FAIR data practices
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Redivis is built on data principles to support data discovery and reusability at all levels. Data practices adhering to these principles emphasize data that is:
Data is findable when (1) data and metadata are assigned a globally unique and persistent identifier, (2) data are described with rich metadata, (3) metadata clearly and explicitly include the identifier of the data it describes, and data and (4) metadata and data are registered or indexed in a searchable resource.
At Redivis:
can be issued for datasets, facilitating authoritative citation and attribution.
All datasets have a section of documentation which auto-populates with administrator actions and allows for additional linking to other artifacts that were part of the dataset creation process.
Redivis has comprehensive search tools that index all aspects of a including the , , variable names and variable documentation.
Data is accessible when: (1) data and metadata are retrievable by their identifier using a standardized communications protocol, (2) the protocol is open, free, and universally implementable, (3) the protocol allows for an authentication and authorization procedure, where necessary, (4) metadata are accessible, even when the data are no longer available.
At Redivis:
Data can be explored through any web browser.
Researcher accounts to apply for data access or do analyses are always free.
Data is interoperable when: (1) data and metadata use a formal, accessible, shared, and broadly applicable language for knowledge representation, (2) data and metadata use vocabularies that follow FAIR principles, (3) data and metadata include qualified references to other data and metadata.
At Redivis:
Data is reusable when: (1) data and metadata are richly described with a plurality of accurate and relevant attributes, (2) data and metadata are released with a clear and accessible data usage license, (3) data and metadata are associated with detailed provenance, and (4) data and metadata meet domain-relevant community standards
At Redivis:
Many dataset, table, and variable metadata fields are automatically populated based on user action, with the ability to be manually adjusted.
All datasets have a Usage tab with information on how it has been viewed and used across Redivis
Redivis cloud-based analysis tools encourage data users to do analysis along the data rather than downloading it and breaking linkages.
Public data and analyses can be explored without an .
Researcher accounts can be linked to .
Robust support interoperability with other tools.
use common languages such as SQL, Python, R, Stata, and SAS.
Data and metadata are available for in multiple common formats.
Datasets are automatically .
containing analyses are self-documenting and capture the full analysis pipeline.
Anyone can a workflow they have access to in order to continue an analysis.