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Context-Driven Discoverability of Research Data

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12246)

Abstract

Research data sharing has been proved to be key for accelerating scientific progress and fostering interdisciplinary research; hence, the ability to search, discover and reuse data items is nowadays vital in doing science. However, research data discovery is yet an open challenge. In many cases, descriptive metadata exhibit poor quality, and the ability to automatically enrich metadata with semantic information is limited by the data files format, which is typically not textual and hard to mine. More generally, however, researchers would like to find data used across different research experiments or even disciplines. Such needs are not met by traditional metadata description schemata, which are designed to freeze research data features at deposition time.

In this paper, we propose a methodology that enables “context-driven discovery” for research data thanks to their proven usage across research activities that might differ from the original one, potentially across diverse disciplines. The methodology exploits the collection of publication–dataset and dataset–dataset links provided by OpenAIRE Scholexplorer data citation index so to propagate articles metadata into related research datasets by leveraging semantic relatedness. Such “context propagation” process enables the construction of “context-enriched” metadata of datasets, which enables “context-driven” discoverability of research data. To this end, we provide a real-case evaluation of this technique applied to Scholexplorer. Due to the broad coverage of Scholexplorer, the evaluation documents the effectiveness of this technique at improving data discovery on a variety of research data repositories and databases.

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Fig. 1.

Notes

  1. 1.

    Scholexplorer, https://scholexplorer.openaire.eu.

  2. 2.

    OpenAIRE, https://www.openaire.eu.

  3. 3.

    Crossref, https://www.crossref.org.

  4. 4.

    DataCite, https://www.datacite.org.

  5. 5.

    EMBL-EBI, https://www.ebi.ac.uk.

  6. 6.

    Scholexplorer API, https://scholexplorer.openaire.eu/#/api.

  7. 7.

    Hadoop, https://hadoop.apache.org.

  8. 8.

    Code repository, https://code-repo.d4science.org/miriam.baglioni/context-propagation.

  9. 9.

    Elasticsearch, https://www.elastic.co/elasticsearch.

  10. 10.

    Evaluation interface: https://propagation-demo.infrascience.isti.cnr.it.

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Acknowledgements

This work was co-funded by the EU H2020 project OpenAIRE-Advance (Grant agreement ID: 777541).

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Correspondence to Miriam Baglioni , Paolo Manghi or Andrea Mannocci .

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Baglioni, M., Manghi, P., Mannocci, A. (2020). Context-Driven Discoverability of Research Data. In: Hall, M., Merčun, T., Risse, T., Duchateau, F. (eds) Digital Libraries for Open Knowledge. TPDL 2020. Lecture Notes in Computer Science(), vol 12246. Springer, Cham. https://doi.org/10.1007/978-3-030-54956-5_15

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  • DOI: https://doi.org/10.1007/978-3-030-54956-5_15

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