DuPont Pioneer Announces Roll-Out of Global Genotypic Data Management System to Accelerate Agriculture Solutions for Farmers
News Sep 22, 2013
DuPont Pioneer and UNIConnect have announced the global roll-out of a lab information management system to help Pioneer accelerate the development of new plant products with higher yields and stronger resistance to pests and diseases.
“Incorporating technology provided by UNIConnect offers flexibility and reliability in our lab information management system, while also supporting our critical global information management implementation,” said John Arbuckle, DuPont Pioneer senior director, Global Marker and Research Information Technologies. “This is helping to sustain our leading position in generating and applying genetic information for plant development scientists who are improving key crops around the world. Integrating this information technology platform into our existing proprietary solutions and processes expands our global production genotyping system capability.”
Now live in eight locations located across four continents, individual global marker technology sites have autonomously-functioning Laboratory Information Management System (LIMS) operations. Arbuckle noted that the project is capturing millions of data points each day and already delivering enhanced performance reliability, traceability and reliability of results. Running in parallel, the new system operates as a federated network – meaning sites can operate independently or as a global network.
“We are proud to provide information technology to a world leader in plant genomics and crop science,” noted William S. Harten, UNIConnect founder and chief executive officer. “Our technology began in the realm of plant genomics R&D, and we are gratified to witness the increasing adoption of UNIFlow by great agricultural businesses like DuPont Pioneer.”
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.