Barriers to Open Science – a more honest take
Open science principles sound simple in the abstract: share your data, share your code, register your hypotheses before you test them. In practice, most researchers run into the same handful of walls. Worth naming them plainly, rather than pretending good intentions are enough.
Time and effort. Cleaning data so someone else can reuse it, writing documentation, preparing a detailed pre-registration – none of this is free. It adds real hours onto schedules that are already stretched, and those hours rarely show up on a CV.
Fear of being scooped. Sharing your hypotheses, methods, or early data publicly means someone with more resources or a faster timeline could get there first. This isn’t paranoia – it happens, and it’s a legitimate cost to weigh.
Unclear incentives. Hiring committees, grant panels, and promotion criteria still lean heavily on publication count and journal prestige. Doing right by open science can occasionally cost you the currency your career actually runs on.
None of these are personal failings – they’re structural. Which means individual willpower alone probably won’t shift them much. Real change likely needs to come from the policies around us: what funders require, what publishers reward, what potential employers choose to consider as “good science” when reviewing applications.
Doing good quality open science likely does require more time but the system has not yet changed to accommodate and actively value this, which would enable researchers to incorporate these practices.
Next week, we’ll look at one concrete example of a policy-level lever – registered reports – and be honest about what it actually fixes, and what it doesn’t.
By Teresa Simoes Steyn