Digging into data using new collaborative infrastructures supporting humanities-based computer science research

  • Michael Simeone University of Illinois
  • Jennifer Guiliano University of South Carolina
  • Rob Kooper National Center for Supercomputing Applications
  • Peter Bajcsy National Center for Supercomputing Applications
Keywords: authorship, collaboration, image data

Abstract

This paper explores infrastructure supporting humanities–computer science research in large–scale image data by asking: Why is collaboration a requirement for work within digital humanities projects? What is required for fruitful interdisciplinary collaboration? What are the technical and intellectual approaches to constructing such an infrastructure? What are the challenges associated with digital humanities collaborative work? We reveal that digital humanities collaboration requires the creation and deployment of tools for sharing that function to improve collaboration involving large–scale data repository analysis among multiple sites, academic disciplines, and participants through data sharing, software sharing, and knowledge sharing practices.

Author Biographies

Michael Simeone, University of Illinois
Program Manager, Institute for Computing in Humanities, Arts, and Social Science
Jennifer Guiliano, University of South Carolina
Associate Director, Center for Digital Humanities Research Assistant Professor, Department of History University of South Carolina Center Affiliate, National Center for Supercomputing Applications
Rob Kooper, National Center for Supercomputing Applications
Research Programmer, Image Spatial Data Analysis Group
Peter Bajcsy, National Center for Supercomputing Applications
Research Scientist Adjunct Professor of Electrical and Computer Engineering and Computer Science Departments Associate Director for Data Analytics and Pattern Recognition, Institute for Computing in Humanities, Arts and Social Science
Published
2011-04-16
How to Cite
Simeone, M., Guiliano, J., Kooper, R., & Bajcsy, P. (2011). Digging into data using new collaborative infrastructures supporting humanities-based computer science research. First Monday, 16(5). https://doi.org/10.5210/fm.v16i5.3372
Section
Articles