Documentation means capturing the work that you do in a way that enables others to understand what you did so they can duplicate the process. To do this, your documentation must include information about what was done, how it was done, why it was done, when it was performed, where it was performed, and who performed the work.
These basic guidelines for creating a notebook with complete and accurate recordings of your research comes from the Massachusetts Institute of Technology (MIT). It has helpful tips on how to format your notes so that you are able to review and understand their contents months after writing them.
DataONE provides concise definitions for best practices when documenting your research data. Click on the best practice headers for a more detailed guide on each topic.
Data documentation should start at the beginning of a project and continue throughout your process. This will make documentation easier and make it less likely that you will forget the details of each process later. Data documentation will also ensure that you and others will be able to interpret, assess, and repeat your work.
Knowing what to include in your documentation depends on your project and the data types you may be generating.
Some possible elements to include:
Purpose of data collection
Data collection procedures
Structure and organization of the data files
Time and timing of data collection
Data validation and quality assurance
Types of manipulation conducted on raw data during analysis
A README tab in your spreadsheet describing the data structure, column headings, abbreviations, codes, history of data collection, collection instructions, software versions, etc.
A Data Dictionary or Coding Manual describing the variable types, what they mean, and coding procedures for each data column.
A .txt file in the folder where you collect information about your project history, collection events, description of files and folders, etc.
Commented code in analysis and/or processing scripts describing the expected operations.
Below are a few examples of README file templates and other types of documentation that you might find useful to adapt for your own work:
o Cornell’s Guide to writing "readme" style metadata
o ICPSR’s Data Preparation Guide: Important Metadata Elements (Social science)
Data Dictionaries & Codebooks
o The Center for Criminal Justice Research's Creating a Codebook
o McGill University Health Center's Codebook cookbook: How to enter and document your data
o Data Ab Initio's Data Dictionaries