Grants

NSF

The Sub-national Nonstate Actor Governance (SNAG) project introduces a new measurement strategy and public dataset to measure territorial control at the local level within conflict zones, tracked over time. Understanding how groups gain or lose territorial control, and thus how conflicts begin, evolve, and end, is essential to national security and preparedness. Yet scholars, policymakers, and military strategists lack reliable and accessible techniques to measure and monitor territorial control within conflict zones. Existing empirical research is focused on a limited number of conflicts for which there happen to exist reliable measures of local-level territorial control over time. This limits ability to understand conflict more generally, and to apply knowledge to new threat environments. This research draws upon open-source information to ensure a transparent process that is easily replicated across contexts and adapted to new measurement challenges. The project uses machine learning and natural language processing (NLP) tools to automatically detect mentions of belligerent activity and control in a corpus of open-source texts, which are then used to produce spatially and temporally disaggregated estimates of rebel and government territorial control. The Subnational Nonstate Actor Governance (SNAG) project measures nonstate actors’ territorial control and governance at the local level, capturing temporal variation throughout conflict, comparable across contexts. This project makes both substantive and methodological contributions, generates new publicly available data capturing nonstate actors’ territorial control, uses an approach that translates across contexts to facilitate comparative analyses. The PIs annotate text from a corpus of news reports from conflict zones, identifying indicators of rebel and government territorial control with location and time information. These annotations are then used to train a new natural language processing pipeline, which is applied to the remainder of the corpus to automate the process of extracting relevant information from the full corpus. The information produced by this process is incorporated into a measurement model to produce fine-grained spatio-temporal data on conflict belligerents’ territorial control within conflict zones, facilitating systematic comparison of these phenomena within and across conflicts. The subnational territorial control data are used to investigate basic research questions related to the causes and consequences of territorial control and governance, fundamental to understanding the security risks in “differently governed” spaces, the efficacy of counterinsurgency aid, and the consequences for state-building after conflict. Methodologically, SNAG contributes new tools for generating geospatial data from text and for developing spatial latent variable models adaptable for additional social science applications. 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

The Sub-national Nonstate Actor Governance (SNAG) project introduces a new measurement strategy and public dataset to measure territorial control at the local level within conflict zones, tracked over time. Understanding how groups gain or lose territorial control, and thus how conflicts begin, evolve, and end, is essential to national security and preparedness. Yet scholars, policymakers, and military strategists lack reliable and accessible techniques to measure and monitor territorial control within conflict zones. Existing empirical research is focused on a limited number of conflicts for which there happen to exist reliable measures of local-level territorial control over time. This limits ability to understand conflict more generally, and to apply knowledge to new threat environments. This research draws upon open-source information to ensure a transparent process that is easily replicated across contexts and adapted to new measurement challenges. The project uses machine learning and natural language processing (NLP) tools to automatically detect mentions of belligerent activity and control in a corpus of open-source texts, which are then used to produce spatially and temporally disaggregated estimates of rebel and government territorial control. The Subnational Nonstate Actor Governance (SNAG) project measures nonstate actors’ territorial control and governance at the local level, capturing temporal variation throughout conflict, comparable across contexts. This project makes both substantive and methodological contributions, generates new publicly available data capturing nonstate actors’ territorial control, uses an approach that translates across contexts to facilitate comparative analyses. The PIs annotate text from a corpus of news reports from conflict zones, identifying indicators of rebel and government territorial control with location and time information. These annotations are then used to train a new natural language processing pipeline, which is applied to the remainder of the corpus to automate the process of extracting relevant information from the full corpus. The information produced by this process is incorporated into a measurement model to produce fine-grained spatio-temporal data on conflict belligerents’ territorial control within conflict zones, facilitating systematic comparison of these phenomena within and across conflicts. The subnational territorial control data are used to investigate basic research questions related to the causes and consequences of territorial control and governance, fundamental to understanding the security risks in “differently governed” spaces, the efficacy of counterinsurgency aid, and the consequences for state-building after conflict. Methodologically, SNAG contributes new tools for generating geospatial data from text and for developing spatial latent variable models adaptable for additional social science applications. 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

Many processes of industrial and technological importance involve the impact of drops on a surface. Drop impact is a key step in applying coatings to solid surfaces and in various printing processes. Controlling the dynamics of drop impact is essential in achieving uniform coatings and in realizing high-resolution printing. One way to regulate drop impact is to tailor the properties of the substrate – the surface upon which drops impact. Prior research demonstrated that soft substrates could inhibit splashing and avoid the formation of small extraneous drops. However, the underlying physics and the role of substrate properties on drop impact are poorly understood. This project will systematically study how variations in substrate properties influence drop impact behaviors. Conducted in collaboration with industrial engineers, the research will focus on substrates most relevant to industrial coating processes. Thus, the proposed research will enrich the fundamental understanding of drop impact dynamics and potentially revolutionize existing industrial coating practices, particularly the processes of multi-layer coating and coating on compliant surfaces. The central hypothesis of this research is that precise tuning of substrate mechanical properties offers a powerful route to control drop impact outcomes with high specificity. This research will systematically investigate how compliant, heterogeneous, and non-Newtonian substrates influence drop impact dynamics. By modulating substrate elasticity to control drop impact, the research aims to reveal the missing link between the onset of splashing and the distribution of impact pressure, addressing a long-standing question in the study of drop impact. The project will unfold in three stages, examining how (1) thin elastic substrates, (2) mechanically heterogeneous substrates, and (3) rheologically complex substrates alter the underlying fluid dynamics of drop impact. Motivated by pressing engineering challenges such as multi-layer coatings and coatings on compliant surfaces, this research will decompose complex impact scenarios into simple model processes, enabling the development of predictive understanding applicable to more complex industrial settings. A strong partnership with industrial researchers ensures that the project remains aligned with the most relevant and urgent engineering needs. In addition to industrial impact, the project will contribute to STEM education through student exchange programs and outreach 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

Many processes of industrial and technological importance involve the impact of drops on a surface. Drop impact is a key step in applying coatings to solid surfaces and in various printing processes. Controlling the dynamics of drop impact is essential in achieving uniform coatings and in realizing high-resolution printing. One way to regulate drop impact is to tailor the properties of the substrate – the surface upon which drops impact. Prior research demonstrated that soft substrates could inhibit splashing and avoid the formation of small extraneous drops. However, the underlying physics and the role of substrate properties on drop impact are poorly understood. This project will systematically study how variations in substrate properties influence drop impact behaviors. Conducted in collaboration with industrial engineers, the research will focus on substrates most relevant to industrial coating processes. Thus, the proposed research will enrich the fundamental understanding of drop impact dynamics and potentially revolutionize existing industrial coating practices, particularly the processes of multi-layer coating and coating on compliant surfaces. The central hypothesis of this research is that precise tuning of substrate mechanical properties offers a powerful route to control drop impact outcomes with high specificity. This research will systematically investigate how compliant, heterogeneous, and non-Newtonian substrates influence drop impact dynamics. By modulating substrate elasticity to control drop impact, the research aims to reveal the missing link between the onset of splashing and the distribution of impact pressure, addressing a long-standing question in the study of drop impact. The project will unfold in three stages, examining how (1) thin elastic substrates, (2) mechanically heterogeneous substrates, and (3) rheologically complex substrates alter the underlying fluid dynamics of drop impact. Motivated by pressing engineering challenges such as multi-layer coatings and coatings on compliant surfaces, this research will decompose complex impact scenarios into simple model processes, enabling the development of predictive understanding applicable to more complex industrial settings. A strong partnership with industrial researchers ensures that the project remains aligned with the most relevant and urgent engineering needs. In addition to industrial impact, the project will contribute to STEM education through student exchange programs and outreach 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

This project aims to serve the national interest by developing undergraduate chemistry students’ competency to engage in the practices of scientists, as well as facilitating students’ beliefs in their abilities to engage in these science practices, which is known as science practice self-efficacy (SPSE). Facility with scientific practices and confidence in carrying them out are critical for students’ ability to apply their scientific understandings both in school and in post-school life. The project team plans to implement a novel approach to engaging chemistry learners in science practices. They also plan to develop new survey instruments to generate evidence that can be used to better understand the relationships among students’ science practice competency, self-efficacy, science identity, and chemistry course performance. The findings have the potential to improve introductory chemistry education and support the development of scientifically literate STEM graduates. This project is a collaboration between researchers at University of Nebraska-Lincoln and California State University-Fullerton. Project activities will include (1) developing and testing high-quality instruments for measuring science practice self-efficacy and science practice competency, (2) conducting a quasi-experimental study in general chemistry and organic chemistry courses to identify how engaging chemistry students in science practices supports their science practice competency and SPSE, and (3) modeling the relationship between SPSE and relevant outcome variables using a structural equation model. The project will leverage the complementary expertise of the research team and tailored expertise of the advisory board members to ensure the quality of all products generated through research activity. The measures of science practice self-efficacy and science practice competency to be developed will have the potential be useful for researchers across STEM education fields. These products will be disseminated through publications and resource databases accessible to the wider chemistry education community such as the Chemistry Instrument Review and Assessment Library (CHIRAL) and Organic Chemistry Educational Resources (OrganicERs). The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

Rochester Institute of Technology, Syracuse University, and the University of Rochester aim to enhance high school computing education by providing them easy to adopt, experiential educational labs. This project will adapt and evaluate the Accessible Learning Labs (ALL)—a suite of experiential educational computing modules—for effective use in grades 9–12. The investigators, in collaboration with high school educators and Science and Technology Entry Program (STEP) programs across Upstate New York, will modify and implement several existing experiential educational labs to align with secondary education contexts. By including engaging, real-world computing topics such as artificial intelligence, cybersecurity, and software development into classrooms, the project empowers students with the skills and confidence needed for STEM careers. The work supports the national interest by promoting the progress of science and broadening participation in computing through experiential learning tools. Collaborations with high schools and NY STEP programs will ensure that the project benefits a future STEM workforce. In addition to directly supporting student learning, the project contributes to research on how experiential teaching methods can improve engagement in STEM fields. This small CSforAll High School Strand project will apply experiential learning principles to teach foundational computing concepts in artificial intelligence, cybersecurity, accessible software design, and machine learning. Through iterative co-design, classroom implementation, and rigorous formative and summative evaluation, the team will assess the educational impact of these labs on both students and instructors. The effort will contribute to pedagogical knowledge on experiential computing education at the high school level, address existing gaps in accessible STEM resources, and generate scalable, open-access materials to support nationwide adoption. 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

To provide high-quality high-school computer science (CS) education experiences for every student in the nation, there is a critical need to develop professional learning programs to support teachers—many of whom did not receive CS instruction in their own educational experiences—to learn CS content and pedagogy. The American Institutes for Research and Marquette University (MU), in collaboration with Milwaukee Public Schools (MPS) and The Learning Partnership, will develop an innovative professional learning program for high school teachers called the CS Bootcamp Experience. The CS Bootcamp Experience will be aligned to the widely used Exploring Computer Science curriculum and combine CS content modules with monthly professional learning community meetings and classroom coaching support. By supporting MPS teachers to strengthen their CS content knowledge and teaching practices, this project will support more MPS high school students to have strong CS learning experiences and better prepare them to use CS in a variety of college and career pathways and to address challenges in their communities. The objectives of the CSforAll High School Strand Research-Practice Partnership are to (a) adapt an existing MU CS bootcamp course to create the CS Bootcamp Experience; (b) implement the CS Bootcamp Experience across 2 school years with a cohort of 15 MPS teachers, gathering feedback to inform improvements; and (c) generate evidence of the bootcamp’s effect—and, thereby, the potential effect of teacher CS content knowledge supports—on outcomes in MPS aligned with the Capacity, Access, Participation, Experience (CAPE) framework for promoting CS education. The project team will use design-based implementation research to support iterative improvements of the CS Bootcamp Experience. In addition to gathering direct feedback from teachers, the project will also monitor implementation of the Exploring Computer Science (ECS) curriculum and periodically assess teachers’ and students’ content knowledge, teachers’ self-efficacy for teaching CS, and students’ attitudes about CS. The project team will continually reflect on these data, using them to gain insight into the impact of the CS Bootcamp Experience on students and teachers and to inform iterative improvements. The project will have direct impact on 15 MPS teachers and 600 MPS students, improving their CS teaching and learning experiences. The CS Bootcamp Experience may also serve as a model for other districts for improving their teaching and implementation of ECS, potentially leading to strong CS learning experiences for high school students across Wisconsin and the nation. 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

Students diagnosed with autism spectrum disorder (ASD) possess many critical qualities and characteristics associated with success in computing fields. Studies have shown that post-secondary graduation rates for students with ASD are lower than the general student population. Several studies have identified underdeveloped verbal/non-verbal communication abilities and social skills as significant factors to these attrition rates. The goal of the Neurodiversity Research Innovation Experience (NEURODRiVE) program is to support students so that they can successfully pursue and obtain employment in computing fields. Special attention will be given to creating workforce opportunities for Palm Beach State College (PBSC) students diagnosed with ASD, as this underutilized talent pool is of strategic importance to Florida. By providing tailored support and immersive learning experiences, NEURODRiVE will improve educational and employment outcomes for all PBSC students. The project team will leverage the power of incorporated technologies that provide progressive, scaffolded experiences leading to real-world relationships in academic and career settings. The program will expose students to customized AR/VR modules where they will learn to navigate verbal/non-verbal communication and social skills in simulated workplace situations. The program will encourage participants to evaluate, critique, and present their AR/VR module experience to practice peer-to-peer communication skills. Industry partners will participate in the development of simulated job interviews in a VR environment to assist students in familiarizing and preparing for internships and job interviews. PBSC will work with local industry partners to provide internships for participating students. The project team will provide “flip-side mentorship” training for faculty and industry partners on evidence-based communication techniques that support student success in the classroom, and the workplace. This project is funded by the Advanced Technological Education program that focuses on the education of technicians for the advanced-technology fields that drive the nation's economy. 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

Modeling is a key scientific practice emphasized in the Next Generation Science Standards (NGSS), but students often need significant support to engage in the practice meaningfully. This project aims to develop an augmented reality (AR) learning environment integrated with a large language model (LLM)-powered pedagogical agent to guide middle school students' modeling practice. It will bring together computer scientists, human-computer interaction researchers, science education researchers, and K-12 teachers to co-design the learning environment. The AR environment will allow students to interact with simulated objects and phenomena as they develop their models. The LLM-powered agent will provide timely assessments of students' diagrammatic models, offer personalized feedback, and share insights with teachers to inform instruction. This project has the potential to transform the way students engage in modeling practice in K-12 science classrooms. Over three years, it will directly impact approximately 10 middle school teachers and 400 middle school students. The project is guided by three core objectives: 1) to build AR simulations for modeling tasks, 2) to develop an on-device multitask LLM to assess diagrammatic models, and 3) to investigate student modeling practice within the AR-LLM learning environment. A key innovation of the project is the closed-loop feedback system: students first receive suggestions for model revisions from the LLM and revisit AR components, and the LLM agent then provides feedback on students' performance to teachers to inform instructional decision-making. The project will investigate students' perceptions of their modeling experiences and how the AR elements and LLM-generated feedback support their model development respectively. Primary data sources will include classroom video recordings of student consensus model building, screen recordings of individual modeling activities, and semi-structured focus student interviews. A multiple-case study approach will be used to investigate student learning. Research products will be widely shared with practitioners, teacher educators, and researchers through publications, conference presentations, teacher workshops, and publicly accessible teacher resources. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and learning. 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

This collaborative project explores how a tiny droplet, powered by internal and surface activity, can propel itself—serving as a simplified model for how primitive cells, or "protocells", move. In experiments, such systems can be created by building networks of actin proteins inside and along the membrane of giant vesicles. To understand how this motion arises, the research team develops mathematical models that describe how forces inside the droplet and on its surface interact with the surrounding fluid. A key focus is to understand how this active droplet pushes against its environment to generate sustained forward motion—behavior that is fundamental to many forms of movement in soft materials and living cells. The project supports graduate education at Florida State University and New Jersey Institute of Technology, and promotes collaboration and dissemination of scientific knowledge through scientific workshops and seminars. The project aims to elucidate the role of steric alignment interactions in the nematic fluid on drop propulsion. The project combines analytical theory, numerical simulations, and comparisons with experimental data from active vesicle systems. The primary investigator Young leads the development of mathematical models and analytical methods, including theory of partial differential equations (PDE), dynamical systems analysis, differential geometry, and asymptotic techniques. The primary investigator Quaife develops efficient numerical algorithms for solving coupled surface-bulk PDEs on both rigid and deforming geometries. These numerical methods include solvers for surface PDEs on evolving interfaces and bulk-surface coupling across moving boundaries. A central challenge is modeling steric alignment interactions at the continuum level and calibrating their strength using experimental observations. The resulting framework has broad applicability to active matter systems described by coupled surface-bulk dynamics on moving domains. 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

This collaborative project explores how a tiny droplet, powered by internal and surface activity, can propel itself—serving as a simplified model for how primitive cells, or "protocells", move. In experiments, such systems can be created by building networks of actin proteins inside and along the membrane of giant vesicles. To understand how this motion arises, the research team develops mathematical models that describe how forces inside the droplet and on its surface interact with the surrounding fluid. A key focus is to understand how this active droplet pushes against its environment to generate sustained forward motion—behavior that is fundamental to many forms of movement in soft materials and living cells. The project supports graduate education at Florida State University and New Jersey Institute of Technology, and promotes collaboration and dissemination of scientific knowledge through scientific workshops and seminars. The project aims to elucidate the role of steric alignment interactions in the nematic fluid on drop propulsion. The project combines analytical theory, numerical simulations, and comparisons with experimental data from active vesicle systems. The primary investigator Young leads the development of mathematical models and analytical methods, including theory of partial differential equations (PDE), dynamical systems analysis, differential geometry, and asymptotic techniques. The primary investigator Quaife develops efficient numerical algorithms for solving coupled surface-bulk PDEs on both rigid and deforming geometries. These numerical methods include solvers for surface PDEs on evolving interfaces and bulk-surface coupling across moving boundaries. A central challenge is modeling steric alignment interactions at the continuum level and calibrating their strength using experimental observations. The resulting framework has broad applicability to active matter systems described by coupled surface-bulk dynamics on moving domains. 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

Models of the physical and chemical behavior of partially molten rocks form a key component of scientists' ability to study and understand volcanic systems. Together with field-based studies, experimental analyses, and investigations of prior eruptions, these models are important for advancing our knowledge of potentially hazardous volcanoes in the United States and globally. This project continues development of a flexible, powerful, and easy-to-use suite of modeling software tools used by thousands of Earth scientists: alphaMELTS. This project will expand the scenarios that alphaMELTS can model, increase integration with igneous rock and experimental databases, link to other geochemical modeling tools written in Python, and support users with workflows for increased reproducibility. Online resources and outreach workshops will extend applications of the software in teaching and training. These workshops, both virtual and in-person, will equip scientists from various career stages and experience levels with quantitative tools for modern Earth science research. This project will implement a framework to align alphaMELTS petrologic modeling software and workflows with FAIR principles (findable, accessible, interoperable, and reusable). Planned developments will make alphaMELTS fully open source, easy to install with standard tools, interoperable with associated modeling and visualization tools and databases, callable from many programming and data visualization environments, and fully versioned, logged, and documented. The work will focus on Python-based tools – in particular, via continued development of the PetThermoTools package for beginner-to-intermediate MELTS users, and machine-learning assisted acceleration for high-end users with high-volume throughputs – but also support those who prefer a simpler graphical user interface. A systematic assessment of model performance will guide users towards applications where the software is verifiably accurate. Expanded functionality will include modeling of reverse crystallization and post-entrapment crystallization of melt inclusions, integration with packages like Thermobar and PySulfSat, and a new trace element engine that gives users total control over partition coefficients. This project will increase integration with IEDA2 databases and the VICTOR portal, and engage with users though workshops, online resources, and creation of instructional materials. The core software development team will grow to include two early-career researchers, as well as two graduate student researchers, which will foster the continuity of alphaMELTS services moving forward. 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

Minerals are essential to understanding our planet's past and guiding its sustainable future. This project builds on Mindat.org, the world's largest public database of minerals and their global distributions, which receives nearly 10 million visitors per year. With previous support from the National Science Foundation, the OpenMindat project has made mineral data more accurate and accessible for scientists and the public alike. This new phase of work, called OneMineralogy, will expand these efforts into a broader open science ecosystem. OneMineralogy aims to make mineral data and tools available to a wider range of users, including students, educators, researchers, and decision-makers. Its data, tools, and services will help them ask and answer big questions about how Earth's systems have evolved over time. The activities of OneMineralogy will strengthen science education, promote open data sharing, support mineral exploration (including critical minerals), and accelerate discoveries related to planetary science, Earth-life co-evolution, and environmental change. By fostering new partnerships and offering training opportunities, OneMineralogy will empower the next generation of scientists and ensure that mineralogical data benefit society as a whole. Scientifically, OneMineralogy will advance the frontier of data-driven geoscience by developing new data curation strategies, computational tools, and community engagement programs. It builds on the success of the NSF-funded OpenMindat project, which has already improved the quality and accessibility of over 6,000 mineral species records and data from more than 400,000 global localities. OneMineralogy will consist of three major activity clusters: (1) extending and curating data to support a wider range of geoscientific research, (2) building a data science toolbox to enable large-scale analysis of mineralogical systems, and (3) conducting workshops and outreach programs to grow the user community and build capacity. The project will integrate Mindat with other open cyberinfrastructure resources related to paleoenvironments, geomicrobiology, tectonics, and biosignatures. The enriched data and tools will provide strong support to the investigation of Earth's dynamic history through deep time. It will also provide a foundation for interpreting the growing body of mineralogical data from planetary missions to the Moon and Mars. Through these activities, OneMineralogy will create a sustained, open, and collaborative ecosystem to support transdisciplinary research and education in the Earth and planetary sciences. This award by the Geoinformatics program is co-funded by the Innovative Technology Experiences for Students and Teachers (ITEST) program, which supports projects that build understandings of practices, program elements, contexts and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers. 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

The Paleobiology Database (PBDB) is one of the most impactful and widely used digital representations of the fossil record, capturing our best understanding of the age, location, identity, and geological context of fossils. Data held in the PBDB have been used in over 2,000 scientific publications and in a wide range of educational and public outreach materials. The PBDB is an essential resource for geoscientists, biologists, students, educators, and the public. Although the research and educational impact of the PBDB is tremendous, there are key issues with its current computer infrastructure, which was designed in the late 1990s and early 2000s. This project will combine PBDB with the Integrative Paleobotany Portal (PBot) to create a more modern and flexible digital system: the PBDB 2.0. Planned project activities will transform the PBDB to be more useful to a broad community of researchers, educators, and students in the Earth and life sciences, as well as the general public. In particular, the PBDB will adopt features from PBot that will allow users to easily contribute and interact with fossil identifications tied to specimen images, and outreach activities will further motivate community participation with PBDB. This project undertakes a complete technological overhaul of the PBDB, one of the most prominent and widely used fossil databases. Technical improvements include coupling the PBDB with the PBot, whose cutting-edge conceptual framework provides innovative user-centric capabilities. Specifically, development of the PBDB 2.0 will: 1) overhaul the PBDB's data model, database, and application logic to integrate PBot functions, streamline data entry, and improve technical sustainability; 2) create a more capable application programming interface; 3) construct a new web application to leverage back-end upgrades and facilitate new science; and 4) host community events and activities to assess progress, train new members, mobilize data, and produce new educational/outreach materials. Long-term costs of paleobiological cyberinfrastructure will be substantially reduced by consolidating development effort, expanding the PBDB's current functionality to better serve a large community, enhancing overall user experiences, mobilizing new science-critical data, and improving alignments with other data systems. Upgrades will provide explicit support for uncertain taxonomic classification, introduce a more specimen-forward approach to organizing fossil data, make high quality specimen images available alongside data, and add workbench capabilities. Researchers from around the world will utilize the PBDB 2.0 to understand Earth history and Earth systems processes, conduct Rules of Life research, and interpret ecological and evolutionary change across all taxonomic groups, continents, and time periods. The refreshed PBDB 2.0 will make it easier for anyone in the broader community, from professional Earth and life scientists to children, to engage with fossil science. This award by the Geoinformatics program within the Division of Earth Sciences is jointly supported by the Infrastructure Capacity for Biological Research program within the Division of Biological Infrastructure. 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

The Paleobiology Database (PBDB) is one of the most impactful and widely used digital representations of the fossil record, capturing our best understanding of the age, location, identity, and geological context of fossils. Data held in the PBDB have been used in over 2,000 scientific publications and in a wide range of educational and public outreach materials. The PBDB is an essential resource for geoscientists, biologists, students, educators, and the public. Although the research and educational impact of the PBDB is tremendous, there are key issues with its current computer infrastructure, which was designed in the late 1990s and early 2000s. This project will combine PBDB with the Integrative Paleobotany Portal (PBot) to create a more modern and flexible digital system: the PBDB 2.0. Planned project activities will transform the PBDB to be more useful to a broad community of researchers, educators, and students in the Earth and life sciences, as well as the general public. In particular, the PBDB will adopt features from PBot that will allow users to easily contribute and interact with fossil identifications tied to specimen images, and outreach activities will further motivate community participation with PBDB. This project undertakes a complete technological overhaul of the PBDB, one of the most prominent and widely used fossil databases. Technical improvements include coupling the PBDB with the PBot, whose cutting-edge conceptual framework provides innovative user-centric capabilities. Specifically, development of the PBDB 2.0 will: 1) overhaul the PBDB's data model, database, and application logic to integrate PBot functions, streamline data entry, and improve technical sustainability; 2) create a more capable application programming interface; 3) construct a new web application to leverage back-end upgrades and facilitate new science; and 4) host community events and activities to assess progress, train new members, mobilize data, and produce new educational/outreach materials. Long-term costs of paleobiological cyberinfrastructure will be substantially reduced by consolidating development effort, expanding the PBDB's current functionality to better serve a large community, enhancing overall user experiences, mobilizing new science-critical data, and improving alignments with other data systems. Upgrades will provide explicit support for uncertain taxonomic classification, introduce a more specimen-forward approach to organizing fossil data, make high quality specimen images available alongside data, and add workbench capabilities. Researchers from around the world will utilize the PBDB 2.0 to understand Earth history and Earth systems processes, conduct Rules of Life research, and interpret ecological and evolutionary change across all taxonomic groups, continents, and time periods. The refreshed PBDB 2.0 will make it easier for anyone in the broader community, from professional Earth and life scientists to children, to engage with fossil science. This award by the Geoinformatics program within the Division of Earth Sciences is jointly supported by the Infrastructure Capacity for Biological Research program within the Division of Biological Infrastructure. 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

The Paleobiology Database (PBDB) is one of the most impactful and widely used digital representations of the fossil record, capturing our best understanding of the age, location, identity, and geological context of fossils. Data held in the PBDB have been used in over 2,000 scientific publications and in a wide range of educational and public outreach materials. The PBDB is an essential resource for geoscientists, biologists, students, educators, and the public. Although the research and educational impact of the PBDB is tremendous, there are key issues with its current computer infrastructure, which was designed in the late 1990s and early 2000s. This project will combine PBDB with the Integrative Paleobotany Portal (PBot) to create a more modern and flexible digital system: the PBDB 2.0. Planned project activities will transform the PBDB to be more useful to a broad community of researchers, educators, and students in the Earth and life sciences, as well as the general public. In particular, the PBDB will adopt features from PBot that will allow users to easily contribute and interact with fossil identifications tied to specimen images, and outreach activities will further motivate community participation with PBDB. This project undertakes a complete technological overhaul of the PBDB, one of the most prominent and widely used fossil databases. Technical improvements include coupling the PBDB with the PBot, whose cutting-edge conceptual framework provides innovative user-centric capabilities. Specifically, development of the PBDB 2.0 will: 1) overhaul the PBDB's data model, database, and application logic to integrate PBot functions, streamline data entry, and improve technical sustainability; 2) create a more capable application programming interface; 3) construct a new web application to leverage back-end upgrades and facilitate new science; and 4) host community events and activities to assess progress, train new members, mobilize data, and produce new educational/outreach materials. Long-term costs of paleobiological cyberinfrastructure will be substantially reduced by consolidating development effort, expanding the PBDB's current functionality to better serve a large community, enhancing overall user experiences, mobilizing new science-critical data, and improving alignments with other data systems. Upgrades will provide explicit support for uncertain taxonomic classification, introduce a more specimen-forward approach to organizing fossil data, make high quality specimen images available alongside data, and add workbench capabilities. Researchers from around the world will utilize the PBDB 2.0 to understand Earth history and Earth systems processes, conduct Rules of Life research, and interpret ecological and evolutionary change across all taxonomic groups, continents, and time periods. The refreshed PBDB 2.0 will make it easier for anyone in the broader community, from professional Earth and life scientists to children, to engage with fossil science. This award by the Geoinformatics program within the Division of Earth Sciences is jointly supported by the Infrastructure Capacity for Biological Research program within the Division of Biological Infrastructure. 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

The Paleobiology Database (PBDB) is one of the most impactful and widely used digital representations of the fossil record, capturing our best understanding of the age, location, identity, and geological context of fossils. Data held in the PBDB have been used in over 2,000 scientific publications and in a wide range of educational and public outreach materials. The PBDB is an essential resource for geoscientists, biologists, students, educators, and the public. Although the research and educational impact of the PBDB is tremendous, there are key issues with its current computer infrastructure, which was designed in the late 1990s and early 2000s. This project will combine PBDB with the Integrative Paleobotany Portal (PBot) to create a more modern and flexible digital system: the PBDB 2.0. Planned project activities will transform the PBDB to be more useful to a broad community of researchers, educators, and students in the Earth and life sciences, as well as the general public. In particular, the PBDB will adopt features from PBot that will allow users to easily contribute and interact with fossil identifications tied to specimen images, and outreach activities will further motivate community participation with PBDB. This project undertakes a complete technological overhaul of the PBDB, one of the most prominent and widely used fossil databases. Technical improvements include coupling the PBDB with the PBot, whose cutting-edge conceptual framework provides innovative user-centric capabilities. Specifically, development of the PBDB 2.0 will: 1) overhaul the PBDB's data model, database, and application logic to integrate PBot functions, streamline data entry, and improve technical sustainability; 2) create a more capable application programming interface; 3) construct a new web application to leverage back-end upgrades and facilitate new science; and 4) host community events and activities to assess progress, train new members, mobilize data, and produce new educational/outreach materials. Long-term costs of paleobiological cyberinfrastructure will be substantially reduced by consolidating development effort, expanding the PBDB's current functionality to better serve a large community, enhancing overall user experiences, mobilizing new science-critical data, and improving alignments with other data systems. Upgrades will provide explicit support for uncertain taxonomic classification, introduce a more specimen-forward approach to organizing fossil data, make high quality specimen images available alongside data, and add workbench capabilities. Researchers from around the world will utilize the PBDB 2.0 to understand Earth history and Earth systems processes, conduct Rules of Life research, and interpret ecological and evolutionary change across all taxonomic groups, continents, and time periods. The refreshed PBDB 2.0 will make it easier for anyone in the broader community, from professional Earth and life scientists to children, to engage with fossil science. This award by the Geoinformatics program within the Division of Earth Sciences is jointly supported by the Infrastructure Capacity for Biological Research program within the Division of Biological Infrastructure. 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

Spectrum sharing in mid-band offers unprecedented opportunity to harness desirable frequencies for commercial 5G operators and for unlicensed use, although higher priority incumbents need to be reliably detected. As an example, the requirement of detecting naval ship-borne radar signals by the environmental sensing capability (ESC) sensors in the 3.55-3.7GHz Citizens Broadband Radio Service (CBRS) band severely limits the transmission power for 5G operators. The objective of this project called MEDUSA: Mid-band Environmental sensing capability for Detecting incUmbents during Spectrum shAring is to detect the presence of static/mobile radar and anomalous transmissions within concurrent and comparatively higher power 5G and 4G-LTE signals. It achieves this through machine learning (reacting to existing interference) and receiver antenna design (proactively avoiding interference). MEDUSA will enable ESC sensors to work with different types of wireless signals, both for individual spectrum monitoring and via collaborative methods for enhanced incumbent detection accuracy. The project will result in open-source release of antenna design files, datasets and learning algorithms for the research community. It also includes several outreach and dissemination activities such as hosting recordings of interviews with spectrum experts on the project website, recruiting students from under-representative groups, and designing course projects that use CBRS-related datasets. The project has three goals for radar detection in the Citizens Broadband Radio Service (CBRS) band but is also generalizable for other frequencies. First, it proposes a deep learning framework to enhance the discriminative abilities of the environmental sensing capability (ESC) sensor while preserving privacy by using spectrogram inputs. These sensors will detect radar pulses within 5G and 4G-LTE signals with powers stronger than FCC-mandated levels by 5 dB. Furthermore, the PIs will develop transfer-learning methods for unseen conditions. Second, it will advance the science of collaborative inference, when multiple ESC sensors make (i) independent and (ii) joint decisions by fusing individual predictions using the algorithms developed as part of the first goal. It also proposes a method of fusion of spectrograms and raw in-phase and quadrature (IQ) samples. Third, when the geographically separated and arbitrarily spaced ESC sensors are time and phase synchronized, they form a massive virtual array for receive beamforming. The PIs will design real-time weight adaptation algorithms and horn antennas that can create nulls towards known 5G/4G-LTE base stations. Finally, the research goals will be validated in emulation as well as over experimental testbeds through the NSF Platform for Advanced Wireless Research (PAWR 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

As our planet's climate continues to change, finding new approaches to alleviate its impact is crucial. Microbiomes, which consist of smaller organisms making a living on larger species, can affect how their hosts respond to environmental change. This project evaluates whether and how we can assemble synthetic microbiomes to help host species cope with the impacts of environmental change. Due to the enormous diversity of microbial communities, there are numerous ways to engineer synthetic microbiomes, making it difficult to identify the principles that underpin their impacts on host species. The researchers will employ high-throughput techniques to simultaneously evaluate the impacts of different synthetic communities and environmental conditions on plant hosts. The results will expand our understanding of how environmental change alters the impacts of microbes on hosts and how engineered synthetic microbial communities may help hosts adapt to a changing climate. The researchers will actively mentor students from middle school to undergraduate level, create a new Course-Based Undergraduate Research Experience Lab course in the cutting-edge field of synthetic microbiology, and design a backyard research program for under-served middle and high school students to provide training to attract their interest to possible careers in STEM. This study will explore the intersection of different research areas to study the interactions between hosts and microbes, including symbiosis, evolutionary biology, and ecosystem functioning. It will assess how elevated temperatures affect three crucial components of host-microbe interactions: (1) microbe-microbe interactions and their impact on host fitness, (2) functional redundancy and its effect on biodiversity and ecosystem functions, and (3) evolvability during microbiome breeding. To achieve this goal, the researchers will collect duckweed, a widespread aquatic plant, across a temperature gradient, acquire bacteria from duckweeds and other sources, and use these collections in large multifactorial experiments on an automated, high-throughput platform. The collections, results, and experimental methods will contribute to the development of microbial consortia that can enhance organismal and ecosystem resilience in the Anthropocene. 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

Catalysts are used widely in industrial processes to improve energy efficiency and direct chemical reactions toward desired products. The project examines novel catalyst designs for an important class of industrial reactions – selective hydrogenations - used for many applications such as the production of plastics, fragrances and pharmaceuticals. The precious metal palladium (Pd) is widely used for selective hydrogenation reactions. The project aims to maximize utilization of the precious metal in the form of isolated atoms, and tailor their properties by anchoring them to covalent organic frameworks (COFs), which are highly crystalline porous materials with specific binding sites for the metal atoms. The investigators will synthesize and characterize the catalysts using a suite of laboratory- and synchrotron-based characterization techniques. The catalysts will be tested for the selective hydrogenation of acetylene to ethylene to determine how the binding sites affect the activity and selectivity of the Pd atoms. Beyond the research, the project involves education of graduate and undergraduate students with cross-training of researchers between the two institutions and focused efforts to increase participation of underrepresented groups in catalysis science. The project aims to tailor the properties of Pd single atoms using covalent organic frameworks (COF) as the support/ligand, and thus circumvent trade-offs between activity and selectivity associated with traditional metal oxide supported nanoparticle catalysts for the semi-hydrogenation of ethylene. Areas of focus include 1) the effects of the COF binding sites and secondary ligands on the Pd electronic properties, 2) hydrogen activation, 3) binding with adsorbates and 4) catalyst activity/selectivity. Those thrusts will be accomplished through precise catalyst synthesis, advanced characterization techniques (in-situ/operando X-ray spectroscopies, microcalorimetry) and detailed kinetic measurements. The project will identify the possibilities and limitations for designing catalysts based on Pd metal single atoms. The interdisciplinary nature of this research, and the integration of research and education plans will lead to a cadre of students obtaining a unique educational experience on heterogeneous catalysis and advanced lab- and synchrotron-based characterization techniques. 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

This project aims to serve the national interest by improving curricula for strengthening students' data analysis skills in solid Earth geoscience courses. The ability to process, analyze, and visualize data is increasingly vital for conducting independent research and preparing students for the STEM workforce. The project plans to advance geoscience education by developing upper-level teaching modules that will help students use advanced data analysis to explore the mechanical behavior of the solid Earth. This collaborative IUSE Level 1 Engaged Student Learning track project will also lead and facilitate faculty professional development to enhance data-centric teaching approaches for solid Earth courses. To support the development and application of data analysis and strengthen the quantitative reasoning and self-efficacy of geoscience students, the goals of the project are to: 1) create undergraduate teaching materials for investigating solid Earth geoscience problems with a focus on data analysis skills; 2) lead and facilitate professional development for increasing faculty confidence in using data-rich teaching activities in solid Earth courses; and 3) evaluate the impact of data-rich teaching modules on changes in students' confidence in data-analysis skills in solid Earth courses. The teaching modules will provide freely available instructional faculty resources to teach data analysis in solid Earth contexts to help students develop critical thinking and other skills necessary to succeed in STEM disciplines. During the project, approximately 30-40 instructors and over 400 students will benefit from these efforts. The Science Education Resource Center (SERC) will provide research support, assessment, project evaluation, and website hosting. The National Association of Geoscience Teachers (NAGT) will support dissemination. The project plans to contribute knowledge of how professional development activities may increase instructors’ confidence in including more data analysis in their courses. The project also aims to enhance the quantitative reasoning and data analysis skills of geoscience students, preparing them for graduate research, careers in the geoscience workforce, and engaging with challenges such as natural hazards and energy resources. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

This project aims to serve the national interest by improving curricula for strengthening students' data analysis skills in solid Earth geoscience courses. The ability to process, analyze, and visualize data is increasingly vital for conducting independent research and preparing students for the STEM workforce. The project plans to advance geoscience education by developing upper-level teaching modules that will help students use advanced data analysis to explore the mechanical behavior of the solid Earth. This collaborative IUSE Level 1 Engaged Student Learning track project will also lead and facilitate faculty professional development to enhance data-centric teaching approaches for solid Earth courses. To support the development and application of data analysis and strengthen the quantitative reasoning and self-efficacy of geoscience students, the goals of the project are to: 1) create undergraduate teaching materials for investigating solid Earth geoscience problems with a focus on data analysis skills; 2) lead and facilitate professional development for increasing faculty confidence in using data-rich teaching activities in solid Earth courses; and 3) evaluate the impact of data-rich teaching modules on changes in students' confidence in data-analysis skills in solid Earth courses. The teaching modules will provide freely available instructional faculty resources to teach data analysis in solid Earth contexts to help students develop critical thinking and other skills necessary to succeed in STEM disciplines. During the project, approximately 30-40 instructors and over 400 students will benefit from these efforts. The Science Education Resource Center (SERC) will provide research support, assessment, project evaluation, and website hosting. The National Association of Geoscience Teachers (NAGT) will support dissemination. The project plans to contribute knowledge of how professional development activities may increase instructors’ confidence in including more data analysis in their courses. The project also aims to enhance the quantitative reasoning and data analysis skills of geoscience students, preparing them for graduate research, careers in the geoscience workforce, and engaging with challenges such as natural hazards and energy resources. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

This project aims to serve the national interest by improving students’ understanding of how social, cultural, economic, and political systems can inform their engineering designs. Traditionally, engineering design courses have focused primarily on the technical specifications and requirements of a design without full consideration of the wider context in which they will be used. There is also a lack of engineering design courses in the middle two years of a typical engineering curriculum. This leads to a gap in learning design principles and skills for students between their first design course and the final capstone design course. The project plans to develop, implement, refine, and evaluate engineering design courses for second and third year students at the University of Illinois-Chicago and the University of Texas-San Antonio. The courses should help students learn how to conduct in-depth analyses of the implications of design choices for society using historical and current examples of engineering design. This project intends to develop an instructional framework for the middle years of engineering programs' courses along with instructional materials for those courses. Using interviews and student surveys, the project will assess the impact of the courses on students. Project results will be disseminated to engineering educators through professional development workshops. The goal of this project is to help students learn how to consider social, cultural, economic, and political factors during the engineering design process. This project is based on Critical Consciousness theory which suggests that students need to develop an awareness of inequitable societal conditions that can occur due to engineering design decisions. A new teaching framework for engineering design in the middle years will be developed and refined so that engineering design includes a contextual perspective. The goals for the new engineering design course are: (1) to empower students to be more socially and critically driven engineers, (2) to help students learn about the engineering design process from a Critical Consciousness perspective, and (3) to help students become validated in their aspirations to pursue an engineering career. An inter-group dialogue approach will be used in the course to establish communication relationships, facilitate dialogue, and encourage collaborations between students. This study will address two research questions: (1) What teaching strategies are most helpful in developing students’ critical consciousness through an engineering design course? (2) How and in what ways do the programmatic features of the project impact students’ learning of engineering design and engineering identity development? The project team will use quantitative and qualitative methods to analyze data from classroom observations, course artifacts, and student surveys. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

Collaborative Research: Tectonic Influence on the Greenland Ice Sheet (TIGRIS) The evolution of the Greenland Ice Sheet (GrIS) strongly depends on its underlying geologic structure. Changes to the ice sheet can cause the Earth’s crust and mantle to deform, with the amount of deformation being controlled by variations in the stiffness and thickness of these geologic layers. How the solid-Earth responds can either hinder or enhance ice loss, and this ice-Earth feedback mechanism plays a critical role in determining GrIS behavior. This project aims to evaluate the stability of the GrIS under different environmental conditions by employing an advanced computer model that combines ice-sheet, atmospheric, and geologic constraints. Results from this work will inform estimates of both past and future global sea-level change. Subglacial solid-Earth parameters are largely based on geophysical observations; however, conflicting interpretations of the geologic structure beneath Greenland limit our understanding of GrIS stability. Key portions of Greenland have been under-sampled, and prior studies have often only utilized data from select seismic networks. This project will develop new, self-consistent models of the solid-Earth structure beneath Greenland by combining geophysical observations from multiple networks with those from a new seismic deployment in central-eastern Greenland. Those new solid-Earth constraints will then be incorporated into a state-of-the-art, fully coupled tectonic-atmospheric-cryospheric modeling framework to evaluate the critical thresholds for ice-sheet recovery under different environmental scenarios. Three fundamental hypotheses will be tested: (a) solid-Earth structure plays a first-order role in the long-term future evolution of the GrIS as well as its response to past warming and cooling episodes; (b) under certain projected future warming scenarios, the GrIS will not fully retreat given feedbacks that are controlled by the solid-Earth structure; and (c) the interplay between different feedback mechanisms will result in at least partial ice-sheet recovery, and the GrIS will be resilient in the long term (10-20 kyrs). 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.