> 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/data-retention-policy.md). # Data retention and long-term availability Retaining research data, documentation, and provenance is essential to supporting reproducibility, persistent citation, and continued reuse. Redivis is designed to keep data available over time while giving dataset owners control over how their data is managed, shared, and, when necessary, removed. Redivis provides infrastructure for long-term data hosting, recovery from recent accidental changes, dataset version history, persistent metadata and landing pages, and data export. These capabilities can support institutional stewardship and preservation practices, but institutions and dataset owners remain responsible for establishing any additional retention or preservation processes required by their policies, funders, disciplines, or agreements. ## Supporting long-term availability Redivis supports the continued availability of research data through long-term hosting, dataset versioning, persistent metadata and landing pages, and tools for exporting data and related materials. These capabilities help institutions and researchers maintain access to data over time while preserving the context needed for citation, reproducibility, and reuse. Redivis also maintains operational backups to support recovery from recent accidental deletion or modification. Data stored in Redivis's default storage environment can be recovered through a seven-day rolling point-in-time window. Metadata has the same seven-day recovery window, with daily metadata backups retained for one year. ## Data integrity and version history Redivis datasets are automatically versioned. Each released version preserves the data, documentation, and metadata associated with that release, allowing a dataset to evolve without changing the materials used in earlier research. Deleting or replacing a table in a new version does not remove it from previous released versions. Researchers with appropriate access can continue to reference and work with historic versions. Redivis also records checksums for files stored in datasets. These checksums can help users compare file contents and confirm that an exported file corresponds to the stored resource. ## Dataset archival Dataset owners may [archive an entire dataset or version](/reference/datasets/create-and-edit-datasets/dataset-lifecycle.md) when it is no longer in use or does not need to remain actively available, but should not be permanently deleted. Archived data cannot be queried, analyzed, or exported, and dependent workflow tables become unavailable. Archival is reversible and preserves the dataset or version for future reference or reuse, while reducing the cost for long-term storage. ## Owner-initiated deletion Dataset owners can [remove datasets](/reference/datasets/create-and-edit-datasets/dataset-lifecycle.md) from Redivis when the data should no longer be available. After deletion, a dataset can be recovered for up to seven days before the deletion becomes permanent and the data can no longer be queried or recovered. To support persistent citation and reproducibility, deleting a dataset does not remove its complete public record. Available metadata and documentation are retained, and bookmarked URLs and DOIs continue to resolve to the dataset's landing page. The dataset is no longer discoverable through search, and its underlying data is removed. ## Redivis-initiated deletion Data storage that exceeds the free tier must be paid for by the data owner or other sponsor. Upon payment failure, every effort will be made to contact the data owner and reestablish payment. If payment cannot be reestablished, the data may be destroyed after 30 days of nonpayment. Available metadata and permanent landing pages will persist. ## Data portability and continuity Subject to applicable access and export restrictions, data and related materials can be exported from Redivis for use in other systems. Researchers can export data, metadata, workflow code, and other analytical materials in supported formats or access them through Redivis APIs and client libraries. This portability allows institutions and researchers to maintain copies of important research materials outside Redivis or transfer them into other storage, computing, repository, or preservation environments when appropriate. Datasets can also be transferred between eligible users and organizations when the person initiating the transfer has the required permissions. Organization-owned datasets can be managed by multiple administrators, helping maintain continuity when a researcher or administrator changes roles or leaves an institution. --- # 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. 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