Jeffrey Sean Grethe

The paper describes the Common Fund Data Ecosystem (CFDE), which integrates NIH Common Fund data resources to enable discovery, analysis, and collaboration across heterogeneous datasets.

The paper introduces a centralized catalog of preclinical TBI models to improve reproducibility and accessibility by consolidating metadata from published studies.

The paper analyzes citation practices for biological resources over 10 years, showing improved findability and adherence to NIH rigor criteria after adopting RRID citations.

The paper introduces a knowledge graph framework for integrating and querying biomedical data across domains, demonstrating its utility in eight use cases.

The paper describes the development of SCKAN, a knowledgebase of autonomic nervous system connectivity, and its use in generating anatomical and functional maps.

The paper develops BIKO, an ontology to standardize terminology and methodologies in TBI research to improve data comparison and translation from preclinical to clinical settings.

The paper constructs a reproductive toxicity knowledge graph (ReproTox-KG) to link birth defects, drugs, and genes, identifying potential molecular mechanisms for drug-induced birth defects.

The paper introduces ODC-TBI, a platform for sharing preclinical TBI data, enabling big data analytics and machine learning to address heterogeneity in TBI research.

The paper describes the development and implementation of a vocabulary extension pipeline for the SPARC program to improve FAIR data practices in autonomic nervous system research.

The paper describes the development of a resource for the autonomic nervous system community, including databases, connectivity maps, and simulation tools.

This paper introduces the SPARC Data Structure (SDS), a FAIR standard for biomedical research data, and describes its rationale, design, and tools for implementation.

The paper describes the harmonization of identifier resolution services to enable persistent, machine-resolvable citations for biomedical data.

The paper outlines ten lessons for designing, provisioning, and reusing persistent identifiers to improve data integration in life sciences.

The paper introduces DATS, a metadata model for dataset discoverability, and describes its implementation for the DataMed data discovery index.

DataMed enhances data discoverability by creating a unified index of biomedical datasets across repositories, supporting FAIR principles for better knowledge discovery.