In today's globalized, technology-driven world, we are inundated with information on a daily basis. Faced with a deluge of data emanating from traditional news outlets, advertising and our social media networks, we must constantly sift through this mountain of data and evaluate which information is the most accurate, important and relevant to our lives.
Within the scientific community, things aren't much different. Technology has now enabled us to collect near real-time ecological information on scales ranging from a 1-hectare plot to an entire continent via space-, air- and ground-based sensors. Global, regional and national monitoring networks and observing systems can now use these technologies to capture information from more geographies and time periods than ever before.
It wasn't always this way. Back in 2001, while commuting between graduate school and an ecological think tank where I was working as an intern, I often reflected on how relatively little environmental and ecological data was available. The limited data that did exist was often incompatible, isolated and collected using non-standardized methods. Furthermore, taking the next step -- integrating natural resource information with socioeconomic data in order to inform conservation and sustainable development efforts -- was mostly done only in small-scale projects.
A decade later, and after spending six years at Conservation International working on the Tropical Ecology Assessment Monitoring Network (TEAM), it's exciting to see how much has changed. It's becoming clear that massive quantities of scientific data -- known in the information technology (IT) world as "big data" -- is essential to accurately measure, monitor and quantify our natural systems.
Governments are increasingly recognizing the need to integrate data collected from many agencies, and private companies like Esri are recognizing the value of big data to help make informed decisions. Global monitoring networks like TEAM, the soon to be implementedVital Signs monitoring system and a host of other multidisciplinary monitoring and observing networks are now busy collecting this data.
These monitoring networks and systems will serve as data providers and synthetic engines to process this ever-expanding volume of environmental and socioeconomic data and share it with the right people. They are also demonstrating that we can use these standardized datasets to address some of the most pressing environmental challenges, such as how our natural resources will respond to climate change or the impact local land-use decisions will have on ecosystem services.
Finally, a critical component for any network with large quantities of data is a cyberinfrastructure -- or robust IT systems and tools --that can facilitate the operations of the network but also to manage, analyze and distribute the large quantities of data.
July's issue of the journal BioScience featured a paper that highlights our experience building a cyberinfrastructure for TEAM, which was originally created by CI and is now a partnership among CI, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society.
TEAM's global network of scientists is collecting and distributing near real-time data on trends in biodiversity, climate, land-cover change and ecosystem services. An important goal of the paper was to provide "lessons learned" for other major scientific monitoring and observing networks. In addition, these experiences are very relevant to REDD+ projects and to national governments, as they implement new national resource monitoring regimes and improve upon existing ones.
My experience working with TEAM has been incredibly exciting so far. Technology changes so fast, and the future will yield even more data as we take advantage of the prevalence of mobile devices. Social networks and citizen science efforts will continue the data deluge and -- importantly -- encourage an open data approach amongst a new generation of young scientists to share data freely.
Ultimately we look forward to the time when no matter the size of the project, from small projects run by students and faculty to global monitoring networks, the data they collect will become part of a public resource, accessible and helpful to all.