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.
To learn more about how our data has been used to advance science visit our listed of peer-reviewed journal publications.
To get involved or find out more, reach out via "Contact Us" below or directly to a team member.
PDS Platform Announcements
New Data: EMD Serono, 2 studies in Glioblastoma and 1 in Pancreatic Cancer, Dec. 2020
New Data: Eli Lilly, 1 study in Non-Small Cell Lung Cancer, Dec. 2020
New Data: G1 Therapeutics, 3 studies in 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.
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 socioeconomic 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.