Diesner to present research at the Open Science 2017 Conference

Posted: March 15, 2017

Assistant Professor Jana Diesner will discuss current issues with open science that involve human-centered and online data and her related research at the Open Science Conference 2017, which will be held March 21-22 in Berlin. The Open Science 2017 Conference is the fourth international conference of the Leibniz Research Alliance Science 2.0, which addresses changes in science and the science system that are related to new forms of participation, communication, collaboration, and open discourse now possible through the web.

This year's conference will focus on open educational resources—course materials (print and digital), modules, streaming videos, software, and other tools, materials, or techniques used to support open access to knowledge. It will offer presentations by international experts, including Diesner, as well as a poster session, a panel discussion, and workshops.

Diesner's presentation, "Innovating compliantly and transparently—road blocks, myths and solutions," will address a set of challenges related to the use of human-centered and online data for research and applications in data science:

From the abstract: The collection, usage and sharing of these data is governed by multiple sets of norms and regulations, including institutional and sectoral norms and rules, intellectual property law including copyright and fair use, privacy and security laws and regulations, terms of service, technical constraints, personal ethics, and national differences in these rules. Problems can arise when students, scholars and practitioners are unaware of applicable rules, uninformed about their practical meaning and compatibility, and insufficiently skilled in implementing them. In this talk, I will discuss strategies for addressing these issues, and provide examples from our research in human-centered data science on solving some of these problems. I will also discuss how intransparencies in data preparation and data provenance – another limitation to openness – can bias research outcomes, and how we can detect and mitigate these shortcomings. 

Diesner is an expert in network science, natural language processing, machine learning, and human-centered data science. She was a 2015-16 faculty fellow at the National Center for Supercomputing Applications (NCSA) at Illinois and is currently a research fellow in the Dori J. Maynard Senior Research Fellows program, which is a collaboration of The Center for Investigative Reporting and The Robert C. Maynard Institute for Journalism Education. She holds a PhD from the Computation, Organizations and Society (COS) program at Carnegie Mellon University's School of Computer Science.

Filed Under: Data Analytics, faculty news, conferences