Riffyn Blog

Category: Process and Assay Improvement

Your Assay Results May Be Wrong
Timothy Gardner
Researchers often take assay data at face value, assuming it’s accurate because it came out of an instrument. Here we explain why that can be dangerous, and how assay validation can make your science faster and better.
A Better Way to Hit-Pick Strains, Enzymes, and Anything Else
Timothy Gardner
Emphasis is placed on collecting and analyzing screening data to identify hits, but once hits are chosen, the selection criteria used to define hits is often lost. Riffyn allows users to associate decision criteria with hit data, enabling improved selection processes and faster product development.
Shaping Scientific Data is Like Growing a Square Tree
Riffyn Team
Scientific data are multi-dimensional with many complex relationships between samples, devices, and systems. Lab data look more like a tree with a network of relationships between data points than the two-dimensional “square” we need it to be in for machine learning and statistics.
Design of Experiments (DoE) Made Easy
Timothy Gardner

If you want to perform DoEs on your system but don’t know where to start, or if you regularly perform DoEs and are looking for a simple way to import your designs to Riffyn Nexus, check out this blog explaining DoEs, why they're powerful, and how DoE designs created in JMP® can easily be pushed to Riffyn Nexus.

Data is Like Ikea Furniture, It’s Best Shipped Flat
Loren Perelman
The key to analyzing data faster and more accurately is in the way we record and organize our data. Loren Perelman shows us the best way to organize our data for analysis and just might change your perspective on what constitutes a beautiful data table!

What is Machine Learning?
Timothy Gardner
If you’re confused about the differences between machine learning, AI, deep learning, supervised learning, or unsupervised learning, you’re not alone. This blog explains the differences, and reveals that you may have even been doing machine learning for years without even knowing it.
The Importance of Measurement Systems
Loren Perelman
Your science is only as good as your ability to measure outcomes. Where should effort be placed in measurement system development? Here are the key drivers that make your measurement capability work for you,enabling maximum throughput without sacrificing data quality or needlessly increasing costs.
Be Your Own Data Scientist — It’s Easier Than You Think
Riffyn Team
If you have assumed that data science is only for data scientists, then you're probably missing some important discoveries. Laura Jennings-Antipov takes us on her personal journey from bench scientist to discovering the power of data and machine learning in science.

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Better Integration with More REST - A Riffyn Nexus API Upgrade

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