I work at the intersection of hydrology, aquatic ecology, data engineering, open science, and research software engineering.

My work focuses on building practical, reproducible, and maintainable data systems for environmental science and natural resource management, with an emphasis on aquatic ecology, habitat restoration, environmental flows, and climate adaptation in California.

This GitHub account is primarily for work-related code, documentation, and technical collaboration.

I use code, data systems, open-source software, and open science practices to help environmental programs move from individual projects and datasets toward shared, reusable infrastructure and reproducible, extensible scientific work.

Current areas of focus include:

• 🌱 Open science workflows for environmental monitoring, restoration, and synthesis
• 🗂️ Data governance and stewardship for multi-agency science programs
• 🔁 Reproducible research pipelines using R, Quarto, GitHub, targets, renv, continuous integration, and related tools
• 📦 Open-source research software and open data packages for environmental analysis and publication
• 🏗️ Environmental data infrastructure including cloud platforms, spatial databases, and data services
• 🗺️ Spatial data pipelines for restoration project tracking, validation, and integration
• 🌊 River network and watershed analysis for barrier removal, connectivity, and restoration planning
• 🏛️ Public-sector technology for government data systems and institutional implementation
• 🤖 Generative AI in science for responsible coding, analysis, and decision support
• 📚 Reusable documentation and templates that make scientific workflows easier to review, maintain, and extend

I primarily work in R, Quarto, GitHub, PostgreSQL/PostGIS, Azure, and structured metadata/schema workflows but enjoy learning new tools to extend my technical skillset and advance my projects!

I care about code and data systems that are:

• Reproducible — analyses can be rerun and reviewed
• Reusable — datasets, code, and documentation are structured for future use
• Transparent — methods and assumptions are visible
• Maintainable — projects can survive beyond one person or one deadline
• Accessible — tools and documentation are approachable for new learners and straightforward to use for people with diverse access needs
• Useful — technical work supports real decisions, collaboration, and learning
• Joyful — workflows are designed to be satisfying, welcoming, and even fun to use

I earned my PhD in Environmental Science, Policy, and Management from UC Berkeley, where I was advised by Dr. Ted Grantham and worked in the UC Berkeley Freshwater Labs. That training continues to shape how I approach environmental data science, open science, and decision-support tools for river and restoration programs.

• Personal website: lucyrandrews.com
• Employer: California Department of Water Resources