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

Complexity Scholarly search interests are as heterogeneous as scientific data. Concerning complexity, information needs can either be very specific and focus on one research domain only, or search interests can be broader and comprise various research fields. In terms of the search results, retrieved datasets may contain the complete answer to a search query or answer queries only partially. For the latter, users might construct new datasets out of various input datasets. Unger et al. [23] characterize the complexity in retrieval tasks along four dimensions: Semantic complexity describes how complex, vague, and ambiguous a question is formulated and if het- erogeneous data have to be retrieved. Answer locality denotes if the answer is completely con- tained in one dataset or if parts of various datasets need to be composed or if no data can be found to answer the question. Derivability describes if the answer contains explicit or implicit information. The same applies for the question. If broad or vague terms appear in the question or answer, additional sources have to be integrated to enrich question and/or answer. Semantic tractability denotes if the natural language question can be transformed into a formal query. Related work This section focuses on approaches that analyze, characterize and enhance dataset search. We discuss studies identifying users’ information needs and introduce existing question corpora used for retrieval evaluation. In a second part, we describe what retrieval methods are used in

existing data portals, and we introduce approaches that aim at enhancing dataset search. User interests Query logs, surveys or question corpora are valid sources for studying user behavior in search. Kacprazal et al [5] provide a comprehensive log analysis of three government open data portals from the United Kingdom (UK), Canada, and Australia and one open data portal with national statistics from the UK. 2.2 million queries from logs provided by the data portals (internal queries) and 1.1 million queries issued to external web search engines (external que- ries) were analyzed. Two authors manually inspected a sample set of 665 questions and deter- mined the main query topics. Most queries were assigned to Business and Economy (20% internal queries, 10% external queries) and Society (14.7% internal queries, 18% external que- ries). Besides query logs, Kacprazal et al [5] also analyzed explicit requests by users for data via a form on the website. Here, users provided title and description which allowed the authors to perform a deeper thematic analysis on 200 manually selected data requests. It revealed that geospatial (77.5%) and temporal (44%) information occurred most, often together with a spe- cific granularity (24.5%), e.g., “hourly weather and solar data set” or “prescription data per hos- pital”. Users were also asked why they had requested data explicitly, and more than 40% indicated that they were not able to find relevant data via the provided search. A large study on user needs in biodiversity research was conducted in the GBIF community

in 2009 [27, 28]. The aim was to determine what GBIF users need in terms of primary data and to identify data gaps in the current data landscape at that time. More than 700 participants from 77 countries took part in the survey. It revealed that scholars retrieved and used primary data for analyzing species diversity, taxonomy, and life histories/ phenology. That mainly required “taxon names, occurrence data and descriptive data about the species” [28]. As biodi- versity is a rapidly changing research field, the authors recommended to repeat content need assessments in frequent intervals [27]. Apart from query logs and surveys, question corpora are another source for identifying search interests. Usually, questions are collected from experts of a particular research field and important terms representing main information needs are labeled with categories or so-called PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 5 / 36