven approach to data repository design|
|Journal||Journal of Cheminformatics|
|Author(s)||Harvey, Matthew J.; McLean, Andrew; Rzepa, Henry S.|
|Author affiliation(s)||Imperial College London|
|Primary contact||Email: rzepa at imperial dot ac dot uk|
|Volume and issue||9|
|Distribution license||Creative Commons Attribution 4.0 International|
|This article should not be considered complete until this message box has been removed. This is a work in progress.|
The design and use of a metadata-driven data repository for research data management is described. Metadata is collected automatically during the submission process whenever possible and is registered with DataCite in accordance with their current metadata schema, in exchange for a persistent digital object identifier. Two examples of data preview are illustrated, including the demonstration of a method for integration with commercial software that confers rich domain-specific data analytics without introducing customization into the repository itself.
Keywords: Data repository, metadata-driven, DataCite, data preview, Mpublish
Turnkey institutional repositories based on platforms such as DSpace were introduced more than 10 years ago, with early applications directed largely towards archival of publication preprints and postprints. The recent increasing requirement for research data management emerging from funding agencies means that the focus is now shifting to the use of repositories as part of the data management processes. More recent data-centric tools such as Figshare and Zenodo reflect these changes. Such services rely on the minting of persistent identifiers or DOIs for the depositions using the DataCite agency. Metadata describing the deposited material is supplied to DataCite and a DOI is returned. An early example of such research data management is illustrated by a DSpace-based project to produce and, 10 years later, curate a library of quantum-mechanically-optimised molecular coordinates derived from a computable subset of the National Cancer Institute's (NCI) collection of small molecules.
One feature of the curation phase of the project aimed to explore the capabilities of the DataCite metadata schemas to improve the discoverability of the deposited data. The metadata can then be exploited to create rich search queries. As a result of the experiences gained from this project, we became aware that one limiting factor to the effective use of metadata was the repository design itself. The next stage therefore was to explore whether what we considered the essential requirements for a data repository could be incorporated into a new design. Here we report the principles used to create such a repository and some of the applications in chemistry that have resulted. These principles may in turn assist researchers wishing to deposit data in identifying the repository attributes that can best expose the discoverability and re-use of their data.
- "DSpace". DuraSpace Organization. http://www.dspace.org/. Retrieved 07 September 2016.
- "Zenodo". CERN Data Centre. https://zenodo.org/. Retrieved 07 September 2016.
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- Harvey, M.J.; Mason, N.J.; McLean, A. et al. (2015). "Standards-based curation of a decade-old digital repository dataset of molecular information". Journal of Cheminformatics 7: 43. doi:10.1186/s13321-015-0093-3. PMC PMC4550659. PMID 26322133. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=PMC4550659.
- Rzepa, H.S.; Mclean, A.; Harvey, M.J. (2015). "InChI as a research data management tool". Chemistry International 38 (3–4): 24–26. doi:10.1515/ci-2016-3-408.
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added. In one case, the original citation was incomplete (#6) and was corrected here.