Lab Informatics Industry News and Views for January 2021
UPCOMING EVENT: Astrix Webinar - Toolkit for Building a Successful Digital Transformation Strategy

January 20th 1:00 PM EST / 10:00 AM PST

One of the core challenges for today’s laboratory scientists is keeping up with the overall picture of data generated, especially as the data extends beyond and across the set of current laboratory data management systems. The old model of disconnected systems such as LES, LIMS, ELN and SDMS used at various places along the development process is being replaced by a more integrated approach. In this webinar, we will take an in depth look at the approaches, tools, and common pitfalls to enable you to break down the barriers and accelerate your digital transformation journey.

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Innovative Solutions and Enabling Technologies Webcast Series: Revolutionizing Scientific Laboratory Informatics

Astrix is bringing together innovative informatics solutions providers from across the industry that are at the forefront of technology to present cutting edge solutions that will enable the transformation of your laboratory into the digital lab of the future. Join us for this unique series to learn more about these revolutionary advancements in laboratory informatics. Upcoming sessions include presentations from BioIT Solutions, Riffyn and Agilent Technologies.

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Case Study – Waters Empower Custom Field Development and Validation

This project involved one of the world’s fastest-growing pharmaceutical compa­nies with an established commercial presence in over 100 different countries and annual revenue of around 15 billion dollars. In order to support its growing product line, the company was interested in engaging a qualified organization to assist with the development and validation of Waters Empower Chromatography Data System (CDS) custom fields. Over 160 custom fields and 200 reports needed to be developed and validated.

Astrix On-Demand Webinar “Avoiding outcomes bias and driving quality decisions in biologics development” – Presented by IDBS

Automation and parallelization are creating more data that teams sift through, often manually, to make decisions against. To achieve this, scientists are adopting compensating behaviors and making decisions partly based on prior outcomes. This is called “outcomes bias”; it is the equivalent of running a red light getting to work to cut the time of your commute, then making running red lights your daily commuting strategy for the next year and assuming it will always turn out the same!

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