Research by Subject: Research Data Services @SDSU: Organizing and Documenting Your Data

Organizing Your Data

Organize and name files meaningfully.

What makes sense for you:

File type
Date
Type of analysis

Use file naming conventions for related files (see File Naming Conventions Worksheet below)

Be consistent
Short yet descriptive
Avoid spaces and special characters

Possible elements for file names:

Project/grant name and/or number.
Date of creation: useful for version control, e.g. YYYYMMDD
Name of creator/investigator: last name first followed by (initials of) first name.
Name of research team/department associated with the data.
Description of content/subject descriptor.
Data collection method (instrument, site, etc.).
Version number.

Use open file formats for long term storage of data: Some file formats are less susceptible to obsolescence than others

pick TXT over DOCX
CSV over XSLX
TIF over JPG

File Naming Conventions Worksheet

Documenting Your Data

Project- & folder-level

Create a readme file. Good example: http://hdl.handle.net/2022/17155
Document any data processing and analyses.
Don’t forget written notes!

Use Readme Files

Names + contact information for people associated with the project
List of files, including a description of their relationship to one another
Copyright + licensing information
Limitations of the data
Funding sources / institutional support
Any information necessary for someone with no knowledge of your research to understand and / or replicate your work.
more about Readme files from Cornell and Harvard.

Datasheets for Datasets. by Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé III, Kate Crawford. This article is a good overview of creating machine and human readable documentation for your data.(last revised v.7, 2020)

Another option is to use an Electronic Lab Notebook. A comparison chart was developed for Harvard researchers if you are considering using one.