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FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics
Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual descript...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760356/ https://www.ncbi.nlm.nih.gov/pubmed/34264488 http://dx.doi.org/10.1007/s12021-021-09529-4 |
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author | Levinson, Maxwell Adam Niestroy, Justin Al Manir, Sadnan Fairchild, Karen Lake, Douglas E. Moorman, J. Randall Clark, Timothy |
author_facet | Levinson, Maxwell Adam Niestroy, Justin Al Manir, Sadnan Fairchild, Karen Lake, Douglas E. Moorman, J. Randall Clark, Timothy |
author_sort | Levinson, Maxwell Adam |
collection | PubMed |
description | Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result’s metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software. |
format | Online Article Text |
id | pubmed-8760356 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-87603562022-10-08 FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics Levinson, Maxwell Adam Niestroy, Justin Al Manir, Sadnan Fairchild, Karen Lake, Douglas E. Moorman, J. Randall Clark, Timothy Neuroinformatics Original Article Results of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result’s metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software. Springer US 2021-07-15 2022 /pmc/articles/PMC8760356/ /pubmed/34264488 http://dx.doi.org/10.1007/s12021-021-09529-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Levinson, Maxwell Adam Niestroy, Justin Al Manir, Sadnan Fairchild, Karen Lake, Douglas E. Moorman, J. Randall Clark, Timothy FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics |
title | FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics |
title_full | FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics |
title_fullStr | FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics |
title_full_unstemmed | FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics |
title_short | FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics |
title_sort | fairscape: a framework for fair and reproducible biomedical analytics |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760356/ https://www.ncbi.nlm.nih.gov/pubmed/34264488 http://dx.doi.org/10.1007/s12021-021-09529-4 |
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