What is Organic Data 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:
- a science approach to tackle the problem of the age of water,
- a technical substrate that facilitates transdisciplinary collaborations, and
- a social approach to engage the community.
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:
- Design the technical aspects of our organic data science framework
- Understand the human-centered computing aspects of organic data science
Contributing to this 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 email@example.com.
We are using a semantic wiki framework with significant extensions to structure collaboration processes. Read more about how this framework works and how to participate and contribute.
This work is supported by the National Science Foundation through the INSPIRE program with grant number IIS-1344272.