Session 5: Open Data and Reproducible Research:
Blurring the Boundaries between Research and Publication


In many scientific and technical fields, research is increasingly based on published data. Researchers also often publish detailed instructions or even executable recipes for reproducing their results. Combined with inexpensive networked computing and mass storage, these trends can radically accelerate the pace of research, by lowering barriers to entry and decreasing the time required to reproduce and extend innovations. These changes may also modify the balance between data collection and data analysis, and between experimental and theoretical work.

Nevertheless, these potentially revolutionary developments are mostly happening below the surface, with uneven progress across disciplines, and little general discussion of how to guide or react to the process. The goal of this panel is to publicize the experience of several communities who have up to two decades of experience with what Jon Claerbout has termed "reproducible research", and to begin a general discussion of the broader implications for scientific, technical and scholarly publication.



Sergey Fomel University of Texas Geophysics (slides)
Patrick Vandewalle  Philips Signal Processing (slides)
Jelena Kovacevic Carnegie Mellon University Signal Processing (slides) (Text)
Sünje Dallmeier-Tiessen Helmholtz Gemeinschaft Geophysics (slides)
Mark Liberman University of Pennsylvania  Speech and Language Technology (slides)
Kai von Fintel MIT Semantics (slides)
Steven Krauwer Universiteit Utrecht CLARIN (Humanities & Soc.Sci.

[Note: Steven Krauwer was unable to participate, even remotely, due to a family emergency.]