Revision as of 22:24, 20 October 2014

What is Organic Data Science?

Organic Data Science will facilitate ad-hoc collaborations in science.

We are investigating Organic Data Science, a new approach aimed to allow scientists to formulate and resolve science processes through an open framework that facilitates ad-hoc participation and entice collaborators based on attractive science goals. Organic Data Science allows scientists to formulate and resolve science processes through an open framework that facilitates ad-hoc participation and entice collaborators based on attractive science goals.

Accomplishing this requires three elements:

1. a science approach to tackle the problem of the age of water,
2. a technical substrate that facilitates transdisciplinary collaborations, and
3. a social approach to engage the community.

Read more about Organic Data Science.

Technical and Social Aspects of Organic Data Science

We are pursuing a social computing approach that takes into account human aspects such as incentives and participation, while providing the fabric for representing and coordinating tasks involved in accomplishing science goals. Our approach will openly expose science tasks, facilitating inspection and engagement of new potential contributors. The collaboration will grow in an organic way, drawing in people and other contributions from existing data providers and cyberinfrastruture resources.

Ongoing Technical Activities

We are working on several major activities:

1. Design the technical aspects of our organic data science framework
2. Understand the human-centered computing aspects of organic data science

Contributing to this project

There is a growing set of contributors to the project.

We are testing this framework with a collaborative research project focused on the theoretical and experimental aspects of the isotopic age of water. Visit the project site.

The contents of this wiki are accessible to everyone. If you would like to contribute new content, please contact us to obtain an account by emailing us at organic.data.science@gmail.com.

Acknowledgments

This work is supported by the National Science Foundation through the INSPIRE program with grant number IIS-1344272.