Research Data Management
Dataset versioning, FAIR compliance, repository integration
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Research Data Management
Part of the worlds-biggest-software-project initiative.
An AI-native, open-source platform for FAIR-compliant research data management, dataset versioning, and repository integration.
Research Data Management (RDM) is a platform for universities, research institutes, and principal investigators to deposit, describe, share, and preserve research datasets in line with funder mandates such as the 2025 NIH Data Management and Sharing Policy and EU Horizon Europe requirements. It targets the gap between heavyweight institutional repositories that demand significant IT investment and lightweight free services that lack governance for sensitive data.
Why Research Data Management?
- Incumbent open-source platforms (Dataverse, Invenio RDM, CKAN) are highly capable but require substantial IT infrastructure and configuration to self-host.
- Free SaaS options (Zenodo, OSF, Figshare) offer minimal access controls for sensitive data and limited metadata depth.
- DMP-focused tools (DMPTool, DMP Online) only cover plan creation and do not manage data through its lifecycle.
- FAIR assessment is currently siloed in standalone tools such as F-UJI rather than embedded into deposit and curation workflows.
- Commercial preservation services (Arkivum, enterprise Figshare) are priced at $20,000–$200,000/yr, putting them out of reach for many institutions.
Key Features
Repository and Dataset Management
- Dataset versioning with change tracking
- Persistent identifier (DOI) minting and assignment
- Cloud storage backend for data preservation
- Audit trail for compliance and reproducibility
Metadata, Discovery, and Identity
- FAIR metadata support (Dublin Core minimum)
- Full-text and faceted search and discovery
- Schema.org markup for search engine discoverability
- ORCID integration for researcher attribution
Access Control and Governance
- Role-based access control for sensitive data
- User authentication via institutional or social credentials
- Sensitive data governance and secure access workflows
- Long-term preservation and archival guarantees
Data Management Plans and Compliance
- Data Management Plan (DMP) templates and creation workflows
- Funder template integration (NSF, NIH, European Commission)
- Automated FAIR assessment scoring with improvement recommendations
- Machine-actionable data management plans (DMP Common Standard)
Integration and Extensibility
- Institutional deployment options (self-hosted or SaaS)
- REST API for integration with analysis workflows
- Linked open data (RDF) export and integration
- Automated metadata harvesting from laboratory information systems
- Version control integration with Jupyter notebooks and code
AI-Native Advantage
AI capabilities address the manual burden that limits adoption of existing RDM tools. Automated metadata generation analyses uploaded datasets and suggests discipline-appropriate fields, controlled vocabulary terms, and structured descriptions. An intelligent DMP drafting assistant generates funder-compliant plans from project descriptions, populating repository selections, sharing timelines, and budget estimates. FAIR compliance scoring provides actionable remediation guidance against FAIRsFAIR metrics, and semantic similarity surfaces related datasets across Zenodo, Dataverse, Figshare, and discipline-specific repositories that should be interlinked for reuse.
Tech Stack & Deployment
The platform is designed for both self-hosted institutional deployment and SaaS delivery. It aligns with established open standards: the FAIR Principles, Dublin Core, the DataCite Metadata Schema, ORCID, Schema.org Dataset, OpenAIRE Guidelines, the DMP Common Standard (RDMO), and BagIt (RFC 8493) for packaging and preservation. A REST API enables integration with analysis workflows and laboratory information systems.
Market Context
The global RDM market is an emerging segment within research informatics, with institutional spending estimated in the hundreds of millions annually and the adjacent open science infrastructure market growing at 10–15% CAGR. Commercial institutional deployments typically run $20,000–$200,000/yr depending on storage and user scale. Primary buyers are research data managers and librarians, principal investigators subject to funder DMP policies, research IT teams, grant administrators, and publishers enforcing data availability statements.
Project Status
This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.
Contributing
We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.
Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.
Licence
Licence to be determined. See discussion for context.