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Project Data Sphere® open-access platform for cancer research

Share, integrate, and analyze patient-level cancer data from the randomized controlled clinical trials shared by pharma and academia.

The Project Data Sphere platform allows researchers to browse, search, and access patient-level data from more than 150 de-identified cancer studies which can be downloaded for analysis.

New Web Site

Welcome to the Project Data Sphere data website data.projectdatasphere.org

If you're looking for the home website, please visit projectdatasphere.org


Our Data

We see power in data to generate solutions for cancer patients. We are dedicated to ensuring that valuable clinical trial data does not remain locked in information siloes.

To that end, we solicit de-identified patient-level data and make it freely available on the Project Data Sphere platform for non-commercial research.

A majority of the datasets are randomized clinical studies from sponsors (some containing only records from comparator arm patients and some containing records from comparator and experimental arms).

A significant number of datasets are the analysis data supporting publications funded by the National Cancer Institute (NCI). These also may be acquired directly from NCI.

There are 13 researcher-curated datasets: 11 studies where Research Triangle Institute augmented the original with Socioeconomic and Health Care Access Variables, 1 dataset from our 2015 Prostate Cancer DREAM Challenge, and 1 dataset of published Prostate Cancer Tumor Growth analysis.

ACCESS LEVEL
DATASETS
Open Access
88+ datasets are accessible via open access
NCI Approval
65+ datasets are accessible with NCI Approval

SAS® Analytics Tools

Project Data Sphere offers access to SAS analytics tools to registered users at no cost.

SAS analytics tools in two programming environments are available:

  • The secure SAS® Life Sciences Analytics Framework (LSAF) environment is available to analyze datasets using Base SAS®, SAS/STAT®, and SAS/GRAPH®.
  • The scalable SAS® Visual Data Mining and Machine Learning (VDMML) in-memory processing environment combines data wrangling, exploration, statistical, data mining and machine learning techniques.
  • Guidance documents about the registration and data sharing processes are available. Links to videos and documentation about the SAS analytics tools are also available.

Registration

Access to datasets is granted through a quick process in which researchers submit a brief application with background information and agree to the terms of use.

No research proposal is required.

Registration allows the ability to access datasets for download or on the Project Data Sphere platform with free analytic tools provided by SAS. Registration is also necessary for data providers to securely share data.