Suppose one day you read an interesting article speculating on the contribution of processes in submarine canyons to the global carbon cycle and decide to explore arctic datasets. Entering the AON data portal, you first encounter icons for terrestrial, atmospheric, oceanic, and human dimensions that contain a summary of data holdings under each discipline. You then have the option to browse datasets by discipline or by theme. Using data exploration tools, you search for canyon processes and determine what relevant meteorological, geophysical, and oceanic datasets are archived, and their availability in space and time.
Although you do not realize it, the information accessed comes from four different data centers in two different countries. For observations that are interesting but unfamiliar, you find links to descriptions of the instrumentation, the methods, and the data processing steps. You also find links to browse images of the datasets and, after inspection of these, you decide flow levels of the X River bear closer investigation, as the X River appears to be associated with the Y Canyon, and both the oceanic and terrestrial environments are well instrumented.
Plotting the time series using the online data display tools, you observe that three years ago, in June, the gauges reported an abrupt drop in water level after a gradual rise through late spring. The screen also shows an icon that looks like the silhouette of a parka. Curious, you click on the icon, and a text box pops up describing a large ice dam that gave way about the time of the abrupt water level drop, with a notation from the Inuit hunter who reported the event. Now you open the relational database interface in the AON portal and frame a query requesting turbidity measurements within 100 km of the mouth of the X River during the timeframe of the ice dam collapse. Within seconds, you have links to data streams and generate another series of plots. These show an increase in turbidity within the Y Canyon two days after the ice dam collapsed. You suspect that you have identified a flow event carrying sediment into the deep Arctic Ocean. Wondering how general these events are, you search for abrupt drops in tide gauge measurements coupled to local increases turbidity measurements for other arctic river systems and find three more candidate events.
It is almost the end of the day and you download your time-series plots and email them to your colleagues twelve time zones away for their review tomorrow. You save your AON session using the password protection you have installed so that you can access the data again tomorrow without having to redo the data searches. Before wrapping up, you post a request to the event detection service, providing the combined tide gauge turbidity criteria as the trigger. Finally, you post a request to the observation scheduling list, starting the process to request time on the docked autonomous underwater vehicle near the mouth of the X River to be triggered on detection of an event. It has been a productive day.
the data management strategy of the AON, it is imperative not to reinvent the wheel. Much has been written about scientific data management (e.g., NRC, 1995; CCSDS, 2002; NSF/LOC, 2003; ICSU, 2004; Hankin and the DMAC Steering Committee, 2005; IPY, 2005; NSB, 2005), and many nations are establishing standards to promote integration and accessibility (e.g., FGDC, 1998; ISO, 2003). A successful AON data management strategy will follow nationally (or internationally) accepted guidelines and tailor data to meet the needs of the arctic user community while remaining flexible enough to allow for unanticipated use of the instruments and data.
Many different countries and organizations make observations in the Arctic. Increasingly, the integration of consistent and high-quality international observations requires a mechanism to prepare regulatory and guidance material relating to data collection, data management, and development of data products.
Recommendation: As a first step toward implementing the AON data management strategy, a permanent AON data management committee should be established to provide (i) oversight and coordination of long-term planning for data acquisition, access, distribution, and preservation; (ii) consistency and development of data policies; (iii) oversight of data management system design and engineering; (iv) collaboration with network designers; (v) distribution of integrative and interpretative products to inform national and international policy; (vi) user outreach, and (vii) oversight for the evolution of AON standards.1
Ideally, this group would include advisory members who establish strategies for various components of the data management system as well as members who can implement the strategic recommendations: for example, selecting and disseminating value-added products to inform policy decisions or arctic communities of observed environmental change. The AON data management committee would promote shared infrastructures for AON observations and provide a central portal in a distributive environment for contribution of and access to all the observations that are a part of the AON.
In Chapter 6 the Committee collects its ideas about implementation steps for the AON and breaks these ideas into near-term (minimum) actions and longer-term actions for an “ideal” system. Because the topic of the present chapter (4) is one of the Committee’s four Essential Functions, and because it is this “essential function” framework on which implementation recommendations are hung, it is more convenient and effective to place the implementation ideas on data management throughout this chapter than to wait until Chapter 6, where the other essential functions are discussed in detail. Most of the ideas in Chapter 4 are considered necessary near-term actions, but two are mostly for the “ideal” system and are marked accordingly.