Welcome to the leading oncology open access data sharing platform. We host de-identified patient-level data contributed by industry, academia, and PDS research programs.
Register for Access
Register now for full access to our datasets and powerful SAS® tools, or to share your data.
Registration is quick and easy. There are no fees, and no research proposal is required.
If you're looking for information on the Project Data Sphere organization, please visit the marketing website.
The marketing website contains information about the various research programs initiated by the Project Data Sphere organization.
PDS Platform Announcements
New Data Contribution
EMD Serono, 2 studies in Glioblastoma and 1 in Pancreatic Cancer, Dec. 2020
Eli Lilly, 1 study in Non-Small Cell Lung Cancer, Dec. 2020
G1 Therapeutics, Small Cell Lung Cancer, Nov. 2020
Improve outcomes for cancer patients by openly sharing data, convening world-class experts, and collaborating across industry and regulators to catalyze new scientific insights that accelerate effective treatments to patients.
Patient Level Datasets
Data is being used to drive new research and treatment methods, develop innovative technology (ML/AI models), and more efficiently plan and design clinical trials. Data contained on the PDS platform is made available through contributions from industry and key partnerships in academia, research, and government.
This data is de-identified, patient-level, randomized clinical trial data and linked or enriched data sets. To learn more about how researchers leverage the data on our platform, please visit our list of peer-reviewed journal publications.
Through an active funding partnership with the Robert Wood Johnson Foundation, PDS has been able to offer enriched randomized clinical trial data containing socio-demographic information prepared by RTI International and made available by the Agency for Healthcare Research and Quality's Medical Expenditure Panel Survey information.
Registration includes access to powerful SAS analytics tools at no cost.
Life Sciences Analytics Framework (LSAF) Securely analyze datasets using Base SAS®, SAS/STAT®, and SAS/GRAPH®.
Visual Data Mining and Machine Learning (VDMML) Scalable, in-memory processing environment combines data wrangling, exploration, statistical, data mining and machine learning techniques.