Open science, or how it should be the default
Note: This is an opinion piece written for the Department of Nutritional Sciences (University of Toronto) newsletter NutriNews. Link to the piece will be put up after the newsletter is published.
Open source. Open access. Open data. You may or may not have heard these terms, but these all encompass ‘open science’. So what are they and why are they important?
Before we get into open science, it’s important to go back to what modern science is. Science is the process of coming up with an idea, collecting data to test that idea, and comparing the data to the idea to see if reality matches the idea.
However, science is increasingly become more difficult to do. Studies cost more, take longer, and are more complicated, in addition to less funding available and more societal pressure to produce impactful and relevant research. Science needs to adapt to these changes, and the open science movement is one such adaptation.
Modern science is intrinsically a collaborative process. While light competition is beneficial for all human activities, the type of competition usually seen in business and industry is particularly harmful to science. In order to progress, research teams need to understand what other groups are doing so that they may replicate and expand on each others work.
The challenges that we all currently face (climate change, prevention diseases) are massive and complicated. No individual scientist or research group can possibly hope tackle them alone and the only solution is to collaborate. Using open science tools, researchers around the globe can work together to solve these problems. There are three main tools in open science: open access, open source, and open data.
Open access is ensuring that any published scientific article is freely available to the public, immediately. Currently, most journals charge a subscription fee (and gain a massive profit) that can impede progress. Interestingly, open access articles tend to also have higher citation rates, a metric commonly used to judge scientists work and funding. This is an argument for more scientists to use open access and yet it is still not the standard among researchers.
Open source is providing the code used to analyze the data and present the results in the journal article. This allows researchers to not only check to make sure the code and analysis were correct, but it also allows researchers to use that code to conduct their own analysis. This in turn makes research better since more eyes are on the code and gets it published sooner because the code isn’t written from scratch. Open source software such as R or Python, two commonly used languages in analyzing scientific data, have mechanisms in place that allow easy sharing of code.
Open data is putting the data collected from a study online, available for use by the research community. This is particularly important for tackling epidemics such as the Zika or influenza viruses. Other researchers working on the same topic can use that data to help them better understand the problem.
As you probably noticed, the common thread between these three tools is that everything is easily available, transparent, and encourages (re-)use. They reduce the time it takes for other researchers to confirm recently published findings, incorporate them into the current knowledge, and use that to help inform or tackle pressing problems.
We as society are facing challenges that require an adaptation. The traditional way of doing science is not enough to keep up with the current demands. We need to evolve and using open science tools is a step in the right direction.