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.
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!
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.
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.
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.
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.
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.
The Global Bioproduction Summit held in San Diego California from February 5-6, 2018 detailed the most pressing issues in biologics manufacturing. It featured 35 speakers from several leading drug development firms. A dominant theme at this conference was drug makers’ focus on reducing the time to market for new drugs. Here we outline some of the ways companies are doing that.
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.
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?
Family-Wise Error Rate (FWER) is an approach for multiple testing correction. It is a scary sounding term, but don’t be deterred. It’s simply the probability that one or more of your “family” of multiple tests is false. In my view, FDR is generally more aligned with what you want as an experimenter. But FWER offers an alternative, generally more stringent, approach to reduce your false findings.
Since the time of Newton and Galileo, the tools for capturing and communicating science have remained conceptually unchanged. But these tools are wholly inadequate for the complexity of today’s scientific challenges. Riffyn created the Scientific Development Environment to change that.