We present a guest post from researcher Falk Lüsebrink highlighting the benefits of data sharing. Falk is currently working on his PhD in the Department of Biomedical Magnetic Resonance at the Otto-von-Guericke University in Magdeburg, Germany. Here, he talks about his experience of sharing early MRI data and the unexpected impact that it is having on the research community.
Early release of data
The first time I faced a decision about publishing my own data was while writing a grant proposal. One of our proposed objectives was to acquire ultrahigh resolution brain images in vivo, making use of an innovative development: a combination of an MR scanner with ultrahigh field strength and a motion correction setup to remediate subject motion during data acquisition. While waiting for the funding decision, I simply could not resist acquiring a first dataset. We scanned a highly experienced subject for several hours, allowing us to acquire in vivo images of the brain with a resolution far beyond anything achieved thus far.
When our colleagues saw the initial results, they encouraged us to share the data as soon as possible. Through Scientific Data and Dryad, we were able to do just that. The combination of a peer-reviewed open access journal and an open access digital repository for the data was perfect for presenting our initial results.
17,000 downloads and more
‘Sharing the wealth’ seems to have been the right decision; in the three months since we published our data, there has been an enormous amount of activity:
- over 90 tweets sharing the manuscript with 70,000 followers
- more than 17,000 downloads of the data from Dryad Digital Repository
- a video on YouTube showcasing the data
- inclusion in the list of neuroscience databases on Wikipedia
- inclusion in BrainBox, a prototype framework for real-time collaboration in neuroimaging allowing people to visualize, segment, and annotate any brain MRI dataset using a web application
- and a first citation (in NeuroImage)
A distinct need for data re-use
MRI studies are highly interdisciplinary, opening up numerous opportunities for sharing and re-using data. For example, our data might be used to build MR brain atlases and illustrate brain structures in much greater detail, or even for the first time. This could advance our understanding of brain functions. Algorithms used to quantify brain structures needed in the research of neurodegenerative disorders could be enhanced, increasing accuracy and reproducibility. Furthermore, by making available raw signals measured by the MR scanner, image reconstruction methods could be used to refine image quality or reduce the time it takes to collect the data.
There are also opportunities beyond those that our particular dataset offers. A recent emerging trend in MRI comes from the field of machine learning. Neuronal networks are being built to perform and potentially improve all kinds of tasks, from image reconstruction, to image processing, and even diagnostics. To train such networks, huge amounts of data are necessary; these data could come from repositories open to the public. Such re-use of MRI data by researchers in other disciplines is having a strong impact on the advancement of science. By publicly sharing our data, we are allowing others to pursue new and exciting directions.
Download the data for yourself and see what you can do with it. In the meantime, I am still eagerly awaiting the acceptance of the grant application . . . but that’s a different story.
The data: http://dx.doi.org/10.5061/dryad.38s74
The article: http://dx.doi.org/10.1038/sdata.2017.32
— Falk Lüsebrink