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

Löffler, Felicitas; Wesp, Valentin; König-Ries, Birgitta; Klan, Friederike

doi:10.1371/journal.pone.0246099

Cited by 30 publications

(28 citation statements)

References 49 publications

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“…For brevity, we assume that datasets of interest have already been identified. However, searchability of data repositories is an active area of research (Pampel et al, 2013;Löffler et al, 2021). We briefly review several common approaches to data acquisition, harmonization, curation, and publication.…”

Section: Current Database Pipelinementioning

confidence: 99%

“…Soil data uses include a broad range of applications such as ecology, biogeochemistry (Iversen et al, 2017;Wieder et al, 2021b), soil engineering, soil taxonomy and classification, geochemistry (Nave et al, 2016;Hengl et al, 2017;Lawrence et al, 2020), micrometeorology (Cheah et al, 2018), agronomy (Lyons et al, 2020), and geomorphology. Datasets, defined as "a collection of scientific data including primary data and metadata organized and formatted for a particular purpose" (Löffler et al, 2021), are assembled by an equally diverse range of organizations. These organizations include government agencies, academic collaborations, nongovernmental organizations, and industry, reflecting a wide range of generators and users including farmers, land managers, students, technicians, scientists, and policy makers.…”

Section: Introductionmentioning

confidence: 99%

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Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

et al. 2022

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Abstract. In the age of big data, soil data are more available and richer than ever, but – outside of a few large soil survey resources – they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

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Section: Current Database Pipelinementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

et al. 2022

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“…Most of this work is so far focused on improving the FAIRness of biodiversity data. It includes work on improvement of discoverability of data by better, semantic descriptions (Löffler et al 2021, Pfaff et al 2017). These investigations have shown which categories of concepts (e.g., organism, environment, process, event) are relevant to biodiversity research.…”

Section: Preliminary Work: Biodiversity Informatics and Semantic Webmentioning

confidence: 99%

INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology

Heger¹,

Zarrieß²,

Algergawy³

et al. 2022

RIO

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Developing a precise argument is not an easy task. In real-world argumentation scenarios, arguments presented in texts (e.g. scientific publications) often constitute the end result of a long and tedious process. A lot of work on computational argumentation has focused on analyzing and aggregating these products of argumentation processes, i.e. argumentative texts. In this project, we adopt a complementary perspective: we aim to develop an argumentation machine that supports users during the argumentation process in a scientific context, enabling them to follow ongoing argumentation in a scientific community and to develop their own arguments. To achieve this ambitious goal, we will focus on a particular phase of the scientific argumentation process, namely the initial phase of claim or hypothesis development. According to argumentation theory, the starting point of an argument is a claim, and also data that serves as a basis for the claim. In scientific argumentation, a carefully developed and thought-through hypothesis (which we see as Toulmin's "claim'' in a scientific context) is often crucial for researchers to be able to conduct a successful study and, in the end, present a new, high-quality finding or argument. Thus, an initial hypothesis needs to be specific enough that a researcher can test it based on data, but, at the same time, it should also relate to previous general claims made in the community. We investigate how argumentation machines can (i) represent concrete and more abstract knowledge on hypotheses and their underlying concepts, (ii) model the process of hypothesis refinement, including data as a basis of refinement, and (iii) interactively support a user in developing her own hypothesis based on these resources. This project will combine methods from different disciplines: natural language processing, knowledge representation and semantic web, philosophy of science and -- as an example for a scientific domain -- invasion biology. Our starting point is an existing resource in invasion biology that organizes and relates core hypotheses in the field and associates them to meta-data for more than 1000 scientific publications, which was developed over the course of several years based on manual analysis. This network, however, is currently static (i.e. needs substantial manual curation to be extended to incorporate new claims) and, moreover, is not easily accessible for users who miss specific background and domain knowledge in invasion biology. Our goal is to develop (i) a semantic model for representing knowledge on concepts and hypotheses, such that also non-expert users can use the network; (ii) a tool that automatically computes links from publication abstracts (and data) to these hypotheses; and (iii) an interactive system that supports users in refining their initial, potentially underdeveloped hypothesis.

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“…However, practical guidance on semantic annotation of biodiversity literature are few and far between and usually refer to English-language text corpora with a focus on taxonomy (see, e.g., Sautter et al, 2007). Beyond mere taxonomic tagging, more recent workflows also cover a much broader thematic range of biodiversity entities, but do not allow multilabel annotation, that is, (possibly) assigning more than one annotation tag to an annotation unit (Löffler et al, 2020;Nguyen et al, 2019;Thessen et al, 2018). Enhancing content retrieval and information fusion by multi-label annotation has since found its way into the biomedical domain, e.g.…”

Section: Introductionmentioning

confidence: 99%

Multiple annotation for biodiversity: developing an annotation framework among biology, linguistics and text technology

Lücking

Driller²,

Stoeckel

et al. 2021

Lang Resources & Evaluation

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Biodiversity information is contained in countless digitized and unprocessed scholarly texts. Although automated extraction of these data has been gaining momentum for years, there are still innumerable text sources that are poorly accessible and require a more advanced range of methods to extract relevant information. To improve the access to semantic biodiversity information, we have launched the BIOfid project (www.biofid.de) and have developed a portal to access the semantics of German language biodiversity texts, mainly from the 19th and 20th century. However, to make such a portal work, a couple of methods had to be developed or adapted first. In particular, text-technological information extraction methods were needed, which extract the required information from the texts. Such methods draw on machine learning techniques, which in turn are trained by learning data. To this end, among others, we gathered the bio text corpus, which is a cooperatively built resource, developed by biologists, text technologists, and linguists. A special feature of bio is its multiple annotation approach, which takes into account both general and biology-specific classifications, and by this means goes beyond previous, typically taxon- or ontology-driven proper name detection. We describe the design decisions and the genuine Annotation Hub Framework underlying the bio annotations and present agreement results. The tools used to create the annotations are introduced, and the use of the data in the semantic portal is described. Finally, some general lessons, in particular with multiple annotation projects, are drawn.

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

Cited by 30 publications

References 49 publications

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential

INAS: Interactive Argumentation Support for the Scientific Domain of Invasion Biology

Multiple annotation for biodiversity: developing an annotation framework among biology, linguistics and text technology

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