Vivli

Repository Name: Vivli
Repository Homepage URL: https://vivli.org/
Repository Description:

Source: https://www.re3data.org/

Vivli is a non-profit organization working to advance human health through the insights and discoveries gained by sharing and analyzing data. It is home to an independent global data-sharing and analytics platform which serves all elements of the international research community. The platform includes a data repository, in-depth search engine and cloud-based analytics, and harmonizes governance, policy and processes to make sharing data easier. Vivli acts as a neutral broker between data contributor and data user and the wider data sharing community.

Data Collection Policy URL: https://vivli.org/resources/sharedata/
Research Areas: Life Sciences; Medicine; Public Health;
Data Types: Experimental; Observational; Code; Survey; Text; Tabular; Synthetic/Simulation; Geospatial; Audio; Video; Image; Genomic/Molecular; Biomedical; 3D Models; Machine learning;
Data Types Explicitly Prohibited: Deep learning models (e.g., CNNs, RNNs, Transformers); Generative AI models (e.g., GANs, LLMs); Any model using proprietary, de-identified, or identifiable external datasets
Fee for JHU Researchers to Deposit: Free under 500GB
Data Limit: Up to 4TB. Fee model applies above 500GB.
Option for Data Access: Controlled Access;
Details on Data Access:

Restricted data is by request. Vivli administrators review requests. Accessing Vivli’s secure data enclave may require a compute fee.

Human Data Accepted: Yes
Level of Deidentification Required: JHU has a contractual agreement with Vivli that JHU affiliates anonymize deposited data following guidelines established by the JHM Data Trust: JHU's Vivli Demographic Anonymization Requirements
Human Participant Data Sharing Policy URL: https://vivli.org/resources/five-safes-framework/
Sensitive Data Policy URL: https://vivli.org/resources/five-safes-framework/
Submission Policy URL: https://vivli.org/wp-content/uploads/2025/05/2025_05_26-Study-Submission-Guide-3.7.pdf
Required Funder: None listed
Persistent Identifier: DOI
Data Retention Period: 10 Years
AI LLM Policy: Deep learning models (e.g., CNNs, RNNs, Transformers); Generative AI models (e.g., GANs, LLMs) are not accepted.
re3data Keywords: clinical trial; clinical trials; data discovery methods; health care; individual participant-level data (IPD); medical treatments; patients; studies
re3data Repository Contact: https://vivli.org/contact/
re3data Record URL: https://www.re3data.org/repository/r3d100012823