ArticleJournal of biomedical semantics2025
The CLEAR Principle: organizing data and metadata into semantically meaningful types of FAIR Digital Objects to increase their human explorability and cognitive interoperability.
Article in Journal of biomedical semantics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Managing complexity in research data management for heterogeneous biotechnological data: a perspective on interacting as data steward.Bioprocess and biosystems engineering · 2026Review
- The Semantic Units Framework, a technology-agnostic representational approach to FAIR and CLEAR knowledge infrastructures.Scientific data · 2026Article
- Expanding the PHES-ODM: A Comprehensive, Open-Source Data Model for the Future of Wastewater-Based Epidemiology.Microorganisms · 2026Article
- Rosetta Statements: simplifying FAIR knowledge graph construction with a user-centred approach.Database : the journal of biological databases and curation · 2026Article
- Mouse Hippocampal Sharp-Wave Ripple Dataset Curated From Public Neuropixels Datasets.Scientific data · 2025Article
- Suggestions for extending the FAIR Principles based on a linguistic perspective on semantic interoperability.Scientific data · 2025Article
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Abstract
backgroundEnsuring the FAIRness (Findable, Accessible, Interoperable, Reusable) of data and metadata is an important goal in both research and industry. Knowledge graphs and ontologies have been central in achieving this goal, with interoperability of data and metadata receiving much attention. This paper argues that the emphasis on machine-actionability has overshadowed the essential need for human-actionability of data and metadata, and provides three examples that describe the lack of human-actionability within knowledge graphs.
resultsThe paper propagates the incorporation of cognitive interoperability as another vital layer within the European Open Science Cloud Interoperability Framework and discusses the relation between human explorability of data and metadata and their cognitive interoperability. It suggests adding the CLEAR Principle to support the cognitive interoperability and human contextual explorability of data and metadata. The subsequent sections present the concept of semantic units, elucidating their important role in attaining CLEAR. Semantic units structure a knowledge graph into identifiable and semantically meaningful subgraphs, each represented with its own resource that constitutes a FAIR Digital Object (FDO) and that instantiates a corresponding FDO class. Various categories of FDOs are distinguished. Each semantic unit can be displayed in a user interface either as a mind-map-like graph or as natural language text.
conclusionsSemantic units organize knowledge graphs into levels of representational granularity, distinct granularity trees, and diverse frames of reference. This organization supports the cognitive interoperability of data and metadata and facilitates their contextual explorability by humans. The development of innovative user interfaces enabled by FDOs that are based on semantic units would empower users to access, navigate, and explore information in CLEAR knowledge graphs with optimized efficiency.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.