Toward interoperable bioscience data

By S. Sansone, P. Rocca-Serra, Dawn Field, E. Maguire, Chris Taylor, Oliver Hofmann, H. Fang, Steffen Neumann, W. Tong, L. Amaral-Zettler and 40 more. Nature Genetics, 2012

428 citations recorded by the source corpus.

To make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open 'data commoning' culture. Here we describe the prerequisites for data commoning and present an established and growing ecosystem of solutions using the shared 'Investigation-Study-Assay' framework to support that vision.

Subfields: Information Systems, Information Systems and Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fc3da030b1a588dd87a233f9ee4324bf7c90d65e

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Carole A. Goble

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Citations in the corpus

Reuse of public genome-wide gene expression data (2012, methodology): “Thus, annotation and data management tools such as Annotare 54 , ISA-creator 55 and others 56 are likely to have a positive impact on data reusability and reproducibility of results.”
The Ontology for Biomedical Investigations (2016, methodology): “The ISA project (short for Investigation, Study, Assay [31]) supports managing and tracking biological experiment metadata to ensure its preservation, discoverability and re-use, by providing a set of software applications centered around a user-friendly tabular data-entry format called ISA-Tab.”
MetaboLights: a resource evolving in response to the needs of its scientific community (2019, background): “Published by Oxford University Press on behalf of Nucleic Acids Research.”
Gateways to the FANTOM5 promoter level mammalian expression atlas (2015, methodology): “experiments, and protocols, we employed the MAGE/ ISA-tab file format [38,39], a standard format to describe”
MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data (2012, background): “At the core of the database implementation is the ISA framework (21).”
Finding Our Way through Phenotypes (2015, neutral): “[42].”
Open PHACTS: semantic interoperability for drug discovery. (2012, background): “The result is a diverse landscape of data sources, with an inherent distribution of data quality, formats, standards, copyright and licensing [4,5].”
Computational mass spectrometry for small molecules (2013, background): “Citing [69], “to make full use of research data, the bioscience community needs to adopt technologies and reward mechanisms that support interoperability and promote the growth of an open ‘data commoning’ culture.”