The eScience Institute is excited to launch an open and ongoing call for Special Interest Groups (SIGs).
eScience SIGs are flexible and dynamic structures that support the development of new connections and communities on diverse data science related topics. Learn more about proposing a new SIG.
The CDWG discuss topics related to: demographic data and computational and statistical methods, share students & faculty tools, develop via demos or tutorials, and graduate student & faculty research workshops. This group is hosted jointly with the UW Center for Studies in Demography and Ecology.
Group Chair: Maryam Fazel
The NSF-funded Institute for Foundations of Data Science (IFDS) is led by UW and is a collaborative partnership with teams at the universities of Wisconsin-Madison, California-Santa Cruz, and Chicago. IFDS brings together researchers from computer science, electrical engineering, mathematics, and statistics to make progress along its four themes of research (complexity, robustness, closed-loop data science, and ethics & algorithms), towards more robust, reliable, privacy-preserving, fair data science algorithms that perform well in dynamic environments.
The Remote Hackweeks Working Group shares lessons learned as we transition community building and educational activities to a remote environment. Together we are developing resources to support our collective efforts to build inclusive and welcoming communities using remote technologies.
Environmental Impacts of Data Science
The computational activities of data science draw on cyberinfrastructure systems that are built, powered, and maintained using renewable and carbon-based energy sources and other natural resources. The externalized impacts that result are influenced by many factors, from data center design to AI modeling trends and cloud data storage policies. This interdisciplinary group explores ways to incorporate sustainability principles into data, computer and information sciences, for practitioners and educators.
Legacy groups are Special Interest Groups that are no longer current, although the archived information may still be useful, or lead to new collaborations in the future.
Group Chair: Ben Marwick
This group aims to promote the alignment of scientific ideals with research behaviors in the UW community. We fulfill this aim by sharing information, facilitating training and development – especially about open source scientific software – and working to implement sustainable campus policies to support transparency, open sharing, and reproducibility.
This group develops innovative teaching methods and formats to make both formal and informal training in data science skills more accessible within and beyond the UW. We also focus on how to create and sustain long-term career trajectories for a new generation of researchers whose work depends crucially on the analysis of massive, noisy, and/or complex data.
Text is a ubiquitous and valued data source in the computer and information sciences, many areas of the natural and social sciences, engineering, business and more. This eScience Special Interest Group is for students, faculty and researchers interested in sharing and learning about UW research and teaching that uses text as data.
UW Data Science Studies was a group of cross-disciplinary researchers interested in the sociocultural and organizational dimensions of data science. It existed to create opportunities for discussing research, reading scholarly work related to this subfield, supporting research collaborations, and leveraging sociotechnical perspectives to inform data science practice. The group convened from 2015 – 2021, and materials from their activities, including slides and video recordings, can be accessed through the archived website below.
Group Chairs: Alice Schwarze & Spencer Wood
Thinking about data and complex systems as networks and using tools of network analysis has been a successful approach in various research problems. Many fields that have benefited from network approaches — e.g., systems biology, neuroscience, biomedicine, engineering sciences, and social sciences — have very active research communities at UW. Our SIG aims to create opportunities and spaces for researchers at UW and their colleagues to meet, learn, and discuss the connections between their research and network science.
Quantitative Cell Biology and Communities (QCBC)
Rapid progress in the quality and quantity of laboratory data on cellular processes enable the development of quantitative models of cells and cell communities that can usher in a new era of materials, personal medicine, environmental remediation and more. We refer to this emerging area as Quantitative Cell Biology and Communities (QCBC). The QCBC special interest group at the eScience Institute focuses on data management, processing pipelines, and modeling, and machine learning methodologies for understanding the biology of cells, viruses, and their communities.