Journal article
Machine actionable metadata models
- Abstract:
- Community-developed minimum information checklists are designed to drive the rich and consistent reporting of metadata, underpinning the reproducibility and reuse of the data. These reporting guidelines, however, are usually in the form of narratives intended for human consumption. Modular and reusable machine-readable versions are also needed. Firstly, to provide the necessary quantitative and verifiable measures of the degree to which the metadata descriptors meet these community requirements, a requirement of the FAIR Principles. Secondly, to encourage the creation of standards-driven templates for metadata authoring, especially when describing complex experiments that require multiple reporting guidelines to be used in combination or extended. We present new functionalities to support the creation and improvements of machine-readable models. We apply the approach to an exemplar set of reporting guidelines in Life Science and discuss the challenges. Our work, targeted to developers of standards and those familiar with standards, promotes the concept of compositional metadata elements and encourages the creation of community-standards which are modular and interoperable from the onset.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.3MB, Terms of use)
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- Publisher copy:
- 10.1038/s41597-022-01707-6
Authors
+ Wellcome Trust
More from this funder
- Funder identifier:
- https://ror.org/029chgv08
- Grant:
- 208381/A/17/Z
+ European Commission
More from this funder
- Funder identifier:
- https://ror.org/00k4n6c32
- Grant:
- 824087
- Publisher:
- Nature Research
- Journal:
- Scientific Data More from this journal
- Volume:
- 9
- Issue:
- 1
- Article number:
- 592
- Publication date:
- 2022-09-30
- Acceptance date:
- 2022-06-10
- DOI:
- EISSN:
-
2052-4463
- ISSN:
-
2052-4463
- Language:
-
English
- Keywords:
- Pubs id:
-
1274439
- Local pid:
-
pubs:1274439
- Deposit date:
-
2022-08-18
- ARK identifier:
Terms of use
- Copyright holder:
- Batista et al.
- Copyright date:
- 2022
- Rights statement:
- ©2022 The Author(s). 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
- Licence:
- CC Attribution (CC BY)
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