Usable Data Abstractions for Next-Generation Scientific Workflows

Project Overview: Supercomputing has become a critical tool for many scientific fields such as climate science and physics. Nevertheless, there are few models that provide insight into how scientists think about their use of supercomputers and how it influences their workflow. In this project, we leverage ethnographic approaches to study current practices in the use of high performance computing in order to gain deeper understanding of how to design usable data abstractions for next-generation scientific workflows.


Nan-Chen Chen, University of Washington, Human Centered Design and Engineering, PhD Student

Sarah Poon, Lawrence Berkeley National Laboratory, Affiliate UX Designer

Lavanya Ramakrishna, LBNL, Scientist

Cecilia Aragon, UW, HCDE, Professor



Chen, N.-C., Poon, S.S., Ramakrishnan, L., Aragon C. Considering Time in Designing Large-Scale Systems for Scientific Computing. In Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW ’16, San Francisco, CA (2016). PDF