Love Your Data Week Feb 13th – 17th 2017

February 17th: Rescuing Unloved Data

Post authored by Lora Leligdon


We are wrapping up Love Your Data week with rescuing unloved data.

As always, please join in the conversation on Twitter (#LYD17 #loveyourdata) or share your insights on Facebook (#LYD17 #loveyourdata).

And while today is the last day of our event, there is still time to register for workshops on data management at Dartmouth. Starting on February 20th, the library will host six data management workshops exploring different stages of the research data life cycle, including data management planning, cleaning, visualizing, storing, sharing, and preserving. Please visit for more information and to register to attend.

Our daily blog posts are courtesy of the 2017 LYD Week Planning Committee. Learn more at!

“Data that is mobile, visible and well-loved stands a better chance of surviving” ~ Kurt Bollacker

Things to consider:

Legacy, heritage and at-risk data share one common theme: barrier to access. Data that has been recorded by hand (field notes, lab notebooks, handwritten transcripts, measurements or ledgers) or on outdated technology or using proprietary formats are at risk.

Securing legacy data takes time, resources, and expertise but is well worth the effort as old data can enable new research and the loss of data could impede future research. So how to approach reviving legacy or at-risk data?

How do you eat an elephant? One bite at a time.

  1. Recover and inventory the data
    • Format, type
    • Accompanying material–codebooks, notes, marginalia
  2. Organize the data
    • Depending on discipline/subject: date, variable, content/subject
  3. Assess the data
    • Are there any gaps or missing information?
    • Triage–consider nature of data along with ease of recovery
  4. Describe the data
    • Assign metadata at the collection/file level
  5. Digitize/normalize the data:
    • Digitization is not preservation. Choose a file format that will retain its functionality (and accessibility!) over time: “Which file formats should I use?”
  6. Review
    • Confirm there are no gaps or indicate where gaps exist
  7. Deposit and disseminate
    • Make the data open and available for re-use



That’s a wrap for our Love Your Data week posts on data quality! Thanks for reading along, and we hope you’ve learned to love your data. 

If you have any questions on data management, please contact Lora Leligdon.

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