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Field stats
IRES: Costa Rica
Statistical Training in the field

P.I. Laura may-collado; co-P.I. Joaquin Nunez

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Welcome to
IRES Costa Rica

An NSF-funded learning experience for students passionate about marine biology and ecology

(NSF Award Number 2246323)

Image by Zdeněk Macháček

2024 Offering: June (Virtual); July 1-31

This IRES program takes place in Santa Elena Bay, located on the northwest Pacific coast of Costa Rica. A unique oceanographic feature of this region is the seasonal upwelling also known as the “Papagayo Upwelling” which brings deep cold and nutrient-rich water to the surface during the trade wind season, driving high productivity and biodiversity in the area. Because of these unique oceanographic and biological characteristics, the gulf is part of the Guanacaste Conservation Area and a UNESCO Heritage Nature site, protecting coral reefs, seagrass, and mangroves ecosystems, which are critical habitats for sharks and rays, northern and southern hemisphere humpback whales and several species of dolphins. 

statistical training

As part of the IRES experience, students will learn data analysis skills using the R programming language. This training will occur through a series of workshops. The first of which will be a pre-field-work online workshop in June. Four more workshops will take place while students are in the field. These "computational nights" will occur on July 6, 7, 13, and 14.

Students will find all relevant information about the IRES computational and statistical training on this page. These workshops are powered by:

Biological Data Science Program

at The University of  Vermont

Image by Camille Minouflet

Accessing the VACC

Basic steps to access UVM computational resources from afar

Computational work for this IRES experience will capitalize on tools provided by UVM's VACC. To access this resource you must first install UVM's VPN client (click here).

 

Once you have installed and activated the VPN proceed to: https://vacc-ondemand.uvm.edu to connect to the supercomputer. 

Further details can be found here: <<TBD>>

If you want to practice using R and R-Studio on your personal computer, you can download the software (they are free) from here. Notice that you will need to install both R and R-Studio.

Image by Samuel Charron

The basics of R

Workshop 1 (June): The basics of R and a refresher of statistics

This day-long intensive workshop will introduce students to the basics of the R programming language through a series of guided practicums. Along the way, students will revisit basic concepts of probability and statistics. These will be key skills for their IRES analyses. 

Resources:

1. The basics of R, pt 1. (Video [15 min], Code)

2. The basics of R, pt 2. (Video [17 min], Code)

3. Data structures (Video [18 min], Code)

4. Libraries and built-in data (Video [9 min], Code)

5. Tidyverse (Video [16 min], Website, Code)

6. Loops (Video [25 min], Code)

7. Functions (Video [15 min], Code)

8. GGplot (Video [10 min], WebsiteCode)

9. Inputs and Outputs (Video  [21 min], Code)

10. Basic Statistics (Video [22 min], Code) *

 

* Rusty or New to stats? Please see the extra resources below for some guides on the basic concepts. Stat basics (good resources from the web; Statquest's "clearly explained" series on YouTube): Mean and Standard deviation; Correlations; P-values; confidence intervals.

Image by Alejandro Piñero Amerio

Power analysis

Workshops 2 & 3 (July 6-7): Univariate statistics and power analyses

During our first two "computing nights," we will explore tools to answer an important question in stats: "How many individuals do I need for my study?"

Resources:

1. Simulating "virtual biology" (Video, Code)

2. Ecological associations pt. 1 (Video, Code)

3. Power analysis (Video, Code)

Image by Claudia Salamone

Multivariate tools

Workshops 4 & 5 (July 13-14): Multivariate statistics. Principal component analysis and spatial visualization

During the third and fourth "computing nights," we will explore tools to answer another important question in stats: "How can I derive insight from multiple measurements (i.e., variables) of my study system?"

Resources:

1. Principal component analysis (Video, Code)

2. Ecological associations pt. 2 (Video, Code)

3. Geo-spatial visualizations (Video, Code)

Image by Conny Schneider

After fieldwork

Data analysis, write-ups, and "office hours"

Once we complete our field season we need to analyze the data to derive biological meaning. To do this, we create virtual office hours to discuss the next steps.

Information:

TBD

Schedule Office hours:

Reach out to Joaquin.Nunez[at]uvm.edu to set up virtual office hours

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