LinkML: an open data modeling framework
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- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Johns Hopkins Univ., Baltimore, MD (United States)
- University of North Carolina, Chapel Hill, NC (United States)
- Siemens AG, Munich (Germany)
- Kapernikov, Leuven (Belgium)
- Simon Fraser University, Burnaby, BC (Canada)
- University of Florida, Gainesville, FL (United States)
- Allen Institute, Seattle, WA (United States)
- Charité-Universitätsmedizin, Berlin (Germany)
- GSK, San Francisco, CA (United States)
- Queen Mary University of London (United Kingdom)
- Leibniz Institute for Catalysis (LIKAT), Rostock (Germany)
- Semanticly, Athens (Greece)
- Knocean, Toronto, ON (Canada)
- University of California, Los Angeles, CA (United States)
- Swiss Institute of Bioinformatics (SIB), Basel (Switzerland)
- European Molecular Biology Laboratory (EMBL), Heidelberg (Germany)
- Univ. of British Columbia, Vancouver, BC (Canada); LinkML Community Contributors. et al.
- Johns Hopkins University, Baltimore, MD (United States)
Background Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. Findings LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics. Conclusions LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- National Institutes of Health (NIH); USDA; USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Grant/Contract Number:
- AC02-05CH11231
- OSTI ID:
- 3027562
- Journal Information:
- GigaScience, Journal Name: GigaScience Vol. 15; ISSN 2047-217X
- Publisher:
- BioMed CentralCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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