• Explore scientific data with a set of tools for human-guided or automated discovery
• Design & configure data processing pipelines
• Define the parameter ranges for your algorithms, available algorithmic choices, and the framework will generate pipeline instances for you
• Use automatically perturbed data processing pipelines to create different data products.
• Easy to use with scikit-dataaccess for integration of a variety of scientific data sets

pip install scikit-discovery

See https://github.com/MITHaystack/scikit-discovery/tree/master/skdiscovery/docs

Project lead: Victor Pankratius (MIT)
Contributors: Cody M. Rude, Justin D. Li, David M. Blair, Michael G. Gowanlock, Evan Wojciechowski, Victor Pankratius

We acknowledge support from NASA AIST14-NNX15AG84G, NASA AIST16-80NSSC17K0125, NSF ACI-1442997, NSF AGS-1343967, and Amazon AWS computing access support.

Example code with complete science case studies are available as Jupyter Notebooks at:

/MITHaystack/science-casestudies