Riffyn Blog

Category: Process and Assay Improvement


How a digital backbone can transform industrial fermentation
Joseph Walker

While the industrial applications of fermentation continue to grow, underlying challenges to scaling up this fundamental process still remain. At the core of this lies a common challenge: a reliance on manual data collection and paper forms and notebooks, ad hoc process and parameter documentation, Excel spreadsheets with multiple versions that are difficult to track, inflexible ELNs and LIMs, and disparate data files from multiple instruments that are difficult to impossible to format and contextualize for statistical analysis. But there's hope: here's how a digital backbone can transform industrial fermentation.

GitHub for Science
Timothy Gardner

Designable process flows can serve as the source code for experiments and a foundation for a GitHub for science. In this post, we illustrate what we mean by “GitHub for Science” and how this could transform the way we advance scientific idea to manufactured products.

Source Code for Science
Timothy Gardner
Like people, ideas need to romance each other: they meet, mix and produce offspring. But sadly, the breeding of scientific ideas is being shortchanged. We need a new kind of dating app: a dating app for nerdy ideas. We need source code for science.
Data Are Like Ikea Furniture: 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!

The Four Pillars of a Digital Ecosystem for R&D
John Conway
Talk of digital transformation is in the air. But is it all hype? Why does it seem so hard to achieve? In this blog post, John F. Conway, Riffyn’s Commercialization Officer, shares what Riffyn has learned about the four pillars that are essential in order to digitally transform an R&D organization.

Where's the fanFAIR?
Timothy Gardner
Choosing to create a FAIR (Findable, Accessible, Interoperable, Reusable) laboratory environment seems obvious. Who wouldn’t want to be able to find and reuse their data? But it’s not as easy as it sounds. The transformation requires scientific technical backbone and knowhow.
What Does "Reproducible Science" Really Mean?
Timothy Gardner
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 and routinely practiced around the world every day.
Proactive and Reactive Strategies to Harness Your Data
Jack Morel
Data governance and data cleaning both fall under the category of “getting data ready for analysis," but the main difference is how and when these methodologies are applied.

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