By Scott Edmunds, Executive Editor at GigaScience.
The most common cause of heart attacks is coronary heart disease. Diagnosis is key in both the treatment and prevention of such events. One useful tool in the fight against this killer is magnetic resonance imaging, which allows the direct examination of blood flow to the myocardium of the heart.
However, for this perfusion analysis technique to work, it needs to compensate for the respiratory motion of the patient, which can only be done through complex image processing methods. Thus, there is a need to improve these tools and algorithms. The key to achieve this lies the availability of large publicly available MRI datasets to allow tests, optimisation and development of new methods.
Published yesterday in the Open Access and Open Data Journal GigaScience, researchers from Universidad Politécnica de Madrid in Spain and the National Institutes of Health in the USA provide a fantastic example of open data sharing to help build these exact tools, a wealth of patient imaging data. Better still, to enable reproducible comparisons between new tools, the researchers and journal have taken the unusual step to publish and package the data alongside the software required to run the experiments. This is available to download from GigaScience’s GigaDB database as a “virtual hard disk” that will allow researchers to directly run the experiments themselves and to add their own annotations to the data set.
As one potential user of these resources, Professor Alistair Young, Technical Director of the Auckland Magnetic Resonance Research Group commented: “Very large amounts of medical imaging data have now become available through registries and large population studies. Well-validated, automated methods are required to derive maximum benefit from such resources.
"The paper by Wollny and Kellman exemplifies how data and algorithm sharing can advance the field through a platform by which existing methods can be tested and new methods validated against current benchmarks. Such benchmark datasets are essential to advance the field through objective metrics and standards.”
With everything wrapped up in a Virtual Machine, things were now simpler for the scientific peer-review and publication process, as the settings, packages and file locations were already set up in a working configuration. One of the people who carried out this test process, Dr Robert Davidson Data Scientist at GigaScience, stated: “Testing the code during review is sadly almost a novel concept and one that needs to roll out as a standard - but even more: if it's easy for the reviewers, it's easy for the community to use too.”
As well as being important for improving the diagnosis for the world's number one cause of death, the rise in recent retractions of published scientific articles makes essential the addition of direct means to improve article reproducibility, both for the ability to be able to trust current findings — on which future studies are built — and to prevent the loss of public confidence in the research communities it funds. Publishing a virtual machine and an interactive and executable publication provides an example to the scientific community, and serves as a test case which demonstrates a potential new type of scholarly output.