Research Remix

May 25, 2011

Data standards address cognitive barriers too

Filed under: Uncategorized — Heather Piwowar @ 12:16 pm

Advocates of data sharing standards — guidelines for which elements ought to be included in a dataset for a particular datatype, controlled vocabularies for describing the data, or formatting specifications — usually tout effective reuse as the main benefit. Data can be understood more accurately, interpreted more easily, and technically stitched together at a much larger scale when it adheres to well-defined standards.

This is important. Crucial. That said, it isn’t what gets me excited about data standards.

I think data standards also make it easier for people to *share* their data. I don’t have evidence of this, but I think it is true. It may not be easier in terms of minutes and hours and curse words… some standards are fiddly unfortunate things. But it is easier in that it turns what was a cognitively difficult task into a cognitively easy one. *How* to share data (well)(or well enough)(or how to know what is well enough) is hard. How to get my data into that format is easier.

To the extent that data sharing becomes more institutionalized, less about whether and more about how, less about how and more about “how in the heck can I do that?”, we are on our way to cultural change.

Consequently, even though data sharing standards haven’t been my thing till now, I’m really happy to have joined in the efforts to collect and publish data sharing standards as part of the BMC series. Data standards are never perfect, they create their own problems, but they are the right sorts of problems.


  1. […] Data standards address cognitive barriers too […]

    Pingback by Guest editor for Open Data series at BMC Research Notes « Research Remix — May 25, 2011 @ 12:18 pm

  2. Heather, interesting thought, but this might go both ways. For example, although data standards might save money down the road with factors such as re-usability, the process of standardization is more costly and takes more person-time given the amount of work required to do it. So, instead of having to just think about doing something and then collecting the data, i have to think about my study, check whether a corresponding standard exists, curate the variables that are missing from existing standards, and only then i can proceed to collect the data.

    Now a different scenario would be if I have a cluster of variables that usually goes together — say, a group of standardized variables to measure patient comorbidity — where I have not only documented that cluster but also validated it with aspects such as the amount of time it takes to fill out (time & motion studies), how it will impact my clinical workflow, how it align with other sites participating in the study. In this second scenario will save me big time and will be a decision support tool alleviating cognitive burden.

    Comment by Ricardo — May 31, 2011 @ 3:26 pm

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