Data Organization Best Practices

Use systematic naming

For quickly finding and sorting files and folders, the names should be consistent but unique. Avoid special characters and spaces (use underscores instead). Consider including the following elements:

• project name/acronym
• experiment/instrument type
• site location information (if applicable)
• researcher initials
• date (consistently formatted, i.e. YYYY-MM-DD)
• version number (e.g. v01)

Likewise, name your folders and directories systematically. Do not depend on folder names to describe files since they may be moved around in the future. Adding a brief text or README file to a folder will help future users (including yourself) understand the content and context.

Track versions

You may need to go back to your original data, so keep track of versions. Always save an untouched copy of the raw data, and do not alter it! Only analyze, sort, refine, or other otherwise manipulate a copy of the original file.

File Formats

Whenever possible, save a copy of your data in a plain text format for long-term preservation (e.g. .csv or .txt). The software you currently use may change versions or even become entirely defunct rendering the file unreadable in the future.  For a quick overview, see our File Format Support Matrix.

Describing Files (a.k.a Metadata)

Describe your data to ensure crucial context about how your data was collected is not lost. This description is called “metadata” (data about data), and it becomes increasingly important as more people use the data. Metadata not only answers questions that are discipline-specific like what instrument was used or the geographic location of sampling, but also very general questions like who created the data, meanings of abbreviations, units of measurement, etc.

Some disciplines have well-defined metadata standards (see the Digital Curation Centre’s resource), while others do not. A simple README file is a good solution in the absence of guidance (see Cornell University’s Guide to writing “readme” style metadata).

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Content on this page has been adapted from Cornell University’s Research Data Management Service Group website with permission under CC BY 3.0 US.