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- R1.2-Explanation type Explanation assertion.
- R1.2 label "R1.2" assertion.
- dint_a_00043 label "Jones et al. 2019" assertion.
- ds-wizard.org label "Data Stewardship Wizard" assertion.
- cedar-template-model label "CEDAR" assertion.
- prov-template label "PROV-Template" assertion.
- R1.2-Explanation label "R1.2 Explanation" assertion.
- for-researchers label "CASTOR" assertion.
- R1.2-Explanation isDefinedBy R1.2-Explanation assertion.
- R1.2-Explanation comment "Detailed provenance includes facets such as how the resource was generated, why it was generated, by whom, under what conditions, using what starting-data or source-resource, using what funding/resources, who owns the data, who should be given credit, and any filters or cleansing processes that have been applied post-generation. Provenance information helps people and machines assess whether a resource meets their criteria for their intended reuse, and what data manipulation procedures may be necessary in order to reuse it appropriately." assertion.
- R1.2-Explanation seeAlso dint_a_00043 assertion.
- R1.2-Explanation seeAlso ds-wizard.org assertion.
- R1.2-Explanation seeAlso cedar-template-model assertion.
- R1.2-Explanation seeAlso prov-template assertion.
- R1.2-Explanation seeAlso for-researchers assertion.
- R1.2-Explanation explains-principle R1.2 assertion.
- R1.2-Explanation implementation-considerations "Current choices are for communities to choose a set of metadata descriptions to optimize provenance to optimally enable machine and human reusability for its own purposes. These choices, and, as argued before the richness of the provenance associated with a digital resource will strongly influence its actual reuse. Therefore, the implementation�considerations for implementing according to this principle are inherently the same as described for principle F2, but now more focused on appropriateness for reuse than on findability per se." assertion.
- R1.2-Explanation implementation-examples "Provenance descriptions can for instance be implemented following community specific templates according to the PROV-Template (https://provenance.ecs.soton.ac.uk/prov-template/) approach. These templates allow to predefine the structure of the intended collection of provenance information using variables which are later instantiated with appropriate data extracted from existing process output. Such templates also reduce the burden on community members to deeply understand the highly structured PROV ontology, and the well-defined data structures that emerge from its use - that is to say, PROV should not be treated as a simple vocabulary from which terms can be selected, but rather as a model that constrains how those terms must be used in relation to one another. Several early tools are under development to make the construction of FAIR metadata easier, including for instance CEDAR (https://more.metadatacenter.org/tools-training/outreach/cedar-template-model), CASTOR (https://www.castoredc.com/for-researchers/) and the knowledge models in the Data Stewardship Wizard (https://ds-wizard.org, doi:10.1162/dint_a_00043)." assertion.