The Collaborative Data Science initiative at NISS brings together experts from various fields to tackle complex data challenges through interdisciplinary teamwork and innovative methodologies.

The goal is to foster progress in:

1. Developing new ideas for experimental and observational data-driven learning and discovery that address key questions at the cutting edge of science and scientific deduction;
2. Quantifying and summarizing uncertainty in data-driven theories, as well as complex Data Science models, algorithms, and workflows; and
3. Establishing new practices for scientific reproducibility and replicability through Data Science.

This initiative promotes the inherently interdisciplinary nature of Data Science, seeking science-led advancements in Data Science and their innovative, significant, or transformative applications in science. It encourages robust collaboration and integration across the broadly defined realms of Science and Data Science via: (i) deep domain, (ii) broader inter-domain, and (iii) cross-domain collaborative research. It advocates for collective scientific advancement through novel collaborative and scientific methods and theories that can enrich the knowledge and strengthen the data practices among domain and data scientists.

Data Science in Science | Taylor & Francis Online is an open access, international journal publishing original research and reviews at the intersection of Science and Data Science. NISS Director David S. Matteson is one of the Editors-in-Cheif. An additional opportunity of becoming part of a NISS collaboration will be to publish a paper on the reasearch done through this initiative in this journal.

About the NISS CoLab