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

mechanisms to handle misspellings but full semantic search approaches are very rare in large data repositories (see also Subsection “Challenges to be addressed in Computer Sci- ence”). Hence, if the desired information need is not explicitly mentioned in the metadata, the search will fail. This is reinforced by the fact that some repositories only integrate partial metadata in their search index [4]. In order to provide improved user interfaces supporting exploratory search paradigms [33], data repositories increasingly offer faceted search facili- ties. Facets are a group of meaningful labels comprising relevant categories of a domain [34]. A faceted search allows users to influence the result set and to narrow its scope, e.g., to filter for datasets in a specific geographic region. If not manually created, facets need to be built on metadata fields or grouped keywords. Numerous studies in computer science explore automatic approaches for facet creation [35–37]. Recent studies also consider the usage of semantic knowledge bases (e.g., [38, 39]). However, so far these techniques have not been integrated into existing search systems in dataset retrieval and can not completely replace predefined domain categories. In recent years, a variety of approaches have emerged to improve dataset search. A common approach is to annotate metadata with entities from schema.org (https://schema.org). Favored by Google [40] and the RDA Data Discovery Paradigm Interest Group [41], the idea is to add descriptive information to structured data such as XML or HTML in order to increase find-

ability and interoperability. These additional attributes help search engines to better disambig- uate terms occurring in text. For example, Jaguar could be a car, an animal or an operating system. By means of schema.org entities, data providers can define the context explicitly. Numerous extensions for specific domains have been developed or are still in development, e.g., bioschemas.org [42] for the Life Sciences. Since Google launched its beta version of a data- set search in Fall 2018 (https://toolbox.google.com/datasetsearch), schema.org entities got more and more attention. Hence, data centers such as PANGAEA [20] or Figshare [19] are increasingly incorporating schema.org entities in their dataset search. Other approaches favor an improved metadata schema. Pfaff et al [43] introduce the Essential Annotation Schema for Ecology (EASE). The schema was primarily developed in workshops and intensive discussions with scholars and aims to support scientists in search tasks. EASE consists of eight top level categories (Time, Space, Sphere, Biome, Organism, Process, Method and Chemical) that were enriched with further elements in a top down approach. Finally, EASE consists of around 1600 elements. The MIBBI project [44] also rec- ognized that only improved metadata allow information seekers to retrieve relevant experi- mental data. They propose a harmonization of minimum information checklists in order to facilitate data reuse and to enhance data discovery across different domains. Checklist devel-

opers are advised to consider “‘cross-domain’ integrative activities” [44] when creating and maintaining checklists. In addition, standards are supposed to contain information on for- mats (syntax), vocabularies and ontologies used. Nowadays, its successor FAIRsharing [45] manually curates metadata on these standards and the relationships between them and relates this back to metadata on the repositories and knowledge bases that implement and use them. Further, FAIRsharing links both standards and databases to journal and funder data policies that recommend or endorse their use. Federated approaches attempt to align heterogeneous data sources in one search index. That allows the use of conventional search engines and keyword-based user interfaces: DataONE is a project aiming to provide access to earth and environmental data provided by multiple member repositories [46]. Participating groups can provide data in different metadata formats such as EML, DataCite or FGDC [47]. DataONE is currently working on quantifying FAIR [48]. Their findability check determines if specific metadata items such as title, abstract or publication date are present. For title and abstract, they additionally check the length and PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 7 / 36