The Arctic Data Center has created a variety of open access curricula that guides individuals on topics including open data, data ethics, software, methodology, and analysis. This course is taught in person, and the content is also available for personal use. Each section in the Fundamentals of Data Management resource encompasses the research and data life cycle processes. This hands-on guide, with survey and environmental data, has been designed for those with research interests in the social sciences and limited R experience. There is also content on ethical and reproducible research practices.
Table of Contents
- Open Data
- Data Ethics
- Social Science Methodology
- Intro to R
- Intro to git
- Data Analysis
- Publishing Data
- Team Collaboration
Open Data and Reproducibility
This section defines and highlights the importance of open data and open science.
Writing Data Management Plans
Data management plans may be required when submitting research proposals or IRB applications. There are helpful tools that guide this process such as the dmptool.org.
Data portals are hosted by the Arctic Data Center’s website and create the opportunity for users to gather published data.
This section provides insight on data ethics in the context of open science and the Arctic Data Center.
Human Subjects Research Considerations
This section discusses general Institutional Review Board (IRB) requirements and research best practices for working with people and Indigenous communities.
Open Data and Ethics Summary
A review of open data and ethics can be found in this section.
Social Science Methodology
Intro to R
Introduction to R
R is an open source statistical software that reads in data and performs statistical analyses.
Introduction to RMarkdown
RMarkdown promotes a reproducible workflow as it is an environment that combines statistics and report writing and can be integrated with GitHub for further collaboration.
Intro to git
Introduction to git
The following link will direct users to a more in-depth explanation on the mechanics of git, Github, and R in the form of an online book. The chapter begins with step-by-step instructions on how to connect git to RStudio, and then dives into the reasons why people use git and GitHub while providing examples along the way.
git collaboration and conflicts
This guide furthers the conversation on git by providing guidance on git and team collaborations.
Data Modeling Essentials
Data modeling requires tidy data to ensure that the computer is correctly understanding the intended analysis. An example of tidy data is having individual csv files for each entity measured along with concise and understandable column names.
Cleaning and Manipulating Data
Prior to uploading data into a programming language, having data that is readable by the computer is an important step.
R packages are a tool that can help simplify code because an R package encompasses prewritten code. There are data visualization R packages that can be downloaded from an R script. An example of an R package being used is downloading ggplot2 to create histograms.
Geospatial analysis is an avenue that can be explored for data that has location and is mappable.
References and Images
Budden, A. E., Clark, S. J., Haycock-Chavez, N., Johnson, N., Jones, M. B. (2022). Fundamentals in Data Management for Qualitative and Quantitative Arctic Research. NCEAS Learning Hub. https://learning.nceas.ucsb.edu/2022-04-arctic/index.html
All of the images used are from the website thenounproject.com.
- “Data Management” by remmachenasreddine from Noun Project
- “Open Access” icon by Duke Innovation Co-Lab from Noun Project
- “Machine Ethics” icon by Trent Kuhn from Noun Project
- “Sociology” icon by Ian Rahmadi Kurniawan from Noun Project
- “Program” icon by Roman from Noun Project
- “Git” icon by Andrejs Kirma from Noun Project
- “Data Analysis” icon by Muhamed Abraham from Noun Project
- “Publishing“icon by Lars Meiertoberens from Noun Project
- “Collaboration” icon by Roberto Chiaveri from Noun Project
- “Source” icon by Mun May Tee from Noun Project