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End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach
BACKGROUND: The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734275/ https://www.ncbi.nlm.nih.gov/pubmed/34991705 http://dx.doi.org/10.1186/s13326-021-00253-1 |
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author | Samuel, Sheeba König-Ries, Birgitta |
author_facet | Samuel, Sheeba König-Ries, Birgitta |
author_sort | Samuel, Sheeba |
collection | PubMed |
description | BACKGROUND: The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment’s computational and non-computational processes. RESULTS: We present the “REPRODUCE-ME” data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps. CONCLUSION: We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13326-021-00253-1). |
format | Online Article Text |
id | pubmed-8734275 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87342752022-01-07 End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach Samuel, Sheeba König-Ries, Birgitta J Biomed Semantics Research BACKGROUND: The advancement of science and technologies play an immense role in the way scientific experiments are being conducted. Understanding how experiments are performed and how results are derived has become significantly more complex with the recent explosive growth of heterogeneous research data and methods. Therefore, it is important that the provenance of results is tracked, described, and managed throughout the research lifecycle starting from the beginning of an experiment to its end to ensure reproducibility of results described in publications. However, there is a lack of interoperable representation of end-to-end provenance of scientific experiments that interlinks data, processing steps, and results from an experiment’s computational and non-computational processes. RESULTS: We present the “REPRODUCE-ME” data model and ontology to describe the end-to-end provenance of scientific experiments by extending existing standards in the semantic web. The ontology brings together different aspects of the provenance of scientific studies by interlinking non-computational data and steps with computational data and steps to achieve understandability and reproducibility. We explain the important classes and properties of the ontology and how they are mapped to existing ontologies like PROV-O and P-Plan. The ontology is evaluated by answering competency questions over the knowledge base of scientific experiments consisting of computational and non-computational data and steps. CONCLUSION: We have designed and developed an interoperable way to represent the complete path of a scientific experiment consisting of computational and non-computational steps. We have applied and evaluated our approach to a set of scientific experiments in different subject domains like computational science, biological imaging, and microscopy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at (10.1186/s13326-021-00253-1). BioMed Central 2022-01-06 /pmc/articles/PMC8734275/ /pubmed/34991705 http://dx.doi.org/10.1186/s13326-021-00253-1 Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Samuel, Sheeba König-Ries, Birgitta End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach |
title | End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach |
title_full | End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach |
title_fullStr | End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach |
title_full_unstemmed | End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach |
title_short | End-to-End provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach |
title_sort | end-to-end provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8734275/ https://www.ncbi.nlm.nih.gov/pubmed/34991705 http://dx.doi.org/10.1186/s13326-021-00253-1 |
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