1. Consider what group/s of files your naming convention will cover. You may need to use different conventions for different file sets.
   Example: This convention will apply to all of my microscopy files, from raw image through processed image.
    
2. What information (i.e. metadata) can support organization and retrieval of these files? What makes each file distinct?
   Ideally, pick three pieces of metadata; use no more than five. This metadata should be enough for you to visually scan the file names and easily understand what contents are in each one.
   Example: For images, I want to know the date, sample ID, and image number for that sample on that date.
   1:
   2:
   3:
   4?
   5?
    
3. Do you need to abbreviate any of the metadata or encode it?
   If any of the metadata from step 2 is described by lots of text, decide how to consistently shorten the information. If any of the metadata from step 2 has regular categories, standardize the categories and/or replace them with 2- or 3-letter codes; be sure to document these codes.
   Example: Sample ID will use a code made up of: a 2-letter project abbreviation (project 1 = P1, project 2 = P2); a 3-letter species abbreviation (mouse = “MUS”, fruit fly = “DRS”); and 3-digit sample ID (assigned in notebook).
    
4. What should be the order for the metadata elements in the file name?
   Think about how you and others may expect to sort and search for your files as you decide which metadata should appear at the beginning of the file name. If date is important, use ISO 8601-formatted dates (YYYYMMDD or YYYY-MM-DD) so that files will sort chronologically.
   Example: If sample ID is most important it will appear first, followed by date, then image number.
   1:
   2:
   3:
   4?
   5?
    
5. What characters will you use to separate each piece of metadata in the file name?
   Many computer systems cannot handle spaces in file names. To make file names both computer- and human-readable, use dashes (-), underscores (_), and/or capitalize the first letter of each word in the file names.
   Example: Use underscores to separate metadata elements and use dashes between parts of sample ID.
    
6. Will you need to track different versions of each file?
   You can track versions of a file by appending version information to end of the file name. Consider using a version number (e.g. “v01”) or the version date (use ISO 8601 standard date format: YYYYMMDD or YYYY-MM-DD).
   Example: As each image goes through analysis workflow, append the version type to the end of the file name (e.g. “_raw”, “_processed”, and “_composite”).
    
7. Write down your naming convention pattern.
   Make sure the convention only uses alphanumeric characters, dashes, and underscores. Ideally, file names will be 32 characters or less.
   Example: If file naming convention is “SA-MPL-EID_YYYYMMDD_###_status.tif”
   then examples are “P1-MUS-023_20200229_051_raw.tif” and “P2-DRS-285_20191031_062_composite.tif”.
    
8. Document this convention in a README.txt** (or save this worksheet) and keep it with your files. See below for more about README files.

   ---

   * This resource is adapted with thanks from Briney, Kristin A. (2020) File Naming Convention Worksheet. Retrieved from https://resolver.caltech.edu/CaltechAUTHORS:20200601-161923247 and generously made available for reuse under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

** The University of Oklahoma University Libraries have produced a very helpful guide to creating readme.txt files: https://libraries.ou.edu/content/how-make-readmetxt-file

A README is a plain text file that provides information about the content and organization of the folders and files that it accompanies. It includes the following elements:

• Folder structure
• File naming conventions
• Any other relevant information needed to work with files in the folder, such as:
  • Dataset description
  • Software or coding dependencies required to open files or execute code

A README is an important component of your data deposit because it:

• Keeps essential information together with the data, so that details like authorship, licensing, and context may not be lost even if files are downloaded or shared separately
• Ensures that others may understand the structure and purpose of the deposited files
• Facilitates data reuse, reproducibility, and transparency
• Supports long-term preservation and reuse of the dataset

These README generators/coding utilities developed at other Canadian universities may be of interest: 

• McMaster - https://rdm.mcmaster.ca/readme
• UBC - https://github.com/ubc-library-rc/dataverse_utils
• U of T - https://dv-readme-gen-dev.deno.dev/