About the Miami Ecological Big Data Initiative

The Miami Ecological Big Data Initiative (MiEBDI) facilitates student and faculty access to and analysis of ecological "big data" from around the world to address critical threats to ecosystems and the valuable services that they provide.


Two emerging frontiers in ecology and environmental science are:

  • increased use of automated, advanced sensors that collect high-frequency data
  • regional- to global-scale networking of large environmental data sets through ecological observatory networks (EONS)

Macrosystem to global scale environmental change has made the use of advanced sensors and large datasets imperative to address the grand challenges in ecology. MiEBDI provides students and faculty a framework to facilitate research with these advanced sensors and large datasets through data management workshops and providing connections to ecological big data resources. In addition to Miami's own large ecological datasets, researchers are able to access many shared datasets, and share their own data to become part of the many rapidly growing data sharing networks.

For more information on accessing and managing ecological big data at Miami University contact:

Beth Mette (dickmaem@MiamiOH.edu)

1. Automated Sensors Allow for the Collection of Detailed Environmental Data

photo of vertical profiling buoy

Advanced sensors such as those found on this vertical profiling buoy on Lake Lacawac, Pennsylvania, collect high-frequency data on a variety of environmental variables including extreme weather events, climate change, and the physical, chemical, and biological responses of lake ecosystems. These data provide new insights into the effects of extreme events on aquatic ecosystems.

2. These Detailed Environmental Data Show the Effects of Extreme Weather Events

graph showing extreme precipitation events (New Jersey, 2011); additional information in text below

Daily precipitation data, such as these from a New Jersey weather station operated as a part of the EPA's Clean Air Status and Trends Network (CASTNET) program, show fluctuations in precipitation events. The high-frequency data collected by these automated sensors reveal extreme events such as the back-to-back tropical cyclones Irene and Lee that hit the region in 2011 and led to fundamental changes in lake ecosystems across northeastern USA (Klug et al. 2012 Env. Sci. Technol. 46: 11693).

Hourly meteorological data obtained from CASTNET December 3, 2016.

3. Extreme Weather Events Are Affecting our Planet at an Increasing Rate

photo of Tropical Cyclone Irene over the northeastern United States

Satellite imagery from the National Aeronautics and Space Administration (NASA) in the summer of 2011 shows Tropical Cyclone Irene as it makes landfall in the northeastern U.S.

4. Large Networks of Detailed Environmental Data Allow for the Analysis of Nationwide Trends

map of United States showing changes in precipitation over the last 50+ years: Northeast 71% increase; Great Lakes 37% increase; Southeast 27% increase; Central States 16% increase; Pacific Northwest 12% increase; Alaska 11% increase; Southwest 5% increase; Hawaii 12% decrease

Ecological Observatory Networks (EONS) integrate these large datasets from advanced, high-frequency sensors, into big data that permit the analysis of continental- to global-scale trends over longer time periods.

For example, analysis of 50+ years (1958-2012) of nationwide precipitation data show that the frequency of extreme precipitation events increased the most in the northeastern United States and the least in the southwest. These trends are illustrated in the above figure, with darker shading corresponding to regions of the United States with greater increases in extreme precipitation from 1958-2012, and lighter shading in regions with lesser increases.

Updated from Karl et al. 2009 in Melillo J , Richmond TC , and Yohe GW (Eds). 2014. Climate change impacts in the United States: the third national climate assessment. Washington, DC : US Global Change Research Program; doi: 10.7930/J0Z31WJ2.