CUAHSI supports data-driven education by collaborating directly with educators to develop lesson plans and by contributing to educational resources posted on the Science Education Resource Center’s (SERC) Data and Model Driven Hydrology Education site (http://serc.carleton.edu/hydromodules) - a pathway to the federated National Science Digital Library (NSDL).
CUAHSI's data access tools allow students to discover diverse types of data from numerous data sources at once. Incorporating CUAHSI data access tools into your classroom will minimize the time it takes students to discover and download data and maximize the time for data visualization and analysis. CUAHSI is interested in collaborating with environmental educators interested in developing data-driven exercises. To get involved please send an email to help@cuahsi.org.
Example Data-Driven Activities on SERC
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Downloading Discharge and Precipitation Data from HydroClient and Creating a Plot in Excel
This lesson demonstrates how to use the HydroClient web interface to get time series data for discharge and precipitation from multiple data sources. The procedure to download data for other stations is identical and can be followed to get precipitation, discharge, and several other parameters for other locations in the United States and around the world. The goal of this lesson is for students to examine the relationship between precipitation and streamflow by getting data from the data access tool, HydroClient, and plotting the data in Microsoft Excel. Also, students are introduced to interpreting metadata for the downloaded time series data. Below is the graph that the students will create following this lesson.
Check out the full lesson here: http://bit.ly/hydroclient-tutorial
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Investigating Air Temperature with HydroClient and RStudio
This lesson demonstrates how to find data in HydroClient then analyze and plot the data in RStudio. The example plot from the tutorial is seen below, along with an example of using the summary() command to return basic summary statistics of a time series data. Both of these examples use daily air temperature observations from a USGS monitoring station. The purpose of this step is to combine HydroClient data discovery abilities with R Studio data analysis tools.
Check out the full lesson here: http://bit.ly/Rtutorial