An ensemble is a collection of data sets, typically produced through a series of related simulation runs. More generally, an ensemble is a set of samples, each consisting of the same set of variables, over a shared high-dimensional space describing a particular problem domain. Ensemble analysis is a form of meta-analysis that looks at the combined behaviors and features of a group of simulations in an effort to understand and describe the underlying domain space. For instance, sensitivity analysis uses ensembles to examine how simulation input parameters and simulation results are correlated. By looking at groups of runs as a whole, higher level patterns can be seen despite variations in the individual runs.
The Slycat™ system integrates data management, scalable analysis, and visualization via commodity web clients using a multi-tiered hierarchy of computation and data storage. Analysis models are computed in parallel on a High Performance Computing platform or on the Slycat™ server. Model artifacts are moved to the Slycat™ server, where they are stored in a project database. These artifacts are the basis for visualizations that are delivered to users’ desktops through ordinary web browsers.
This is a demonstration version of Slycat™, a web-based system for analysis of large, high-dimensional data, developed to provide a collaborative platform for remote analysis of data ensembles. This version encapsulates the server functionality into a container that runs on your local machine, enabling you to locally experiment with Slycat™ models and locally view them with a browser, but not to share them with other people. This version provides two Slycat™ models: Canonical Correlation Analysis (CCA) to model relationships between inputs and output metrics in table data, and the Parameter Space model which can be used to explore in situ generated media results.
A demo version of Slycat is running at https://myslycat.com
The data in this demo is erased on a regular basis, so please don't upload anything you intend to keep.
Run Your Own Instance of Slycat
You can run your own instance of Slycat using Docker. If you need help installing or running Docker, visit https://www.docker.com
- Download and install Docker.
- Pull the Slycat image with the following command:
docker pull slycat/slycat-developer
- Get Slycat™ running on localhost:
docker run -p 2222:22 -p 80:80 -p 443:443 -p 5984:5984 -d --name slycat slycat/slycat-developer
(that's one long command, not two separate ones)
- Visit your local instance of Slycat at https://localhost
You can log in with any username as long as the password is the same as the username. For example:
Also, your browser will probably notify you of a privacy issue because we provide a self-signed certificate. You can proceed anyway.
- You can ssh to your local slycat container:
ssh slycat@localhost -p 2222
The password is
- Once inside your container, you can update the Slycat source code like so:
Slycat will automatically restart to pick up any new changes.
- To exit your container:
- Once you're out of your container, you can stop Slycat:
docker stop slycat
- And start it back up:
docker start slycat
(don't run the
docker runcommand from step 2 again, it's only required the first time you start Slycat)