Working with data

Being purposeful in handling data will be a benefit to you and to others who may someday also use your data.

• What is data management?

  • A good place to start is with this UMN link, which defines data management, lists some benefits, and provides some local helpful resources.

  • FAIR data principles provides a high level set of guidelines for data management.

  • Some simple guidelines for data management.

  • Many facets of effective data management.

  • Recommendations from data to manuscript and in between.

  • Many of these eleven tips for working with large data sets apply even for small data sets.

• Data structure

  • Make your data tidy.

  • How to organize data in spreadsheets.

  • More about best practices for spreadsheets.

  • Metadata

  • Best practices for metadata.

• Archiving and documenting data

  • Simple steps to data archiving.

  • GitHub for version control and some other aspects of workflow and reproducibility.

  • A good place to start looking at archives is DataOne.

• Graphs and figures:

  • • Check out this masterful series of Tweets on design.

    • Clean, legible visuals are important. Here is a compendium of clean graphs in R with code to help you create your own and here is R code for "Tufte style" graphics.

• Statistics:

  • Here is a crowd-sourced tabulation of resources to help learn and use R.

  • Looking for examples of data analysis using different computer packages including R?

  • Very approachable description of common statistical mistakes.