DBGen
AI-powered synthetic data generation for building production-ready test environments.

The world's leading open-source research data repository for sharing, citing, and archiving scholarly datasets.
Harvard Dataverse is a robust, open-source research data repository software designed to facilitate the sharing, preservation, and citation of scholarly data. Built on a Java-based architecture (utilizing Payara and PostgreSQL), it serves as a central node in the global Dataverse Project network. As of 2026, it remains the primary implementation of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, providing researchers with automated DOI (Digital Object Identifier) minting via DataCite. Technically, it offers a modular schema for metadata, supporting domain-specific standards like DDI, Dublin Core, and Schema.org. The platform's API-first design enables deep integration with computational notebooks like Jupyter and RStudio, as well as institutional identity providers via Shibboleth and OAuth2. Its market position is solidified by its status as a non-profit, community-governed alternative to commercial repositories, offering unmatched granularity in metadata management and long-term digital preservation through its integration with Archivematica. It is optimized for both individual researchers needing to meet funder mandates and large-scale institutions requiring a scalable data infrastructure.
Integration with DataCite and EZID to automatically assign a persistent Digital Object Identifier upon publication.
AI-powered synthetic data generation for building production-ready test environments.
The first metadata-driven AI engine for hyper-automated data management and autonomous governance.
The most advanced WordPress form builder to create data-driven web applications.
The global registry for data standards, databases, and data policies.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Supports the Open Archives Initiative Protocol for Metadata Harvesting to increase dataset discoverability.
Permissions can be set at the Dataverse, Dataset, or individual File level, including IP-based restrictions.
Automatically extracts metadata and variable-level information from SPSS, Stata, and R files.
Customizable forms that users must fill out before downloading data, capturing lead and usage information.
Configurable backends for S3, Swift, or local file systems via the Dataverse storage abstraction layer.
Full semantic versioning (Major.Minor) with side-by-side comparison of metadata changes.
Researchers must provide public access to data to comply with NIH or NSF requirements.
Registry Updated:2/7/2026
Include DOI in grant report.
Journals require data availability statements and peer-review access to underlying data.
Tracking changes in a dataset over several years while maintaining citeable versions.