Provenance support for quality assessment of scientific results: A user study
Applications deployed on cyber-infrastructures often rely on multiple data sources and distributed compute resources to access, process, and derive results. When application results are maps, it is possible that non-intentional imperfections can get introduced into the map generation processes because of several reasons including the use of low quality datasets, use of data filtering techniques incompatible for the kind of map to be generated, or even the use of inappropriate mapping parameters, e.g., low-resolution gridding parameters. Without some means for accessing and visualizing the provenance associated with map generation processes, i.e., metadata about information sources and methods used to derive the map, it may be impossible for most scientists to discern whether or not a map is of a required quality. ^ This thesis presents a quantitative user study on how provenance can help scientists discriminate between high and low quality contour maps. The study had the participation of twenty active scientists with different levels of expertise with regards to gravity data and GIS. The study demonstrates that only a very small percentage of the scientists can identify imperfections using maps without the help of provenance. The study also demonstrates that most scientists, whether GIS experts, subject matter experts (i.e., experts on gravity data maps) or not, can identify and explain several kinds of map imperfections when using provenance to inspect maps. ^
Del Rio, Nicholas, "Provenance support for quality assessment of scientific results: A user study" (2007). ETD Collection for University of Texas, El Paso. AAI1449723.