The voluminous discussion and conflicting language on scientific reproducibility over the past decade has obscured its meaning. But in fact, there is a very clear scientific meaning for reproducibility—one that has been thoroughly developed by brilliant minds over the past 70+ years, and routinely practiced around the world every day.
The 6th Accelerating Biopharmaceutical Development Conference (AccBio2019), hosted by AIChE from February 17-19, 2019 focused on the confluence of new therapy pathways, biologics process intensification, and using process development data to speed innovative therapies to market.
If modern software engineering worked like science, programmers would not share open source code; they would take notes on their work and then publish long-form articles about their software. Months or years later, their colleagues would attempt to reproduce the software based on the article. It sounds a bit silly, and yet even, this level of prose-based methodological discourse has deteriorated in science communication.
Given the nature of constantly evolving R&D processes, traditional LIMS systems are out of date the minute they are configured. In contrast, Riffyn SDE is designed to evolve as your process evolves. It is configured on the fly by scientists, not by IT teams. This article explains how we do it and why many companies are saying “Good-bye LIMS. Hello Riffyn.”
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.
To celebrate the ground-breaking publication of Riffyn SDE in the journal Scientific Data, Riffyn is launching Open Access which provides free use of the SDE for next-generation design and publishing of experiments.
Just a few miles away from the birthplace of microbiology, the Pronk Lab in the Department of Industrial Microbiology at Technical University Delft, has completed a complex study of yeast physiology using Riffyn SDE. This is a first-of-kind use of Riffyn SDE for scientific publishing and sharing of experimental methods and data.
OSIsoft PI Server software provides real-time data infrastructure with best-in-class abilities to collect and contextualize sensor data for insight, trends, and analytics. Riffyn now provides out-of-the-box integration with PI Server.
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.
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.
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.
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.
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.
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.