Trends in Ecology & Evolution
ReviewSpecial Issue: Ecological and evolutionary informaticsEcoinformatics: supporting ecology as a data-intensive science
Section snippets
Ecology as an evolving discipline
Ecology is increasingly becoming a data-intensive science (see Glossary) 1, 2, relying on massive amounts of data collected by both remote-sensing platforms [3] and sensor networks that are embedded in the environment 4, 5, 6, 7. New observatory networks, such as the US National Ecological Observatory Network (NEON) [8] and Global Lake Ecological Observatory Network (GLEON) [9], provide research platforms that enable scientists to examine phenomena across diverse ecosystem types through access
What is ecoinformatics?
Ecoinformatics is a framework that enables scientists to generate new knowledge through innovative tools and approaches for discovering, managing, integrating, analyzing, visualizing and preserving relevant biological, environmental, and socioeconomic data and information. Many ecoinformatics solutions have been developed over the past decade, increasing scientists’ efficiency and supporting faster and easier data discovery, integration and analysis; however, many challenges remain, especially
The data life cycle
Knowledge is derived through the acquisition of data and the transformation of those data into information that can be incorporated into the corpus of scientific facts, principles and theories. Figure 1 illustrates the different stages that data might progress through during the processes that lead to new information and knowledge. Two stages are reflected in this depiction of the data life cycle. First, projects that include collection of new data typically proceed through steps 1–5 (i.e.
Supporting the full data life cycle
New ground, aerial and satellite-based environmental observing systems coupled with the rapid growth in the use of in situ environmental sensor networks for field research and monitoring, as well as an ever-growing number of citizen-science programs, will soon push ecology and the environmental sciences into a new era where petabytes of data are being collected annually. Powerful informatics platforms will be required to support scientists as they move into this age of data-intensive science.
Remaining challenges
Despite the emergence of ecoinformatics solutions that enable science, several technical and sociocultural challenges and research opportunities remain. First, from the technical side, it is difficult to transport terabyte- and petabyte-sized data sets. Possible solutions include adding computing capabilities to data repositories so that data sets can be processed prior to transport and colocating high-performance computing with large data resources. Second, new visualization approaches and
Concluding remarks
In a manner analogous to the transformation undertaken in the physics domain, new environmental observational systems are moving ecology into the realm of big science, whereby scientists and institutions share observation platforms, accumulate and analyze massive amounts of data, and collaborate across institutions to address environmental grand challenge questions. NEON, GLEON, OOI and other observational platforms play a key role in this scientific transformation, much like telescopes,
Acknowledgments
This work was supported by National Science Foundation awards #0619060, #0743429, #0722079, #0753138, #0814449, #0830944, #0918635, and the National Center for Ecological Analysis and Synthesis [funded by NSF (Grant #EF-0553768), the University of California, Santa Barbara, and the State of California].
Glossary
- Cloud computing
- provision of computing cycles, storage resources and software as a service that is accessible from the Internet via a standardized approach that treats these shared resources as a commodity utility.
- Data-intensive science
- a transformative, new way of doing science that entails the capture, curation and analysis of massive amounts of data from an array of sources, including satellite and aerial remote sensing, instruments, sensors and human observation.
- Data life cycle
- the data life
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