Browsing by Author "Klug, Jennifer L."

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    Analysis of high-frequency and long-term data in undergraduate ecology classes improves quantitative literacy
    Klug, Jennifer L.; Carey, Cayelan C.; Richardson, David C.; Gougis, Rebekka Darner (Ecological Society of America, 2017-03)
    Ecologists are increasingly analyzing long-term and high-frequency sensor datasets as part of their research. As ecology becomes a more data-rich scientific discipline, the next generation of ecologists needs to develop the quantitative literacy required to effectively analyze,visualize, and interpret large datasets. We developed and assessed three modules to teach undergraduate freshwater ecology students both scientific concepts and quantitative skills needed to work with large datasets. These modules covered key ecological topics of phenology, physical mixing, and the balance between primary production and respiration, using lakes as model systems with high-frequency or long-term data. Our assessment demonstrated that participating in these modules significantly increased student comfort using spreadsheet software and their self-reported competence in performing a variety of quantitative tasks. Interestingly, students with the lowest pre-module comfort and skills achieved the biggest gains. Furthermore, students reported that participating in the modules helped them better understand the concepts presented and that they appreciated practicing quantitative skills. Our approach demonstrates that working with large datasets in ecology classrooms helps undergraduate students develop the skills and knowledge needed to help solve complex ecological problems and be more prepared for a data-intensive future.
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    A Practical Guide for Managing Interdisciplinary Teams: Lessons Learned from Coupled Natural and Human Systems Research
    Henson, V. Reilly; Cobourn, Kelly M.; Weathers, Kathleen C.; Carey, Cayelan C.; Farrell, Kaitlin J.; Klug, Jennifer L.; Sorice, Michael G.; Ward, Nicole K.; Weng, Weizhe (MDPI, 2020-07-09)
    Interdisciplinary team science is essential to address complex socio-environmental questions, but it also presents unique challenges. The scientific literature identifies best practices for high-level processes in team science, e.g., leadership and team building, but provides less guidance about practical, day-to-day strategies to support teamwork, e.g., translating jargon across disciplines, sharing and transforming data, and coordinating diverse and geographically distributed researchers. This article offers a case study of an interdisciplinary socio-environmental research project to derive insight to support team science implementation. We evaluate the project’s inner workings using a framework derived from the growing body of literature for team science best practices, and derive insights into how best to apply team science principles to interdisciplinary research. We find that two of the most useful areas for proactive planning and coordinated leadership are data management and co-authorship. By providing guidance for project implementation focused on these areas, we contribute a pragmatic, detail-oriented perspective on team science in an effort to support similar projects.