Research Data Management

Documentation is your way of explaining your project, procedures, and data to others. Clear documentation makes it easier for others to reuse your data in the future, for other researchers to understand your research, and for you to keep track of your own research process. Without good documentation, even data that is shared may not be usable.

Types of Documentation

File Naming and Structure - giving clear, descriptive titles to your files that can be understood by all, and organizing your files in a clear and organized fashion

Readme Files - plain text files provided with the research data that give the necessary context to understand and use the data

Data Dictionaries - a dictionary that explains key terms and abbreviations needed to understand use the data

What Should You Document?

Who - Who collected the data? Who were the subjects under study?

What - What data was collected and for what purpose? What is the content and structure of the data?

Where - Where and under what conditions were the data collected?

When - When was the data collected

Why - Why was the experiment performed and how does it relate to your research question?

At the beginning of a project, you should develop a file naming convention and follow it throughout the course of the project. Following a file naming convention will make it easier to find your files, keep track of changes, avoid duplication of work, and avoid misplacing files. A clear file naming structure will also make it easier to others to use your data.

File Naming Tips

• Document your naming conventions clearly, including the use of acronyms and abbreviations
• Have a consistent order of components
• Brief (less than 25 characters is best)
• Use abbreviations
• Use underscores instead of spaces to separate words/dates
• Stick to letters and numbers (no special characters)
• Use the NISO standard for dates: YYYYMMDD
• Include a version number

File Structure

File organization is key to finding the information quickly.  It should make sense for all team members.

Some options for file organization:

• Separate folders for each project
• Separate folders for each type of data
• Separate folders for each day, if you are collecting a lot of raw data
• All data in one location, with regular back ups
• Stored on one type of media
• Files should be in consistent formats when possible

Readme files are simple text files provided with the research data to give key context needed to understand the data.

Types of Readme Files

General Project Files - describe overall organization and models, responsible parties, instruments, timeframes, etc

Specific Data Files - define parameters contents, date and time formats, measurements, etc. Anything that will facilitate the use of the data

Analyzed/Processed Data Files - for analyzed and processed data, include descriptions and references to software, code, or tools used to process the data

A data dictionary is a simple document that describes all variables, acronyms, and abbreviations used in your data collection. This can include:

• Names and definitions of collected data
• Properties of each data element
• Definitions for acronyms and abbreviations used in data collection
• Missing data codes
• Relationships between types of data
• Any other information needed to understand your data