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DataVizHub is an open-source Python framework for building powerful, reproducible data-driven visualizations. Originally developed to produce 2:1 equirectangular global imagery and animations for NOAA’s Science On a Sphere platform, it is designed as a modular pipeline that handles data acquisition, processing, rendering, and dissemination. Our goal is to make it as useful to developers building custom workflows as it is to educators and the public exploring scientific data.
This wiki is the central hub for architecture overviews, design goals, and best practices, along with guides, examples, and reference materials to help you get the most out of DataVizHub—whether you’re generating high-resolution geophysical maps, creating animations, or extending the platform into new domains. Mission Statement
To empower scientists, educators, and the public with an accessible, extensible, and open-source toolkit for transforming raw data into compelling, accurate, and educational visual stories—bridging the gap between complex scientific information and clear, engaging visualizations.
If it helps, there is an easy to understand introduction.
- Open Science First – Built entirely on open-source technologies, with a focus on reproducibility, transparency, and free public access.
- Modular by Design – A composable architecture where data ingestion, processing, visualization, and distribution can be used independently or as an end-to-end pipeline.
- Scientifically Accurate – Visuals grounded in authoritative datasets and proven methods, with careful attention to projection, scaling, and context.
- Usability for All – Equally valuable to developers scripting automated pipelines and to educators or communicators seeking ready-to-use visuals.
- Extensible and Future-Facing – Flexible enough to handle new data sources, formats, projections, and output media as visualization needs evolve.
- Educational Impact – Designed to not only display data but to help explain it, supporting learning, outreach, and public engagement.
