Students use small mammal data from the National Ecological Observatory Network to understand necessary steps of data management from data collection to data analysis by estimating small mammal population sizes using the Lincoln-Peterson model.
This module series covers how to import, manipulate, format and plot time series data stored in .csv format in R. Originally designed to teach researchers to use NEON plant phenology and air temperature data; has been used in undergraduate classrooms.
Quantifying The Drivers and Impacts of Natural Disturbance Events – The 2013 Colorado Floods
Version: 1.0
This lesson focuses on ways that scientists identify and use data to understand ecological disturbance events using data from five public datasets. The main lesson focused on interpretation of figures, while optional coding extensions teach R skills.
Products from the NEON Data Education Faculty Mentoring Networks
Intro to Data Types and Graphing Lab
Version: 2.0
This is the third lab in an Introductory Physical Geography/Environmental Studies course. It introduces students to different data types (qualitative vs quantitative), basic statistical analyses (correlation analysis s, t-test), and graphing techniques.
Students build on fundamental concepts of spatial patterns and combine this knowledge with the open-data from the National Ecological Observatory Network to quantify spatial autocorrelation and complexity.
Remote sensing data processing – NEON airborne data
Version: 1.0
Concepts of remote sensing and data logistics, and NEON remote sensing data are introduced. Students learn how to apply spatial data processing and visualization skills using R coding program to process NEON airborne data to address scientific questions.
This module assesses the role of wildfire in the eastern US and its impact on bird communities using NEON bird survey data from pre- and post- a major wildfire in the Great Smoky Mountains National Park (GRSM) in November 2016.
Mosquito Vector Ecology of the East Coast using NEON
Version: 1.0
This data module examines the relationship between mosquito vector ecology and climate across the east coast of the United States. The module is designed to merge core concepts in ecology with budding interests of the largely pre-heath student body.
This lesson focuses on understanding metadata, the data about the data, using aquatic macroinvertebrate abundance and species information from a variety of NEON sampling locations.
Data simulation and randomization tests
Version: 1.0
A participatory live-coding lesson on simulating data and performing randomization tests on ecological data in R.
Working with plant phenology data and fitting a nonlinear model using least squares in R
Version: 1.0
A participatory live-coding lesson on working with NEON phenology data and fitting a sine-wave model to
determine when different species get and lose their leaves.
NEON Data in the Classroom: Implementing and Adapting an Open Education NEON Resource “Quantifying the Drivers and Impacts of Natural Disturbance Events-The 2013 Colorado Floods” for the Southern California Classroom.
Version: 1.0 Adapted From: Quantifying The Drivers and Impacts of Natural Disturbance Events – The 2013 Colorado Floods v1.0
Natural disasters and subsequent ecological disturbance events illustrate the complexity of problems in ecology and environmental science, and can be used as effective teaching tools. Unpacking the diverse real-world factors leading up to events offers op
Outstanding Oaks: Quercus Phenology at NEON Sites
Version: 1.0
This resource uses an app hosted on QUBES to visualize similarities and differences in Quercus phenology at three NEON sites in California, Florida, and Massachusetts.
Students build on fundamental concepts of ecosystem production and carbon cycling, combining this knowledge with open long-term data from ecological and meteorological networks to uncover the environmental drivers of carbon fluxes.
Gaining familiarity with R and Work with NEON OS & IS data – Plant phenology & temperature - adaptation
Version: 1.0.0 Adapted From: Large Datasets in R - Plant Phenology & Temperature Data from NEON v1.0
This adaptation introduces students to R and the NEON OS & IS data – Plant Phenology & Temperature tutorial. A planned extension will prompt students to develop a testable hypothesis, based on their tutorial insights, that could be investigated locally.
Resource for conducting data intensive research on CyVerse
More In Depth Spreadsheet Management Adaptation of Data Management using NEON Small Mammal Data
Version: 1.0.0 Adapted From: Data Management using National Ecological Observatory Network's (NEON) Small Mammal Data with Accompanying Lesson on Mark Recapture Analysis v2.0
This adaptation consists of three exercises that introduce students to 1) format spreadsheet data tables, 2) carry out spreadsheet quality control, and 3) count/sort/filter data of interest in order to conduct a pilot analysis on NEON small mammal data.
In addition to the resources shared on QUBESHub, FMN participants have shared the following modules on other platforms:
- Dr. Matthew Aiello-Lammens developed and shared NEON-on-Mobile Data science with NEON on mobile devices. The goal of this work is to provide a means to learn basic data science skills using NEON data on a mobile device.
Products from the EREN-NEON Flexible Learning Projects collaboration.
This project makes use of publicly-available datasets on lichen presence and abundance and wet deposition, paired with geospatial data on air quality, tree canopy cover, and locally collected field data, to better understand how lichens respond to changes
Mosquito Surveys Along Anthropogenic Impact Gradients: EREN-NEON Flexible Learning Project
Version: 1.0
This project explores the role of human land use and other environmental factors that affect native and invasive mosquito species distributions and the diseases that they can vector.
Students observe insect pollinators and other floral visitors in their backyards, or campus, or nearby natural areas to describe plant-pollinator networks and assess how the assemblages from their sites compare to those in a range of landscapes.
A project to compare effects of a continuum of landscape alteration intensities on plant diversity, biomass, and ecosystem services, and to explore human socioeconomic connections to plants in the environment.