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

content. Based on these criteria, they evaluated their data and found out that concerning Find- ability around 75% of the available metadata fulfilled the self-created criteria. The German Federation for Biological Data (GFBio) [49] is a national infrastructure for research data man- agement in the green Life Sciences and provides a search over more than six million heteroge- neous datasets from environmental archives and collection data centers. As described above, numerous approaches have been proposed and developed to improve dataset search. However, what is lacking is a comprehensive analysis on what exactly needs to be improved and how large the actual gap is between user requirements and given metadata. A—Information needs in the biodiversity domain Question corpora are common sources to determine user interests for a particular domain. Therefore, we asked biodiversity scholars to provide questions that are specific for their research. We analyzed the questions and identified search topics that represent scholarly infor- mation needs in this domain. Methodology Our methodology for the question analysis is based on common question collection and anno- tation procedures in information retrieval and question answering [9, 10, 50]. In the following subsection, we describe the methodology in detail. It comprises four parts: (1) question collec- tion, (2) category definition, (3) annotation process, (4) annotators and category assignment. (1) Question collection. We gathered questions in three large biodiversity projects,

namely CRC AquaDiva [14], GFBio [15] and iDiv [1]. We explicitly requested fully expressed questions to capture the keywords in their search context. These projects vary widely in their overall setting, the scientists and disciplines involved and their main research focus. Together, they provide a good and rather broad sample of current biodiversity research topics. In total, 73 scholars with various research backgrounds in biology (e.g., ecology, bio-geochemistry, zoology and botany) and related fields (e.g., hydro-geology) provided 184 questions. This number is comparable to related question corpora in Information Retrieval (e.g., bioCADDIE [25]) which typically consist of around 100—150 questions. The scholars were asked to provide up to five questions from their research background. Questions varied with respect to granu- larity. The corpus contains specific questions, such as List all datasets with organisms in water samples. as well as questions with a broader scope, e.g., Does agriculture influence the ground- water?. We published the questionnaires that were handed out in AquaDiva and iDiv as sup- plementary material in our repository. In the GFBio project, questions were gathered via email and from an internal search evaluation. All questions were inspected by the authors with respect to comprehensibility. We discarded questions that were not fully understandable (e.g., missing verb, misleading grammatical structures) but left clear phrases in the corpus that were not fully expressed as a question. If scholars provided several questions, they were treated indi-

vidually even if terms referred to previous questions, e.g., Do have earthworm burrows (bio- pores) an impact on infiltration and transport processes during rainfall events? and Are the surface properties influencing those processes?. In this case, no further adaption towards com- prehensibility has been made. The questions were also not corrected with respect to grammar and spelling since changing the grammar could lead to an altered statement. We did not want to lose the original question statement. In some questions, abbreviations occurred without explanations. In these cases, we left the questions as they are and did not provide full terms, since these abbreviations can have various meanings in different biological fields. It was up to the domain experts to either look them up or to leave the term out. After the cleaning, the final PLOS ONE Dataset search in biodiversity research PLOS ONE | https://doi.org/10.1371/journal.pone.0246099 March 24, 2021 8 / 36