Today’s Love Your Data Week’s post is by Tiffany Grant PhD, Interim Assistant Director for Research and Informatics at the Health Sciences Library (HSL) and Research Informationist.

Data, FAIR Data

If asked to define good data, the definitions would run the gamut, as the interpretation of the term will be specific to the types and formats of data typically collected by the individual. However, simply put, good data meets the standard of being of good quality, and data quality generally refers to the ability of data to serve the use it was intended. In short, data quality hinges on the reliability and application efficiency of data. The combination of good data quality and data documentation ensures accurate interpretation and reproducibility. Beyond documentation, a number of federal mandates dictate that data be shared beyond one’s own lab notebook, and in order to ensure proper interpretation and reproducibility of your data, it must be FAIR.

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Today’s Love Your Data Week’s post is by Tiffany Grant PhD, Interim Assistant Director for Research and Informatics at the Health Sciences Library (HSL) and Research Informationist.

The Big 3 of Data

Documenting, describing and defining your data are the 3 most critical components of good data management and your data legacy. If done properly, documentation ensures accurate interpretation and reproducibility of your data. Additionally, it improves the integrity of the scholarly record by providing a more complete picture of how your research was conducted.

Data Things to Do

1. Document all file names and formats associated with your project
2. Describe how your data was derived including a description of any equipment and/or software used in the process
   1. Describe your file naming conventions and folder structures
3. Define any abbreviations, variables or codes used in your data or your file names/folders

Big 3 Data Basics

Who: Who are the contributors?

What: What kind of data was collected and what analyses were done to generate the data?

Why: Why was the project started, i.e. what questions did you hope to answer?

Where: Where did you get your data (if you aren’t the creator)? What is the physical location of the data?

How: How was your data generated?  

Message of the day

Good documentation tells people they can trust your data by enabling validation, replication, and reuse.