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The mission of the Long Term Ecological Research program (LTER) Network is to “provide the scientific community, policy makers, and society with the knowledge and predictive understanding necessary to conserve, protect, and manage the nation’s ecosystems, their biodiversity, and the services they provide.” A specific goal of the LTER is education and training - “to promote training, teaching, and learning about long-term ecological research and the Earth’s ecosystems, and to educate a new generation of scientists.

The goal of this package is to provide a sampler to gather feedback from the community of what will be a larger package containing 28 datasets - one from each of the existing US LTER sites. Those datasets are subsets of the original data and have been updated - sometimes substantially - from the raw data. They are aimed to be useful for teaching and training in environmental data science. This content is thus not suitable for research and should only be used for teaching purposes.

We encourage you to explore existing LTER teaching and training initiatives, and the many other available LTER datasets which can be accessed via the Environmental Data Initiative. Please contact cited researchers directly to discuss using data for research purposes or in publication.


You can install the development version of lterdatasampler from GitHub with:

# install.packages("remotes")

The dataset samples

Dataset samples currently included in the package are summarized below; see individual Articles for data and source details. Note: the three letter prefix for each dataset indicates the LTER site (see full list of site abbreviations).

Which data sample should I use?

These data samples are selected because they have features we feel are commonly useful in introductory environmental data science and statistics courses.

In the table below, we list some introductory methods / skills, then share which data samples in this package we think are well-suited to use when teaching or learning them! It is not comprehensive - there are many different analyses & skills that these data samples would facilitate. Here we highlight a few that we think would be commonly useful

Data sample For example you could:
Linear relationships pie_crab Model the relationship between fiddler crab size and latitude using pie_crab , while learning about Bergmann’s Rule!
ntl_icecover Investigate the relationship between winter temperatures and ice cover duration for Wisconsin lakes using ntl_icecover
hbr_maples Explore seedling height-mass relationships for sugar maples using hbr_maples
Non-linear relationships knz_bison Model the relationship between bison age and mass for male and female bison using knz_bison, for example estimating parameters in the Gompertz model
and_vertebrates Model the length-mass relationships for cutthroat trout and salamanders in Mack Creek, Oregon
Time series analysis arc_weather Explore seasonality, wrangling dates, or practice forecasting using daily meteorological records from Toolik Station, Alaska
luq_streamchem Investigate the impact of a hurricane on stream water chemistry
Spatial data introduction nwt_pikas Introduce basics of spatial data (e.g. CRS, projections) and tools for working with spatial data by visualizing pika locations at Niwot Ridge in the Colorado Rockies
Comparing groups hbr_maples Compare sugar maple seedling heights in previously calcium-treated versus untreated watersheds using hbr_maples, using the exercise as an opportunity to think about acid rain and soil acidification
and_vertebrates Explore differences in size and abundance of cutthroat trout and salamanders in old growth versus previously clear cut forest sections (2 groups) or in different conditions (> 2 groups, e.g. pool, cascade, riffle) of Mack Creek, Oregon

How to provide feedback

The best way to provide feedback on this package is to open an issue and assign the feedback label. Thank you!


Thank you to the amazing students who contributed to this project: Sam Guo, Adhitya Logan, Lia Ran, Sophia Sternberg, Karen Zhao as part of their UCSB Data Science capstone project. Thank you also go to their Course Advisor Prof. Sang-yun Oh.

People / organizations who supported this project:

We gratefully acknowledge all authors and contributors of the roxygen2, usethis, pkgdown, devtools, tidyverse and metajam packages. This website relies heavily on themes created by Dr. Desirée DeLeon and Dr. Alison Hill.