Research Data Management (RDM): Folder Structure

Like file naming, systems to organise folder and file directories require coherency and consistency.

A logical folder structure facilitates access to your files for you and others.

Using multiple folder and subfolders, creating a hierarchy, will make it easier to keep track of your research files.  Some examples of data-related files you will want to organise are raw data, results, documentation, procedures, field notebooks, and others.

Here are some things to keep in mind when creating a folder structure:

	Structure of the folders should be agreed to and adopted by all participants. This makes sure that it is coherent and understandable to all.
	Folder names should always be short and explicit. Users need to understand what files are within even without opening them.
	Follow the same chosen file naming convention for folders as well
	Keep all of the original data files in a folder to archive them. Make copies of these files to work with. Keep your working files in a separate folder from your original files.
	In your working files folder keep track of different versions of your data.  Save and archive versions any time you make significant changes to your data.
	Create a folder for documentation. This documentation may include data dictionaries, lab or field notebooks, metadata, procedures, and anything else that would help you or others understand your research. [See more in the Documention guide]
	Put your final results data in it's own folder. This allows easy access to the final data files used to support your research claims and to write your paper.
	The hierarchy of your folders should be consistent and logical. Go from a general, high-level folder (starting with a single folder for the project, using its name or acronym) to more specific lower-level folders.
	Think about your folder structure's hierarchy: does it work better for your research to have a deep folder structure (multiple subfolders under a limited number of main folders) or flat structure (many main folders, few subfolders)?
	Your structure should not be too deep nor too shallow. In general, the advice is to put no more than three or four subfolders in a folder and no more than ten files in a folder.
	Put all the relevant material for understanding your data, such as information about data collection and data processing, together in a folder.
	If you have multiple independent data sets, it is recommended to create a separate folder for each data set.

This example adapted from the UK Data Service presents one way your project folders could be structured:

Folders					The first folder level is the project (ENBIOproject)   The second separates the data from the documentation   A further level is used to distinguish between different data types   The final level divides items based on the research activity     You could do it the other way around: research activities could be the second-level folder, in which case they would contain their own data and documentation folders.   The only requirement is that your structure is clear and coherent with the project.
	ENOBIproject
		Data
			Databases
				ConsumerSurvey
				StakeholderNetworkAnalysis
				StakeholderSurvey
			Images
				FocusGroupImages
				LandscapeImages
			Models
			Sound
				FocusGroupRecordings
				InterviewRecordings
			Text
				FocusGroupTranscripts
				InterviewTranscripts
		Documentation
			ConsentForms
				CF_FocusGroups
				CF_Interviews
			InformationSheets
				IS_ ConsumerSurvey
				IS_FocusGroups
				IS_Interviews
				IS_StakeholderSurvey
			Methodology
				Method_ConsumerSurvey
				Method_FocusGroups
				Method_Interviews
				Method_Modelling
				Method_SNA
				Method_StakeholderSurvey

Example of a Folder Structure (adapted from Macalester College)

From Knowledge clip: Keeping research data organized [Video], by UGent Open Science, 2021, Ghent University. (https://www.youtube.com/watch?v=7Ogbkx74Ym8). CC BY NC ND.

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