Today’s final theme is “Rescuing Unloved Data.” Data in need of rescue can range from at-risk paper archives containing structured information to recently created digital files at risk of loss because of the collapse of support or infrastructure for that particular file format. Other data that can use a helping hand include data in a stable format that simply needs some further enrichment to see their full potential, and data at risk of disappearance because of a change in administrative oversight or ideological outlook.

Recently, Data Services hosted a data enrichment hack-a-thon for Soviet-era maps that had been digitized in tiff format, but not yet georeferenced so that they could be displayed in a GIS system. Moving that spatial data from a basic digital format to an image format with embedded spatial referents involved a bit of manual labor by hack-a-thon participants, reminding us that some of the best data rescues involve collective efforts. In the end, a helpful new resource was built for researchers out of a set of files that had been in need of a little love and attention.

We hope you’ve taken some time out of your schedule this week to think about the future of your research data. Check back for next year’s Love Your Data Week 2018 for more tips and new themes.

Sometimes, it is also useful to set parameters for good data by considering what constitutes bad data. If you haven’t come across this resource, it is always useful to consult the “Quartz Guide to Bad Data,” an extensive (maybe even exhaustive!) list of commonly found elements of bad data. Or check out this collection of “how not to do data” examples.

All too often, we also think of good data as data that is suitable for the research question at hand (meaning it is crafted and understandable by the single researcher and sometimes the single researcher only). But well-attested data can also be data that is attuned to community standards–and not just community standards within a single discipline, but across the wider data community. Selecting helpful authorities and formats for the way data is represented, an approach that can encompass everything from deploying a specific set of terms from a curated medical terminology to utilizing established formats for geographic locations, can boost the quality of one’s data by asserting specificity and standardization.

No matter how data quality is conceived, remember that achieving it very often requires peer/community review, so it is always useful to bring colleagues into the conversation!