Documentation

The expectations for research data documentation are discipline dependent, and some disciplines are more prescriptive than others. In general, research data documentation should include enough information for users to independently understand and replicate the results. There are many options for documenting your research data, including codebooks, data management plans, lab manuals, metadata, project manuals, READMEs, etc. A single project may use multiple types of documentation for different purposes. 

One set of principles that can guide the creation of documentation is ALCOA+. ALCOA+ comes from the Food and Drug Administration (FDA) and is commonly used in clinical trials, the pharmaceutical industry, manufacturing, and other industries. ALCOA+ principles, when followed, are generally considered a sign of data integrity, where the accuracy and consistency of the data is preserved regardless of changes made. 

• Attributable – where did the data come from? Who acquired it and when? Who performed what actions? What systems/devices/etc. were used to collect/process the data?
• Legible – the documentation is readable and understandable for all involved.
• Contemporaneous – documentation should occur as events occur.
• Original – the original documentation is preserved, and changes are documented. 
• Accurate – the information should be error-free and precise.
• +Complete – the documentation should include all data.
• +Consistent – the data does not include conflicting or contradictory information, and are in the expected order.
• +Enduring – the data is accessible and retrievable for the time required by regulations and sponsor requirements.
• +Available – authorized individuals are able to access and review the data.

There are also standard operating procedures around Good Documentation Practices, such as this one from the Frederick National Laboratory for Cancer Research. 

Other resources that may be helpful:

Codebooks 

• ICPSR – What is a Codebook? 
• DDI – Create a Codebook

Data management plans

• Ten Simple Rules for Creating a Good Data Management Plan
• ICPSR – Elements of a Data Management Plan

Electronic Lab Notebook 

• UNL provides access to RSpace (training video)

Lab manuals 

• APHL’s How to Write a Laboratory Quality Manual
• Biologist – How to…write a lab handbook
• Nature – The key to a happy lab life is in the manual

Metadata

• Cornell’s Guide to writing “readme” style metadata
• ARDC – Metadata Guide
• UNC – Metadata for Data Management
• NIH's Plan to Increase Findability and Transparency of Research Results
  Through the Use of Metadata and Persistent Identifiers

Project manuals 

• U of Michigan’s Project Manual template

READMEs

• Harvard – README Files
• Cornell ReadmeTemplate.txt

Other

• Prepare Supplementary Materials for publishing
• Good Enough Practices for Scientific Computing