Valuable lessons from sharing and non-sharing of data

A vivid story from Buzzfeed "Scientists Are Hoarding Data And It’s Ruining Medical Research" describes two related cases - one where researchers voluntarily shared their entire dataset and how the re-analysis  found errors and miscalculations and another one where the data or any results from a largest drug trial were not released for 7 years because the researchers feared criticism and continued double-checking their data. The details from each of the cases are worth following up, but the author of the story comes to an important conclusion that we need to accept that science works through checks and corrections, stop unfair criticisms and doubts in researchers' credibility, and start sharing data for better science, better knowledge, and ultimately, better informed decisions that impact our lives:

And here is where I think the threads come together. The press releases on the reanalysis of the Miguel and Kremer deworming trial in Kenya will go live this week. Somewhere, I’m sure, people will attack or mock them for their errors. One way or another, I can’t believe they won’t feel bruised by the reanalysis. And that is where we have gone wrong. It’s not just naive to expect that all research will be perfectly free from errors, it’s actively harmful.

There is a replication crisis throughout research. When subjected to independent scrutiny, results routinely fail to stand up. We are starting to accept that there will always be glitches and flaws. Slowly, as a consequence, the culture of science is shifting beneath everyone’s feet to recognise this reality, work with it, and create structural changes or funding models to improve it.