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The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
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LinkML: an open data modeling framework.

Sierra A T Moxon1, Harold Solbrig2, Nomi L Harris1

  • 1Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

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Summary
This summary is machine-generated.

LinkML (Linked Data Modeling Language) is an open framework that standardizes data at its origin, improving interoperability and FAIR data compliance. This approach simplifies data integration, validation, and sharing across diverse scientific fields.

Keywords:
AI-ready dataFAIR datadata integrationdata modelingontologiesopen dataopen sourceschemasemantic modeling

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Area of Science:

  • Data science
  • Computational biology
  • Information management

Background:

  • Scientific data is often unstructured (e.g., free-text notebooks, spreadsheets), hindering interoperability.
  • Lack of data standardization complicates integration, validation, and reuse.
  • Existing data models can be complex and lead to proliferation of single-use formats.

Purpose of the Study:

  • Introduce LinkML (Linked Data Modeling Language) as a solution for data standardization.
  • Demonstrate LinkML's capability to simplify data authoring, validation, and sharing.
  • Promote adoption of LinkML for enhanced data interoperability and FAIR compliance.

Main Methods:

  • Utilizes an open framework with an approachable syntax for describing data schemas, classes, and relationships.
  • Supports diverse data structures from simple lists to complex, normalized models with inheritance.
  • Allows seamless integration with existing frameworks and import of schemas.

Main Results:

  • LinkML enables the creation of well-defined, stable, and ontology-aligned data structures.
  • Facilitates interdisciplinary collaboration through accessible data semantics.
  • Simplifies the process of defining and sharing data models.

Conclusions:

  • LinkML reduces data heterogeneity and complexity, promoting FAIR data standards.
  • It has broad adoption across various scientific and commercial domains.
  • LinkML makes implicit data models explicit and computable, standardizing data at its source.