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Data Management

Resources in documenting, storing and preserving research data

File Naming Best Practices

Why Should We Care?

Having a good file naming and organization method is one of the simplest things you can do to make a huge impact on your data management! However, it can also be one of the hardest things to change in your data management practices, because it’s often something we do by hand and changing our personal habits can be difficult.

Though it can be difficult to implement, a good file naming convention and folder organization method can make quick improvements to your research process. It makes your data easier to search through and it makes it easier to distinguish similar files or versions from one another. It also provides built-in description about the contents of the file and can make it easier to share documents with collaborators, as they’ll be able to find and understand the file.

One of the most important things for file naming is to develop a naming convention, or a template of standard information you’ll use in most file names, and to always use that convention anytime you have multiple related files in a folder. Without a set convention, you may end up recording haphazard information, or not capturing enough important information, each time you create a file. This will make it harder to remember what keywords you can use to search for the file.

There is not one recommended naming convention that will work for everyone. Each project and person are different, but on the right column we’ve laid out some suggestions for creating a file naming convention.

How to Create a File Naming Convention 

  • Be brief: use 25 of fewer characters if possible. Pick 3 to 4 key pieces of information about the files. Consider using the following information: project or experiment name or acronym, location/spatial coordinates, researcher name/initials, date or date range of experiment, type of data, conditions, version number of file
  • Be meaningful: your file name should provide a clear description of the data
  • Use date standards: dates should always be formatted YYYYMMDD or YYYY-MM-DD. This is an international standard, ISO 8601. Using a standard makes sure the date will be interpreted the same way every time by yourself or others.
    • Example: Mendota_Buoy6_20180711_v2.pdf
  • No special characters or spaces: special characters and spaces can cause interoperability issues across hardware and software and can also prevent data from being imported to certain programs. Use dashes ( – ), underscores ( _ ), or camelcase (capitalizing the first letter of each word, e.g. CamelCase) instead. 
    • Examples: NYC_climate_data.csv or NYCclimateData.csv
  • Avoid common words like "draft" or "final": words like “draft” and “final” can cause confusion if you’re creating multiple drafts. They often stack up and we end up with file names like “project_final_final_draft,” which causes confusion. Instead, use version numbers or dates to provide more information regarding the stage of your project and which draft to use. For very complex versioning needs, consider using git.
    • Example: ASIST_abstract_v3.docx or ASIST_abstract_20180522.docx.

Folder Best Practices

Another key component of an effective file management strategy is establishing a well-organized hierarchical folder structure to go along with your file naming conventions. While there’s no easy answer as to how many folders you should have or how to best organize them, the trick is to create a structure that balances breadth versus depth. You don’t want to create so many layers in your hierarchy that accessing the actual data files becomes difficult. However, you also don’t want to have too many files within each folder that may also make finding your data more difficult.

Below we have an example for creating an organized and useful folder structure.

  • PROJECT 01

    • Administrative

    • Outputs

      • Publications

      • Presentations

    • Grant 01

    • Grant 02

    • Data

      • Raw

      • Analyzed

  • PROJECT 02

  • PAST PROJECTS 

    • 2022

      • Project 03

        • Data

    • 2021

Best Practices:

  • Apply your naming conventions to your folder names. Make sure each folder has a brief but descriptive name. Avoid folder names that are ambiguous or overlap with the names of other folders. In the example above, the folder names are clear and indicate the category or subject of the files within.
  • Group by similarity, function, or topic instead of by file type.
    • If you collect a lot of data in a certain file type, like images, you can end up with a folder with too many files in it. Notice that in the example, none of the folders are specific file formats or types.
  • Distinguish between past and active work. Create archive or yearly folders that you can put past work in. This can help clean up your folder structure quite quickly!
    • In the above example, you can see the use of a “past” folder which archives work from previous years. Using this type of structure means you’re narrowing your search scope and looking among a smaller set of folders to locate data. The folder structure archives past work by the year that work was done—you can choose to organize your “past” folder in whatever way is most effective for you.
  • Create folders to keep your raw data separate from your processed data. This allows for your research to be replicable, and for you to backtrack should an error surface during data processing/analysis.
    • In the above example, which has a “Data” folder. This is a great way to ensure that you always know where to look for your data files.

The content on this page was modified from https://learn.library.wisc.edu/research-data-management/lesson-2-file-naming-organization/ 

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