Guest article by Prof. Dr.-Ing. Tobias Knopp* , Institute for Biomedical Imaging
Openness and science have been closely connected since the beginning of mankind. The goal of science is to discover new insights based on existing knowledge. Accordingly, access to knowledge is absolutely essential to avoid having to reinvent the wheel over and over again. Despite this obvious connection, openness does not seem to be a given in all branches of science. This is also evident from the fact that the term Open Science had to be established in the first place.
The bad news right at the beginning: Open Science involves effort. This is because in an Open Science publication, not only the methods and results are published in the form of a manuscript. In addition, as many building blocks as possible are provided to enable other researchers to replicate their own results. Two essential components here are software and data, which are well documented and published in a structured way. In the case of experimental work, this can also include audio or video recordings documenting the experiments. The compilation and preparation of all documents and data naturally means more work.
Every scientist wants their own research results to be noticed by other scientists. By making code and data openly available, we make it much more attractive for other researchers to engage with our research. So, quite obviously, we are increasing the quality of research.
Reproducibility is the key element that makes a research result a knowledge gain in the first place. Through an open publication approach, the hurdle to reproducing research results becomes much lower for other researchers.
Research becomes much more sustainable. If scientists want to validate their own results 20 years after publication, the code and data provided can be used for this purpose.
These three points clearly show that it is worthwhile to implement Open Science. They also illustrate that the same care should be taken in preparing data and program code as in the actual manuscript. Rather, the object of research should be understood in its entirety as a mixture of different media.
It is also worthwhile here to take a look at open software development, which has significantly advanced society in recent decades. Open software allows scientists worldwide to understand how programs work and thus both identify bugs and initiate further development. These advantages have created a unique development model in which people collaborate with each other in a flexible way, regardless of space and time, even across organizational boundaries.
Science itself has also benefited enormously from the open source movement, as a large proportion of research results are based on open software. For example, the field of machine learning is based on open software packages such as PyTorch and TensorFlow and is used in most scientific fields today. It is therefore only logical that scientists recognize the advantages of an open approach to research and thus increase the usability of the results for mankind.
For me, the link between open source and open science also has very practical advantages. We develop our research software directly in the form of an open source project in which new algorithms are directly incorporated. This creates a coherent software infrastructure across publication boundaries, which is useful for myself as well as for other researchers.
I hope my contribution has made clear that Open Science is an important topic in today’s science. To get started, I recommend a small project to understand the process and see the benefits for oneself.
Open Science at the Institute for Biomedical Imaging in Hamburg
The Institute for Biomedical Imaging at UKE and TUHH is committed to open science in research. In this video (German), they give an overview of the areas in which they pursue Open Science.
*Tobias Knopp was born in 1982 and studied Computer Science from 2002 until 2007 at the University of Lübeck. He received his PhD in 2010 from the University of Lübeck as well, where he worked on the tomographic imaging method Magnetic Particle Imaging (MPI). The thesis was awarded with the Klee award from the DGBMT (VDE) in 2011. In 2011 he joined the company Bruker Biospin to work on the first commercial MPI system. From 2012 until 2014 he worked at the company Thorlabs in the field of Optical Coherence Tomography (OCT) as a software developer. Since 2014 he is a Professor for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology. For his contributions in the field of Open Science he received the Hamburg Open Science Award in 2020.
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