Good Data Examples – Love Your Data Week 2017

Information from Love Your Data Week.

Message of the day

Good data are FAIR – Findable, Accessible, Interoperable, Re-usable

Things to consider

What makes data good?

  1. It has to be readable and well enough documented for others (and a future you) to understand.
  2. Data has to be findable to keep it from being lost. Information scientists have started to call such data FAIR — Findable, Accessible, Interoperable, Re-usable. One of the most important things you can do to keep your data FAIR is to deposit it in a trusted digital repository. Do not use your personal website as your data archive.
  3. Tidy data are good data. Messy data are hard to work with.
  4. Data quality is a process, starting with planning through to curation of the data for deposit.

Remember! “Documentation is a love letter to your data”


Example: This dataset is still around and usable more than 50 years after the data were collection and more than 40 years after it was last used in a publication.

Counterexample: This article: promises:

“Statistical scripts and the raw dataset are included as supplemental data and are also available at”


(Used by recommendation of the author who has long since become enlightened. The data have made it into a trusted repository too.)

Hadley Wickham tells you how to tidy your data:


Project TIER teaches undergraduate students how to structure data for reproducible research:

UK Data has great instructions for how to document your data:

If you want to go all in, look at the instructions for documenting data in ICPRS’s Guide to Social Science Data Preparation and Archiving  

Example: Data can take many forms. This compilation of “Morale and Intelligence Reports” collected by the UK Government during and after the war is a great example of qualitative historical data:


What is your favorite data set? How/why is it good for your project? Try out the FAIR Principles to describe and share examples of good data for your discipline. Tell us on Twitter at #loveyourdata or #LYD17!