LinkML: an open data modeling framework

Authors:
Open Access
Publisher website
Google Scholar
Abstract
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/.
Funding Information
  • U.S. Department of Energy (DE-AC02-05CH11231)
  • National Human Genome Research Institute
  • National Institute of Mental Health (4DP2MH129986)
  • National Institutes of Health (U24NS120057)
  • National Institutes of Health (1U24MH130919-01)
  • National Institutes of Health (1U01DE033978-01)
  • National Institutes of Health (1OT2TR005712-01)
  • National Cancer Institute (19×077Q HHSN261201500003I)

This publication has 17 references indexed in Scilit: