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

various repositories focusing on different domains. In a survey conducted by the Research Data Alliance (RDA) Data Discovery Paradigm Interest Group [4], 35% of the 98 participating repositories stated that they host data from Life Science and 34% indicated they cover Earth Science. All of these are potentially of interest to biodiversity researchers. However, the offered search services at public data providers do not seem to support schol- ars effectively. A study by Kacprzak et al. [5] reports that 40% of users attempting to search within two open data portals could not find the data they were interested in, and thus directly requested the data from the repository manager. In several studies, ecologists report on the dif- ficulties they had when looking for suitable datasets to reuse [3, 6, 7]. In a recent, large-scale survey by Gregory et al [8], the vast majority of participants characterized data discovery as a sometimes challenging (73%) or even difficult (19%) task. 49% of the respondents stated that the greatest obstacles are “inadequate search tools, a lack of skill in searching for data, or the fact that their needed data are not digital” [8]. Hence, many researchers do not search for data in repositories, but rather follow data mentions in literature or employ general search engines for their dataset search [8]. Thus, there is a high demand for new techniques and methods to better support scholars in finding relevant data in data repositories. In this study, we explore what hampers dataset retrieval in data repositories for biodiversity

research. We analyze two building blocks in retrieval systems: information needs (user queries) and underlying data. We want to find out how large the gap is between scholarly search inter- ests and provided data. In order to identify scholarly search interests, we analyzed user ques- tions. In contrast to user queries, which are usually formulated in a few keywords, questions represent a search context, a more comprehensive information need. Characteristic terms or phrases in these textual resources can be labeled and classified to identify biological entities [9, 10]. Scientific data are not easily accessible by classical text retrieval mechanisms as they were mainly developed for unstructured textual resources. Thus, effective data retrieval heavily relies on the availability of proper metadata (structured information about the data) describing available datasets in a way that enables their Findability, one principle of FAIR data [11]. A sur- vey conducted by the Research Data Alliance (RDA) Data Discovery Group points out that 58% of the 98 participating data repositories index all metadata and partial metadata (52%). Only one third of the participating repositories integrate data dictionaries or variables (the actual primary data) [4]. We argue that Findability at least partially depends on how well metadata reflect scholarly information needs. Current retrieval evaluation methods are basically focused on improving retrieval algorithms and ranking [12, 13]. Therefore, question corpora and documents are

taken as given and are not questioned. However, if the underlying data do not contain the information users are looking for, the best retrieval algorithm will fail. We argue that in dataset search, metadata, the basic source for dataset applications, need to be adapted to match users’ information needs. Thus, the following analysis aims to explore: • What are genuine user interests in biodiversity research? • Do existing metadata standards reflect information needs of biodiversity scientists? • Are metadata standards utilized by data repositories useful for data discovery? How many metadata fields are filled? • Do common metadata fields contain useful information? We propose a top-down approach starting from scholars’ search interests, then looking at metadata standards and finally inspecting the metadata provided in selected data repositories: PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 2 / 36 10.25829/idiv.1864-15-2987 (Question Corpus) https://doi.org/10.25829/idiv.1881-12-3202 (Metadata Analysis). Funding: We acknowledge the Collaborative Research Centre AquaDiva (CRC 1076 AquaDiva, DFG Project Number: 218627073) of the Friedrich Schiller University Jena, the GFBio project (DFG Project Number: 229241684) and the Open Access Publication Fund of the Thueringer Universitaets- und Landesbibliothek Jena (DFG Project Number: 433052568), all funded by the Deutsche Forschungsgemeinschaft (DFG). The funder had no role in study design, data collection and analysis,

decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.