10 Open Science Myths

In our stride to change academia, we frequently face misconceptions about Open Science. Time we bust some of these myths. 

  • Open Science = Open Access

Making publications freely available to all (Open Access) is good stuff. But Open Science is about way more. It’s about making each and every part of the research cycle more open and transparent. That includes, for instance, writing out your research plans before you collect or analyse the data (preregistration) and sharing data and materials whenever possible. This way, what ends up in a publication will be more reproducible and of better use to anyone (hopefully freely) accessing it.

  • Open Science is a hype

Open Science is certainly raising a lot of excitement. But it’s not a passing hype like Tamagotchis (1995), UGG’s (2005), or sweatpants with printed text on your ass (2015). Open Science practices are getting solid ground in (inter)national policies. Have a look, for instance, at the recent position paper by Dutch public knowledge institutions and funders of research: “Room for everyone’s talent; towards a new balance in the recognition and rewards of academics“. One of their major goals: stimulating Open Science practices. Such developments signal that Open Science is here to stay and hopefully will have become the new default by 2025.

  • Open Science is about pointing fingers

Open Science is not about pointing fingers at individual researchers – the vast majority of scientists are trying to do good work. Open Science is about pointing towards ways to collectively improve our way of working. If anything, Open Science is about researchers themselves pointing other researchers and interested parties towards information about how they ran their studies and why. 

  • Open Science dismisses exploratory research

There is hypothesis-generating (exploratory) and hypothesis-testing (confirmatory) research. Both are important. Exploratory research can spot unexpected trends. It cannot, however, generate a new hypothesis and test it at the same time. Open Science does not dismiss exploratory research. It merely asks you to specify your intentions in advance, before you gather data. And if your intentions are to explore data, that’s completely fine. As long as you don’t present your research findings as confirmatory afterwards.

  • Open Science is a straitjacket

Preregistrations, in which you specify your research plans in advance (e.g., via the Open Science Framework), are a guide. A well thought-out, but not infallible guide. If advanced insights or practical issues force you to deviate from your original plan, do so. Just make sure you report on what you changed and why.

  • Open Science is the end of privacy

The privacy of research participants needs to be protected regardless, whether you practice Open Science or not. In some cases, this could mean that you cannot publicly share all of your data. However, more often than not, a large part of the data can be safely shared or at least made findable for other researchers by a good description of your dataset (metadata). 

Other Open Science practices such as sharing your analysis codes and research materials also go a long way in increasing the transparency of your research, without being a threat to privacy. 

  • Open Science means everyone will see your mistakes

This is actually true. If we share our research plans, code, and data, others might find mistakes. The myth is that this would be a bad thing. Hell yes, it can make you feel vulnerable. But know that all researchers make mistakes. And if there are better ways to do something, you should want to know. Preferably as soon as possible. Your own work, and the quality of science as a whole, will ultimately benefit from it. 

  • Open Science enables stealing of your work

Open Data means your data can be freely used, re-used and redistributed by anyone. If that happens, that’s not stealing. That’s just making good use of available data. Do note that Open Data does not necessarily mean you can’t require others to attribute your work – that depends on the license you choose. The more others cite your dataset, the more visible your work becomes. Ideas and research plans are difficult to protect, but if you submit them as a registered report, you are quite certain your work will be published if you follow through with the registered methodology – even if someone else ‘scooped’ you. 

  • Open Science is mainly pain with little gain

There’s no denying that getting acquainted with new ways of working costs time. But at the end of the day, it will also save you time. For instance, preregistration forces you to consider issues that could otherwise have bitten you in the ass afterwards (e.g., a lack of statistical power). Moreover, a detailed plan allows for a swift analysis once the data comes in. There are an increasing number of open-source tools available that will help you make your workflow more reproducible and efficient at the same time. And there’s another gain for those interested in an academic career: more and more universities and funders are seeking candidates who implement Open Science practices in their work. 

  • Open Science is an unclimbable mountain

Open Science is the umbrella term for a multitude of initiatives, which can make it hard to know where to start. The Open Science Community Groningen is there to help. We facilitate Open Science practices and connect Open Science enthusiasts. Sign up, and let’s start climbing that mountain together. 

Lead author: Jojanneke Bastiaansen

With contributions from: Daan Ornée, Rink Hoekstra, Vera Heininga

Image: pexels.com