|Why web GIS may not be enough: a case study with the Virtual Research Vessel|
Wright, D.J.; O'Dea, E.; Cushing, J.B.; Cuny, J.E.; Toomey, D.R. (2003). Why web GIS may not be enough: a case study with the Virtual Research Vessel. Mar. Geod. 26(1-2): 73-86
In: Marine Geodesy. Taylor & Francis: Philadelphia, PA etc.. ISSN 0149-0419, more
|Authors|| || Top |
- Wright, D.J.
- O'Dea, E.
- Cushing, J.B.
During several decades of investigation, the East Pacific Rise seafloor-spreading center at 9°-10°N has been explored by marine geologists, geophysicists, chemists, and biologists, and has emerged as one of the best studied sections of the global midocean ridge. It is an example of a region for which there is now a great wealth of observational data, results, and data-driven theoretical studies. However, these have yet to be fully utilized, either by research scientists or educators. While the situation is improving, a large amount of data, results, and related theoretical models still exist either in an inert, noninteractive form (e.g., journal publications) or as unlinked and currently incompatible computer data or algorithms. Presented here is the prototype of a computational environment and toolset, called the Virtual Research Vessel, to improve the situation by providing marine scientists and educators with simultaneous access to data, maps, and numerical models. While infrastructure is desired and needed for ready access to data and the resulting maps via web GIS in order to link disparate data sets (data to data), it is argued that data must also be linked to models for better exploration of new relations between observables, refinement of numerical simulations, and the quantitative evaluation of scientific hypotheses. For widespread data access, web GIS is therefore only a preliminary step rather than a final solution, and the ongoing implementation of the Virtual Research Vessel (scheduled for final completion in 2004-2005) is a case study for the midocean ridge community to test the effectiveness of moving beyond the data-to-data mode towards data-to-models and data-to-interpretation.