Skip to main content
It looks like you're using Internet Explorer 11 or older. This website works best with modern browsers such as the latest versions of Chrome, Firefox, Safari, and Edge. If you continue with this browser, you may see unexpected results.

Data Management and Record Keeping

Tools, tips and checklists for creating a data management plan and managing data you generate

Data Integrity

All researchers have an interest in, and responsibility to, protect the integrity of the research record.

Why do we care? 
-To reconstruct/reproduce what was done (validation)
-To assign credit to researchers
-To prepare reports, papers, etc.
-To teach others how to analyze results, develop new tests, identify errors
-To meet contractual requirements
-To avoid fraud or carelessness (both of which could call the research into question)
-To defend patent claims  (US patent law follows first-to-conceive not first-to-file system)

Source: https://www.hhmi.org/sites/default/files/Educational%20Materials/Lab%20Management/Making%20the%20Right%20Moves/moves2.pdf

What are Data?

Data can be defined as measurements, observations, or any other primary products of research activity. It is the recorded factual material commonly retained by and accepted in the scientific community as necessary to validate research findings.

Data are the empirical basis for scientific findings. The integrity of research depends on integrity in all aspects of data management, including the collection, use, storage, and sharing of data.

Data are not just numbers in a lab notebook. Depending on the research, data might include: Tangible data such as images, audio or video recordings, genetically modified organisms, specialized software, ancient artifacts, or geological samples; Intangible data such as observations, conclusions and next steps; Numerical/Statistical data such as calculations, statistical analyses, etc.

Source: http://research-ethics.org/topics/data-management

Data Lifecycle