Arctic Data Center Training (October 2020)

Dates: October 19 - October 23, 2020
Location: Online
Venue: NCEAS, 735 State St., Suite 300, UC Santa Barbara The Arctic Data Center provides training in data science and data management, as these are critical skills for the stewardship of the data, software, and other research products that are preserved in the Arctic Data Center. A goal of the Arctic Data Center is to advance data archiving and promote reproducible science and data reuse. This 5-day workshop will provide researchers with an overview of best data management practices, data science tools and concrete steps and methods for more easily documenting and uploading their data to the Arctic Data Center.

Overview:

Workshop topics will include:

• Arctic Data Center and NSF Standards and Policies
• Data Management Plans
• Effective Data Management for Data Preservation using R
• Publishing Data at the Arctic Data Center
• Data and Metadata Quality
• Provenance for Data and Software

For more detailed information on how to prepare for the workshop, see preparing for the workshop (below).

Materials

Link to course materials

Organizers

Name	Email
Matthew Jones	jones@nceas.ucsb.edu
Amber Budden	budden@nceas.ucsb.edu
Jeanette Clark	jclark@nceas.ucsb.edu

Acknowledgements

Work on this package was supported by:

• NSF award #1546024 to M. B. Jones, S. Baker-Yeboah, A. Budden, J. Dozier, and M. Schildhauer

Additional support was provided for working group collaboration by the National Center for Ecological Analysis and Synthesis, a Center funded by the University of California, Santa Barbara, and the State of California.

Preparing for the Workshop

Required software

We will primarily be using a web browser along with an instance of RStudio server set up especially for this course. However, we also recommend setting up R, RStudio, and git on your local system to more easily prepare you to utilize the skills you learned once the course ends.

• R: We will use R version 4.0.2, which you can download and install from CRAN

• RStudio: To download RStudio, visit RStudio’s download page.

• R packages: We will be using the following packages:

  • devtools
  • dplyr
  • DT
  • ggplot2
  • leaflet
  • tidyr
  • scales
  • sf

  You can install these packages quickly by running the following two code snippets:

  packages <- c("devtools", "dplyr", "DT", "ggplot2", "leaflet", "tidyr", "scales", "sf")
  

  for (package in packages) {
    if (!(package %in% installed.packages())) {
      install.packages(package)
    }
  }
  

• git: Download git and install it on your system.

• GitHub: We will be using GitHub so you will need create (or remember your existing) GitHub login

Refresh your skills

This workshop assumes a base level of experience using R for scientific and statistical analyses. However, we realize that not everyone will be at the same place in terms of familiarity with the tools we’ll be using. If you’d like to brush up on your R skills prior to the workshop, check out this list of resources we like:

• Learn R in the console with swirl
• The Introduction to R lesson in Data Carpentry’s R for data analysis course
• The Stat 545 course materials
• The QCBS Introduction to R lesson (in French)

If you’re a fan of cheat sheets, RStudio provides some fantastic ones on their Cheat Sheets page. Please make sure to print ahead of time if you prefer hard copies. In particular, check out:

• Base R
• Data Import
• Data Transformation with dplyr
• R Markdown
• Data Visualization with ggplot2
• R Markdown Reference Guide
• The leaflet package

Participants