Riffyn Blog

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.

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.

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.

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!

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.

As a full-time scientist you test a lot of hypotheses — a lot more than one per week. 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?

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?