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End date: 2024 × Start date: 2020 × DDC: 570 × Has files: Yes × WGL Institute: TIB ×

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Now showing 1 - 4 of 4
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    BEXIS2: A FAIR-aligned data management system for biodiversity, ecology and environmental data
    (Sofia : Pensoft Publishers, 2021) Chamanara, Javad; Gaikwad, Jitendra; Gerlach, Roman; Algergawy, Alsayed; Ostrowski, Andreas; König-Ries, Birgitta
    Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantially. Researchers are encouraged to manage the data as per the FAIR data principles and ensure that the raw data, metadata, processed data, software, codes and associated material are securely stored and the data be made available with the completion of the research.
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    Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research
    (London : BioMed Central, 2022) Vogt, Lars; Mikó, István; Bartolomaeus, Thomas
    Background: In times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term. Results: We argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept. Conclusions: We conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.
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    Latent Class Cluster Analysis: Selecting the number of clusters
    (Amsterdam [u.a.] : Elsevier, 2022) Lezhnina, Olga; Kismihók, Gábor
    Latent Class Cluster Analysis (LCCA) is an advanced model-based clustering method, which is increasingly used in social, psychological, and educational research. Selecting the number of clusters in LCCA is a challenging task involving inevitable subjectivity of analytical choices. Researchers often rely excessively on fit indices, as model fit is the main selection criterion in model-based clustering; it was shown, however, that a wider spectrum of criteria needs to be taken into account. In this paper, we suggest an extended analytical strategy for selecting the number of clusters in LCCA based on model fit, cluster separation, and stability of partitions. The suggested procedure is illustrated on simulated data and a real world dataset from the International Computer and Information Literacy Study (ICILS) 2018. For the latter, we provide an example of end-to-end LCCA including data preprocessing. The researcher can use our R script to conduct LCCA in a few easily reproducible steps, or implement the strategy with any other software suitable for clustering. We show that the extended strategy, in comparison to fit indices-based strategy, facilitates the selection of more stable and well-separated clusters in the data. • The suggested strategy aids researchers to select the number of clusters in LCCA • It is based on model fit, cluster separation, and stability of partitions • The strategy is useful for finding separable generalizable clusters in the data.
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    Semantic units: organizing knowledge graphs into semantically meaningful units of representation
    (London : BioMed Central, 2024) Vogt, Lars; Kuhn, Tobias; Hoehndorf, Robert
    Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results We introduce “semantic units” as a conceptual solution, although currently exemplified only in a limited prototype. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs by adding another layer of triples on top of the conventional data layer. Semantic units and their subgraphs are represented by their own resource that instantiates a corresponding semantic unit class. We distinguish statement and compound units as basic categories of semantic units. A statement unit is the smallest, independent proposition that is semantically meaningful for a human reader. Depending on the relation of its underlying proposition, it consists of one or more triples. Organizing a knowledge graph into statement units results in a partition of the graph, with each triple belonging to exactly one statement unit. A compound unit, on the other hand, is a semantically meaningful collection of statement and compound units that form larger subgraphs. Some semantic units organize the graph into different levels of representational granularity, others orthogonally into different types of granularity trees or different frames of reference, structuring and organizing the knowledge graph into partially overlapping, partially enclosed subgraphs, each of which can be referenced by its own resource. Conclusions Semantic units, applicable in RDF/OWL and labeled property graphs, offer support for making statements about statements and facilitate graph-alignment, subgraph-matching, knowledge graph profiling, and for management of access restrictions to sensitive data. Additionally, we argue that organizing the graph into semantic units promotes the differentiation of ontological and discursive information, and that it also supports the differentiation of multiple frames of reference within the graph.