You might’ve come across words like “transparency” or “reproducibility” in the past few years. Perhaps you’ve overheard colleagues talking about data sharing or preregistering a study plan. If you aren’t familiar with open science, it can sound like a collection of jargon or buzzwords.
However, open science is best understood as both a set of research practices and a broader commitment to making the processes by which scientific knowledge is produced more transparent, accessible and open to scrutiny.
The contemporary open science movement gained particular momentum in psychology and neighbouring fields over the past decade, partly in response to growing evidence of systemic problems in research practice. These included questionable research practices such as p-hacking, which is when you fiddle with your data to get it to give you a smaller p-value; high-profile cases of fraud; increasing numbers of retractions and difficulties reproducing supposedly well-established findings.
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These problems were not reducible to a few badly behaved scientists. Many arose from ordinary research practices and incentive structures that allowed for considerable undisclosed flexibility in how studies were analysed and reported.
The barriers
To many of my colleagues, open science is simply “science done right”. After all, we should be conducting, reporting and disseminating research transparently, and in ways that allow others to scrutinise our findings. We should make research transparent, accessible where possible, and take reasonable measures to reduce bias and error. Right?
In principle, yes, but in practice, adopting open science can mean changing established workflows, learning unfamiliar tools and adding work to research pipelines that are already crowded. That can make getting started daunting, particularly in disciplines where open practices are not yet routine.
Remember that openness does not mean putting everything online. Sensitive data, qualitative materials and research involving vulnerable communities may require restricted access, careful contextualisation or sometimes non-sharing. Responsible open science means thinking explicitly about these trade-offs rather than treating maximum openness as an end in itself.
The bread and butter
There are many dimensions to open science, but two relatively accessible places to begin are sharing and preregistration. I am talking primarily about sharing data, materials and code, although almost any resource produced during a research project can potentially be shared.
Where ethically and legally appropriate, repositories such as Zenodo provide stable places to store and disseminate these resources. Simply uploading files is not enough, however, and without clear metadata explaining what they are, how they were produced and how they should be interpreted, ostensibly “open” materials can be practically unusable.
Preregistration involves documenting research questions, hypotheses, methods and planned analyses before examining the relevant data, usually in a time-stamped repository. Tools such as AsPredicted.org lower the barrier to entry by providing a short, structured template. Preregistration helps distinguish decisions made in advance from those made during exploration, while still allowing researchers to deviate from their plans when there is good reason to do so – at least, as long as those deviations are reported transparently.
Registered Reports go further by peer-reviewing the research question and methods before data collection, with publication provisionally agreed on that basis, rather than on whether the eventual findings are exciting or statistically significant.
And for qualitative researchers, here is the good news: neither preregistration nor Registered Reports belongs exclusively to quantitative research. Qualitative researchers have also developed approaches for making plans, methodological decisions and changes more transparent without pretending that qualitative research should follow a rigid, predetermined plan.
Sample the buffet
Open science can still seem like an enormous list of things you are suddenly supposed to be doing, but it needn’t be. Open science is a buffet, to borrow a metaphor from researcher Christina Bergmann. You don’t need to adopt every practice at once or even at all (and it’s not as if every practice suits every research design anyway).
I recommend starting somewhere practical and low effort. Put the materials or data from your next project on Zenodo and accompany it with a detailed and informative readme file. Preregister one study. Make just one paper open access. Consider which parts of your existing workflow would genuinely benefit from greater transparency, accessibility or scrutiny, and begin there. Begin anywhere, really – experience tells me that it will get easier and less burdensome once you start!
Cultivating openness
One of the easiest places to change scientific culture is in how we train new researchers. Students are remarkably willing to question assumptions about how science works if we give them permission and the support to do so.
Talk to your master’s students about error and failure in science; complicate the issue and show them where things have gone wrong. I promise you, they can handle it.
Teach your undergraduates that registering a study plan or thinking carefully about whether and how to share data can simply be part of doing research; that it’s normal. Challenge PhD candidates to engage with the ethical dilemmas of sharing sensitive materials, and to question sharing mandates if they don’t feel right.
For all of them, disrupt and destabilise the rose-coloured view of science to which they’ve likely been exposed. It does them a disservice to enter research without understanding how the flawed but functional science machine really works to produce the knowledge it does. Open science, ultimately, is about cultivating a research culture in which our decisions, evidence and claims can be examined, questioned and improved.
Sarahanne Field is assistant professor at Groningen University.
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