BRINGING FIELD STATION DATA INTO THE COLLEGE CLASSROOM: LESSONS LEARNED FROM THE DIGITAL RMBL PROJECT

The National Science Foundation (NSF) recently mandated that data collected through NSF funded projects be shared with the public through a variety of means. Many new NSF-funded projects are attempting to facilitate data sharing at a community wide scale (e.g. EarthCube, etc.) down to the scale of single projects or institutions (e.g. the Digital RMBL project). In 2010, Digital RMBL was developed with NSF funding to share long-term ecological datasets collected with NSF support at the Rocky Mountain Biological Laboratory (RMBL) in Gothic, Colorado. The goal of Digital RMBL is to bring the ideas, data, and scientific results developed by RMBL scientists since its inception in 1928 into college classrooms through online data-rich, inquiry-based modules.

Two of the four modules, The Biology of Climate Change and The Warming Meadow Experiment, explore the effects of changing climatic conditions on ecologic systems. Using data from both long-term observations and long-term experiments, the modules examine changes in the timing of life cycle events (phenology) of plants and animals as compared with weather observations since the early 1970s by billy barr, RMBL’s business manager.

Over the last three years, the modules were extensively tested by 15-20 faculty that teach at a wide variety of institutions across the nation. Instructors and their students have rated the modules highly, largely because of the incorporation of authentic data and open-ended investigations. Instructors claim large gains in their students’ content knowledge and data literacy skills, and all plan to use the modules in the future – an important measure of success.

The Digital RMBL project is one model of how scientists can share their data with the public through curricula designed to improve scientific and data literacy. In the future, Digital RMBL modules will be posted through science curricula clearinghouses (e.g. ESA’s EcoEd Digital Library, SERC etc.) for maximum impact.