Alejandra N Gonzalez-Beltran

The paper introduces the ISA API, a Python library for handling ISA-Tab and ISA-JSON metadata formats, enabling programmatic manipulation and interoperability in life science experiments.

COPO provides a metadata platform to enable FAIR data sharing in life sciences by standardizing metadata description and repository integration.

This paper presents standardization efforts for cell migration data to improve data sharing and analysis.

The paper introduces metadata elements in the DATS schema to describe data access, use conditions, and consent for biomedical data reuse.

The paper introduces a microservices-based framework for scalable data analysis, validated in metabolomics, enabling dynamic scaling and integration of software tools.

PhenoMeNal provides a cloud-based platform for metabolomics data analysis, integrating tools and workflows to enable reproducible and scalable research.

The paper introduces FAIRsharing, a community-driven resource that evaluates and promotes standards, databases, and repositories to improve data reuse and interoperability in research.

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.

The paper describes BioSharing, a resource that catalogs metadata standards to enhance the FAIRness of scientific data and connects them to repositories and policies.

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

Galaxy workflows reproduced SOAPdenovo2 bacterial assembly results almost exactly, while ISA, Nanopublication and Research Object models exposed reporting errors leading to a published correction.