The Damaged leaf dataset
The Damaged Leaf Dataset (DLD) is a collection of over 15,000 leaves collected in Colchester, Vermont, during the Lymnatria dispar (spongy moth) outbreaks of 2021 and 2022. The dataset is the foundational material of Phytomechatronics. Each collected leaf of the DLD was cleaned, pressed, and photographed.
DLD Gen I:
The first generation includes the leaves damaged by the Lymantria dispar caterpillar in the 2021 and 2022 outbreaks.
DLD Gen II:
Incredibly, a healthy tree that the caterpillar has defoliated in the spring will produce a second set of leaves in the same season. A collection of this second flush of leaves makes up the second generation of the DLD.
DLD Gen III:
Artworks that transform and heal the Gen I leaves make up the third generation of the DLD.
The Whole Leaf Dataset
The Whole Leaf Dataset (WLD) is a collection of whole red oak leaves collected in 2023 and 2024 after the Lymnatria dispar outbreaks of 2021 and 2022 subsided. This dataset serves as a counterpoint to DLD Gen I; its collection of leaves grew from trees that survived the outbreaks.
the athena dataset
The Athena Dataset was created through a series of paper collages. Each glyph is made of eight triangles and an octagon, and represents a fallen tower.
The Athena Dataset deconstructs the centralized concentrated power that is symbolized by tower architecture. In its simplest form, the tower is an octagon shape topped by eight slanting triangles that meet at a center point in the sky, a place of one-pointed knowing and privilege. Each glyph in the Athena Dataset is a flattening of the tower’s hierarchy, with irregular triangles and octagon parts scrambled and reassembled into new energetic circuits.
synthetic data
Synthetic data is artificially generated information created by analyzing and recombining elements from an original dataset while maintaining its core rules and characteristics. In the Athena Dataset, this process began with handmade geometric glyphs, each composed of eight triangles and one irregular octagon, which were digitized into SVG files. A genetic algorithm then generated new glyphs by treating the individual shapes as genetic components, remixing them into new compositions while strictly following the original artistic rules. This automated process expanded a limited handmade dataset into a larger collection of synthetic glyphs that preserved the authentic qualities of the artist’s work while creating enough variations to train more sophisticated machine learning systems, such as GANs (Generative Adversarial Networks). The resulting synthetic data maintained both the statistical patterns and the artistic integrity of the original compositions while generating entirely new combinations.