Barriers to open sharing of research data using social tools? Q and A with Dr. Pieter Dorrestein

In this clip, Dr. Pieter Dorrestein discusses the challenge of encouraging researchers to share data openly using collaborative tools.

Dr. Dorrestein is professor at the Skaggs School of Pharmacy and Pharmaceutical Sciences at the University of California at San Diego. He delivered a lecture at NIH in April 2015 on “Social Networks For Molecular Analysis.”

Watch his lecture here:

Q: Have there been barriers to researchers sharing their data openly?
A: So yes, I think the scientific community has been trained to be quite afraid of sharing data beforehand. There’s a couple of ways that you can limit that concern. One is if you upload raw data you can still make cross-correlations with other data sets. There’s limited context with what’s put in and so. But our idea is with our tool is to actually make the analysis so powerful that if you don’t make your data publicly accessible, you can spend many more man-years to figure out the same thing. And so by making it publicly accessible, you actually get way more information than we would otherwise be able to get. And so that’s a real incentive for a lot of people to make the data publicly accessible. And I think it’s well, should it be mandated? Probably not. It really should be driven from the standpoint that these tools are so useful that they will want to use it. Now if you look at the breakdown, however, of people who are using it, you can clearly see that this younger generation is not afraid to share. They don’t have this fear of being scooped. They know how to work collaboratively, and that to me is really an exciting direction that the scientific community is going to. We’re no longer isolated silos. No, we work as a group even though each individual person can put out their own publication, we can still cross-learn from all the information that was provided.