Improving Online Data Accessibility

As the commercial use of machine learning expands, the need for clean, interesting, and useful data becomes more apparent. Despite the increasing need for easily accessible data, there has not been a successful push towards mass distribution of useful data. Prior attempts at free online data have fallen into the trap of publishing data that is uninteresting, unreliable or undocumented.    

To address this need for more and better data, the indico SCOPE team developed Cloudburst: an open-source data aggregator where users can search for data according to their interests, then explore it in-browser before downloading it to their computers. The team’s commitment to interesting data is paired with a high standard for clean and usable data that provide our users with everything they need to get started on their projects.

indico Poster

Faculty Advisor
John Geddes

Team Members
Adam Coppola
Deborah Hellen
Mitchell Kwock
Allison Patterson
Emily Tumang