The Changing Face of Digital Science:
New Practices in Scientific Collaborations
CHI 2009 Workshop
April 4 - 9, 2009 - CHI 2009 in Boston, USA
Sunday, April 5, 2009 - 9am to 5pm - Workshop
The confluence of two major trends in scientific
research is leading to an upheaval in standard scientific
practice. A new generation of scientists, working in
large-scale collaborations, is repurposing social
software for use in collaborative science. Existing
social tools such as chat, IM, and FriendFind are being
adopted and modified for use as group problem-solving
facilities. At the same time, exponentially greater and
more complex datasets are being generated at a rate
that is challenging the limits of current hardware,
software, and human cognitive capability. A concerted
effort to develop new software tools to handle this data
tsunami is redefining the collaboratory and represents a
new frontier for computer supported cooperative work.
We are hoping this workshop can build community
among researchers studying and/or building software
for scientific collaborations.
Cecilia Aragon, Lawrence Berkeley National Lab
Sarah Poon, Lawrence Berkeley National Lab
Claudio Silva, University of Utah
Cecilia Aragon, CRAragon@lbl.gov
As computation has become a fundamental tool for the
scientific method in recent years, there has been a rise
in scientific collaborations of both collocated and
geographically distributed teams. This trend towards
collaboration introduced a need for supporting
technologies, and scientists were among the first to
adopt information and communication technologies into
their work practices. Tools such as wikis, instant
messaging, and email, have been widely adopted to aid
in collaborative efforts .
Today, a new generation of physicists, biologists, and
other scientists is changing the course of scientific
research. Scientists who grew up with Facebook,
Twitter, and IM are developing and applying new
means of collaborating to the scientific process.
Scientists are beginning to explore the use of social
networking sites, blogging, and microblogging for
information exchange. Existing social tools such as
chat, IM, and FriendFind are being adopted and
modified for use as group problem-solving facilities.
At the same time, the landscape of science itself is
shifting. Increasingly, scientific research is conducted
by large, multi-institution and interdisciplinary project
teams, processing exponentially vaster and more
complex data flows. This overwhelming increase in the
amount of scientific data being generated has been
called the data tsunami. The inability to gain insight
into complex scientific phenomena using current
software tools is a bottleneck facing virtually all
endeavors of science.
The confluence of the growing availability of social
software with the increasing need for scientific
collaboration to generate, analyze, and derive insight
from vast and complex data sets is leading to a number
of interesting developments.
Scientific Social Data Analysis
A new class of Web site has recently emerged that
enables users to upload and collectively analyze many
types of data (e.g., Many Eyes  and Swivel ).
These are part of a broad phenomenon that has been
called social data analysis. This trend is expanding to
the scientific domain where a number of collaboratories
are under development. As the cost of hardware
decreases over time, the cost of people goes up as
analyses get more involved, larger groups need to
collaborate, and the volume of data manipulated
increases. Science collaboratories aim to bridge this
gap by allowing scientists to share, re-use and refine
their computational tasks (workflows).
To analyze and understand scientific data, complex
computational processes need to be assembled and
insightful visualizations need to be generated, often
requiring the combination of loosely coupled resources,
specialized libraries, and grid and Web services. The
heterogeneity of the data, its size, and location, greatly
complicate the data analysis pipelines.
What are the requirements for the successful
development of scientific social data analysis software?
Can such tools, e.g. VisTrails , facilitate scientific
Barriers to the Adoption of New Collaboration Technologies
Although it was once predicted that scientists would
"lead the way in making boundaries of distance
obsolete and would be the first to take advantage of
new technologies to assemble larger-scale efforts
across distance," barriers exist that make scientific
collaboration difficult . Studies have shown that
adoption of technologies can be hindered when they do
not complement or are incompatible with existing work
An issue of trust arises when data are made available on
the web, since the web can "by-pass many of the social
and technical processes by which communities decide
what is known, what is to be trusted, what is accepted
as public, published information" . The appropriation
of social software for information exchange further
complicates the questions regarding the reliability and
security of information exchange.
In addition, although many technologies make sharing
of information increasingly simple, knowledge is still
difficult to transfer . Knowledge is often difficult to
represent and changes rapidly, but common
understanding can be negotiated. What role can social
software play in developing common ground in relation
to knowledge artifacts?
How is the confluence of these two major trends
(generational turnover in the scientific field and the
oncoming data tsunami) impacting science and the
process of scientific collaboration? How can HCI
research elucidate the nature of scientific collaboration
infrastructure? Can the study of human cognitive
limitations provide insight into the difficulties facing
scientific collaborations? Are there new approaches to
scientific collaboration software that are proving
successful in addressing scientists' challenges?
In this workshop, we hope to discuss these and other
questions and topics, and develop a framework for
further study of the changing nature of scientific
collaborations and the software developed, modified,
and used in such collaborations. We further hope to
develop community among researchers involved in this
study, from academia, industry and government labs.
1. Exchange information about new ideas and
research and development of software for scientific
Workshop will be limited to no more than 25
participants to facilitate in-depth discussion.
The workshop will be one day in length. The papers
will be grouped into three topic areas. Participants will
be asked to read all papers, with special attention to the
papers in their topic area.
We will spend the first hour on 3-4 minute introductions
and presentations of the key idea of each person's
research. Then we will split into the three topic area
groups for the next hour. We will break for lunch,
and encourage each topic area group to have lunch
together. After lunch, each topic area group will make
a 15-minute presentation on their thoughts and
conclusions. We will then split into small groups again,
this time based upon interest. This will last one hour.
For the final hour of the workshop, we will reconvene
as a large group to discuss ideas, conclusions, and
We will solicit position papers 2-4 pages in length.
Papers will be peer-reviewed. Selected papers will be
classified into one of three topic areas (to be
determined after submission). Authors of selected
papers will be notified of their selection and asked to
read and comment on the other papers in their topic
area before the conference.
A workshop poster will be designed and created; a
group mailing list for further communication and
collaboration will be generated. We will produce a
summary paper from the workshop discussion. We will
then initiate further discussion among the group as to
whether we should try to publish a book or journal
special issue on the changing face of scientific
We encourage papers on the following topics (but not
2. Develop a community of researchers on this topic.
3. Publish a book or journal special issue based on
expanded or revised contributions to the
workshop, if there is interest.
Collaborative scientific applications and case
studies, including data gathering, analysis and
Social networks of scientists
Repurposing social software for science
Participatory design and/or rapid prototyping for
Distributed data gathering and analysis
Time-critical scientific applications
Use of mobile devices in science
Studies of generational differences in how science
Cross-functional applications and comparisons of a
scientific to a non-scientific field
Paper Submission Instructions:
Submissions should be position papers 2-4 pages in length. Please use
the CHI archival publication format (see below for link). All
submissions will be reviewed. The
possibility of a journal special issue or book based on expanded
versions of the submissions will be explored following the workshop.
At least one author of each accepted paper must register for one or
more days of the CHI 09 conference as well as the workshop, which will
be held on Sunday, April 5, 2009. Papers (in .pdf or .doc format)
should be submitted via email to CRAragon@lbl.gov.
** Submissions are no longer being accepted **
Link to the CHI publication format (please use archival format):Organizers' backgrounds
Cecilia Aragon has been a Staff Scientist in the
Computational Research Division at Lawrence Berkeley National Laboratory
since 2005, after earning her Ph.D. in Computer Science
from UC Berkeley in 2004. Her current research focuses on
scientist-computer interaction, a subfield of
human-computer interaction, specifically computer-supported
cooperative work for scientific collaborations.
She studies the development of novel visual interfaces
for collaborative exploration of very large scientific
data sets, and has published in the areas of visualization,
computer-supported cooperative work, visual analytics,
image processing, and human-computer interaction.
Her work on the Sunfall data visualization and workflow management
system for the Nearby Supernova Factory helped advance
the study of supernovae in order to reduce the statistical
uncertainties on key cosmological parameters that categorize
dark energy, one of the grand challenges in physics today.
She has received four best paper awards since 2004.
She has an interdisciplinary background, including over 15
years of software development experience in industry and
NASA, and a three-year stint as the founder and CEO of a
small company. She earned her B.S. in mathematics
from the California Institute of Technology.
She is also active in program service and the support of diversity in computing; she is the current chair of the IEEE Computer Society's Entrepreneur and Pioneer Awards committee, a founding member of Latinas in Computing, and a board member of the Computing Research Association's Committee on the Status of Women in Computing Research.
Sarah Poon received her master's degree from the
School of Information at UC Berkeley, where her
interests included human-computer interaction and
information visualization. Upon graduation, she began
work as a user interface engineer at the Lawrence Berkeley
National Lab (LBNL). Using participatory design
methods, she developed a data warehouse and
workflow visualization application, a groupware
interface for both synchronous and asynchronous
evaluation of data, and a prototype fisheye visualization
for data exploration.
Claudio T. Silva received the BS degree in mathematics
from the Federal University of Ceara, Brazil, in 1990,
and the PhD degree in computer science from the State
University of New York at Stony Brook in 1996. He is an
associate professor of computer science and a faculty
member of the Scientific Computing and Imaging (SCI)
Institute at the University of Utah. Before joining Utah
in 2003, he worked in industry (IBM and AT&T),
government (Sandia and LLNL), and academia (Stony
Brook and OGI). He coauthored more than 100
technical papers and eight U.S. patents, primarily in
visualization, geometric computing, and related areas.
He is an active member of the visualization, graphics,
and geometric computing research communities,
having served on more than 50 program committees.
He is co-editor of the Visualization Corner of the IEEE
Computing in Science and Engineering. Previously, he
was on the editorial board of the IEEE Transactions on
Visualization and Computer Graphics. He was papers
co-chair for IEEE Visualization conference in 2005 and
2006. He received IBM Faculty Awards in 2005, 2006,
and 2007, and best paper awards at IEEE Visualization
2007 and IEEE Shape Modeling International 2008. He
is a member of the ACM, Eurographics, and IEEE.
 Sonnenwald, D. 2007. Scientific Collaboration: A
Synthesis of Collaborations and Strategies. In Cronin,
B. (ed.), Annual Review of Information Science and
Technology, vol. 4. Medford, NJ: Information Today.
 Many Eyes,
 Swivel, http://www.swivel.com.
 The VisTrails Project, http://www.vistrails.org.
 Bos, N., Zimmerman, A., Olson, J., Yew, J., Yerkie,
J., Dahl, E., et al. (2007). From shared databases to
communities of practice: A taxonomy of collaboratories.
Journal of Computer-Mediated Communication, 12(2),
 Duque, R.B., Ynalvez, M., Sooryamoorthy, R., Mbatia, P., Dzorgbo, D.S., & Shrum W. (2005). Collaboration paradox: Scientific productivity,
the internet, and problems of research in developing areas. Social Studies of Science, 35(5),
 Van House, N., Butler, M., Schiff, L. (1998). Cooperative Knowledge
Work and Practices of Trust: Sharing Environmental Planning Data Sets. CSCW
98: The ACM Conference On Computer Supported Cooperative Work. Proceedings.
Seattle, WA. ACM, 1998, p. 335-343.
 Szulanski, G. (1992). Sticky Knowledge: Barriers to Knowing in the
Firm. London: Sage Publications.