DATA SHARING: TWO CASE STUDIES
Some progress has been made in data management. Case 1: As of 2011 Shell, ConocoPhillips and Statoil have signed an agreement with the National Oceanic and Atmospheric Association to formally share scientific information that the companies have acquired individually and jointly in the Arctic region.a
This collaboration leverages the complementary strengths of NOAA’s scientific expertise and the industry’s significant offshore resources. Scientific datasets for the Arctic region are shared, including weather and ocean observations, biological information, and sea ice and seafloor mapping studies. NOAA’s ability to monitor climate change and provide useful products and services that inform energy exploration activities in the Arctic will likely be improved through the sharing of high-quality data. The integration of these data could also provide a greater national capacity to effectively manage and respond to environmental disasters in an area where limited personnel and facilities exist. Data and information are shared with the public through NOAA’s existing outlets. Quality control on all data provided is conducted by NOAA before it is incorporated into its products and services.
Case 2: Since 2011, all National Science Foundation proposals must include a supplementary “data management plan,” which is subject to peer review. The primary goal of this new data-sharing policy is to “assure that products of research help NSF achieve its mission to promote the progress of science and engineering.”b The plan should outline the types of data to be produced, the standards to be used, the policies for data access and reuse, and the plans for archiving.
discovery and access. Recent Web standards provide distributed databases that appear uniform and singular to the user. Therefore it is not necessary to create new archives, but rather to leverage existing infrastructure. A key characteristic of the central information hub and the individual components that lie behind it is the timeliness of the available resources. This is particularly critical for applications related to seasonal sea ice predictions, which require real-time data access for model output, in situ observations, satellite and aircraft survey data, etc. The centralized hub could also serve as an integrating resource, providing access to information on the various elements of the Arctic sea ice system (e.g., ocean, atmosphere, and sea ice.)
A separate, but related issue is long-term data storage limitations. In the climate modeling community, the push toward high-resolution and complex models coupled with diverse stakeholder needs has resulted in a rapid and increasing demand for data storage, analysis and distribution (NRC, 2012a). Thus many climate modeling