Initial Publication Date: July 28, 2017

About this Project

Project Goals

Our objective is to develop stand-alone modular classroom activities for undergraduate students using long-term and high-frequency data frame by the following pedagogical goals:

  1. Develop skills required to manipulate large datasets.
  2. Conduct inquiry-based investigations.
  3. Develop students' reasoning about statistical variation.
  4. Engage students in authentic scientific discourse.
  5. Foster conceptions about the nature of environmental science.

What are Large Data Sets?

Across science and engineering fields, the analysis and synthesis of large datasets is increasingly common. In many ways, the environmental sciences, including earth science and ecology, are undergoing an "informatics" revolution, with networks of sensors and people generating unprecedented amounts of data at a range of spatial and temporal scales

Both long-term and high frequency datasets are typically large and complex, containing many variables, multiple sites, missing data points, and incorrect sensor readings. Large datasets can be long-term data collected manually over many years. High-frequency data generated by automated sensor-based systems (Schimel & Keller, 2015, Benson et al. 2009), are increasingly being used to measure and record data for multiple parameters at high frequencies (readings every 15 minutes or even more frequently) and over long time spans (years). These sensors provide records of change that are essential research and monitoring tools. Sensor technologies are now used to collect high-frequency data on ecologically relevant variables ranging from soil moisture to stream conditions to correlating animal movements with environmental conditions. From a practical perspective, large datasets are ones for which there is more data than can be easily viewed on a single computer screen, thus necessitating the use of software keyboard commands and graphing as ways to conduct initial explorations of these data. Thus young scientists should have opportunities to learn how to manage, analyze, and interpret large datasets

Where Do Our Data Sets Come From?

The table below lists current module topics and data sources.

ThemeHigh frequency/long-term datasets and online sources
Climate Change: Atmospheric CO2NOAA Earth System Research Laboratory: Mauna Loa CO2
Lake Ice PhenologyNational Snow and Ice Data Center: Global Lake and River Ice Phenology
Climate Change: Air TemperaturesNOAA Earth System Research Laboratory: NCEP/NCAR Reanalysis Air Temperatures
Flood FrequencyUSGS: River Discharge
Food WebsGlobal Lake Ecological Observatory Network: Chlorophyll and Cyanobacteria
Climate Change: Physical LimnologyGlobal Lake Ecological Observatory Network: Temperature Profiles
Soil RespirationFLUXNET: Atmospheric CO2 Global Lake Ecological Observatory Network: Dissolved Oxygen

Project Partners

Project Support

Project EDDIE is supported by funding from NSF DEB 1245707. Project EDDIE is sponsored by the National Association for Geoscience Teachers.