University of Georgia Research Foundation Inc

NSF Awards · FY 2024 · 2024-08

Mental health and well-being are rising concerns nationally. University faculty are no exception, as academia is known as a high stress environment. This is a national study of the prevalence and severity of mental health problems among faculty working in STEM disciplines. The project will examine specific factors that positively and negatively impact STEM faculty mental health and well-being and how academic systems affect these factors for different demographic groups. In doing so, the project will ultimately contribute to improved support for faculty by informing practices and policies that can promote well-being and more inclusive academic environments. The project leverages an exploratory mixed methods design informed by the Job-Hindrance-Support-Control model, specifically the hindrance appraisal component of the model, and Collins and Bilge’s model of multiple strata interacting to create unique hindrances for some. Results will confirm, extend, or modify the Job Hindrance-Support-Control model, thereby expanding occupational well-being literature in academic contexts. Collins and Bilge describe a model of systems of power within an organization and highlights structural, disciplinary, cultural, and interpersonal domains of power. The qualitative phase of the project will include exploratory interviews with 60 STEM faculty and 20 administrators at U.S. institutions. These interviews will be leveraged to develop a novel survey validated through cognitive interview and pilot data collection phases. Once distributed nationally to an estimated 1,244 STEM faculty members through institutional partnerships, the project will become the largest study to date on faculty mental health and well-being. Through this large-scale data collection, the project will identify stressors for faculty strata that are often excluded (e.g. non-tenure track) and contribute to understanding how multiple faculty identities impacts their mental health and well-being. As part of the partnership agreement, the project will return customized reports to partner institutions in addition to workshops reviewing the institutional findings and research-based workshops on supporting faculty. The project results will be shared broadly with research communities and the public, including but not limited to scholarly publications, popular media, and a project website that hosts community resources. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

A challenge in mathematics education is that students often do not see math as an opportunity for exploration and creativity. However, these tenets are central to how mathematicians engage in mathematics and could prove transformative in how students experience mathematics learning. Mathematicians identify and formalize patterns, develop and prove conjectures, and explore mathematical structures in creative and playful ways. Mathematical play can also be used to help students explore new ideas, experiment with different solutions, and develop mathematical reasoning. Mathematical play can also bolster motivation and engagement, which are critical factors in supporting students’ abilities to understand and persist in mathematics and the STEM fields. The project will investigate (a) how to meaningfully incorporate playful elements into the foundational secondary and undergraduate mathematics topics of algebra and calculus, and (b) the potential outcomes of “playifying” classroom mathematics for students’ learning and enjoyment. The project will investigate tasks that can be used for students to explore mathematical ideas such as rates of change, functions, derivatives, and integrals. This study will examine the following traits of mathematical play: (a) exploration, (b) self-selection of goals, and (c) immersion, investment, and/or enjoyment. The research questions address students' mathematical activity, reasoning in algebra and calculus, the nature of mathematical play, and the learning benefits for students. In parallel to the student experience, the research questions also examine task design elements, pedagogical moves, and classroom features that support mathematical play. The project will implement a multi-phase design experiment model, leveraging clinical and stimulated recall interviews, small-scale teaching experiments, and whole-class teaching experiments, with each phase building on the findings of the prior. The research activities will produce a set of findings about the aspects of task design, instruction, and classroom interactions that support mathematical play, as well as the learning benefits of mathematical play for adolescents and undergraduates. This project is supported by NSF's EDU Core Research (ECR) program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. Investments are made in critical areas that are essential, broad and enduring: STEM learning and STEM learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This research investigates the challenges faced by farmers in adapting to environmental change, focusing on differences among various types of farmers and how their practices change over time. This project specifically examines farmers are in a position to adapt farming practices to climate change, how does this ability vary among farmers, and what are the long-term impacts of adaptation to climate change on the sustainability of agriculture. This project addresses these questions by combining field data with spatial models of farmer decision-making. Research findings are disseminated at the community-level, and also to governmental and non-governmental officials connected to agricultural support services. Global environmental change processes have particular impacts on farmers given the tight linkage between agricultural production and climate/weather dynamics. Farmers face a complex decision context in deciding how and when to modify farming practices to cope with changing environmental conditions. This project investigates this decision making nexus at the farmer level taking into consideration farmer attributes and broader environmental, political, social, and economic contexts that affect these decisions. The research studies farmer adaptation through two decision cycles: the initial adoption of climate smart agriculture practices and whether farmers continue those practices after adoption. Data from focus groups, interviews, and surveys are integrated to examine how individual and community factors affect farmer adaptation, highlighting decision-pathways of historically disadvantaged groups. These data are combined to develop a spatially explicit agent-based model to understand how differences in adaptations and interactions impact the resilience of farming systems to climate change. The project generates policy insights to aid farmer adaptation and inform the deployment of agricultural support services. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Electrical and computer engineers make significant contributions to society and help maintain a robust economy. They design a wide range of products and systems that are crucial to modern life, working in a variety of sectors such as consumer electronic devices, telecommunications, healthcare, industrial automation, information technology, and aerospace and defense, among others. In order for the US to maintain its globally competitive workforce, it is important for universities to attract and graduate a diverse group of engineering students. This project contributes to NSF’s goal of broadening participation by exploring new ways that electrical and computer engineering programs can support students during their engineering training. Specifically, the research team will explore how students view their family backgrounds and life experiences as contributing to their success in engineering. The team will also determine the extent to which university faculty, staff, and administrators recognize the value of these experiences in the students’ professional development as an engineer. The results can be used to improve the way we train engineers to meet complex challenges in the 21st century. The project will use a qualitative research design to expand the use of asset-based thinking in electrical and computer engineering undergraduate education. The research team will conduct 25-30 interviews with a cross-section of undergraduate electrical and computer engineering students, faculty, staff, and administrators at a large public university. Using qualitative analysis techniques, the research team will compare these perspectives to determine what possible alignments – or misalignments – exist in how these groups perceive the value of the different experiences that students bring to their undergraduate engineering education. Interviews will focus on the various aspects of student’s community cultural wealth and to what extent they feel these linguistic, resistant, navigational, familial, social, and aspirational capitals have been leveraged in their undergraduate electrical and computer engineering program. Researchers will employ inductive and deductive thematic analysis techniques in combination with narrative analysis to help elevate the experiences of diverse engineering students. The results of this research will generate knowledge about how to best leverage the diversity of student assets in electrical and computer engineering and other engineering disciplines. These results will also be used to inform transformative leadership strategies to integrate asset-based thinking into the day-to-day operations of an electrical and computer engineering department led by the principal investigator. This positions the principal investigator to make significant positive impact on 30 electrical and computer engineering faculty and staff and more than 480 students per year. The advisory board of electrical and computer engineering department heads will support the research team in interpreting findings and tailoring dissemination to other leaders who aspire to adopt asset-based perspectives in their organizations. The project supports NSF’s significant investment in research initiation grants in the last 10+ years by studying the mechanisms by which research mentoring relationships succeed. The research team will conduct a collaborative autoethnographic study of this project. The findings will expand the knowledge of how mentorship of engineering faculty can build research capacity in engineering education and will have implications for NSF research initiation programs such as the Research Initiation in Engineering Formation (RIEF) and Building Capacity in STEM Education Research (BCSER) programs. The autoethnographic findings will also offer the potential to improve other peer-to-peer or hierarchical mentor training. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

As society’s problems continue to become increasingly more complex, engineers need new skills to tackle these problems head-on. One approach to arming engineers with this skillset is through interdisciplinary engineering education. This method is different from traditional single disciplinary engineering education and helps foster broader thinking and creative insights. For the US to maintain its competitive edge in the global workforce, engineers must learn to work effectively in cross-functional teams. To address this need, universities are offering interdisciplinary programs that allow students to expand the boundaries of their education in ways that match their career goals and industry needs for creative problem solvers. Understanding student career choice can help universities better tailor programs to meet student needs. This project contributes to the NSF’s goal of broadening participation by exploring new ways in which multi- and interdisciplinary engineering programs affect students’ career goals and choices during their education. The research team will explore factors that guide students’ academic and career pursuits and determine the student perceived value in engaging with interdisciplinary learning and experiences during their undergraduate education. The results can be used to improve the way we train engineers to solve the complicated and multidimensional issues of the 21st century. The project will use a qualitative research design to build on the knowledge of how engagement in multi- and interdisciplinary programs impact engineering students’ career choices. The research team will conduct interviews with students in both multi-disciplinary and single disciplinary engineering programs at a large public university. Interviews will be framed using social cognitive career theory and will explore how learning experiences, personal characteristics, and environmental influences impact students’ decision-making process for career selection, as well as outcome expectations and their confidence in their ability to succeed on that path. Researchers will employ inductive and deductive thematic analysis techniques in combination with narrative analysis to elevate the experiences and perspectives of diverse engineering students. The results of this research will generate knowledge about how multi- and interdisciplinary programs influence students’ career choices and decisions to persist in an engineering career. These findings on interdisciplinary engineering education will help educators design programs to support tomorrow’s industry needs and adapt to evolving career paths. As a director of an interdisciplinary engineering program, the PI is well-positioned to make significant positive impact on the nearly two hundred students who are enrolled annually in an integrated business and engineering program at a large public university. The advisory board will support the research team in interpreting findings and tailoring dissemination to other leaders of similar programs in the US. The project also supports NSF’s significant investment in research initiation grants in the last decade by using a collaborative autoethnographic study of this project to explore the mechanisms by which research mentoring relationships succeed. The findings of the collaborative autoethnographic study will expand the knowledge of how structured mentorship of engineering faculty can build research capacity in engineering education. This will have useful implications for NSF research initiation programs such as the Research Initiation in Engineering Formation (RIEF) and Building Capacity in STEM Education Research (BCSER) programs and will also offer the potential to improve other peer-to-peer or hierarchical mentor training initiatives. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Across the nation, many school districts are experiencing rapid expansion in the enrollment of multilingual learners, yet many high school teachers do not have corresponding opportunities to learn how to effectively support these students’ engagement in scientific and engineering practices. This exploratory project will address this issue by developing and testing a model of professional learning for high school teachers in which they learn how to embed the Instructional Conversation pedagogy within standards-aligned scientific and engineering practices. This professional learning model is designed to benefit all students, including those receiving English for Speakers of Other Languages (ESOL) services. Under this model, high school science teachers will collaborate with high school ESOL teachers to co-develop linguistically-sustaining instructional materials that provide students with intentionally-scaffolded opportunities to use scientific dialogue as they collaborate to explain natural phenomena or design solutions through engineering. High school science teachers will use these co-developed instructional materials to teach classes which include students receiving ESOL services, and they will reflect and debrief with an instructional coach regarding whether and how their instructional approaches supported students’ dialogue-rich engagement with scientific and engineering practices. Research will explore whether and how the professional learning experiences supported shifts in the teachers’ instruction. Materials associated with the field-tested professional learning model will be disseminated widely via professional networks of science, engineering, and language educators and researchers. Ultimately, this project is likely to broaden participation in science and engineering fields by advancing quality science education for multilingual learners in high schools. Researchers will investigate whether and how a promising professional learning model contributes to shifts in high school teachers’ science and engineering instruction with multilingual learners. The professional learning model includes three Learning Lab cycles, each aligned with a specific science or engineering practice. During each cycle, high school science and ESOL teachers will co-create Joint Productive Activity task cards, aligned with principles of the Instructional Conversation pedagogy, which guide student collaboration and dialogue as they engage in a specific standards-aligned task. Instructional coaches will debrief and reflect with educators regarding their implementation of the task cards. During the next Learning Lab, the high school teachers will reflect on the implementation feedback to co-design improved task cards based on a different science standard. To investigate whether this professional learning approach supports the teachers in enacting practices aligned with the principles of the Instructional Conversation pedagogy, researchers will generate the following types of data: teacher reflection logs; teacher-generated artifacts, such as the task cards; transcripts from teacher focus groups; and video-recorded observations of instruction. Thematic and text analysis will explore potential shifts in teachers’ pedagogical practices with multilingual learners. Additional research will explore how specific supports, as embedded within teacher professional learning experiences, foster effective multidisciplinary collaboration among educators. This research purpose will be achieved through thematic analysis of transcripts from focus groups with the educators and teacher reflection logs, in addition to targeted purposeful transcriptions of recordings of the professional learning sessions. The resulting professional learning model and associated materials will be shared via an existing professional learning community, focused on the Instructional Conversation pedagogy, of over 2,500 educators. Pedagogical materials and empirical findings will be shared widely through professional networks of practitioners, professional development providers, and researchers in relevant fields. The Discovery Research preK-12 program (DRK-12) seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Predicting how species will be impacted by ongoing and future changes to their environment is critical. Species responses to these changes will determine how well ecosystems function and the ability of the Earth to continue providing food and other resources. Most such predictions focus on changes where a species can occur, but not changes in the numbers of a species, which can be more important for ecology. One major difficulty is that the environment can differ a lot from year to year, which makes it harder to predict how species will be impacted by gradual changes over a long period of time. Also, individuals of widespread species are often adapted to do best in their local environments, which means that the same species in different areas can have different environmental requirements. This project uses transplant experiments and long-term monitoring of wild populations to overcome these challenges, and tests how two common plant species are impacted by environmental change from New Mexico to arctic Alaska. The researchers also team up with educators to create middle and high school curriculum to teach students how to think critically and use real data to investigate ecological and environmental questions. This research relies on a comprehensive dataset spanning 15-28 years documenting demographic trends in two widespread, long-lived tundra plant species (Silene acaulis and Polygonum viviparum) in 29 populations across western North America. By continuing to monitor these wild populations, the researchers will develop a functional definition of rare climate events and assess their demographic impacts. The project also uses common garden transplants and controlled thermal performance experiments to assess local adaptation to climate and the demographic mechanisms driving it. This project will follow the performance of transplants for a total of 9 years, allowing researchers to test the importance of environmental extremes and cumulative abiotic effects on the magnitude and spatial scale of local adaptation. The researchers will integrate demographic and experimental datasets to develop environmentally-explicit and density-dependent demographic models to make range-wide predictions of distribution and local abundance, considering environmental variability and local adaptation. Notably, predictive models will be validated by testing their ability to “present-cast” current patterns of abundance and occurrence in new locales across the species’ latitudinal ranges before forecasting responses to projected climate change. The project’s goals are to both better predict how these particular species will respond to changes in their environment and to develop and test methods for making predictions that can be used for many other species. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

The project aims to initiate a new paradigm for statistical inference of high-dimensional time series. High-dimensional time series refer to a sequence of large dimensional data collected over time, and examples include large panel data in economics, functional magnetic resonance imaging data in neuroscience, stock price data for a large set of companies in finance, cellular usage data over time for a large number of users in telecommunication, high-resolution spatio-temporal data in climate science, and many others. An intrinsic feature of high-dimensional time series data is the temporal dependence among high-dimensional data vectors collected at different time points, while many existing results on high-dimensional data analysis require such vectors to be independent of each other. By allowing dependence to exist not only among different components of the data vector at any given time point but also among data vectors collected at different time points, the project results are expected to lead to a new paradigm for statistical inference and uncertainty quantification of high-dimensional time series. In addition, the project products are expected to be transformative and useful in a wide range of applications to provide the practitioners with a powerful and convenient statistical toolbox for scientific discoveries involving the analysis of high-dimensional time series data. The research developed is expected to be integrated into the undergraduate and graduate education and equip the students with advanced yet accessible statistical tools for analyzing high-dimensional time series data. The project involves the development of a new paradigm to quantify the accumulative uncertainty of self-normalized statistics over an increasing dimension, and a number of its guided statistical inference problems and real applications. Self-normalization refers to the technique of using recursive estimators to pivotalize the asymptotic distribution of the statistic of interest, which has enjoyed considerable development in the low-dimensional setting. Its extension to the high-dimensional setting, however, can be a nontrivial challenge and directly applying self-normalization to high-dimensional objects can lead to singularities or substantial size distortions. A major gap that prevented self-normalized statistics from prevailing in high-dimensional time series analysis is their non-standard limiting distribution which has been mostly tabulated through numerical approximations. To address this, the project seeks a new approach in connection with harmonic analysis to provide an analytical characterization on how the uncertainty of self-normalized distributions accumulates over an increasing dimension. The results then would guide the development of various statistical methods and their asymptotic theory for self-normalized inference of high-dimensional time series. These include, for example, self-normalized high-dimensional feature selection, simultaneous uncertainty quantification of high-dimensional objects, and extensions to general quantities such as the median, variance, quantiles, autocovariances, ratio statistics, and others. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This award supports the Neotoma Paleoecology Database. Neotoma is one of the most widely used and trusted international data resources for fossil data, growing rapidly in the volume and variety of its data holdings, functionality of its software services, and the size and scope of its user community. This award will allow Neotoma to grow and enhance systems to support higher rates of data additions, more streamlined data curation, and better support solutions for new communities seeking to use Neotoma data. This project provides access to publicly funded data and supports researchers, educators, and the public by providing a high-quality, expert-curated open data resource for paleoecological and paleoenvironmental data. Specific activities for this project include better support for rapid upload of hundreds to thousands of datasets from participating research teams through enhancements to the Data Bulk Uploader System (DataBUS), with newly added ORCID user authentication and support for the popular Linked Paleodata (LiPD) format. Embargo Manager will support early data contributions and better data management practice, in alignment with NSF Division of Earth Sciences (EAR) Data and Sample Policy. The Hierarchical Vocabulary and Taxonomy Manager (HVTM) will improve data quality and interoperability by enabling efficient viewing and curation of controlled vocabularies. Neotoma will freely upload supported data types, with priority for NSF-EAR PI data, and will help on-board major geoscience paleodata communities. Neotoma PIs will develop and provide multiple training support activities for scientists, with focused workshops for early career researchers (ECRs) and scientists from underserved regions, multi-lingual support for workshops and online resources, publicly posted training videos, and model workflows for data handling. Neotoma developers will reduce barriers to access and support artificial intelligence and machine-learning applications by deepening Neotoma’s metadata provisioning to Science-on-Schema and DataCite. Lastly, Neotoma stewards will create custom-tailored training and leadership opportunities for ECRs by designing workshops, videos, and code vignettes to address ECR-identified challenges. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This CAREER project will study transfer pathways from community college to CS majors in the University of California (UC) system to provide the first comprehensive picture of degree and early career trajectories followed by transfer students in CS. Community college transfer students represent a high-achieving and often untapped pool of talented emerging computer scientists. Thus, supporting their success is essential to growing the CS workforce, and future research is needed to understand how community college transfer students navigate their CS degree pathways and obtain success in reaching their educational and professional goals. This mixed-methods study will rely on longitudinal data in the form of surveys, student records, and ethnographic interviews, beginning from the time transfer students enter their UC campus and following them as they matriculate through their CS degree programs. Specific analyses will be guided by two overarching questions: (1) What trajectories do community college transfer students follow in their computer science bachelor’s degree pursuits? (2) How do community college transfer students following varying degree trajectories describe and make meaning of their experiences? Person-centered statistical analyses and ethnographic interviews will also explore variation among transfer students. In particular, this study will add nuance and complexity in terms of how we understand community college transfer student success, pushing us to define success beyond traditional metrics (e.g., degree efficiency; four- or six-year graduation rates, etc.). In doing so, this study will build a more robust knowledge base that can contribute to efforts to support community college transfer students as they exercise agency throughout their degree programs and obtain their professional goals. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

While biology education researchers strive to be inclusive and equitable, they can fall short of their goals due to the use of traditional research methods grounded on a historically Western perspective. These approaches fail to address the role of identity and culture in teaching and learning. This network will bring biology education researchers in dialogue with K-12 education and other social science researchers who have successfully expanded the research paradigms used in their own fields to support research and practices that consider these issues and support student learning and success. The intellectual merit of the project lies in the potential to generate innovative research, advance knowledge and design novel solutions to answer important and pressing questions of equity and inclusion in the field. Broader impacts include fostering systemic change in biology education research that will lead to practices that remove barriers and enhance the success of marginalized students. This will in turn, contribute to the recruitment and retention of scientists from diverse backgrounds that will themselves advance NSF's mission to foster scientific progress and advance national prosperity. The overall goals of the network incubator are to: 1) Assess how current/dominant epistemology, ontology, and practices in Biology Education Research (BER) impact the field's ability to envision and enact equity and inclusion in biology education; 2) Identify which approaches create barriers for achieving equity and social justice in biology education; and 3) Propose new research approaches for BER that better align with inclusive frameworks. To accomplish these goals, the network will host a 3-day community building and planning meeting followed by multiple virtual meetings. Findings will be disseminated through publication and a web-based presence. These activities align with RCN-UBE's goal to "catalyze positive changes in biology undergraduate education." This project is being jointly funded by the Directorate for Biological Sciences, Division of Biological Infrastructure, and the Directorate for STEM Education, Division of Undergraduate Education as part of their efforts to address the challenges posed in Vision and Change in Undergraduate Biology Education: A Call to Action (http://visionandchange/finalreport/). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Human interactions with plants and animals on dynamic and integrated natural and cultural landscapes, have provided the agrarian foundation for civilization. Agrarian responses to environmental flux depend on complex human interactions with the landscape, water management, and choices in plant and animal management, especially in environmentally fragile ecosystems. This project investigates how human civilizations respond, both successfully and unsuccessfully, to pronounced environmental changes. Specifically, this research illuminates the often-precarious relationships between agrarian societies, and their ecological settings over the long term. Profound environmental stress about four thousand years ago has been linked with major societal disruptions across the ancient world. Today, some ecosystems are warming almost twice as fast as the global average, and water demand is expected to double or triple in the next few decades, which will continue to stress already-vulnerable ecosystems, economies, and societies. With the dynamic sensitivity under consideration this investigation of social decision making and agrarian responses to environmental stress in the deep past potentially reveals fundamental implications for human resilience and sustainable agriculture in the present and the future. This project is an interdisciplinary study integrating social and natural scientists. Such studies are particularly well-suited to applying deep time perspectives to elucidate long-term environmental changes and assess how agricultural communities cope with environmental stress. Communication of project results will be facilitated by archaeologically informed artistic depictions of ancient landscapes and agrarian communities. Outreach to popular and university audiences in the United States and beyond will foster a sense of archaeological stewardship, an appreciation of deep human heritage, and an awareness of the varied responses to environmental change implemented by agrarian societies. Project data will be accessioned into the University of California’s Digital Library Merritt repository, where it will be managed by Open Context, and available for broad audiences to investigate agricultural practices during the rise and collapse of ancient civilizations. Project results contribute a new comparative approach for the study of ancient social flux across broad geographic and temporal scales, while offering opportunities for student and popular engagement in scientific research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Microbes such as viruses often exchange genetic material with the organisms they infect. Although this genetic material is usually lost, it can sometimes serve as an important source for the acquisition of new traits. This award seeks to understand the process by which virus-derived genetic material can be integrated into host organisms such as insects and then used in a novel setting for the benefit of the host. Research will focus on multiple examples of these processes that have repeatedly occurred in nature, making the results from this award significant for discerning physiological and genetic patterns that lead to evolutionary leaps in form and function. This award will also contribute to the development of the next generation of scientists, who are diverse and globally competitive, and public science outreach. A network of scientists engaged in insect and microbe-related research in the southeastern US will be established. The principal investigators will organize two symposia, inviting expert speakers in the field and facilitating interactions between trainees and members of the network. Viruses are a common source for genetic material entering eukaryotic genomes, and although most DNA acquisitions are non-functional and ultimately degrade, some have given rise to new traits that have significantly influenced eukaryotic evolution. This award will investigate how genes from microbial ancestors have evolved to operate in eukaryotic genomes to produce new beneficial traits. Research will focus on endocrine and intracellular factors that regulate beneficial virus function in insect cells. Extending findings to a comparative evolutionary framework has the potential to transform knowledge of the evolution of biological complexity from microbial traits at a molecular genetic level by comparing and contrasting the processes that have been co-opted to control viral gene expression. This project was co-funded by the Plant-Biotic Interaction Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Dynamic binary translation (DBT) is a technology that converts executable code from one type of computer architecture to another. This process is crucial for various applications, like hardware emulators and software testing tools. However, creating an efficient and accurate DBT system is very challenging. These systems often encounter bugs and performance issues that can severely affect their reliability and usability. The aim of this project is to improve DBT systems by developing methods to detect and fix hidden bugs and performance problems. The project introduces new verification techniques to identify bugs and practical methods to test performance. These advancements are expected to significantly enhance the reliability, usability, and efficiency of DBT systems. By making these improvements, the project will help expand the use of DBT technology across different applications that require precise and efficient systems. Additionally, the project includes teaching and mentoring activities to support a rich learning environment, providing research opportunities for undergraduate students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

As the U.S. invests in efforts to build its capacity to lead the world in the development of artificial intelligence and other areas of computer science, it faces a critical workforce bottleneck—a shortage of computer scientists to meet workforce demands in industry and academia. One way to effectively expand the computer science workforce is to develop a pathway from community colleges to undergraduate and graduate degree programs. Community college transfer students in computer science are a particularly high-achieving group, and community colleges are a primary entry point into higher education for many students. Despite the talents and assets that transfer students bring to the computer science major, they also face unnecessary barriers at their universities, which constrain their opportunities to become leaders in their field. The goal of this project is to produce new knowledge that will address these barriers and guide efforts to transform university structures, policies, and practices to foster success among community college transfer students in computer science. This study will engage a convergent, multi-phase mixed methods design. Drawing on five years of longitudinal survey and interview data from computer science transfer students and other key university agents (e.g., university faculty, staff, administrators) across six campuses in the California State University system, the project aims to address the following overarching research questions: (1) What are the structures, policies, and practices that community college transfer students identify in shaping their degree trajectories in computer science? (2) From the perspective of key university agents (e.g., advising staff, faculty, administrators), what are the relevant structures, policies, and practices that shape community college transfer student degree trajectories and opportunities in computer science? The inquiry will be informed by social cognitive career theory (SCCT) and theories of administrative burden and street-level bureaucracy. The quantitative stream of this work will rely on descriptive and multivariate analysis of student surveys and registrar data. The qualitative stream of this research will use phenomenological methods, relying on student and university agent interview data to better understand the experienced phenomena (i.e., structures, policies, and practices that shape transfer student trajectories in computer science). This approach aligns with the research questions, which collectively focus on how participants with varying positionalities (e.g., student, staff, faculty) make meaning of the structures, policies, and practices that shape community college transfer student success and degree trajectories. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This award provides support to U.S. researchers participating in a project competitively selected by a 55-country initiative on global change research through the Belmont Forum. The Belmont Forum is a consortium of research funding organizations focused on support for transdisciplinary approaches to global environmental change challenges and opportunities. It aims to accelerate delivery of the international research most urgently needed to remove critical barriers to sustainability by aligning and mobilizing international resources. Each partner country provides funding for their researchers within a consortium to alleviate the need for funds to cross international borders. This approach facilitates effective leveraging of national resources to support excellent research on topics of global relevance best tackled through a multinational approach, recognizing that global challenges need global solutions. This award provides support for the U.S. researchers to cooperate in consortia that consist of partners from at least three of the participating countries. The teams will develop transdisciplinary and convergent research approaches on cultural heritage and climate change, foster collaboration among the research community across several regions, and contribute to knowledge advances at the global level. The project focuses on the role that cultural landscapes can play as essential ecological and sociocultural services to help address climate change. The project will develop a toolkit to assist communities assess the vulnerability and resilience of cultural landscapes and further develop our understanding of the vulnerability and resilience of cultural landscapes and culturally informed strategies for climate mitigation and adaptation. The project team will work with local communities in France, Norway and Spain to develop assessment and planning tools for cultural landscapes based on the integrated analysis of social and ecological data. This information will be coupled with downscaled climate projections with clearly defined timescales to assist the communities in understanding potential risks and vulnerabilities and serve as a basis for decision-making. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

This award supports an interdisciplinary project at the interface of philosophy, linguistics, cognitive science, and artificial intelligence. The quality of machine translation has greatly improved over the last ten years, especially with the advent of neural networks and large language models. This has not resulted in making human translation redundant; rather, it is rapidly changing the nature of the everyday work of human translators. An increasing number of stakeholders are coming to view the relationship between human translation and machine translation as symbiotic, not adversarial. Tech giants are investing vast amounts of capital into translation technologies. They are also becoming increasingly interested in specialists who combine technical training with a background in the humanities. Larger translation agencies are actively working on improving human-machine interaction in the context of specialized language-specific tasks, such as translating healthcare materials from English to Catalan. By pursuing the goals of this project, the PI will contribute to nurturing the next generation of translation specialists. The PI will disseminate the results of this project at both academic industry conferences and will reach out to broader audiences, from professional translators to the public at large. He will make the project data, the conclusions, and the educational and research tools illustrating the complex interaction of human translation and machine translation widely available on a new, dedicated website. This project is the first empirically grounded study of human translation and machine translation conducted from the complementary perspectives of philosophy, linguistics, and cognitive science. It combines a theoretical component focused on the representation and transfer of linguistic meaning in various human, machine, and hybrid human-machine translation systems, and a practical component focused on the human-machine symbiosis in realistic translation scenarios and ways of improving it. Philosophers have approached the problem of meaning from many angles, but never in the context of recent groundbreaking developments in translation technologies. A close examination of how human and machine translation interact in real life may offer new insights into how physical systems represent linguistic meaning and, more ambitiously, into the nature of linguistic meaning itself. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The study will investigate whether participation in early intervention positively predicts graduate school aspirations and enrollment among upward transfer community college computing students and whether this differs by gender, race/ethnicity, first generation college status, or socioeconomic status. The college study also will consider factors that shape their decision making. The intervention will be implemented across five campuses within the University of California system, tracking students from the time they transfer to one of the campuses through matriculation unto graduate programs in computing. By empirically examining the impact of different aspects of the intervention, the study will provide guidance for how to most efficiently promote graduate school aspirations and intentions among upward transfer students in computing and other STEM fields. Guided by Social Cognitive Career Theory (SCCT), the study will investigate four research questions: (1) Does participation in the early intervention program positively predict graduate school aspirations and enrollment among upward transfer computing students? Does this differ by gender, race/ethnicity, first-generation college status, or socioeconomic status? (2) What other post-transfer experiences predict graduate aspirations and enrollment among upward transfers in computing? (3) How do upward transfer students experience and make meaning of the early intervention program? And (4) How do upward transfer students aspire to graduate school in computing? What factors shape their decision making? Investigators will employ multiple methods to examine the longitudinal impact of the proposed intervention and a Staged Innovation Design to analyze the data. The project could inform the design of pathways from community college to doctoral programs in computing, thereby, diversifying the computing faculty. The award is funded by the EHR Core Research (ECR) program, STEM professional workforce development theme. ECR supports fundamental research that addresses STEM learning and learning environments, broadening participation in STEM, and STEM workforce development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

Many trace elements (TEs) such as iron are required for the growth of phytoplankton in the ocean, and their availability can limit biological production. One important pathway by which these scarce elements are delivered to surface ocean waters is through aerosol particles in the atmosphere. This project will address important questions about the variability in the atmospheric delivery of iron and other TEs to the surface waters of the North Atlantic Ocean near Bermuda through aerosol sampling, laboratory experiments, and modeling. Increased accuracy of atmospheric deposition and aerosol iron solubility representation will improve global biogeochemical models and our understanding of how changes in climate may influence the marine carbon cycle through changes in atmospheric iron deposition. The project will provide opportunities for undergraduate and graduate student involvement and support an early-career scientist. Data will be posted on the BCO-DMO website and made widely available to scientists working in similar fields by being included in the SCOR Working Group 167 (Reducing Uncertainty in Soluble aerosol Trace Element Deposition) data compilation. This project will conduct a two-year time-series of size-fractionated aerosol sampling at the Tudor Hill Marine Atmospheric Observatory in Bermuda to accomplish four goals: 1. Analyze temporal variations in the size distribution of aerosol Fe and other bioactive, pollutant, and tracer TEs, as well as major cations and anions over the western North Atlantic Ocean and link these variations to aerosol sources and transport pathways. 2. Apply a range of chemical extractions to size-fractionated and bulk North Atlantic aerosols to quantify lower and upper estimates of potentially bioavailable Fe and explain trends in bulk aerosol Fe solubility in the context of variations observed in size-fractionated aerosols. 3. Use elemental ratios, Fe stable isotopes, Fe-mineralogical partitioning and redox state, and air mass back trajectories to directly probe the chemical controls on aerosol Fe solubility as a function of aerosol particle size and source over a two-year period. 4. Parameterize concurrently measured meteorological conditions to model deposition velocities for each aerosol size-fraction during weekly sampling periods, thereby constraining supply rates of total and soluble TEs to North Atlantic surface waters. The results of this study will provide a deeper understanding of the factors influencing trace element solubility in North Atlantic aerosols and the role that aerosol size distribution plays in these variations. Rates of dry deposition calculated using size-fractionated aerosol collections will give a better understanding of the potential overestimation of bioavailable trace element supply rate that may arise from previous calculations based solely on bulk aerosol concentrations. This project will thus improve representation of atmospheric trace element solubility and deposition flux in global and regional deposition models. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.