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FAIR data representation in times of eScience: a comparison of instance-based and class-based semantic representations of empirical data using phenotype descriptions as example
BACKGROUND: The size, velocity, and heterogeneity of Big Data outclasses conventional data management tools and requires data and metadata to be fully machine-actionable (i.e., eScience-compliant) and thus findable, accessible, interoperable, and reusable (FAIR). This can be achieved by using ontolo...
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613519/ https://www.ncbi.nlm.nih.gov/pubmed/34823588 http://dx.doi.org/10.1186/s13326-021-00254-0 |