DankeSuper is a cannabis and psychedelic experiential data science collective headquartered in Brooklyn, New York adjacent to Domino Park & the Williamsburg bridge. Our network of partners reflect over a century of cumulative experience across Botany, Mycology, Cannabis, Psilocybin, Culinary and Data Science fields. As an organization, we have dedicated over five years towards product development, navigating complex regulatory environments, and building a community around cannabis & mushrooms. Our mission is to leverage our intimate experience to contribute to development of an accessible, transparent, navigable, safe marketplace for previously illicit medicinal plants. 

Empowered by our local & online community, DankeSuper merges experiential data with cutting edge research in the cannabis & psychedelics to create a higher level experience. Our small batch infused, artisanal edibles have served as the focal platform of our platform since our inception in 2019. In 2022, we opened a “Cannabis Pop Up” in South Williamsburg which has since undergone constant metamorphosis in its evolutionary path our flagship lab. In cannabis and mushrooms, strain doesn’t necessarily explain or predict experience. It’s true across Maitake, Blue Meanie, Penis Envy, Runtz, Sour Diesel, tomatoes, or strawberries. Upon isolating for environmental factors, consistency in the plant or mushroom experience is conditional on genetics. In a 2017 Cannabis Now interview, Sherbinski describes how Gelato started with 200 phenotypes numbered one through two hundred. Typically, a grower preserves a few mother plants while determining the optimal genetics to offer patients.

In the case of Gelato, Sherbinski presented 25 different “cuts” at a legendary dinner. The numbers #41, #33, #25, etc. were numbers marked on the pot of each original phenotype’s grow. As Sherbinski’s sampled Gelato, our founder found himself immersed in a completely different type of number set building models for complex derivatives portfolios. Working across markets as varied as interest rates, crude oil, electricity, precious metals, and equities he understood the risk drivers behind an asset could be decomposed into a set of systematic ‘explanatory’ factors and idiosyncratic risk.