Riffyn SDE published in a ground-breaking study in the journal Scientific Data

Riffyn SDE was created to address the crisis in scientific reproducibility, and demands for more cost-efficient drug development. Its potential to transform the quality and reusability of scientific work was recently demonstrated in a study published in Scientific Data by Delft University of Technology that harnessed Riffyn SDE to execute a complex set of microbial fermentation experiments.

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Timothy Gardner
R&D Data as a Competitive Advantage

In recent years, several pharmaceutical companies have invested millions of dollars in collecting and storing ever increasing amounts of R&D data. However, these companies will only see a return on their investments if they can determine how to use these data to create a sustainable competitive advantage. Riffyn’s Doug Williams walks us through a use case of how data could be used to create a competitive advantage in R&D and explains where to start if you want to begin using all those data you collect in the most effective way.

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Douglas Williams
Riffyn Spotfire Starter Template

Tibco Spotfire is one of the most popular visualization applications within R&D organizations. Many Riffyn customers use it as their primary data visualization software.  To support these efforts, we have created the Riffyn Spotfire Starter Template to serve as a starting point for creating dashboards that import Riffyn data as a Data Tables.


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Douglas Williams
Your Assay Results May Be Wrong

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.

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Timothy Gardner
A Better Way to Hit-Pick Strains, Enzymes, and Anything Else

Emphasis is often 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, even though the decision criteria themselves are an important factor in optimizing screening assays. Riffyn allows users to directly associate decision criteria with hit data to enable improved selection processes and ultimately smarter, faster product development.

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Timothy Gardner
Shaping Scientific Data is Like Growing a Square Tree

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” (a.k.a., row vs. column table) we need it to be in for machine learning and statistics. Doug Williams explains to us how Riffyn solves this problem to deliver data you can use “out of the box”, and how this is driving a new approach to scientific discovery.

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Douglas Williams
Design of Experiments (DoE) made easy

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, check out this article by Tim Gardner. Tim explains what a DoE is and why it’s a powerful technique for optimizing multiple parameters simultaneously. He also shows, via a short video, how DoE designs created in JMP® can easily be pushed to Riffyn to create an experimental template with optimal combinations of parameters.

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Timothy Gardner
Data is Like Ikea Furniture, It’s Best Shipped Flat

The key to analyzing data faster and more accurately is in the way we record and organize our data. It turns out those pretty tables we all make in Excel—the ones where data are categorized using merged cells, and average values are displayed—are going to cause you problems in the long run. 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!

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Loren Perelman
What is Machine Learning?

If you’re confused about the differences between machine learning, AI, deep learning, supervised learning, unsupervised learning, etc., you’re not alone. Tim Gardner explains the differences between all these terms, and reveals that you may have even been doing machine learning for years and not even known it.

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Timothy Gardner
Riffyn Introduces its Tableau Web Data Connector

Given the tremendous popularity of Tableau as a data visualization and analysis tool, we are super excited to introduce the Riffyn Web Data Connector (WDC) for Tableau users! It allows Riffyn users to securely transfer Riffyn-generated data tables directly into Tableau for immediate analysis and visualization. All you need is a web connection. No need to mess with files.

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Douglas Williams
The Importance of Measurement Systems

Your science is only as good as your ability to measure outcomes. Where should effort be placed in measurement system development? Here we outline the key drivers that make your measurement capability work for you and enable maximum throughput without sacrificing data quality or increasing costs needlessly.

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Loren Perelman
Process Models are Leading the Way in Bioprocess Development

The BioProcess International West conference, held in San Francisco CA from March 19-22 2018, reinforced the state of the bioprocess industry for biotech companies looking to accelerate and intensify their manufacturing processes. The most consistent theme that emerged was the importance of establishing data-driven process models across each of the functional areas within process development. Here we discuss ways companies are taking action towards their goals.

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Douglas Williams
False Discovery Rate: The Most Important Scientific Calculation You Never Do

As a full-time scientist you test a lot of hypothesis—a lot more than one per week. Every sample or condition you compare in an experiment is a hypothesis test. If you test tens or hundreds or even thousands of samples in a single experiment, it’s guaranteed that you’ll find many false positive results, in every experiment. So what can you do about it? You can calculate your False Discovery Rate (FDR), which tells you what fraction of your accepted tests are false.

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Timothy Gardner Comment
Understanding False Discovery Rate

FDR is a very simple concept. It is the number of false discoveries in an experiment divided by total number of discoveries in that experiment. But there is a problem, you never know how many discoveries are actually real or false when you accepted them. So how do you estimate FDR from your data?

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Timothy GardnerComment