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Research Data Management

Why File Organisation is Important

It is important for you and your colleagues to decide how you will name and structure your research data files and folders early in the project planning process. Maintaining consistent file organisation strategies can save you lots of times and headaches, and it is to make sure you and others who need access to the research data can locate and understand it in different periods.

Do you ever have the same experience? Do not know where to find your data files? Do not remember the contents of the data files?

File Naming

Source: PHD Comics

File Structure

How can you store your data in folders systematically?

Define the types of data and file formats

Here are some examples:

  • Images from the field (.jpeg)
  • Progress reports & presentations (.docx, .pptx & .pdf)
  • Field observations (.xlsx & .csv)
  • Analysis files & graphics (.xlsx & .R)

Include important contextual information

When you are looking for a specific data file, how do you think about it? You may want to consider incorporating the following types of contextual information when you organise your data folders?

  • Date
  • Collaborator
  • Data collection method
  • Location
  • Data type

Creating a hierarchical file folder structure

Hierarchical file structures can make your file organisation more systematic.

File Structure

Organise folders by meaningful categories

Based on the contextual information, you will be able to organise the folder in a more meaningful and systematic way. Here are some examples:

  • [Project] / [Sub-project] / [Experiment] / [Instrument] / [Date]
  • [Research area] / [Project] / [Data or documentation] / [Date]

File Naming

Develop a naming convention based on elements that are important to the project. It allows you and others who need access to the data find the files easily and track data records efficiently.

You may consider the following elements in your file conventions:

  • Date/ Date range
  • Study title/ Project name
  • Location
  • Version number
  • Contents of the file
  • Date type
  • Name/Initials of researchers

Here are some other tips:

  • The file naming should be consistent
  • Try not to have very long file names (usually no more than 25 characters)
  • Avoid special characters (i.e. ~ ! @ # $ % ^ & * ( ) ` ; < > ?. , [ ] { } ' " |) in a file name
  • Do not use spaces. Instead, here are some alternative options: capitals (e.g. FileName.xxx), underscores (e.g. File_Name.xxx)
  • Use date format YYYYMMDD
  • Include a version number

Data Versioning

You should be aware of the versioning of your research data when you save new copies of your file. Applying proper data versioning policies to your dataset will save a lot of time when you need to retrieve specific versions of your files in the future.

Here are some suggestions:

  • Include a version number, e.g "v1," "v2," or "v2.1. For e.g., DataFileName_1.0 = original document; DataFileName_1.1 = original document with minor revisions; DataFileName_2.0 = document with substantial revisions
  • Include information about what changes were made, e.g. "cropped" or "normalized"

Source: Data best practices and case studies, Stanford University Libraries;
Research Data Management, University of  Pittsburgh Libguide