Open Science

What is Open Science?

Open Science is the idea that science should be as transparent and comprehensible as possible, making it accessible to everyone. It is based on the principles of

1. Open Methodology,

2. Open Source,

3. Open Data,

4. Open Access

Sometimes the principles of 5. open peer review and 6. open educational resources are also mentioned (Kasberger, 2013).

Open Source.

Brain imaging has become a data heavy discipline. A lot of different software exists that can help researchers make sense of their data which differs in terms of licensing. On the one side of the spectrum there is software that is part of the public domain, and by that anyone is allowed to use it freely, modify it and redistribute the software and code. Then there are permissive licenses differing in how restrictive they are regarding usage, modification and redistribution. These are the licenses often used by open source software. On the other side of the spectrum there is proprietary software, which usually costs money and where all rights are reserved.

Examples of this would be Microsoft Word being proprietary, in that you have to pay for it and are not allowed to redistribute it in any way, and Libre Office, which is open source and completely free. Software not only differs in terms of licensing, but also in terms of documentation. As mentioned, open science strives to be as transparent as possible. In fMRI and in brain imaging in general a lot of complicated procedures are applied to the data in order to end up with interpretable results. This makes it even more important for researchers to understand what the used software is actually doing. This is why a thorough documentation is another important aspect of the principle of Open Source.

Open Methodology.

Open Methodology can be summed up as the effort to provide all the information necessary to reproduce a study. Although articles in scientific journals always contain a methods section, it rarely contains all the information that one would need to reproduce the study step by step (Kraker et al., 2011). One way to make this possible is to practice open notebook science. This basically means that everything that was formerly documented in the lab notebook (e.g., experimental procedures, measuring parameters, code etc.) will be made public, usually in an electronic form (“Open-notebook science”, 2019). In a discipline where there is a lot of data processing, it is important to share the analysis scripts, so that the data analysis can be made even more transparent. Another aspect that falls into the area of Open Methodology is that of preregistration. Preregistration means that a study is registered at a journal before it is conducted. The journal guarantees publication of the study, regardless of significant effects being found. This approach is there to prevent publication bias (also termed the “file drawer problem”; Rosenthal, 1979), bad research practices such as p-hacking and cognitive fallacies that a researcher might fall into (Nuzzo, 2015).

Open Data.

Research is only maximally transparent if all data are shared as well. As in neuroimaging there are large amounts of complex data, the question for the last years has been in what way imaging they should be shared. Today there are a number of platforms that a researcher can choose from to upload their data. Some of them are more curated than others, allowing only data in a specific format to be uploaded.

Uploading data from participants also brings some legal issues, which will also be addressed in the chapter on Open Data. These questions revolve around the anonymization of imaging data and what else is necessary in order to publish them.

Open Access.

Journals with Open Access enable everyone to profit from the results of research. There are different kinds of Open Access journals, namely green Open Access (journal allows the publication of preprints) and gold Open Access (the peer-reviewed version of the article is accessible by everyone).

Please have a loot at the paper by Poldrack et al. for a thorough discussion on what you can do as a neuroscientist (Poldrack, 2017).

Resources

Kasberger, S. (2013). Open Science. Retrieved July 23, 2019, from OpenscienceASAP website: http://openscienceasap.org/open-science/

Kraker, P., Leony, D., Reinhardt, W., & Beham, G. (2011). The case for an open science in technology enhanced learning. International Journal of Technology Enhanced Learning, 3(6), 643. https://doi.org/10.1504/IJTEL.2011.045454

Nuzzo, R. (2015). How scientists fool themselves – and how they can stop. Nature News, 526(7572), 182. https://doi.org/10.1038/526182a

Open-notebook science. (2019). In Wikipedia. Retrieved from https://en.wikipedia.org/w/index.php?title=Open-notebook_science&oldid=902373988

Poldrack, R., Baker, C., Durnez, J. et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci 18, 115–126 (2017). https://doi.org/10.1038/nrn.2016.167

Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638-641. http://dx.doi.org/10.1037/0033-2909.86.3.638