Machine actionable metadata models
| Main Authors: | Dominique Batista, Alejandra Gonzalez-Beltran, Susanna-Assunta Sansone, Philippe Rocca-Serra |
|---|---|
| Format: | info publication-preprint Journal |
| Bahasa: | eng |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/5596465 |
Daftar Isi:
- 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 narrative in form as intended for human consumption. 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 our approach to the creation of machine-readable models, and our open source toolkit. 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 tools and others who are 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.