Lessons learned while building the Deepwater Horizon Database: Toward improved data sharing in coastal science

摘要: Process studies and coupled-model validation efforts in geosciences often require integration of multiple data types across time space. For example, improved prediction hydrocarbon fate transport is an important societal need which fundamentally relies upon synthesis oceanography chemistry. Yet, there are no publically accessible databases integrate these diverse a georeferenced format, nor guidelines for developing such database. The objective this research was to analyze the process building one database provide baseline information on sources sharing document challenges solutions that arose during major undertaking. resulting Deepwater Horizon Database approximately 2.4GB size contained over 8 million points collected from industry, government databases, volunteer networks, individual researchers. technical were overcome reconciliation terms, units, quality flags necessary effectively disparate sets. Assembling required development relationships with researchers managers involved extensive e-mail contacts. average number emails exchanged per set 7.8. Of 95 relevant sets discovered, 38 (40%) obtained, either whole or part. Over third (36%) requests went unanswered. majority responses received after first request (64%) within week (67%). Although fewer than half potentially datasets incorporated into database, level high compared some other disciplines where can be as low 10%. Our suggestions integrated include budgeting significant exchanges, being cognizant cost versus benefits pursuing reticent providers, trust through clear, respectful communication flexible appropriate attributions. Display Omitted integrates points.40% obtained; 36% our unanswered.Most week.Major flags.Significant needs budgeted negotiation trust.