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The Predictive Oncology Model and Data Clearinghouse (MoDaC) is a data repository and model clearinghouse developed to transition resources to the broader research community. These resources consist of predictive oncology datasets and mathematical models (such as machine learning and deep learning models) developed within NCI and in collaborative programs. These programs include the NCI-DOE Collaboration projects and the Accelerating Therapeutics for Opportunities in Medicine (ATOM) Consortium. 

MoDaC supports the community priority for making both data and predictive models FAIR (Findable, Accessible, Interoperable, and Reusable). MoDaC also fosters reproducibility and supports collaborations among groups developing computational predictive models, including collaborations with the US Department of Energy.

) allows registered and authorized NCI-DOE investigators to upload their datasets with appropriate metadata into a data sharing repository via a web interface. The following table summarizes the tasks you can perform in the MoDaC web application (GUI):

TasksRequires Login Signup or Permission?Supported Device
  • Search data
No
  • Download data
Yes, requires login
  • Upload data
  • Edit metadata
Yes, requires login and upload permission
  • for publicly available assets by metadata keywords
  • View the metadata associated with a public collection
NoDesktop or Mobile



Yes, requires signupDesktop only
  • Upload data
  • Generate predictions
  • Evaluate models
  • View upload or prediction status
  • Share collections or predictions with groups.
  • View predictions shared with you
  • Manage edit permissions
  • Edit metadata
  • Delete your own file or empty collection
Yes, requires signup and upload permissionDesktop only

For information on the MoDaC REST API, select About > API Documentation.

For instructions on signing up, refer to Registering Your User Account. To request permission to upload data, select About > Contact. For details, refer to Contacting Us.

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Consider searching within or browsing this user guide:

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This user guide won an Excellence award in the Society for Technical Communication (STC) 2021 Alliance Competition.

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