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Data literacy is a critical skill for developing the next generation of scientists and informed citizens. Authentic engagement with real-world Earth science datasets in K–12 classrooms is often limited by the complexity of research data and the need for effective pedagogical strategies to support sensemaking. Data Puzzles, a collaborative effort between scientists, curriculum developers, and educators housed at the Cooperative Institute for Research in Environmental Sciences (CIRES), addresses this challenge by transforming authentic Earth and climate science datasets into structured, instructional modules designed for secondary classrooms. Each Data Puzzle is anchored in a relevant Earth system phenomenon and pairs authentic datasets (e.g., from satellite observations, field sensors, and physical samples) with research-based instructional practices from Ambitious Science Teaching (AST, Windschitl et al., 2020 ) to support data-driven reasoning, uncertainty exploration, and evidence-based explanation. Data Puzzle lessons have been adopted by hundreds of educators, empowering thousands of students to analyze and interpret authentic datasets while building critical data literacy skills. In parallel, the emerging Puzzle Piece resources allow students and teachers to extend structured learning into open-ended investigations using large, secondary datasets within a free, web-based analysis platform (CODAP). This combination of structured and open-ended resources creates an accessible pathway from curated datasets toward exploratory science practices, amplifying the’ broader impacts of science by translating complex datasets into classroom-ready learning experiences. This presentation will demonstrate how Data Puzzles serve as a model for scientists to broaden the impact of their research, enabling authentic data use and discovery in classrooms nationwide. We will share examples of how scientists’ data have been integrated into Data Puzzle lessons, highlight outcomes from teacher professional learning experiences, and discuss how this framework helps foster equitable data access and supports the next generation of Earth scientists and informed citizens. 

This mixed-methods study explores the value of the “adult learner hat” in professional learning (PL) for science teachers engaging with climate science and data sensemaking. As science education shifts towards “figuring out” versus “learning about,” teachers must learn new content, high-quality materials, and effective pedagogies. While “teacher hat” and “student hat” discursive roles have been shown to be valuable, teachers often “break” from these roles. This study focuses on the “adult learner hat,” where teachers leverage their existing knowledge to deeply engage with complex scientific concepts and data. Through pre- and post-surveys and video-based analyses of a 3-day PL workshop, we found teachers' confidence in climate science, data sensemaking, and the instructional framework significantly increased. Qualitative data revealed “adult learner hat” activities, particularly working directly with data, were identified as the primary source of this growth. We present a thematic account of PL design choices that supported this role, including surfacing teacher “teacher hat” concerns, withholding instructional documents, and purposefully redirecting teachers back to the learner role. This study highlights how the “adult learner hat” provides a productive space for teachers to grapple with uncertainty, critique visualizations, and collaborate in meaning-making, ultimately building intellectual resources for ambitious, data-rich science instruction. 

The Data Puzzles BRIDGE Research Practice Partnership builds middle school science teachers' confidence in teaching data sensemaking skills using real-world climate data. Through an ongoing teacher PLC (n=19), we seek to bring a higher degree of student epistemic agency in classrooms investigating climate phenomena. The theoretical framework of the project uniquely centers the common ground among 1) climate science education (USGCRP, 2024), 2) data sensemaking (Hunter-Thomson, 2019; Lee & Wilkerson, 2018), and 3) the Data Puzzles instructional framework (Griffith & Braaten, 2023; NRC, 2012). This project’s methods of inquiry are oriented within design-based research that centers the practices of teachers as they include Data Puzzles instructional materials in their classrooms. As part of the larger design study, we gathered teacher survey data to understand their confidence and frequency of classroom instructional practices that foreground data sensemaking (Hayes, et al., 2016) and climate literacy and education principles (e.g., USGCRP, 2024). Given that these three fields have different priorities, the central pursuit of the project is the overlapping epistemic practice (e.g., Rouse, 1996) of working with data: Students explore, analyze, interpret, and reason with data to investigate climate phenomena (Figure 1). Our design principles for building professional learning are: 1) The use of high quality, standards-aligned instructional materials is the locus for teacher learning (Harris et al., 2015; Hestness et al., 2014) that originate the laboratory work of professional interdisicplinary earth scientists 2) Teachers and students learn by working with data in supported, consequential environments, and 3) Students should work with datasets that are connected to real-world phenomena, and 4) Data sensemaking opportunities should be flexible and open-ended such that students make decisions about how to visualize and derive meaning. Results. We investigated teachers’ confidence in the three domains that intersect in this theoretical space. The initial data are teachers’ responses to a 5-point retrospective Likert item about their confidence related to data sensemaking, climate science education, and the Data Puzzles Instructional Framework before and after the professional learning. Wilcoxon’s signed-rank test was used because data were ordinal (Likert). For data sensemaking practices, confidence significantly increased from pre-test (M = 3.37, SD = 0.90) to post-test (M = 4.11, SD = 0.66) on a 5-point scale, V = 0, p < .001. Similarly, confidence in ESS-related instruction significantly increased from pre-test (M = 3.42, SD = 0.69) to post-test (M = 4.16, SD = 0.69) on a 5-point scale, V = 7, p < .001. Confidence with the Data Puzzles Instructional Model also showed a significant –and the largest–increase from pre-test (M = 1.89, SD = 0.94) to post-test (M = 4.11, SD = 0.46), V =0, p < .001. These results indicate statistically significant improvements in teacher confidence across all three interdisciplinary areas following the professional learning experience, which are likely to lead to better implementation of the Data Puzzles materials in the fall. These findings substantiate the conclusion that interdisciplinary, epistemic-practice oriented teacher learning supported by high-quality instructional materials are a promising method for an integrated, meaningful future in data science education. 

Inundation of coastal stormwater networks by tides is widespread due to sea‐level rise (SLR). The water quality risks posed by tidal water rising up through stormwater infrastructure (pipes and catch basins), out onto roadways, and back out to receiving water bodies is poorly understood but may be substantial given that stormwater networks are a known source of fecal contamination. In this study, we (a) documented temporal variation in concentrations ofEnterococcusspp. (ENT), the fecal indicator bacteria standard for marine waters, in a coastal waterway over a 2‐month period and more intensively during two perigean spring tide periods, (b) measured ENT concentrations in roadway floodwaters during tidal floods, and (c) explained variation in ENT concentrations as a function of tidal inundation, antecedent rainfall, and stormwater infrastructure using a pipe network inundation model and robust linear mixed effect models. We find that ENT concentrations in the receiving waterway vary as a function of tidal stage and antecedent rainfall, but also site‐specific characteristics of the stormwater network that drains to the waterway. Tidal variables significantly explain measured ENT variance in the waterway, however, runoff drove higher ENT concentrations in the receiving waterway. Samples of floodwaters on roadways during both perigean spring tide events were limited, but all samples exceeded the threshold for safe public use of recreational waters. These results indicate that inundation of stormwater networks by tides could pose public health hazards in receiving water bodies and on roadways, which will likely be exacerbated in the future due to continued SLR. 

Ion mobility spectrometry (IMS) using Structures for Lossless Ion Manipulations (SLIM) is an emerging powerful tool for rapid isomer separations. This technology offers high mobility resolution due to prolonged ion mobility path lengths that are achieved on a small form factor separation device. In this work, we interface SLIM IMS separation with a chemical ionization source, which allows one to sample from the gas- and particle-phases directly. As such, one can monitor dynamic isomer populations in ambient air in real-time without prior sample preparation. This technology opens the door to new possibilities in atmospheric chemistry where isomer distribution is expected to play a key role in gas phase processes and in the formation of organic aerosols. The CI-IMS-TOF instrument built by TOFWERK produces ions via a two-step chemical ionization process, which involves 1) producing reagent ions and 2) ionizing neutral analyte molecules via the reagent ions through either charge transfer or adduct formation. Once generated, these secondary ions travel into the SLIM IMS region, where a series of DC- and AC-electrodes on printed circuit boards create a traveling wave driving force. As the ions travel through the helium buffer gas, they separate based on their rotationally averaged collision cross-sections. In the present work, various experiments were performed using an aerosol flow tube reactor and an atmospheric simulation chamber to recreate atmospheric conditions. Gas-phase oxidation of isoprene was used to explore the capabilities of the CI-SLIM IMS-MS under atmospheric relevant conditions. Firstly, the most important oxidation products produced from the OH-oxidation of isoprene were used including methacrolein, methyl vinyl ketone, isoprene epoxy diols (IEPOX), isoprene hydroxy hydroperoxide (1,2 and 4,3-ISOPOOH), and other C5H10O3 reactive uptake products as a single component or as a mixture to first evaluate the capabilities of the CI-SLIM IMS-MS at resolving the different isomers. Secondly, the reactive uptake of IEPOX onto acidic particles and OH-initiated oxidation (low and high NO regimes) of isoprene were studied to characterize the dynamic of the isomers generated within the simulation chamber under various environmental conditions. Results will be presented to demonstrate the capabilities of the newly developed CI-SLIM IMS-MS at resolving isomers in real time. How to cite: Riva, M., Gerber, S., Claflin, M., Mettke, P., Frauenheim, M., Rice, R., Gold, A., Surratt, J., Yatsyna, V., Graf, S., Canagaratna, M., Herrmann, H., Rohner, U., Kamrath, M., and Lopez-Hilfiker, F.: Real-time monitoring of dynamic isomer populations with CI-SLIM IMS-MS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6074, https://doi.org/10.5194/egusphere-egu24-6074, 2024. 

Narragansett Bay (Rhode Island, USA) is an estuary undergoing changes from a combination of rising water temperatures, nutrient fluxes, and human uses. In this study, we created an ecosystem food web model and evaluated its ability to predict functional group biomasses. Specifically, we used Ecopath to construct 2 mass-balanced models covering different time periods in Narragansett Bay: a historical model using data from 1994-1998 and a present-day model that represents 2014-2018. With the historical model as a starting point, we used Ecosim fit to time series data and projected forward to present-day values, forcing the model with both phytoplankton biomass and fishing mortality. The biomass of most mid- and upper trophic level groups increased by 2018, with the exception of carnivorous benthos, which experienced a large decline. There were changes in the composition of fisheries, with a large increase in recreational benthivorous fish landings and a decrease in commercial landings of planktivorous fish and suspension feeding benthos. The inclusion of fishing mortality and phytoplankton biomass as forcing functions, as well as adjusting the vulnerability levels of prey, greatly improved our model fits for all functional groups with the exception of gelatinous zooplankton. Our ecosystem model was able to correctly predict the direction of change for all fish and fished invertebrate groups with a relatively high degree of precision, particularly for the upper trophic levels. Thus, this ecosystem model is broadly applicable and suitable to project trends in the Narragansett Bay food web associated with localized and adaptive ecosystem-based management.