FY27 Stanford Dataset Sharing for Research (D-ShaRe) - Program
Better access to data, and more efficient use of data, are increasingly central to scholarship. The Dataset Sharing for Research (D-ShaRe) program provides resources to acquire datasets of broad scholarly value, including the lifecycle costs of access — storage, curation, licensing, and the personnel effort associated with initial setup. D-ShaRe is modeled on the Community of Shared Research Platforms (C-ShaRP) Instrumentation & Enhancement Program and applies the same shared-resource philosophy to data: coordinate acquisition so that University-funded datasets serve the broadest possible community rather than a single laboratory.
D-ShaRe is a coordination program, not a consolidation of data ownership. It works with Stanford's existing data-acquisition hubs, which retain their roles and expertise, and it channels University funds toward datasets whose reach and reuse justify shared investment. We plan to accept applications twice per fiscal year, across all disciplines. Any Stanford PI may apply.
- Acquiring datasets with broad utility for Stanford scholars
- Accelerating research on large datasets, including data science and artificial intelligence
- ecuring the use rights researchers need — including text-and-data-mining and AI/ML model-training rights — as a standard term of acquisition
- Facilitating data access for early- and mid-career investigators
- Enhancing the visibility and discoverability of datasets available to Stanford scholars, and Reducing redundancy in data acquisition across schools and units
All Stanford faculty are eligible to apply as the primary applicant. Please see the PI eligibility criteria guidelines in the Research Policy Handbook.
At least one co-applicant must represent the partnering data hub (e.g., the SUL licensing librarian, a PHS or GSB data lead, the FSRDC/IRiSS administrator, or a school data-platform manager), unless the primary applicant is themselves a data-hub representative.
Supported Activities
- Acquisition, or initial-term subscription licensing, of datasets with broad scholarly utility
- Lifecycle costs of first access: storage setup, curation to an appropriate tier (see Proposal Format §5), and personnel effort for initial setup and on-boarding
- Licensing and negotiation support to secure required use rights, including text-and-data-mining, AI/ML model training, and derivative-works (e.g., retrieval-augmented generation) rights
- Preparation of data for governed, shareable access: disclosure-risk review or de-identification, and metadata and catalog records that make the dataset discoverable
- One-time enhancements that make an already-licensed dataset reusable or AI-ready
Research Compliance Questionnaire
