As an academically employed scientist of 20 years, the notion that scientific communication suddenly needs better standards puzzles me. The core research curriculum of nearly every scientific field I’ve seen, STEM or otherwise, is that the data needed for replication are non-negotiable. A paper that doesn’t include it would be table rejected by any editor. Or one would hope. This is taught at the UNDERgraduate level, for heaven’s sake.

The thought clusters emerging from the recent “replication crisis” are a fascinating rabbit hole to crawl into. If you stay near the surface, you will find mostly young scholars cheerleading open science as the obvious solution to replication difficulties. The concepts of pre-registering your study, committing to sharing data, and publishing online are all various components of this idea, varying in their necessity by the author’s devotion to their cause.

But there are several downsides to such a system that aren’t immediately obvious. For example, does the skill set of the successful scientist broaden to include how skilled they are at poaching ideas from public data that wasn’t immediately seen by their authors?

Some of the more recent criticisms invoked the spectre of “platform capitalism”, and suggested the Facebook and Linkedin-ification of science by dumping all its data on a centralized platform would likely have a net negative effect.

This article was written in 2018, and most of the discussions I’ve read since then have suggested that the open science initiative has failed despite the rapid penetration of Jupyter and visualization tools in the scientific process. Perhaps, like most things, the unseen market will pick and choose the good out of the dubious.