AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data
Agricultural research has been traditionally driven by linear approaches dictated by hypothesis-testing. With the advent of powerful data science capabilities, predictive, empirical approaches are possible that operate over large data pools to discern patterns. Such data pools need to contain well-d...
| Autores principales: | , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Frontiers Media
2021
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/115543 |
Ejemplares similares: AgroFIMS: A tool to enable digital collection of standards-compliant FAIR data
- AgroFIMS v.1.0 - User manual
- Enabling reusability of plant phenomic datasets with MIAPPE 1.1
- AgroFIMS v.2.0 - User manual.
- DataScribe—Enabling collection of Findable, Accessible, Interoperable, Reusable survey data
- Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale
- Requesting new terms in ontologies: the example of Crop Ontology and the Agronomy Ontology