Zug/Hamamatsu - Hamamatsu Photonics is integrating the Jetraw technology developed by Dotphoton in its camera systems for research purposes and the pharmaceutical industry. Jetraw works by compressing image data to a greater extent than has ever previously been achieved. This solves problems related to storage volumes and costs.

The Zug-based firm Dotphoton has announced that it has agreed a strategic partnership with Hamamatsu Photonics. The globally leading Japanese manufacturer of optical systems and photonics for scientific purposes is to use the Jetraw raw data compression technology developed by Dotphoton to significantly reduce the ever-growing volume of biomedical image data.

For applications in the field of scientific research and the pharmaceutical industry such as light sheet microscopy, high-throughput screening and histopathology, the generation of large amounts of data can cause some real headaches. The two partners published identical press releases, in which they mention problems in this context including lower scalability, high costs and the complexity of the required IT infrastructure.

“In industry and academia, storage budgets grow exponentially every year, the increase of data center costs and its CO2 impact reduce the amount of resources available for research”, comments Eugenia Balysheva, CEO of Dotphoton, in the press release, before adding: “We have built Jetraw to address all these problems at once, and are very happy to partner with Hamamatsu Photonics”.

One of the users of both technologies in the Oates Group at the Swiss Federal Institute of Technology in Lausanne (EPFL). “Working in developmental biology we need to acquire large time-lapse movies to capture the dynamics of the growing zebrafish embryo”, explains Arianne Bercowsky, a member of the Oates Group. “We struggle to interact with our tera-byte size data due to slow data transfer and expensive storage. With Jetraw’s compression (from 913 GB to 115 GB per movie, as an example) we solved these issues with the perk of not losing any information on the data even when performing our routine analysis”, she adds.

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