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  • 1 Data Science Training for the Arctic Data Center
    • 1.1 Schedule
      • 1.1.1 Acknowledgements
  • 2 Introduction to the Arctic Data Center and NSF Standards and Policies
    • 2.1 Learning Objectives
    • 2.2 Materials
  • 3 Best Practices: Data and Metadata
    • 3.1 Learning Objectives
    • 3.2 Preserving computational workflows
    • 3.3 Best Practices: Overview
    • 3.4 Organizing Data: Best Practices
  • 4 Data Documentation and Publishing
    • 4.1 Learning Objectives
    • 4.2 Data sharing and preservation
    • 4.3 Data repositories: built for data (and code)
    • 4.4 Metadata
    • 4.5 Structure of a data package
    • 4.6 DataONE Federation
    • 4.7 Publishing data from the web
      • 4.7.1 Download the data to be used for the tutorial
      • 4.7.2 Login via ORCID
      • 4.7.3 Create and submit the dataset
      • 4.7.4 File and variable level metadata
      • 4.7.5 Add workflow provenance
    • 4.8 Publishing data from R
      • 4.8.1 Logging in
      • 4.8.2 Obtain an ORCID
      • 4.8.3 Log in to the test repository and copy your token
      • 4.8.4 Modifying metadata
      • 4.8.5 Uploading A Package Using R with uploadDataPackage
  • 5 RStudio and Git/GitHub Setup and Motivation
    • 5.1 Learning Objectives
    • 5.2 Reproducible Research
      • 5.2.1 What is needed for computational reproducibility?
      • 5.2.2 Conceptualizing workflows
    • 5.3 Why use git?
      • 5.3.1 The problem with filenames
    • 5.4 Checking the RStudio environment
      • 5.4.1 R Version
      • 5.4.2 RStudio Version
      • 5.4.3 Package installation
    • 5.5 Setting up git
      • 5.5.1 Note for Windows Users
  • 6 Introduction to R and RMarkdown
    • 6.1 Learning Objectives
    • 6.2 Introduction and Motivation
      • 6.2.1 Resources
    • 6.3 R at the console
      • 6.3.1 Error messages are your friends
      • 6.3.2 Logical operators and expressions
      • 6.3.3 Clearing the environment
    • 6.4 RMarkdown
      • 6.4.1 Your Turn
      • 6.4.2 Code chunks
      • 6.4.3 Your turn
    • 6.5 R functions, help pages
      • 6.5.1 A simple example
      • 6.5.2 Getting help
      • 6.5.3 Your turn
      • 6.5.4 Use a function to read a file into R
    • 6.6 Using data.frames
      • 6.6.1 Your Turn
    • 6.7 Troubleshooting
      • 6.7.1 My RMarkdown won’t knit to PDF
      • 6.7.2 I just entered a command and nothing is happening
      • 6.7.3 R says my object is not found
    • 6.8 Literate Analysis
  • 7 Writing Good Data Management Plans
    • 7.1 Learning Objectives
    • 7.2 Materials
  • 8 Version Control With git and GitHub
    • 8.1 Learning Objectives
    • 8.2 The problem with filenames
    • 8.3 Version control and Collaboration using Git and GitHub
    • 8.4 Let’s look at a GitHub repository
    • 8.5 The Git lifecycle
    • 8.6 Create a remote repository on GitHub
    • 8.7 Working locally with Git via RStudio
    • 8.8 On good commit messages
    • 8.9 Collaboration and conflict free workflows
    • 8.10 Exercise
    • 8.11 Advanced topics
  • 9 Git: Collaboration and Conflict Management
    • 9.1 Learning Objectives
    • 9.2 Collaborating with Git
      • 9.2.1 Activity: Collaborating with a trusted colleague
    • 9.3 Merge conflicts
    • 9.4 How to resolve a conflict
      • 9.4.1 Abort, abort, abort…
      • 9.4.2 Checkout
      • 9.4.3 Pull and edit the file
    • 9.5 Workflows to avoid merge conflicts
  • 10 Data Modeling & Tidy Data
    • 10.1 Learning Objectives
    • 10.2 Benefits of relational data systems
    • 10.3 Data Organization
    • 10.4 Multiple tables
    • 10.5 Inconsistent observations
    • 10.6 Inconsistent variables
    • 10.7 Marginal sums and statistics
    • 10.8 Good enough data modeling
      • 10.8.1 Denormalized data
      • 10.8.2 Tabular data
    • 10.9 Primary and Foreign Keys
    • 10.10 Entity-Relationship Model (ER)
    • 10.11 Merging data
    • 10.12 Simple Guidelines for Effective Data
    • 10.13 Data modeling exercise
    • 10.14 Related resources
  • 11 Data Cleaning and Manipulation
    • 11.1 Learning Objectives
    • 11.2 Introduction
    • 11.3 Setup
    • 11.4 About the pipe (%>%) operator
    • 11.5 Selecting/removing columns: select()
    • 11.6 Changing shape: gather() and spread()
    • 11.7 Renaming columns with rename()
    • 11.8 Adding columns: mutate()
    • 11.9 group_by and summarise
    • 11.10 Filtering rows: filter()
    • 11.11 Sorting your data: arrange()
    • 11.12 Joins in dplyr
    • 11.13 separate() and unite()
    • 11.14 Summary
  • 12 Creating R Functions
    • 12.1 Leaning outcomes
    • 12.2 Why functions?
    • 12.3 Temperature conversion
    • 12.4 Creating a function
    • 12.5 Exercise
    • 12.6 Documenting R functions
    • 12.7 Summary
  • 13 Data visualization for web-based maps
    • 13.1 Learning Objectives
    • 13.2 Introduction
    • 13.3 A Minimal Example
    • 13.4 A Less Minimal Example
      • 13.4.1 Load salmon escapement data
      • 13.4.2 Static Plots
      • 13.4.3 Interactive Maps
    • 13.5 Resources
  • 14 Thinking preferences and meeting facilitation
    • 14.1 Learning Objectives
    • 14.2 Resources
  • 15 Social aspects of collaboration and data policies
    • 15.1 Learning Objectives
    • 15.2 Resources
    • 15.3 References
  • 16 Creating R Packages
    • 16.1 Learning Objectives
    • 16.2 Why packages?
    • 16.3 Install and load packages
    • 16.4 Create a basic package
    • 16.5 Add your code
    • 16.6 Add documentation
    • 16.7 Checking and installing your package
    • 16.8 Sharing and releasing your package
    • 16.9 More reading
  • 17 Open Data in R and ROpenSci
    • 17.1 Learning Objectives
    • 17.2 Introduction
    • 17.3 Open data
    • 17.4 What is rOpenSci?
    • 17.5 Overview of some of the interetsing packages rOpenSci provides
      • 17.5.1 rnoaa: R interface to many NOAA data APIs
      • 17.5.2 mapr: Mapping Species Occurrence Data
    • 17.6 Summary
    • 17.7 Resources
  • 18 Reproducibility and Provenance
    • 18.1 Learning Objectives
  • 19 Using NetCDF files
    • 19.1 Learning Objectives
    • 19.2 Introduction
    • 19.3 Reading in data
    • 19.4 Reshaping the data into a data.frame
    • 19.5 Plotting
  • 20 References
  • Published with bookdown

Arctic Data Center Training

15 Social aspects of collaboration and data policies

15.1 Learning Objectives

In this lesson, you will learn:

  • Technique for group facilitation
  • Policies surrounding data, authorship and community participation

15.2 Resources

  • Meeting facilitation technique

Example Code of Conduct

  • Carpentries Code of Conduct
  • Mozilla Science Code of Conduct
  • Mozilla Community Participation Guidelines

Policy Templates:

  • Authorship Policy
  • Data Policy

15.3 References

  • Cheruvelil, K. S., Soranno, P. A., Weathers, K. C., Hanson, P. C., Goring, S. J., Filstrup, C. T., & Read, E. K. (2014). Creating and maintaining high-performing collaborative research teams: The importance of diversity and interpersonal skills. Frontiers in Ecology and the Environment, 12(1), 31-38. DOI: 10.1890/130001