MANAGING GEOLOGIC FIELD DATA - DEVELOPING AN END TO END STRATEGY FOR GEOLOGIC SPATIAL DATA MANAGEMENT AND DISSEMINATION IN A DIGITAL ENVIRONMENT

In an ongoing NSF REU mapping project in coastal Maine, research teams collect, create and compile a significant amount of geologic field, survey and image data involving up to 50 GB and 16,000 files each year, accumulated during 8 weeks and over 3000 person hours of field and lab work. Rapid accumulation of project data presents problems including inconsistent data management protocols and limited end-user accessibility. With field data collection involving multiple workers, instruments and study areas, data management in the field becomes an even more complex process. Building upon earlier REU work (Sigrist et al. 2008) we have developed a solution, adapting standards such as the North American Geologic Map Data Model (NADM), the Open Geospatial Consortium (OGC), and the Federal Geographic Data Committee (FGDC), to catalogue and organize all collected, processed and production data within a central, networked, data management and dissemination system. Incorporation of a standardized data management protocol dramatically improves accessibility and compatibility of resulting datasets and associated metadata. A networked multi-platform PHP (Preprocessed Hypertext Protocol) user interface, supported by a powerful SQL Database infrastructure, allows our data to be entered, updated, or retrieved via the internet through any standard web browser. It can also generate FGDC compliant metadata on request. However, during data collection, the absence of a connection to the central data management system requires a stand-alone system in the field that could be seamlessly incorporated with a central database. The use of spatial databases, such as PostGRES (open source), for data storage and dissemination allows the quick and efficient distribution of data to a mass audience. At present, lacking format standardization, a spatial database solution is not addressed. However, the SQL database solution utilized here was selected in part because the basic database infrastructure is very similar to that of the widely accepted open-source spatial database PostGRES. Similarities in the database structure of PostGRES, and the currently implemented MySQL, will make the future integration of this type of spatial database possible with minimal reconfiguration.