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Mastering digitalization of fermentation for optimizing production
Why I Wrote This Letter? I decided to write this letter because Gencovery is increasingly working with companies specialized in fermentation — organizations that generate data ac...
The Future of Skills is driven by AI — World Economic Forum 2025 Insights
I wrote this post to share key insights from the World Economic Forum’s Future of Jobs Report 2025. I added a section explaining why AI should be separated from Big Data</stro...
Gencovery Newsletter #6 - Constellab new features
Your digital transformation guide With the Constellab data and AI platform, fully leverage your data and turn it into a strategic asset! ✅ Fast and secure digitalization ✅ An intuitive and high-perfor...
Metadata explained: meaning, examples for everyday use
Metadata are everywhere. They are essential for describing and harmonizing information, particularly in multi-source and multi-operator environments. Without well-structured metadata, information beco...
Streamlit components showcase
App to showcase the custom streamlit component developed an maintained by Gencovery and integrated in gws_core package. You will find :
Machine learning copilot
App to visualize, transform, clean table to generates statistics.
Biolector app
If you own a BioLector XT by Beckman Coulter and are looking for a way to efficiently analyze the data it generates, you’ve come to the right place! The BioLector XT is a cutting-edge microbioreactor ...
Payfit time tracking aggregator
Migrate gws_biolector to gws_plate_reader
Automatic note for Iris pipeline
Fix ubiome after migration
gws_gaia
GAIA (Gencovery Artificial Intelligence Analytics) provides core features for machine learning and deep learning modelling. It offers a broad range of AI algorithms, covering among others classification, regression, clustering methods, neural networks.
gws_ode
Brick for Ordinary Differential Equations
gws_stats
STATS (Statistical Tools Suite) provides core features for statistical analysis of biological data. It offers the most widely used statistical methods for biological data analysis to quantitatively assess your hypothesis from your data.
gws_gena
GENA provides core features to create and use actionable digital twins of cell metabolism to understand and predict cell mechanisms of action.
