> For the complete documentation index, see [llms.txt](https://docs.redivis.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.redivis.com/redivis-for-open-science/research-data-management-and-sharing-plans.md). # 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. {% hint style="info" %} 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. {% endhint %} ## 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. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.redivis.com/redivis-for-open-science/research-data-management-and-sharing-plans.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.