Stephanie Freund Indulged Her Satisfaction in Well-Prepared Datasets

By Jenny Seifert

As a soil ecology technician, Stephanie Freund realized that many of her favorite moments of the research process – from project design to analysis and writing – came on the heels of a freshly acquired dataset.

“I loved discovering tricks for making our work more efficient,” she said of her previous work with the University of Nevada, Reno.

This interest is what drove her to dive deep into environmental data science and programming through the Data Science Fellowship. She has spent the fellowship as part of the Data Task Force, a team of data wranglers for the State of Alaska’s Salmon and People (SASAP) project, which has allowed her to be immersed in learning and applying best practices in preparing datasets for analysis and preservation.

“This has been an immensely effective way to develop new data wrangling skills and become more adept at programming in R, helping to fill in exactly the gaps in my skillset that I identified prior to the fellowship,” said Freund.

What are the most valuable things you learned from the fellowship?

SF: The most valuable things I have learned would definitely include being introduced to version control using Github and understanding how R packages are constructed by participating in package development with the other fellows.

Version control: Data science software systems can contain many versions and configurations of files, and version control enables multiple people to collaborate and track changes across these versions. Github is a commonly used platform for version control.

R package: R is a programming language and free software environment many scientists use to analyze their data, and an R package is a formatted collection of functions, data and compiled code

How do you hope to apply what you learned during the fellowship in your career?

SF: I believe that principles of open science are widely applicable for both scientific research and its applications, and that data accessibility is critical for problem solving. This is a perspective I have gained from the fellowship – on top of a foundation for growing my technical skillset – and I plan to carry it throughout my career.

Data accessibility: the ability to retrieve and use data, which is typically stored in an online data repository

Why do you think the data science work you've done through the fellowship is valuable for science, policy and/or management?

SF: There are many challenges facing ecosystems now and into the future, covering large regional or global scales. Ambitious goals for understanding and meeting these challenges seem far less nebulous and more attainable with large datasets and large collaborative teams.

What’s your favorite or most frequently used emoji?

SF: Although the "science parrot" emoji is likely flouting important lab safety protocols, it represents the satisfaction I feel when I successfully move a project forward, or the excitement over a new tool or discovery that helps me do so.

Meet other data science fellows in this NCEAS Portrait series >>