How you organize and name your files will have a big impact on your ability to find those files later and to understand what they contain. You should be consistent and descriptive in naming and organizing files so that it is obvious where to find specific data and what the files contain.
It's a good idea to set up a clear directory structure that includes information like the project title, a date, and some type of unique identifier. Individual directories may be set up by date, researcher, experimental run, or whatever makes sense for you and your research.
File names should allow you to identify a precise experiment from the name. Choose a format for naming your files and use it consistently.
You might consider including some of the following information in your file names, but you can include any information that will allow you to distinguish your files from one another.
Another good idea is to include in the directory a readme.txt file that explains your naming format along with any abbreviations or codes you have used.
Consider these additional tips as you develop a file naming scheme:
You may already have a lot of data collected for your project and wish to organize and rename these files for easier data management. If you have too many files to rename them all by hand, try one of the following applications for renaming your files:
This is an example from a collection of digital research data collected by Science Data Librarian Amy Hodge from 1997-1999 for her dissertation research. It illustrates some of the problems that you might experience if you do not establish appropriate naming conventions for your files.
Amy still understands what some portions of these file names mean:
Some of the information in the file names no longer makes any sense to Amy. For example, she no longer knows what "-10," "-20" or "noPrim" refer to. She also no longer remembers what DM1A and 3F10 are, though they may be other antibodies. When the 12CA5 and HA notations are used in different file names do they indicate the same thing or different things about the experiments? Amy doesn't know.
These file names also lack a lot of Information that Amy would need to know to be able to understand what each of these experiments is, such as what kind of yeast were used in each experiment, whether the expression of the protein was turned on or not, and what portion of the protein is present for all those file names that do not say "1-284."
This is an example from a research project conducted by a group led by Professors Douglas McCauley and Fiorenza Micheli. It illustrates the organized and thorough method they used to name the thousands of image files that they collected for this project.
The project involved installing approximately 180 tiles in an underwater area near the Palmyra Atoll in the South Pacific and leaving them in place for a specified amount of time. At the end of that time, the plates were retrieved for analysis. The researchers photographed the plates in place during the research, and then again after they were retrieved. The images above show one particular plate in place during the study (left) and then again after retrieval (right).
The researchers wanted to track several things about the plates:
Here is the general naming convention decided upon for the photographs:
The example photo shown on the right above was named using this convention as
How does this translate?
Imagine how easy it will be for these researchers to track these files and to search or scan through their thousands of images to find all the whole tile images, all the images from deep water, or all the images of tiles that had been uncaged.
The use of a well-documented and consistent naming scheme containing relevant and descriptive information about your files will make your research faster and easier to manage as well.
And don't forget to include your naming scheme documentation in a readme.txt file in your data folder.