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
Abstract 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 usin...
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| Formato: | article |
| Lenguaje: | EN |
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BMC
2021
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| Acceso en línea: | https://doaj.org/article/c9ed335667ea417582a15ce9c309b953 |
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