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

Methodology Metadata can be considered as additional information about primary data such as experi- ments, tabular data, images, sound and acoustic files to make scientific data understandable and machine-readable. In order to enable machine processing, metadata in the Life Sciences are mostly stored in structured formats such as XML or JSON. A metadata schema [56] for- mally describes the syntax and structure of metadata, e.g., which elements and attributes can be used and which elements are mandatory and/or repeatable. If a schema has become estab- lished in a research community, it is usually adopted as a metadata standard. Another oppor- tunity to become a metadata standard is the formal adoption by a standards’ organization such as the International Organization for Standardization, https://www.iso.org. In this work, we use the term ‘metadata standard’ in a liberal way and include both schemata being adapted by the community and by an organization. We use the term ‘metadata field’ when discussing indi- vidual elements of a concrete metadata file. The term ‘metadata structure’ comprises all used fields in a metadata file. As the outcome of the question analysis reveals (Section “A—Information Needs in the Bio- diversity Domain”), biodiversity research is a broad field spanning a variety of disciplines. Therefore, in the following analysis, we selected metadata standards from two sources (re3data [57] and RDA Metadata Standards Catalog (version 2) [58]) with a broader perspective. In par-

ticular, we wanted to focus on metadata standards being used in current data portals and archives. In re3data, we filtered for “Life Sciences” (https://www.re3data.org/search?query= &subjects%5B%5D=2%20Life%20Sciences). Under the facet ‘Metadata standards’, a list of 24 standards appeared that we used for our further analysis. From RDA Metadata Standards Cat- alog, we selected all top-level standards (https://rdamsc.bath.ac.uk/scheme-index) labeled with “Science” (https://rdamsc.bath.ac.uk/subject/Science) resulting in a list of 30 standards. We merged both lists and cleaned the final outcome according to the following criteria: The cate- gories Other and Repository-Developed Metadata Schema have been omitted. The MIBBI stan- dard is outdated and has been integrated into ISA-Tab, so we left it out, too. The same applies to the Observ-OM and CIM standard. The information on the website is deprecated and not fully available (e.g., dead links). We also omitted the “Protocol Data Element Definitions” (data elements that are required for data archival for clinical trials), all astronomy and astro- physics related standards and standards for social and behavioral studies, as these fields are out of our scope. Table 2 presents the final list of 21 metadata standards used for the following analysis. They are ranked by the number of data repositories supporting them (obtained from re3data). We analyzed whether the standard supports semantic web formats, e.g., RDF or OWL. According to the FAIR principles [11], community standards, semantic formats, and

ontologies ensure interoperability and data reuse. The last column provides some examples of data repositories supporting the standard. Further, supplementary material on the selec- tion process can be found in our GitHub repository (https://github.com/fusion-jena/ QuestionsMetadataBiodiv/tree/master/metadataStandards). The standard supported by most repositories is Dublin Core, a general metadata standard based on 15 fields, such as contributor, coverage, creator, date, description, format, and identi- fier. In addition, data repositories utilize further domain-specific standards with richer vocab- ulary and structure such as ISO19115 for geospatial data or EML for ecological data. The RDF Data Cube Vocabulary is not used by any of the data centers. We suppose, the abbreviation RDF DC might lead to a misunderstanding (Dublin Core instead of RDF Data Cube). All stan- dards provide elements that can be described along the questions: Who? What? Where? When? Why? and How?. In particular, contact person, collection or publication date and PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 16 / 36