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Dataset Search in Biodiversity Research: Do Metadata in Data Repositories Reflect Scholarly Information Needs?
Date of this Version
PLoS ONE (2021) 16(3): e0246099
Editor: Hussein Suleman, University of Cape Town, South Africa
Received: July 5, 2019 Accepted: January 13, 2021 Published: March 24, 2021
Peer Review History
PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: https://doi.org/10.1371/journal.pone.0246099
Data Availability Statement
The code and data are available in GitHub repository: https://github.com/fusion-jena/QuestionsMetadataBiodiv. In addition, the data has been submitted to the iDiv data portal (https://idata.idiv.de/)
The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. In particular, we focus on scholarly search interests and metadata, the primary source of data in a dataset retrieval system. We show that existing metadata currently poorly reflect information needs and therefore are the biggest obstacle in retrieving relevant data. Our findings indicate that for data seekers in the biodiversity domain environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important information categories. These interests are well covered in metadata elements of domain-specific standards. However, instead of utilizing these standards, large data repositories tend to use metadata standards with domain-independent metadata fields that cover search interests only to some extent. A second problem are arbitrary keywords utilized in descriptive fields such as title, description or subject. Keywords support scholars in a full text search only if the provided terms syntactically match or their semantic relationship to terms used in a user query is known.
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Copyright 2021, the authors. Open access material
License: Creative Commons Attribution (CC BY)