VISUALISE: Visualization for Informal Science Education

May 8–9, 2019

Data visualizations are an increasingly important part of presenting science to the public. Join museum educators, computer scientists, artists, and other professionals for this conference on how we create and learn about visualizing data for the public.

SPEAKERS

This is a private, ticketed event.

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About VISUALISE

We encounter visualizations of science every day, whether we’re following the spread of a wildfire, checking our heart rate on a wearable device, or watching an animation on gene editing. After years of researching and designing visualizations for the public, what have we learned about what works and what doesn’t?

On May 8 and 9, 2019, the Exploratorium will be hosting VISUALISE: Visualization for Informal Science Education, the first conference focused on creating effective visualizations for science museums and other venues for informal science education. VISUALISE will bring together museum professionals, learning researchers, computer scientists, artists, and technology developers to share their work and identify opportunities, knowledge gaps, and emerging research.

The conference program will explore themes of interpretation, narration, broadening participation, applying research to practice, collaboration, and the affordances of technology.

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Confirmed Speakers

Vetria Byrd
Purdue University

Andreas Bueckle
Indiana University, Bloomington

Steve Franconeri
Northwestern University

Janet Iwasa
University of Utah

Bryan Kennedy
Science Museum of Minnesota

Scott McCloud
Author of Understanding Comics

Carrie McDougall
Office of Education, NOAA

Joyce Ma
Exploratorium

Tamara Munzner
University of British Columbia

Nadja Popovich
The New York Times

Mónica Ramírez-Andreotta
University of Arizona

Eric Rodenback
Stamen Design

Susan Schwartzenberg
Exploratorium

Heather Segale
UC Davis

Barbara Tversky
Columbia University

Fernanda Viegas
Big Picture Group, Google

Ryan Wyatt
California Academy of Sciences

Photo credit: Science on a Sphere, Pat Izzo, NASA Goddard. AR Sandbox, Oliver Kreylos, UC Davis

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Visualization at the Exploratorium

The Exploratorium has created visualizations of natural phenomena throughout its history, beginning in 1969 with Drawing Board, in which a pen traces the patterns of a swinging table’s harmonic oscillations. As huge scientific datasets have become more prevalent, we’ve expanded our focus on making visualizations for visitors, and our research on the best approaches in design and development.

For the Living Liquid project, we conducted design-based research as we developed interactive visualizations of three different marine science datasets. At the Visualizing the Bay Area exhibit, we project multiple geographic datasets onto a large topographic model, helping visitors to discover invisible processes that shape our region. Check out some of our other visualization-based exhibits.

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Exploratorium Conference Leadership Team

Jennifer Frazier, PhD, VISUALISE Conference Chair, has led several projects that bring together scientists, computer scientists, artists, and others to create and study visualizations, including the Living Liquid project and the NISE Network Visualization Laboratory.

Joyce Ma, PhD, was co-director of the Living Liquid project, and has led the visitor research efforts on several projects that engage visitors with visualizations or visually complex data, including NISE Network Visualization Laboratory and Seeing Scientifically.

Mary Miller is program director for environmental science partnerships, and leads the Exploratorium’s Wired Pier project—a suite of oceanic and atmospheric sensors and data visualization platforms.

Susan Schwartzenberg is director of the Fisher Bay Observatory at the Exploratorium. She has led numerous visualization-based exhibition and arts projects, including Invisible Dynamics and the Ocean Observatory.

Check this site for further developments, or follow #VISUALISE2019 on Twitter or Instagram.

Questions? Please email visualise@exploratorium.edu.

This material is based upon work supported by the National Science Foundation under Grant No. 1811163. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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