Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

metadata fields potentially contain the most relevant search terms and are also candidates to be enriched with text mining tools. Hence, we aim to explore what information is currently available in general, descriptive metadata fields. We downloaded descriptive metadata fields, namely, dc:title, dc:description and dc:subject in OAI-DC format from all repositories in October and November 2019. Parallel to the download, we collected the key- words used in the subject field and counted their presence in a separate csv file. In order to further inspect the content with Natural Language Processing (NLP) [24] tools, we selected a subset of representative datasets. We limited the amount to 10,000 datasets per repository as the processing of textual resources is time-consuming and resource-intensive. A variety of applications have been developed to determine Named Entities (NE) such as geo- graphic locations, persons and dates. Thessen et. al [31] explored the suitability of existing NLP applications for biodiversity research. Their outcome reveals that current text mining sys- tems, which were mainly developed for the biomedical domain, are able to discover biological entities such as species, genes, proteins and enzymes. We concentrated on the extraction of entity types that (a) correspond to the identified search interests (Section “A—Information Needs in the Biodiversity Domain”) and for which (b) text mining pipelines are available. We used the text mining framework GATE [62] and its ANNIE pipeline [63] as well as the Orga-

nismTagger [64] to extract geographic locations, persons, organizations and organisms. In our previous research, we developed the BiodivTagger [65], a text mining pipeline extracting envi- ronmental terms, materials, processes and data parameters. Results In this subsection, we present the results of the harvested and processed metadata. We ana- lyzed the data along the introduced criteria, beginning with statistics and timelines, a study on the use of metadata fields and a deeper content analysis of metadata fields. Statistics. The overall statistics of the analyzed data repositories are presented in Table 6. Most repositories support general standards for metadata creation; only PANGAEA and GBIF utilize metadata based on discipline-specific metadata standards. Dryad and Figshare already provide metadata in semantic formats such as RDF. In addition, Figshare offers Qualified Dub- lin Core (QDC), an extended Dublin Core that allows the description of relations to other data sources. We checked the fields for a publication date in a valid format. We could not use all harvested datasets as for some metadata files publication dates were not available. Dryad had a Table 5. The date stamps used for each metadata standard and their descriptions obtained from the standard’s website. Format Element URL Description OAI-DC/METS/QDC RDF(Dryad) dc:date http://www.dublincore.org/specifications/dublin-core/ dces/ “A point or period of time associated with an event in the lifecycle of the resource.” EML pubDate https://knb.ecoinformatics.org/external//emlparser/docs/

eml-2.1.1/eml-resource.html#pubDate “The ‘pubDate’ field represents the date that the resource was published.” DATACITE3/4 OAI-DATACITE/3 publicationYear https://support.datacite.org/docs/schema-40 “The year when the data was or will be made publicly available.” DIF DIF_Creation- _Date https://gcmd.gsfc.nasa.gov/DocumentBuilder/ defaultDif10/guide/metadata_dates.html “refers to the date the data was created” ISO19139/ISO19139.iodp gco:DateTime https://geo-ide.noaa.gov/wiki/index.php?title=ISO_Dates (CI-DataTypeCode=publication), publication Date PAN-MD md:dateTime http://ws.pangaea.de/schemas/pangaea/MetaData.xsd publication date (contact to data repository) RDF(Figshare) vivo: datePublished https://codemeta.github.io/terms/ “Date of first broadcast/publication” https://doi.org/10.1371/journal.pone.0246099.t005 PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 22 / 36