Presentation Video and Slides: “Why Data Sharing & Reuse Are Hard To Do?”

New online is a Big Data to Knowledge (BD2K) Initiative webinar presentation recorded on April 21, 2017 by Distinguished Professor Dr. Christine Borgman & Ph. D Candidate Irene Pasquetto, of UCLA Informatics Studies titled, “Why Data Sharing & Reuse Are Hard To Do?”
Webinar Abstract

Researchers are producing an unprecedented amount of data by using new methods and instrumentation. By accessing and reusing these data, scientists can answer complex research problems that need systemic approaches to knowledge discovery. However, research data are often not readily available, and even when data are shared, they cannot be reused outside their original context of production.
Based on our studies of data practices in science, we compare data sharing and reuse challenges faced by researchers in life sciences, oceanography, astronomy, molecular biology, and genomics. Data sharing difficulties include determining what to release, when, in what format, and by what means. Data reuse challenges include determining what data could be reused, by whom (expertise required), with whom (collaborative environments), under what conditions (issues of data quality and curation), why (needs for data integration, control a and comparison), and to what effects (types of analysis).

Presenter Bios

Christine L. Borgman, distinguished Professor and Presidential Chair in Information Studies at UCLA, is the author of more than 250 publications in information studies, computer science, and communication. She directs the Center for Knowledge Infrastructures with research grants from the Alfred P. Sloan Foundation, the National Science Foundation, and other sources.
Irene Pasquetto is a research assistant at the Center for Knowledge Infrastructures and a Ph.D. Candidate in the Department of Information Studies at UCLA. She is working on her dissertation on data sharing and reuse practices in molecular biology and genomics. Her research interests include open science frameworks, science governance models, and, more in general, the ethics and policies of data and code practices.

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