lterdatasampler

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.

Installation

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

# install.packages("remotes")
remotes::install_github("lter/lterdatasampler")

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 here).

and_vertebrates: Records for aquatic vertebrates (cutthroat trout and salamanders) in Mack Creek, Andrews Experimental Forest, Oregon (1987 - present)
arc_weather: Daily meteorological (e.g. air temperature, precipitation) records from Toolik Field Station, Alaska (1988 - present)
hbr_maples: Sugar maple seedlings at Hubbard Brook Experimental Forest (New Hampshire) in calcium-treated and reference watersheds in August 2003 and June 2004
knz_bison: Bison masses recorded for the herd at Konza Prairie Biological Station LTER
ntl_icecover: Ice freeze and thaw dates for Madison, Wisconsin Area lakes (1853 - 2019), North Temperate Lakes LTER
ntl_airtemp: Daily average air temperature data for Madison, Wisconsin (1869 - 2019), North Temperate Lakes LTER
nwt_pikas: Pika observations for habitat and stress analysis at Niwot Ridge LTER, Colorado
pie_crab: Fiddler crab body size recorded summer 2016 in salt marshes from Florida to Massachusetts including Plum Island Ecosystem LTER, Virginia Coast LTER, and NOAA’s National Estuarine Research Reserve System

Which dataset should I use?

These datasets are selected because they have features we feel are commonly useful in introductory data science and statistics courses - in other words, a bunch of Data Science and Statistics 101 stuff can be taught using these data samples!

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 just highlight a few that we think would be commonly useful.

Skill / method	Amenable data samples	We recommend…
Data visualization	All of them!	Visualize trout and salamander length-weight relationships in `and_vertebrates`, exploring different groupings / faceting by other categorical variables in the data sample, including reach (e.g. in the old growth or clear cut forest sections of Mack Creek).
Simple linear regression	`hbr_maples`, `ntl_icecover` , `pie_crab`	Model the relationship between fiddler crab size and latitude using `pie_crab` , while learning about Bergmann’s Rule!
Differences between groups (t-test, effect size, distributions, etc.)	`hbr_maples` , `and_vertebrates`	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.
Nonlinear relationships	`knz_bison`, `and_vertebrates`	Model the relationship between bison age and mass for male and female bison using `knz_bison`, for example estimating parameters in the Gompertz model by nonlinear least squares (as shown in the vignette).
Intro to spatial data wrangling and visualization	`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 with `nwt_pikas` .
Intro to time series wrangling, visualization and analysis	`arc_weather`	Explore seasonality, wrangling dates, or practice forecasting using daily meteorological records from Toolik Station, Alaska in `arc_weather` !

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!

Acknowledgements

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:

LTER Network Office
LTER Information Managers
LTER Education Committee
All the LTER Researchers and Site PIs
Cyber-infrastructures: EDI and DataONE