Biological data
partnerships.

We welcome conversations with biopharma, diagnostics companies, hospitals, and academic research groups.

Retrospective datasets can help address new research questions when their scientific context, quality, and available rights are well understood. Potential collaborations include biological modeling, biomarker research, and the development or evaluation of scientific AI.

The starting point is a specific research opportunity and a clear understanding of what each partner can contribute.

• Molecular measurementsTranscriptomic and other omics data, experimental measurements, and the methods used to produce them.
• Longitudinal and clinical contextRelevant sample timing, disease characteristics, treatment exposure, outcomes, and follow-up, where available and permitted.
• Provenance and qualityCohort definitions, collection protocols, processing history, metadata completeness, and known limitations.
• Permissions and accessThe rights available for the proposed use, governance requirements, and an appropriate way to access or deliver the material.

The agreement should reflect the dataset, the intended application, and your priorities.

• Scope and permitted usesDefine the included material, project purpose, access period, and rights for analysis, model training, evaluation, or other agreed work.
• Access and governanceAgree on de-identification, transfer or controlled access, security requirements, and responsibilities for handling the data.
• Commercial termsFees and payment structure are negotiated for the engagement. Options may include a defined project license, usage-based terms, or a broader research collaboration.
• Ownership and future useAddress existing ownership, permitted outputs, publication, and reuse. Nonexclusive licensing can be considered where it fits both parties’ objectives.

Availability, scientific fit, rights, and economics are assessed for each opportunity. A discussion begins the evaluation of a potential partnership.