> 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.md). # Redivis for open science Redivis is a platform for data distribution and analysis that prioritizes [open access](/redivis-for-open-science/open-access.md) and [FAIR data practices](/redivis-for-open-science/fair-data-practices.md). We believe that supporting open science principles drives better research outcomes, collaboration, and scientific impact. Redivis is creating tools not just for data distribution and analysis, but also to help drive forward standards for transparent research that is accessible to all. Data repositories play an essential role in making completed research outputs discoverable, accessible, and available for reuse. Redivis supports these repository functions while extending open science infrastructure into the active stages of research. Within Redivis researchers can: * [Discover and securely access](/guides/discover-and-access-data.md) research data. * Organize, document, and manage data in an active [project](/reference/projects.md). * [Transform](/guides/analyze-data-in-a-workflow/reshape-data-in-transforms.md) and [analyze](/guides/analyze-data-in-a-workflow.md) data in collaborative [workflows](/reference/workflows.md) and [notebooks](/guides/analyze-data-in-a-workflow/work-with-data-in-notebooks.md). * [Publish datasets](/guides/export-and-publish-your-work.md) and reproducible research outputs. * Support continued discovery, [citation](/redivis-for-open-science/citations.md), and reuse. Bringing these activities into one environment reduces fragmentation between data storage, access management, documentation, analysis, and publication. For institutions, this creates a more consistent connection between [data stewardship](/redivis-for-open-science/institutional-governance-and-stewardship.md) and the day-to-day work of research. ## Reproducibility Every Redivis dataset has a [versioned history](/reference/datasets/versions.md) that is automatically generated while the data owner uploads and edits the data contents. This history is fully exposed to end users and the dataset can be accessed at any version. Citation and provenance information is automatically generated for every version of a dataset, which include the ORCID iDs for all creators. Data owners can opt to create Digital Object Identifier (DOI) for each version of their dataset, which is available at a persistent URL along with the required metadata describing the content at that point in time. As users analyze data in a Redivis [workflow](/reference/workflows.md), each step they take is recorded in a visual tree that can be navigated sequentially. Every step has a full history, which can be viewed at any point in time. ## Availability Datasets in a wide range of formats can be published on Redivis and made openly available at no cost to researchers. Public datasets and workflows can be viewed without a Redivis account. Researchers can [create a free account](/reference/your-account/creating-an-account.md) to work with public data or [request access](/guides/discover-and-access-data/apply-to-access-restricted-data.md) to restricted resources. Public data can also be accessed programmatically without an account through the Redivis [API](/api/master.md) and client libraries, making it easy to incorporate public datasets into applications, dashboards, and research tools. ## Longevity The stable technical infrastructure and dedicated Redivis team are funded by a coalition of academic institutions paying license fees for large-scale data distribution. This diverse group of funders ensures the long-term availability of the datasets. ## For institutions supporting open science Libraries, research offices, data centers, and other university units can use Redivis to provide shared infrastructure for active research data management, responsible access, reproducible analysis, publication, and long-term stewardship. --- # 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 by asking a question. 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