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Loyola University Chicago Libraries

Spreadsheet Therapy: Good data practices

Principles of "data tidiness" to make spreadsheets easier to manage, use, and understand.

Preserving and protecting spreadsheets

After taking such care to design good spreadsheets and workbooks, a few extra steps can help you make sure you retain access to your data and the context needed to understand and interpret them months or years later. These include keeping extra copies of your data, using nonproprietary file formats, and including comprehensive metadata about your data set.

Protecting your data

Protecting against data loss does not have to be arduous. A few good habits can go a long way

  • ALWAYS keep a copy of your raw data in a separate file from any analysis or manipulation
    • Set this file to read-only
  • Use separate tabs or documents to clean and analyze your data
    • Keep track of all steps you perform on your data in a separate tab or metadata file
  • Save early and often
  • Keep multiple backups of your data in at least two physical locations
    • Example: keep one copy on your hard drive, one in SharePoint, and one on another cloud-based server like Google Drive
  • Keep multiple versions of files in case one gets corrupted
    • Use a clear naming convention like file_v1.txt, file_v2.txt
  • When not actively working with data, set files to read-only or open in read-only mode

Metadata files

Metadata files contain any sort of information about your data that give you or others the context needed to understand and interpret them. Depending on your project, this could be a text file like a Readme, or a separate spreadsheet workbook.

Things you might place in a metadata file:

  • A "data dictionary" that explains any special terminology that appears in your data files
  • A description of how different project files relate to each other
  • The units or data types associated with spreadsheet columns, or any restrictions that were placed on input values
  • General experimental conditions
  • Explanations of the analyses conducted
  • An explanation of how missing data were handled
  • Interview questions or wording of general prompts