Agilent, Bruker Enter Chromatographic Data System Agreement
News Jun 21, 2014
Agilent Technologies, Inc. and Bruker Corp. have announced a joint agreement to provide customers with more freedom of choice when choosing a chromatography data system. The companies will exchange information for the development of instrument drivers for their respective gas chromatographs (GC).
With the development of these drivers, both companies’ GC systems can be interfaced and controlled by either Agilent’s OpenLAB Chromatography Data System (CDS) EZChrom Edition, or Bruker’s Compass CDS™ chromatography data system. This exchange will enable customers to preserve their investments in workflows and operating procedures.
Later this year, OpenLAB CDS software will be able to support Bruker’s 3000- and 400-series gas chromatographs, including Bruker’s latest SCION 436 and 456 models. Likewise, Compass CDS™ software drivers will support Agilent’s 5890, 6890, 7890 and similar gas chromatographs.
Both Agilent and Bruker plan to have these drivers available by the end of 2014.
“Agilent continues to offer flexible solutions for all of our customers’ needs,” said Bruce von Herrmann, Vice President and General Manager of Agilent’s Software and Informatics Business. “We are a leading proponent of open systems for the laboratory, and this collaboration with Bruker increases the availability of open system options for our customers.”
“Bruker is dedicated to providing our customers with the flexibility and versatility they need to address modern analytical challenges. This agreement will serve to open up the choices laboratories have to select the preferred solutions that meet their needs.” said Joe Anacleto, Bruker Daltonics Vice President of the Applied, Industrial and Clinical Research Business Unit.
Google has signed an agreement to join CERN's openlab program. openlab is a public-private partnership with companies and other research organizations to develop information and communication technology (ICT) solutions. Google wants to explore possibilities for joint research and development projects in cloud computing, machine learning, and quantum computing.
With machine learning systems now being used to determine everything from stock prices to medical diagnoses, it's never been more important to look at how they arrive at decisions. A new approach out of MIT demonstrates that the main culprit is not just the algorithms themselves, but how the data itself is collected.