Human Centered Computing to Support Organic Data Science

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Our research on human-centered computing to support organic data science centers in three major areas:

  1. The use of social computing for collective problem solving, where scientist contributions will be driven to answering science questions, as we believe this is a great incentive for participation. Answering these questions will be the overarching shared goal, and will require contributors to formulate and collaborate in a variety of tasks such as decomposing the high level questions into smaller tasks, sharing datasets, describing data characteristics, preparing them, and running models.
  2. Contributor credits. Our goal is to understand how to enable ad-hoc collaborations and contributions of any size and form, while exposing the credit for all forms of contributions in the context of the final publications.
  3. Study of emerging normalization and standardization agreements as contributors describe their data, models, and other science products to integrate them in service of collective tasks. We are analyzing the drivers for convergence on agreements, and their effect on productivity, data reuse, and facilitation to new contributors.
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