Problems of Animal Study Reproducibility & Their Solutions
10.00 - 13.00 EST | 7.00 - 10.00 PST
According to a 2012 study published by Amgen researchers, 90% of the 50+ in vivo oncology studies were found to be irreproducible. This underscores the dire need to critically examine our current standard animal study conduct processes. Researchers will walk away with an understanding of the factors which contribute to poor animal study reproducibility.
Attendees will also get to experience and see for themselves what animal study workflow software, such as Studylog v4, is like and how it can solve many of the problems of study reproducibility.
• Factors contributing to the problem of animal study irreproducibility
• Publicly available and online resources and practically applicable concepts, such as the ARRIVE guidelines, PHISPS protocols, and the N3R Design Assistant
• First-hand experiences with a variety of in-house-built and commercially available animal study workflow software applications and databases and their direct and indirect impacts on the problems contributing to animal study irreproducibility
• Hands-on exploration of and learning about Studylog Animal Study Workflow Software in the context of solving problems, improving animal study reproducibility and faithfully preserving detailed study results
for future access
Chief Executive Officer
Investigating Common Tools for Genomic Research in Cancer
14.00 - 17.00 EST | 11.00 - 14.00 PST
Computer databases are an increasingly necessary tool in the digital age and hold a wide host of relevant information. However, according to an article in Nature Education, there are upwards of 3,000 distinct genomic resources, tools, and databases publicly available with varying levels of size and complexity.
So where do you find the data you need? And how can you utilize this data to progress your preclinical studies? This workshop will answer these questions and arm you with the confidence to successfully access the data you need.
• Which database should you be using?
• How to effectively gain the information you need from a vast database
• Using this data to guide your preclinical decisions
• Combining multiple databases to understand different targets
Senior Data Scientist