DataRemix: Designing The Datamade Through ArtScience Collaboration

Ruth West just presented our data remix paper at IEEE VIS Arts Program (VISAP), Atlanta, Georgia, October 2013

the full paper is available on line


Roger Malina

DataRemix: Designing The Datamade Through ArtScience Collaboration

Ruth West, Roger Malina, John Lewis, Member, IEEE, Scot Gresham-Lancaster,
Alejandro Borsani, Brian Merlo, and Lifan Wang

Abstract—ArtScience is emerging as one approach for creating novel ways of seeing and new ways of knowing. We propose a role
for ArtScience research and creative work in contributing to the necessary shifts to go beyond the current crisis of representation. We
specifically describe DataRemix, a recombination and reappropreation practice intended to trigger novel subjective experiences and
associations. The narratives framing data creation and representation circumscribe what we can see and know, and how we see and
know. How do we see and know beyond what our instruments, algorithms, representational schemas and training guide us to see
and know? How do we look for what we don’t know we’re looking for when we can only examine at most a tiny fraction of the available
data? Our argument is grounded in and will be illustrated by experience with several ArtScience collaborations spanning genomics,
astrophysics, new media, and holographic sound design.


R. West, R. Malina, J. Lewis, S. Gresham-Lancaster, A. Borsani, B. Merlo, and L. Wang. Dataremix: Designing the datamade through artscience collaboration. In Proceedings of the IEEE VIS Arts Program (VISAP), Atlanta, Georgia, October 2013.


  1. Hi Roger, thanks for linking to the Dataremix paper! In addition to your collaboration with Ruth West, a number of other papers were presented at the IEEE VIS Arts Program (VISAP 2013) on art-science topics. This includes research by Eleanor Gates-Stuart (of CSIRO) and Francesca Samsel (of University of Texas), as well as discussions of art projects informed by real-time data, immersive environments, and computational aesthetic evaluation. The full list of papers and artworks can be found here:

    We’re already planning IEEE VISAP 2014 (in Paris next October) and it would be great to have more contributions from people within the art-science community. We’ll make an announcement with a CfP probably in January or February.

    -Angus Forbes, co-chair IEEE VISAP

  2. [1] Understanding of the possible response of severe convective precipitating storms to elevated greenhouse gas concentrations remains elusive. To address this problem, telescoping, multimodel approaches are proposed, which allow representation of a broad range of processes that could regulate convective storm behavior. In the global-cloud approach (G-C), the NCEP-NCAR Reanalysis Project (NNRP) global data set provides initial and boundary conditions for short-term integrations of a mesoscale model and nested convective-cloud-permitting domain. In the global-regional-cloud approach (G-R-C), the NNRP data set provides initial and boundary conditions for long-term integrations of a regional climate model, which in turn forces short-term integrations of a mesoscale model and nested convective-cloud-permitting domain. Upon applying these approaches to historical extreme convective storm events, it was found that the global-scale data could be dynamically downscaled to produce realistic convective-scale solutions. In particular, tornado proxies computed from the model-simulated winds were shown to compare well in relative numbers to those of tornado observations on many of the days considered. This supports the telescoping modeling concept as a viable means to address effects of elevated greenhouse gas concentrations on convective-scale phenomena. In an evaluation of the two approaches, it was also found that simulations of the historical events by the G-C were superior to those by the G-R-C. Sensitivity of the convective-scale processes to details in the downscaled synoptic-scale flow, and to the placement of the mesoscale model domain within the regional climate model, reduced the effectiveness of the G-R-C.

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