DISK DISK: Automated DIscovery of Scientific Knowledge

Portals

The DISK framework can be configured to work with different scientific domains. Each domain has its own selection of possible hypotheses and is connected to an appropriate data source for performing analysis. Most results and visualizations are available for all users. To run new analysis an account is necessary, please contact us to request access.

Documentation

In the DISK Documentation website you will find a complete description of the DISK framework and how to configure and use it. The portal provides several figures on how the services interact between each other and how software components are connected. The documentation provides guide at three levels:

Domain portals

DISK can be configured to work in domain specific problems. Portals are provided for demo purposes and can go down at any time. Snapshots provide a static visualization of a given portal.

NeuroDISK

NeuroDISK is an implementation of DISK for neuroscience, it uses genetic data from the ENIGMA project to run neuroscience workflows. Visit the project webpage for more information.

The NeuroDISK portal automates the analysis of neuroscience data through artificial intelligence (AI). NeuroDISK is an AI-driven discovery engine that will continually process neuroscience workflows when new data is available in the ENIGMA project.

Climate DISK

The Climate DISK portal uses scientific workflows to analyze climate data stored in the LinkedEarth platform. The LinkedEarth platform contains observational paleoclimate data stored in the LiPD format. These datasets consist of paleoclimate measurements (such as tree ring width, the isotopic composition of ice, bulk composition of marine and lake sediments) as a proxy for past environmental variables such as temperature and precipitation.

Bike-rental example

The Bikes-rental DISK portal is an example to illustrate DISK capabilities where we will understand if the Bikes-rental process is correlated to the environmental and seasonal settings. For instance, weather conditions, precipitation, day of the week, season, the hour of the day, etc. can affect rental behaviors.