And the difference in costs is substantial. Researchers needing to analyze their data are not holding up our equipment and thus hindering new research. The data can be accessed at any time and in any place. “The virtual machine, on the other hand, has none of these disadvantages. Thirdly, these machines are very costly in both purchase and maintenance.” Secondly, when researchers use the machines for analysis, it cannot be used for research. Firstly, the data is only accessible in one physical place. “Some universities are looking for answers in creating a large cluster of local computers that could deliver the necessary computing power. Jack Fransen explains that everyone in his field is looking for faster and cheaper ways to process the huge amount of data they collect. For the past five months or so, we have been using DRE to analyze data on different virtual machines in parallel, and the results are very promising." I'm enthusiastic about the new possibilities of Azure DRE 2.0. "The first version of Azure DRE did not have the computing power to process these microscopic images. On a high-end PC, this can take up hours to days.” New possibilities The data in these images need to be averaged and fitted using special algorithms on a pixel-by-pixel basis to calculate one final image that we can work with. “You’re talking about computing power to be able to process some 100,000 images. To give you an idea of how large: I remember traveling with a hard drive that contained three images – a terabyte each – from the United States.” I had to physically send data on a hard drive to collaborating researchers. He started working with the Digital Research Environment (Azure DRE) when it was first launched in 2017, but at the start, it could not serve his needs. Celluar biologist Jack Fransen is a manager at Radboudumc’s Technology Center Microscopy.
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