Unwrapped Earth

Every summer leaves a few memories that really stick with me. Sometimes it’s a weird airport experience, sometimes a great sunset, sometimes making an unexpected friend, sometimes – apparently – it’s pulling population data to understand rainfall patterns in Maui.

Last year, during my sophomore year, I joined the TOPS School Open Science Team, working under Professor Kytt MacManus, Juan Martinez, and Professor Antonio Tovar on a project that at first seemed way beyond me. “TOPS” stands for NASA’s Transform to Open Science, and the full program name is Science Core Heuristics for Open Science Outcomes in Learning (SCHOOL) – but what that really means is making science something more collaborative, transparent, and accessible to everyone. I got the chance to help build a learning module that used open data tools to investigate a real environmental event.

Our project had a big name: Flood Watch Report: Maui Heavy Rainfall Analysis Using Census API. At first, I thought research was always about finding answers. But the Maui lesson wasn’t about answering a specific question. The point was to demonstrate, share, and democratize powerful tools so anyone – researcher, college student, or curious high schooler – could use Open Science and Open Source resources for their own investigations. If you’re curious, the project homepage is linked here. It shows how science can be open and collaborative through a variety of case studies, the Maui Flash Flooding that I worked on being one of the many in the “disasters” module of this big project.

My working group’s focus was on an extreme rainfall event in Maui in January 2024. Our goal wasn’t to solve the entire problem – it was to demonstrate how publicly available data and simple tools can help people better understand and respond to disasters. My role involved using U.S. Census data to look at where people live on Maui and how those areas aligned with rainfall patterns. That meant figuring out how to request very specific data from large government datasets – a process that sounded complicated at first but became more manageable as I got deeper into it. One of my main jobs was to pull population data for census tracts in Maui using something called a Census API. At the start, I didn’t even know what an API was – three letters that sounded intimidating and super techy. But it turns out, an API, or Application Programming Interface, is basically a way to ask a giant online database for the exact info you need and plug it straight into your coding project – kind of like asking a librarian for exactly the right book instead of searching the whole library yourself.

There were definitely frustrating moments. Sometimes things didn’t load properly. Sometimes the data didn’t match up the way I expected. But as I kept working, I slowly began connecting the dots – learning how to pull geographic data, match it with population statistics, and visualize it all on a map. Along the way, I also learned how to use GitHub and Visual Studio Code, tools that helped me organize the code and collaborate with the team efficiently. It was incredibly satisfying to see those visualizations take shape, not just because they looked cool, but because they told a story. A story about where people live, where the rain hit hardest, and what that might mean for disaster planning and emergency response.

One of the best parts of the experience was that it wasn’t just about learning – it was about creating something others could use too. I got to help write and shape an actual open lesson that walks people through the process step by step. That’s what open science is all about: making tools and knowledge freely available so others can build on them. If you want to check it out, here’s my working group’s page (Flood Watch Report: Maui Heavy Rainfall Analysis) that I worked on.

This project also connected to my growing interest in using technology to solve real-world environmental problems. Last summer, I worked on sea level rise through a program at Fordham University that focused on how AI could be used for public benefit. I created a project on using AI to help detect sea level changes in NYC. Since then, I’ve been especially interested in how tools like AI and open data can be used for the environment – not just as buzzwords, but as real, useful instruments for research and awareness

I reached out to Professor Kytt MacManus early in my sophomore year, and I’m incredibly thankful he introduced me to Juan Martinez and this project. Now that the TOPS School project has officially come to an end, I’m writing this reflection with a lot of gratitude – for the opportunity, the mentorship, and what it means going forward. It reminded me that meaningful research doesn’t always require a lab coat – it sometimes just takes curiosity, persistence, and the willingness to learn new tools.

This summer, I was also on Columbia’s campus for the Environmental Studies pre-college program that I have previously mentioned, and it was honestly so special to finally meet Professor MacManus and Juan in person instead of just Zoom meetings, phone calls, and emails. Getting a tour of the Mudd building and seeing the labs where this work happens made everything feel more real and exciting. Although the TOPS SCHOOL program has now ended, it’s been a journey since then, one that I am incredibly grateful for.

I came away from this understanding more than just how to navigate datasets. I saw firsthand how science can be more inclusive and open-ended – less about having all the answers, and more about exploring the right questions. Being part of that process made me feel like I wasn’t just learning about science. I was doing science.

So here’s to confusing files, surprising insights, and maps that help tell important stories. And here’s to the inspiration this experience has given me – maybe someday, down the road, I’ll even write and publish my own research paper (of course, under the proper mentors & with the guidance), a meta-analysis on flooding. But if that happens, it’ll be a little while from now.