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20/15 Visioneers Virtual Conference

October 11-14, 2021

The World's First Cloud-Lab Oriented Virtual Conference

OCT. 12TH, 12:30–1:00 PM
Addressing the Need for More Effective Analytical Data Processing and Management in HTE
Nikki Dare

High throughput experimentation (HTE) has transformed the pharmaceutical industry in recent years. The driving force being a desire to increase productivity, profitability, and shorter discovery-to-commercialization timelines. Furthermore, HTE has significant implications for data science technologies such as machine learning (ML) and artificial intelligence (AI) due to the generation of large amounts of data that can be used to feed models.

While there has been significant investment in robotics and automation for HTE, processing and review of related analytical data is still a largely manual process. As a result, scientists spend too much time on tedious, error-prone tasks using several different software packages. Being able to have direct association of analytical data with array design and plate location, automating the processing of analytical data, and visualization of analytical results and trends for fast, confident decisions is therefore of great interest in the HTE community. Additionally, the ability to export normalized experimental and analytical information is key for further utilization of HTE data by data scientists.

This presentation will examine analytical data management challenges in HTE and present a solution to help address them.