The Force 2016 conference sought to analyze and improve scholarly communications. Our KI team contributed with a presentation by Irene Pasquetto, and one poster each by Peter Darch, Ashley Sands, and Bernie Randles. Pasquetto’s presentation The Data Are Open: Can We Reuse Them was related to her research on the collaboration between domain scientists and software engineers in an effort to enable data sharing in a biomedical research community. Randles’ poster, Too Big to Share? Scaling Up Knowledge Transfer Workflows in Computational Sciences, which was recognized with the Best Poster Award, presented how a group of astronomy researchers systematize their workflow through using Jupyter Notebooks. Darch presented a poster How Do Scientists Assess Trustworthiness of Data Produced by Others related to his research of data dynamics at the Center for Dark Energy Investigations (C-DEBI). Sands’ poster, Data Management in the Sloan Digital Sky Survey and the Large Synoptic Survey Telescope Projects, included findings from her dissertation work analyzing data management in astronomy. The Force 2016 conference took place in Portland, April 18-19.

Following the conference, on April 20, our full KI team participated in the Workshop on field studies of data and software work in science. Participants included researchers from the University of Texas at Austin, Carnegie Mellon University, the University of California, Berkeley, the University of Washington, New York University, the University of Illinois at Urbana–Champaign, and our UCLA KI team. The meeting was supported by the Alfred P. Sloan Foundation and hosted by the University of Texas at Austin.


Pasquetto’s presentation at Force 2016



Panel discussion



KI team at Force 2016 conference



Poster session: Darch



Poster session: Sands



Poster session: Randles



Poster session: Randles



Best Poster Award for Randles



Best Poster Award for Randles



Sands, Darch, and Borgman on the Portland Aerial Tram


2016-04-20 09.42.25

Borgman presenting at the Workshop on field studies of data and software work in science