
Jen Schripsema enhanced documentation workflows and discoverability across two open-source repositories. For fpsim/fpsim, Jen improved SEO by removing the robots.txt file from the documentation directory, enabling full indexing by search engines and increasing user access to technical content. In starsimhub/starsim, Jen migrated the SIR calibration example into the project’s documentation, updated configuration files, and added a Jupyter notebook demonstrating stochastic SIR model fitting using Optuna. These contributions, implemented in Python and Jupyter Notebook, focused on reproducibility, onboarding, and maintainability, reflecting a methodical approach to both technical documentation and optimization workflows without introducing new bugs during the development period.

August 2025 focused on documentation and workflow enhancement for the Starsim SIR calibration feature set. Key delivery: moved the SIR calibration notebook into Starsim docs, updated the configuration to include the new notebook, and added a notebook demonstrating fitting stochastic SIR models to synthetic data using Optuna with standalone and Starsim-Optuna calibration (commit 317c7f9d05d5f93934e56c399c18d301e10b6544). Impact: improves onboarding, reproducibility, and accessibility of calibration workflows for users of starsimhub/starsim. No major bugs fixed this period. Demonstrated technologies/skills: Python, Jupyter notebooks, documentation tooling, config management, Optuna-based optimization, stochastic modeling, and reproducible research practices.
August 2025 focused on documentation and workflow enhancement for the Starsim SIR calibration feature set. Key delivery: moved the SIR calibration notebook into Starsim docs, updated the configuration to include the new notebook, and added a notebook demonstrating fitting stochastic SIR models to synthetic data using Optuna with standalone and Starsim-Optuna calibration (commit 317c7f9d05d5f93934e56c399c18d301e10b6544). Impact: improves onboarding, reproducibility, and accessibility of calibration workflows for users of starsimhub/starsim. No major bugs fixed this period. Demonstrated technologies/skills: Python, Jupyter notebooks, documentation tooling, config management, Optuna-based optimization, stochastic modeling, and reproducible research practices.
Month: 2025-05. Focused on delivering business value through SEO optimization and improved content discoverability for fpsim/fpsim. Key feature delivered: SEO Enhancement by removing robots.txt from the docs directory to enable full crawling and indexing, improving visibility of documentation content. No major bugs reported this month; the work centered on docs visibility and maintainability. Overall impact includes increased documentation reach, easier access for users, and stronger alignment with customer self-service goals. Technologies and skills demonstrated include Git/version control discipline, SEO best practices, and cross-team collaboration between engineering and documentation.
Month: 2025-05. Focused on delivering business value through SEO optimization and improved content discoverability for fpsim/fpsim. Key feature delivered: SEO Enhancement by removing robots.txt from the docs directory to enable full crawling and indexing, improving visibility of documentation content. No major bugs reported this month; the work centered on docs visibility and maintainability. Overall impact includes increased documentation reach, easier access for users, and stronger alignment with customer self-service goals. Technologies and skills demonstrated include Git/version control discipline, SEO best practices, and cross-team collaboration between engineering and documentation.
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