Brandon Bucher, Head of Research at ADInstruments, shares best practices, technical considerations and expert advice on how to avoid common data acquisition system and data analysis mistakes in order to produce higher quality data.
There are many variables to consider when designing a preclinical research experiment, and while scientists plan their studies to a high degree, the critical function of digital data acquisition and analysis systems is often overlooked. The technical nature of this subject can be daunting, demanding time and head-space from extremely busy investigators; however, understanding the essential elements of digital data acquisition and analysis is fundamental to ensure high quality experimentation translates to high quality results.
In this webinar, Brandon Bucher, Head of Research at ADInstruments, lifts the veil on common, costly and harmful data acquisition and analysis errors, showing how to avoid them in order to produce optimal data and quality results. He covers key concepts such as setting up your data acquisition system correctly, proper signal conditioning, artifact rejection and preparing data for analysis, exporting data and how elements of your experimental protocol may impact decisions made at each stage.
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