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

(A) We first identified main entity types (categories) that are important in biodiversity research. We collected 169 questions provided by 73 scholars of three large and very diverse biodiversity projects in Germany, namely AquaDiva [14], GFBio—The German Federation for Biological Data [15] and iDiv—The German Research Center for Integrative Biodiversity Research [1]. Two authors of this publication labeled and grouped all noun entities into 13 categories (entity types), which were identified in several discussion rounds. Finally, all proposed categories were evaluated with biodiversity scholars in an online survey. The scholars assigned the proposed categories to important phrases and terms in the questions (Section “A—Information Needs in the Biodiversity Domain”). (B) Most data providers use keyword-based search engines returning data sets that exactly match keywords entered by a user [4]. In dataset search, the search index mainly consists of metadata. The metadata schema required or recommended by the data repository greatly influences the richness of metadata descriptions and determines available facets for filtering. Therefore, we inspected common metadata standards in the Life Sciences and analyzed, to which extent their metadata schemata cover the identified information categories (Section “B—Metadata Standards in the Life Sciences”). (C) There are several data repositories that take and archive scientific data for biodiversity research. According to Nature’s list of recommended data repositories [16], repositories

such as Dryad [17], Zenodo [18] or Figshare [19] are generalist repositories and can han- dle different types of data. Data repositories such as PANGAEA [20] (environmental data) or GBIF [21] (occurrence data) are domain specific and only take data of a specific format. We harvested and parsed all publicly available metadata from these repositories and determined whether they utilize metadata elements that reflect search interests. For GBIF, we concentrated on datasets only, as individual occurrence records are not avail- able in the metadata API. We explored how many fields of the respective schemata are actually used and filled (Section “C—Metadata Usage in Selected Data Repositories”). (D) Finally, we discuss the results and outline how to consider and address user interests in metadata (Section “D—Discussion”). In order to foster reproducibility, questions, scripts, results and the parsed metadata are publicly available: https://github.com/fusion-jena/QuestionsMetadataBiodiv. The structure of the paper is as follows: The first part “Definitions” focuses on the clarifi- cation of various terms. Afterwards, we introduce “Related Work”. The following four sec- tions contain the individual research contributions described above. Each of these sections describes the respective methodology and results. Finally, section “Conclusion” summarizes our findings. Definitions Since dataset retrieval is a yet largely unexplored research field [22], few definitions exist describing what it comprises and how it can be characterized. Here, we briefly introduce an

existing definition and add our own definition from the Life Sciences’ perspective. Chapman et al [22] define a dataset as “A collection of related observations organized and formatted for a particular purpose”. They further characterize a dataset search as an applica- tion that “involves the discovery, exploration, and return of datasets to an end user.” They dis- tinguish between two types: (a) a basic search in order to retrieve individual datasets in data portals and (b) a constructive search where scholars create a new dataset out of various input datasets in order to analyze relationships and different influences for a specific purpose. PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 3 / 36