UW Scientific Software Engineering Center
Official@uw-ssec · United States of America
The UW Scientific Software Engineering Center (SSEC) at the eScience Institute leverages local software engineering talent to advance scientific frontiers.
Agent Skills by UW Scientific Software Engineering Center
Showing 16 vetted skills indexed across 1 GitHub repositories.
uw-ssecdata-visualization
Compose overlays, layouts, and interactive streams for multi-dimensional data with HoloViews.
uw-ssecparameterization
Automate parameterized configurations with validation and dependency tracking.
uw-sseclumen-dashboards
Create interactive dashboards from multiple data sources using YAML specifications.
uw-sseccolormaps-styling
Apply Colorcet perceptually uniform palettes to Holoviews, Panel, and hvPlot visualizations.
uw-ssecgeospatial-visualization
Create interactive geographic visualizations and spatial analyses with GeoViews and GeoPandas.
uw-sseclumen-ai
Translate natural language data questions into executable SQL queries, visualizations, and insights.
uw-ssecplotting-fundamentals
Create interactive hvplot and holoviews visualizations from pandas DataFrames.
uw-ssecadvanced-rendering
Renders large-scale data visualizations using Datashader, HoloViews, and Panel.
uw-ssecpanel-dashboards
Build interactive dashboards and data apps with Panel and Param.
uw-ssecpixi-package-manager
Unify conda-forge and PyPI dependencies in pyproject.toml for reproducible environments.
uw-ssecpython-testing
Design and implement pytest-based tests for scientific Python projects.
uw-ssecpython-packaging
Creates and configures scientific Python projects with pyproject.toml, Hatchling, src-layouts, licenses, and PyPI publishing workflows.
uw-sseccode-quality-tools
Configure Ruff, MyPy, and pre-commit for scientific Python projects.
uw-ssecscientific-documentation
Organizes scientific Python software documentation using Diátaxis-aligned Sphinx/MkDocs templates and workflows.
uw-ssecxarray-for-multidimensional-data
Open and analyze labeled multidimensional scientific data with Xarray.
uw-ssecastropy-fundamentals
Process astronomical data with Astropy core modules for units, coordinates, time, and FITS I/O.
Frequently Asked Questions About UW Scientific Software Engineering Center
FAQPage SchemaWhat specific scientific tasks are enabled by these capabilities?▼
These capabilities enable processing astronomical FITS files, analyzing labeled multidimensional scientific datasets, and creating perceptually uniform geographic visualizations. Users can build interactive dashboards from complex data sources and implement rigorous testing and documentation standards for research-grade software projects.
Who is the target persona for these software engineering practices?▼
The target personas are research software engineers, data scientists, and academic researchers working within scientific domains. These practices are designed for professionals managing complex computational environments who require reproducible results and high-performance visualization for large-scale datasets.
What are the prerequisites for implementing these software environments?▼
Implementation requires a foundational understanding of the scientific ecosystem, specifically familiarity with pyproject.toml configurations and dependency management. Users should be prepared to integrate standard testing frameworks like pytest and documentation generators like Sphinx or MkDocs to maintain project integrity.