Galderma R&D Adopts Agilent Technologies’ Electronic Lab Notebook
News Feb 09, 2010
Agilent Technologies, Inc. and Galderma R&D announced that Galderma has adopted the Agilent Electronic Lab Notebook (ELN) to automate scientific data management at its main R&D centre, the largest facility of its type devoted to dermatological research and development.
“We involved our end users in demonstration sessions based on our workflow for chemistry,” said Catherine Parigot, project leader, Information Management Department, Galderma R&D in Sophia-Antipolis. After comparison with the leading solutions on the market, the user choice of Agilent ELN was unanimous. This was based largely on the ergonomics of the interface and the intuitive navigation through the different functions. Agilent was also able to demonstrate integration with existing information systems, such as our SDMS and our molecular registration system.”
“Electronic lab notebook systems is a very competitive category and we are extremely pleased that the Galderma team recognized the efficiencies, ease-of-use and high-level of support of the Agilent ELN,” said Debra Toburen, Agilent senior product manager, Informatics. “This is just the latest example of the rapid growth of the Agilent ELN product line.”
The Agilent ELN’s open architecture is designed to simplify and accelerate the R&D process by streamlining data capture, speeding data search and retrieval, and eliminating redundant data entry. The system facilitates cross-team collaboration and easily integrates with existing processes and procedures. Agilent ELN contains safeguards for intellectual property and maintains record traceability.
“The use of the intrinsic Analytical Request Module (ARM) to manage our analytical workflow will enable us to replace our existing LIMS application,” Parigot added. “The scope of this initial project will therefore provide an ELN to our chemists and our analysts and trace all the interactions between these two groups.”
An artificial intelligence (AI) approach based on deep learning convolutional neural network (CNN) could identify nuanced mammographic imaging features specific for recalled but benign (false-positive) mammograms and distinguish such mammograms from those identified as malignant or negative.
8th Edition of International Conference and Exhibition on Separation Techniques
Jul 29 - Jul 30, 2019
International Women Health and Breast Cancer Conference
Jul 03 - Jul 05, 2019
2nd International Conference on Biological & Pharmaceutical Sciences
Jul 12 - Jul 13, 2019