For the complete documentation index, see llms.txt. This page is also available as Markdown.

Research data management and sharing plans

Data management and sharing plans describe how research data and other outputs will be organized, documented, protected, maintained, and shared throughout a project. Creating a plan is an important first step, but researchers must also put that plan into practice as their data and research processes evolve.

Redivis helps researchers and institutions translate data management and sharing into consistent, repeatable practices. Data storage, documentation, access management, transformation, analysis, versioning, and data publication can take place within the same research environment.

Redivis can support the implementation of many common data management and sharing plan components. Researchers should still consult the requirements of their funder, institution, discipline, and applicable laws or agreements when preparing and carrying out a plan.

From planning to implementation

Redivis helps researchers implement their plans within the same environment where they work with data. Projects provide a shared space for organizing the datasets, workflows, documentation, and collaborators associated with a research effort, helping researchers keep planning decisions connected to their day-to-day work.

This makes it easier to maintain consistent practices throughout a project and reduces the need to reconstruct documentation, provenance, and sharing decisions when research outputs are ready for publication.

How Redivis supports common plan components

Plan component
How Redivis can support implementation

Data organization, metadata, and documentation

Datasets bring related tables, files, metadata, and documentation together, allowing project information to remain connected to the data.

Storage and active management

Datasets and workflows provide shared environments for managing data throughout an active research project.

Versioning and provenance

Automatic dataset version control and documented workflow steps help preserve relationships among source data, transformation, and analytical outputs.

Access controls

Institutions and dataset owners can configure access systems for public, restricted, and application-based access and apply appropriate usage restrictions.

Sharing timelines and publication

Datasets can remain unpublished while they are prepared and released when the project, institutional, or funder requirements call for sharing.

Licensing and reuse

Dataset documentation can communicate licenses, access conditions, and information needed to understand and reuse the data.

Supporting researchers at the institutional level

Libraries, research offices, data services, and other university units can use Redivis to provide researchers with a consistent, institutionally supported approach to managing and sharing data.

Using a shared platform does not eliminate the need for project-specific planning. It can, however, give researchers a clearer and more consistent way to carry out the commitments described in their plans.

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