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EmmaLi Tsai, a Digital Service for the Planet (DSP) Fellow, joins us for a conversation about why clean and collaborative data is essential to tracking and addressing environmental changes over time. EmmaLi has spent years using digital tech to collect the best possible data on our waters as well as the life teeming in them—from tagging tiny computers to seals’ flippers to measuring wetland elevation. We spoke with her about her fieldwork, how it’s impacted her views on maximizing environmental progress, and what she hopes to accomplish through the DSP fellowship.

The following conversation has been edited for brevity and clarity.

Emily Tavenner: What sparked your interest in working with and helping our sea life?

EmmaLi Tsai: My interest in sea life goes all the way back to the beginning. I was really fortunate to grow up exploring the coast of North Carolina with my parents in our compact, 16-foot sailboat. During these early adventures, I got to see some really beautiful and magical places like Cape Lookout National Seashore and Shackleford Banks. I spent as much time as possible either in the water looking at fish or peering over the edge of a dock watching all the little marine critters go about their day. I’ve always been fascinated by the underwater world and have always wanted to pursue a career to preserve these environments so future generations can experience the same magic I felt growing up.

Tavenner: You’ve done a lot of fieldwork over the years. What was that like?

Tsai: I have so many great memories doing fieldwork and feel very fortunate to learn from so many incredible scientists throughout my career! Most of my time in the field occurred during my undergraduate degree, when I had the opportunity to spend two semesters helping with research projects at University of North Carolina at Chapel Hill’s Institute of Marine Sciences. My undergraduate was focused on how a species of fish uses Earth’s magnetic field to navigate, and I have some really fond memories fiddling with wires, getting into carpentry, and watching the sun rise after running experiments all night. By spending two semesters at the coast, I was also fortunate to help with a variety of other research projects. I collected scallops, tested water quality in canal systems, surveyed seagrass beds, took care of sea turtles, and also participated in the After these amazing experiences, I knew I wanted to dive into animal sensory ecology and navigation in graduate school.

My graduate research is really where I found my love for data science. I spent most of my time at the computer we have of freely diving marine mammals and investigating how the diving patterns of Weddell seals changed from the 1970s to the 2010s. My project didn’t require me to be in the field, but I had the incredible opportunity to go down to Antarctica as a research technician for a seal reproductive physiology project after finishing my degree. I had an absolute blast wrangling and tagging seals with recording devices, and it was definitely some of the most physically challenging fieldwork I’ve ever done. But, working with a great team (and a great Lizzo soundtrack) made it easier to process samples in the lab together until two am, and sometimes we had the occasional penguin that wanted to help with our research!

Once I got back from Antarctica, I also had the opportunity to do more fieldwork during my time as a contractor with the U.S. Fish and Wildlife Service. In addition to helping build Inventory and Monitoring plans for National Wildlife Refuges, I assisted with a lot of coastal wetland elevation monitoring research. As part of a , we visited and measured the elevation of wetlands on refuges to determine whether they were keeping pace with sea level rise. We found that a lot of wetlands were sinking relative to sea level rise, which really pushed me to pursue more wetlands research during my time at and DSP.

Tavenner: You have mentioned in your bio that you’re excited to dive more into the human dimension of climate change. What does that mean to you?

Tsai: During my academic career, a lot of my research was focused on how climate change and other human activities have impacted marine systems. While everything is all connected, I’m really excited to apply my skills to build datasets and tools to directly benefit people and communities.

Tavenner: What inspired you to focus your work not just on data but also on what can be done with that through technological developments?

Tsai: My interest for data science really picked up during my research on the diving patterns of Weddell seals. Although I spent many sleepless nights being extremely frustrated with the  endless problems in the 1970s dive records, I actually found a lot of joy in it! It was basically like solving a series of mini puzzles with code. This led me to do more data-heavy research during my time with the U.S. Fish and Wildlife Service on wetland elevation trends.

From looking at long-term trends and patterns in data for both of these projects, it was clear just how quickly our ecosystems were changing. So, I decided to make the pivot from being a seal wrangler to a data wrangler and became really interested in data harmonization and interoperability. There is so much environmental data being collected in many different places and across many different scales—bringing datasets together allows us to make more efficient, data-driven decisions about our environment. 

Tavenner: Why were you interested in becoming a DSP fellow?

Tsai: In addition to my love for building datasets and tools to help communities, I also liked that the DSP fellowship brings people together from different backgrounds and subject matter to build solutions for a shared goal. Our work intersects and complements one another in various ways. For example, there is a connection between my work on drinking water and JR Washebek’s work on how digital twins can be used in forestry because forests actually . It has been really exciting to  overlap and take a more multidisciplinary approach to building solutions together, especially in an age in which our environments are rapidly changing

Tavenner: What do you think is missing when it comes to how data is leveraged for a healthy planet?

Tsai: For me, I think it all boils down to investing in the environmental datasets of the past, present, and future. Looking at trends over long periods of time has been central to my work in understanding shifts in our ecosystems, but that work is only possible when we have data. We need to support strong monitoring networks and environmental datasets that adhere to FAIR data standards to be able to understand how our ecosystems and their functions are changing. In addition, there is so much data in file cabinets, floppy disks, and old hard drives that are at risk of being lost to time. These older records contain really rich information from a time prior to modern conditions and would help us establish better baselines, but they need to be recovered and ideally digitized before files get thrown out or hard drives become corrupted.

In addition to investing in datasets, I think collaboration and communities of practice can be very powerful in the environmental space. For wetlands, we developed a community of practice to understand how the data landscape can be better connected such that we have access to more up-to-date information. This group, which contains all sorts of brilliant wetland experts that I deeply admire, has really expanded over the past two years, and we’ve started to build solutions to address some key issues in the wetlands data space. Having a common place for people to discuss wetlands data has also resulted in more collaboration and knowledge sharing between people that might not normally cross paths. It’s been really amazing to be part of this group and I’m really excited about all we will accomplish together this year. For me, it really demonstrated how a lot of data harmonization work really starts with people.

Tavenner: What are you hoping to accomplish in the DSP fellowship?

Tsai: So many things! Most of my work really spans across the drinking water, wetlands, and a sprinkle of side quests for other projects. For drinking water, I’ve been really excited about the Drinking Water Dataset and Explorer Tool. The drinking water data landscape is very fragmented, which makes it difficult to untangle the complex web of factors that influence drinking water issues. For this tool, we did a lot of difficult data work to harmonize over 100 drinking water datasets across over 25 datasets to the water system service area boundary in our tool. One of the most challenging datasets was drinking water advisory data, which we collected and standardized from about 13 public state websites. We’ve even had some folks publish data with the intent of it being displayed in our tool, which is really exciting. Our codebase is also fully public—we would love for people to plug in, use our code, or submit GitHub issues so we can improve this resource!

I’m also very excited about our wetlands work. In addition to advocating for the value of wetlands and data, we have also been designing the OpenWetlandsMap with our community of practice. This map, in collaboration with OpenStreetMap US, will leverage the OpenStreetMap framework to build a community-maintained dataset of wetland observations. This map of wetland field observations will complement existing wetland mapping work and help us fill a key data gap.

Tavenner: If you were to envision a future in which environmental data and tech were being used seamlessly so that conservation efforts were successful, what would that look like?

Tsai: On the data side, I think this would really involve investing in our environmental datasets, collaborative problem solving, adhering to FAIR data standards, and great data documentation. Where long-term environmental monitoring data could be accessed in real-time, easily combined with a variety of supplementary datasets, and quickly accessible to identify potential conservation actions. I also think a lot of this work really begins with people. There is a lot of power in collaboration and community-maintained datasets, where everyone can have the opportunity to improve and contribute data in locations that are most important to them. A lot of this is what we’re working toward together with DSP, and I’m really excited to pursue this shared goal with this group of fellows!