Skip to content Skip to footer

Your tasks: Data organisation

What is the best way to name a file?


Brief and descriptive file names are important in keeping your data files organised. A file name is the principal identifier for a file and a good name gives information what the file contains and helps in sorting them, but only if you have been consistent with the naming.


  • Best practice is to develop a file naming convention with elements that are important to your project already when the project starts.
  • When working in collaboration with others, it is important to follow the same file naming convention.


Tips for naming files

  • Balance with the amount of elements: too many makes it difficult to understand vs too few makes it general.
  • Order the elements from general to specific.
  • Use meaningful abbreviations.
  • Use underscore (_), hyphen (- ) or capitalized letters to separate elements in the name. Don’t use spaces or special characters: ?!& , * % # ; * ( ) @$ ^ ~ ‘ { } [ ] < >.
  • Use date format ISO8601: YYYYMMDD, and time if needed HHMMSS.
  • Include a unique identifier (see: Identifiers)
  • Include a version number if appropriate: minimum two digits (V02) and extend it, if needed for minor corrections (V02-03). The leading zeros, will ensure the files are sorted correctly.
  • Write your file naming convention down and explain abbreviations in your data documentation.
  • If you need to rename a lot of files in order to organize your project data and manage your files better, it is possible to use applications like Bulk Rename Utility (Windows, free) and Renamer4Mac (Mac).

Example elements to include in the file name

  • Date of creation
  • Project number / Experiment / Acronym
  • Type of data (Sample ID, Analysis, Conditions, Modifications, etc.)
  • Location / Coordinates
  • Name / Initials of the creator
  • Version number
  • Reserve the last 3-letters for file format (e.g. .xls, .rtf, .mov, .tif, .doc)

Examples of good file names

  • Honeybee project, experiment 2 done in Helsinki, data file created on the second of December 2020
    • File name: 20201202_HB_EXP2_HEL_DATA_V03.xls
    • Explanation: Time_ProjectAbbreviation_ExperimentNumber_Location_TypeOfData_VersionNumber
  • Cropped image of an ant head taken on the third of December 2020 by Meg Megson
    • File name: 20201203_MM_HEAD_CROPPED_V1.psd
    • Explanation: Time_CreatorData_TypeModification_Version

How do you manage file versioning?


File versioning is a way to keep track of changes made to files and datasets. While the implementation of a good file naming convention will indicate that different versions exist, this will not explain the difference between two (or more) versions. File versioning will enable transparency about which actions and changes were made and when. This makes it easy to backtrack and find something that was present in a previous version, but was later deleted or changed.


  • Do you need to collaborate on files, perhaps at the same time?
  • Is there a need to be able to backtrack and restore a previous version?
  • Will there be many changes made?


  • Smaller demands of versioning can be managed manually e.g. by keeping a log where the changes for each respective file is documented, version by version.
  • For automatic management of versioning, conflict resolution and back-tracing capabilities, use a proper version control software such as Git, hosted by e.g. GitHub and BitBucket.
  • Use a Cloud Storage service (see Data storage page) that provides automatic file versioning. It can be very handy for spreadsheets, text files and slides.

How do you organise files in a folder structure?


A carefully planned folder structure, with intelligible folder names and an intuitive design, is the foundation for good data organisation. The folder structure gives an overview of which information can be found where, enabling present as well as future stakeholders to understand what files have been produced in the project.


  • The decisions on how to organise the files should be made during planning and design of the project, so that the strategy can be implemented from the start.
  • Consider to consistently apply the same strategy in every project within the research group.


Folders should:

  • follow a structure with folders and subfolders that correspond to the project design and workflow
  • have a self-explanatory name that is only as long as is necessary
  • have a unique name – avoid assigning the same name to a folder and a subfolder

The top folder should have a README.txt file describing the folder structure and what files are contained within the folders. This file should also contain explanation of the file naming convention. See also A Quick Guide to Organizing Computational Biology Projects.

An example:

  code/                 code needed to go from input files to final results   
  data/                 raw and primary data (never edit!)   
  doc/                  documentation of the study  
  intermediate/         output files from intermediate analysis steps  
  logs/                 logs from the different analysis steps  
  notebooks/            notebooks that document your day-to-day work  
  results/              output from workflows and analyses  
  scratch/              temporary files that can safely be deleted or lost  
  README.txt            file and folder description  

Related pages

More information

Relevant tools and resources

Skip tool table
Tool or resource Description Related pages Registry
BisQue Resource for management and analysis of 5D biological images Data Steward: research Data analysis Bioimaging data Tool info
Bitbucket Git based code hosting and collaboration tool, built for teams. Data Steward: research Data Steward: infrastructure Standards/Databases
Bulk Rename Utility File renaming software for Windows Data Steward: research Researcher
Cookiecutter A command-line utility that creates projects from cookiecutters (project templates), e.g. creating a Python package project from a Python package project template. Data Steward: infrastructure Data Steward: research
Git Distributed version control system designed to handle everything from small to very large projects Data Steward: research Data Steward: infrastructure Training
GitHub Versioning system, used for sharing code, as well as for sharing of small data Data publication Data Steward: infrastructure Data Steward: research Standards/Databases Standards/Databases Training
GitLab GitLab is an open source end-to-end software development platform with built-in version control, issue tracking, code review, CI/CD, and more. Self-host GitLab on your own servers, in a container, or on a cloud provider. Data publication Data Steward: infrastructure Data Steward: research Standards/Databases Training
HumanMine HumanMine integrates many types of human data and provides a powerful query engine, export for results, analysis for lists of data and FAIR access via web services. Data Steward: research Researcher Human data Data analysis Tool info Standards/Databases Training
pISA-tree A data management solution for intra-institutional organization and structured storage of life science project-associated research data, with emphasis on the generation of adequate metadata. Microbial biotechnology Researcher Data Steward: research Documentation and metadata Plant Phenomics Plant Genomics Tool info
Renamer4Mac File renaming software for Mac Data Steward: research Researcher
Research Object Crate (RO-Crate) RO-Crate is a lightweight approach to packaging research data with their metadata, using An RO-Crate is a structured archive of all the items that contributed to the research outcome, including their identifiers, provenance, relations and annotations. Documentation and metadata Data storage Data Steward: research Researcher Microbial biotechnology Machine actionability Data provenance Standards/Databases
SMASCH SMASCH (Smart Scheduling) system, is a web-based tooldesigned for longitudinal clinical studies requiring recurrent follow-upvisits of the participants. SMASCH controls and simplifies the scheduling of big database of patients. Smasch is also used to organize the daily plannings (delegation of tasks) for the different medical professionals such as doctors, nurses and neuropsychologists. TransMed
National resources

CESNET-hosted ownCloud is a 100 GB cloud storage freely available for Czech scientists to manage their data from any research projects.

Researcher Data Steward: infrastructure Data storage