The Changing Face of Digital Science:
New Practices in Scientific Collaborations

CHI 2009 Workshop

Important Dates
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

Primary Contact:
Cecilia Aragon,

Long Description


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 [1].

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 [1] and Swivel [2]). 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 [3], facilitate scientific insight?

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 [5]. Studies have shown that adoption of technologies can be hindered when they do not complement or are incompatible with existing work practices [6].

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" [7]. 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 [8]. 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.

Workshop Goals

1. Exchange information about new ideas and research and development of software for scientific collaborations.
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.

Workshop Format

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 future plans.

Pre-workshop activities

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.

Post-workshop activities

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 collaborations.


We encourage papers on the following topics (but not limited to):

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

** 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.


[1] 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.
[2] Many Eyes,
[3] Swivel,
[4] The VisTrails Project,
[5] 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), article 16.
[6] 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), 755-785.
[7] 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.
[8] Szulanski, G. (1992). Sticky Knowledge: Barriers to Knowing in the Firm. London: Sage Publications.