BuckAI Open-Source Handbook

Turn your research code into a citable, installable, peer-reviewed open-source package. A step-by-step walk-through from repo hygiene to a Zenodo DOI (30 min), pip install + Docker (1–2 days), and a peer-reviewed JOSS paper (2–4 weeks). Includes chapters on mixed open/closed training data, AI-assisted development with JOSS disclosure requirements, and a common-pitfalls appendix.

Quarto - Open Source - JOSS

Maintainer: Moortgat

BuckAI HPC Practicum

A 1-credit, self-guided course that prepares graduate students to run research code on real HPC clusters. Covers SSH setup, shell environments, job scheduling with Slurm, environment management, and right-sizing computational resources, culminating in a reproducible capstone project.

Quarto - HPC - Slurm

Maintainer: Moortgat

AI/ML for Astronomy — NASA Cosmic Origins STIG

A 25-session open-source lecture series across 9 modules from the NASA Cosmic Origins Program AI/ML Science and Technology Interest Group (AI/ML STIG), co-chaired by BuckAI SAB member Yuan-Sen Ting. Covers deep learning frameworks (PyTorch / JAX), neural network architectures (CNNs, GNNs, Transformers, RNNs), physics-inspired and equivariant networks, generative models (normalizing flows, diffusion, flow matching), large language models and autonomous agents, reinforcement learning, and simulation-based inference. Free and open to everyone — no prerequisites or institutional affiliation required.

Lecture Series - AI/ML - Astronomy

Co-chair & instructor: Yuan-Sen Ting (BuckAI Observatory SAB · OSU Astronomy)

sentinel-sarawak

A 10 m per-pixel archive of canopy disturbance across Sarawak, Malaysian Borneo (2015–2024, Sentinel-2), classifying each event as bare-soil, canopy, or wet-substrate clearing. Captures selective-logging and plantation-maintenance events that fall below the 30 m Landsat threshold used by Hansen Global Forest Change and JRC Tropical Moist Forest. Includes the labeled archive (194 M pixels), reproducible pipeline code, and an interactive browser dashboard. (Paper under review.)

Sentinel-2 - Tropical Forests - Disturbance Detection

Authors: Ting & Moortgat

DL_bathy

A two-step deep learning pipeline that estimates shallow-water bathymetry from Sentinel-2 multispectral imagery, trained against airborne LiDAR ground truth. Built on a DeepLabV3+ segmentation backbone with cloud masking (Google Earth Engine) and tidal correction (DTU23).

Sentinel-2 - Bathymetry - Deep Learning

Maintainer: Moortgat group

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