An orchestration platform for the development, production, and observation of data assets.

An open-source JavaScript library for world-class 3D globes and maps :earth_americas:

A Python package for interactive geospatial analysis and visualization with Google Earth Engine.

:earth_americas: machine learning tutorials (mainly in Python3)

Specification for streaming massive heterogeneous 3D geospatial datasets :earth_americas:

:earth_africa: :clipboard: A web dashboard to inspect Terraform States

[CVPR2023] The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

:earth_americas: Simple and ready-to-use tutorials for TensorFlow

Geospatial resources for web development :earth_africa: 🗺️

A powerful, format-agnostic, and community-driven Python package for analysing and visualising Earth science data