By Kathryn Meyer
While working in environmental management, Rachel Carlson noticed a common problem: organizations often have trouble sharing their data. Observing how this led to unused research and unnecessary costs, Carlson became motivated to address the problem head-on and learn about creating open data networks – which led her to become a Data Science Fellow.
“I also came to NCEAS because of its reputation for considering diverse ‘ways of knowing,’” said Carlson, who herself sees the world through multiple lenses, as a holder of degrees in English and civil and environmental engineering. “I wanted to learn from NCEAS’ model of creating a space where unlike fields can collaborate.”
As a fellow, Carlson has specialized in analyzing geospatial data for the State of Alaska Salmon and People (SASAP) project, including writing reproducible scripts, or data processing “recipes” that allow other researchers to reproduce the analysis, for analyzing salmon habitat.
Now preparing to start her PhD in Environment and Resources at Stanford this fall, Carlson suspects her time as a fellow at NCEAS will be the “gift that keeps on giving” throughout her career.
What are the most valuable things you learned from the fellowship?
RC: This fellowship has been transformative. I’ve learned specific skills, like how to document and publish an R script, graph an ecological network in Python, and code beautiful data visualizations—as well as general lessons on good data stewardship.
How do you hope to apply what you learned during the fellowship in your career?
RC: In the short term, I will be reentering academia and plan to use the power of programming to make my own research reproducible (big fan of the dissertation on Github!). I’m also inspired by the culture of data science at NCEAS—for example, peer learning groups like Eco-Data-Science and RLadies—and plan to carry this spirit of peer support with me as I begin work at Stanford.
In the long term, I hope to build a career at the intersection of data science, marine ecology, and policy. This fellowship has given me many concrete skills towards that path.
Why do you think the data science work you've done through the fellowship is valuable for science, policy and/or management?
RC: Data science helps environmental managers and scientists work more efficiently by accessing and building upon each other’s knowledge. As we confront climate change, building research efficiencies is all the more urgent. I think data science is a great example of using 21st-century tools to address 21st-century environmental problems.
Emojis are frequently used during internal correspondence to enhance the efficiency and tone of communication – what’s your favorite or most frequently used emoji?
RC: There’s literally no situation the "deal-with-it parrot" can’t improve, but "female-technologist" is also up there for all of us STEM ladies.
Meet other data science fellows in this NCEAS Portrait series >>