Grant List
Represents Grant table in the DB
GET /v1/grants?sort=-program_reference_codes
{ "links": { "first": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1&sort=-program_reference_codes", "last": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=1392&sort=-program_reference_codes", "next": "https://cic-apps.datascience.columbia.edu/v1/grants?page%5Bnumber%5D=2&sort=-program_reference_codes", "prev": null }, "data": [ { "type": "Grant", "id": "9536", "attributes": { "award_id": "2030139", "title": "Compounding Crises: Facing Hurricane Season in the Era of COVID-19", "funder": null, "funder_divisions": [], "program_reference_codes": [ "CK090", "RND123" ], "program_officials": [], "start_date": null, "end_date": null, "award_amount": 199890, "principal_investigator": null, "other_investigators": [], "awardee_organization": null, "abstract": "Test", "keywords": [ "covid", "research" ], "approved": true } }, { "type": "Grant", "id": "1883", "attributes": { "award_id": "2029032", "title": "RAPID: A mixed-method investigation of the role of faculty mindset beliefs during the transition to online education as compelled by the COVID-19 pandemic", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Education and Human Resources (EHR)" ], "program_reference_codes": [ "8817", "096Z", "7914", "8212" ], "program_officials": [ { "id": 4988, "first_name": "Jolene", "last_name": "Jesse", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-15", "end_date": "2022-04-30", "award_amount": 196563, "principal_investigator": { "id": 4989, "first_name": "Christopher S", "last_name": "Hulleman", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 517, "ror": "", "name": "University of Virginia Main Campus", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 517, "ror": "", "name": "University of Virginia Main Campus", "address": "", "city": "", "state": "VA", "zip": "", "country": "United States", "approved": true }, "abstract": "The transition to online instruction necessitated by the COVID pandemic has created significant uncertainty for the nation’s college faculty, many of whom have not previously taught online. This research would examine how faculty mindsets about themselves and their students, as well as faculty perceptions of their university/college system’s messages about the transition, relate to their responses to the challenge of rapidly transitioning their teaching online, the quality of instruction they deliver, and, as a result, students’ attitudes and learning outcomes in STEM courses during the Spring 2020 COVID-19 pandemic. Given the high levels of stress caused by the disruption to daily life brought by COVID-19, faculty attitudes and beliefs may be critical in determining the pattern of faculty responses, and in particular, whether and how they adapt and transition their courses online. Faculty responses may reflect deep-seated beliefs about (a) the nature of STEM content (as relatively fixed and unchanging); (b) the view of themselves as simply deliverers of that relatively fixed content, and (c) students’ abilities to learn STEM material. Understanding how faculty mindsets influence the teaching practices that STEM faculty adopt as they move their teaching online – and how these practices influence students’ motivation, learning, and performance – can help faculty become more adaptable in the future by developing professional development in advance. This award is made by the EHR Core Research program in the Division of Human Resource Development, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.This study will build on an existing partnership between the Motivate Lab, based at the University of Virginia, and the University System of Georgia, which includes 26 four-year colleges and universities throughout Georgia. The 26 institutions in Georgia are divided roughly equally into four sectors: Research-intensive, Masters-comprehensive, State university, and State college. This variation in institution type will be important to consider as attitudes towards teaching (vs. research) and the type of students vary significantly across the system, with the more research-focused institutions being more selective in their admissions policies. Participants will include 900 faculty across the University System of Georgia and the students enrolled in their courses. Retooled surveys, course artifacts, learning management system data, and administrative data will capture how faculty mindsets about themselves and their students, as well as faculty perceptions of their system’s messages about the transition, relate to their responses to the challenge of rapidly transitioning their teaching online, the quality of instruction they deliver, and students’ attitudes and learning outcomes in STEM courses during the Spring 2020 COVID-19 pandemic. Supplementary student data will assess student perceptions about the changes that faculty made to their instructional practices during the move to online, and the changes to the quality of the learning experience. Although prior research demonstrates that faculty mindsets shape teaching practices even under normal circumstances, this research will test hypotheses about whether mindset beliefs will especially matter during periods of rapidly changing circumstances. Learning how faculty mindsets matter will significantly advance theory and understanding of the role of faculty mindsets in STEM education.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1323", "attributes": { "award_id": "2036064", "title": "EAGER: Computationally Predicting and Characterizing the Immune Response to Viral Infections", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "7916", "7931" ], "program_officials": [ { "id": 3404, "first_name": "Mitra", "last_name": "Basu", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2023-07-31", "award_amount": 200000, "principal_investigator": { "id": 3405, "first_name": "Marc", "last_name": "Riedel", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 227, "ror": "", "name": "University of Minnesota-Twin Cities", "address": "", "city": "", "state": "MN", "zip": "", "country": "United States", "approved": true }, "abstract": "Pathogens such as the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) affect different people differently. Whether an individual mounts a strong response or not depends, at least in part, on their genes. Specific genes code for the proteins on the surface of cells that present viral protein fragments to the immune system. Killer T cells recognize these fragments and kill the infected cells. The immune response to SARS-CoV-2 hinges on whether the viral protein fragments bind into a groove in these cell-surface proteins -- like a key into a lock. The molecular biology is well understood. Whether a protein fragment binds or not is a question of 3D structure and simple atomic force calculations. The full set of proteins associated with SARS-CoV-2 was published in March, so the requisite data is available. This project will predict, through purely computational means, whether such binding happens for all viral protein fragments, for all common variants of the cell surface proteins -- so for all keys into all types of locks. This computational ability will be transformative for a scientific understanding of the pandemic If successful, the same computational infrastructure could be deployed in the future for other pandemics -- those caused by viruses or by bacteria. It could also be transformative in characterizing the human immune system, in general, and its response to pathogens. In technical terms, the goal of the project is to predict, through computational means, which peptides derived from SARS- CoV-2 will bind to each allelic variant of MHC-I molecule commonly found in the U.S. population. Human leukocyte antigen (HLA) typing can be performed to establish the allelic variants of MHC-I molecules of individuals. With population-wide typing, the tools developed by this project will predict which individuals in a population are most likely to mount a strong antiviral immune response to the virus, given their MHC-I alleles. Immunopeptidome profiling will be performed of all common allelic variants of MHC-I molecules, first using machine-learning algorithms. Next immunopeptidome profiling will be performed using custom-developed atomic-level simulation software, deployed on graphical processing units.The project will provide a public implementation of the tool set. The results of the research will be promptly disseminated on a website hosted by the University of Minnesota. The front-end will exploit modern software infrastructure for data analytics and visualization. The back-end will consist of a MySQL database, directly linked to the computational engine, running on a distributed platform.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1376", "attributes": { "award_id": "2042007", "title": "Collaborative Research: EAGER: Advancing Pedagogy and Inclusivity through Multimodal Upper Level Geophysics Education", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [ "7916" ], "program_officials": [ { "id": 3545, "first_name": "Eva", "last_name": "Zanzerkia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2021-07-31", "award_amount": 5445, "principal_investigator": { "id": 3546, "first_name": "Tolulope M", "last_name": "Olugboji", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 464, "ror": "https://ror.org/022kthw22", "name": "University of Rochester", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 464, "ror": "https://ror.org/022kthw22", "name": "University of Rochester", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Today, classrooms are challenged by the need for flexible learning due to the COVID-19 pandemic and for dynamic course materials that attract students from diverse backgrounds and allow them to thrive. This EAGER initiates a pilot effort to develop research-based learning modules for the upper level geophysics classes that can be used by geophysics programs across the country. At the upper level there are fewer resources for teaching and course development guides, and there is a notable drop off in students from underrepresented groups. This EAGER will explore how to implement high quality teaching materials to create a more equitable and inclusive learning environment that can build student confidence and allow students from a variety of diverse backgrounds to succeed. The PI team brings together complementary academic expertise facilitating the creation of a broad range of teaching modules and the convergence between upper level geophysics students and students in core STEM disciplines such as physics, mathematics, and computational science, which are fundamental disciplines on which global seismology relies.The project will design multimodal pilot modules on (1) Elastodynamics, (2) Seismology and Plate Tectonics, and (3) Near surface seismological methods and applications to the developed environment. The work has the potential to transform the traditional teaching mechanisms for upper level geophysics courses, and be used as a means to attract underrepresented groups to geophysics who may not have otherwise considered Earth science as a research focus. The project will incorporate flipped course design and mastery learning to help build student confidence, with the materials created in such a way that they are adaptable to both on-line and in-person teaching. In addition, the team will further develop inclusivity through the creation of biographies featuring scientists from underrepresented groups that will be incorporated directly into the learning modules. The pilot modules will specifically focus on upper level geophysics courses to address and support students transitioning from undergraduate training to graduate school or to professional work in the geosciences, thus extending the pedagogical support from lower level to upper level geophysics teaching.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1367", "attributes": { "award_id": "2041967", "title": "Collaborative Research: EAGER: Advancing Pedagogy and Inclusivity through Multi-modal Upper Level Geophysics Education", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [ "7916" ], "program_officials": [ { "id": 3525, "first_name": "Eva", "last_name": "Zanzerkia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2021-07-31", "award_amount": 5530, "principal_investigator": { "id": 3526, "first_name": "Derek L", "last_name": "Schutt", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 323, "ror": "https://ror.org/03k1gpj17", "name": "Colorado State University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 323, "ror": "https://ror.org/03k1gpj17", "name": "Colorado State University", "address": "", "city": "", "state": "CO", "zip": "", "country": "United States", "approved": true }, "abstract": "Today, classrooms are challenged by the need for flexible learning due to the COVID-19 pandemic and for dynamic course materials that attract students from diverse backgrounds and allow them to thrive. This EAGER initiates a pilot effort to develop research-based learning modules for the upper level geophysics classes that can be used by geophysics programs across the country. At the upper level there are fewer resources for teaching and course development guides, and there is a notable drop off in students from underrepresented groups. This EAGER will explore how to implement high quality teaching materials to create a more equitable and inclusive learning environment that can build student confidence and allow students from a variety of diverse backgrounds to succeed. The PI team brings together complementary academic expertise facilitating the creation of a broad range of teaching modules and the convergence between upper level geophysics students and students in core STEM disciplines such as physics, mathematics, and computational science, which are fundamental disciplines on which global seismology relies.The project will design multimodal pilot modules on (1) Elastodynamics, (2) Seismology and Plate Tectonics, and (3) Near surface seismological methods and applications to the developed environment. The work has the potential to transform the traditional teaching mechanisms for upper level geophysics courses, and be used as a means to attract underrepresented groups to geophysics who may not have otherwise considered Earth science as a research focus. The project will incorporate flipped course design and mastery learning to help build student confidence, with the materials created in such a way that they are adaptable to both on-line and in-person teaching. In addition, the team will further develop inclusivity through the creation of biographies featuring scientists from underrepresented groups that will be incorporated directly into the learning modules. The pilot modules will specifically focus on upper level geophysics courses to address and support students transitioning from undergraduate training to graduate school or to professional work in the geosciences, thus extending the pedagogical support from lower level to upper level geophysics teaching.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1374", "attributes": { "award_id": "2042061", "title": "Collaborative Research: EAGER: Advancing Pedagogy and Inclusivity through Multimodal Upper Level Geophysics Education", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [ "7916" ], "program_officials": [ { "id": 3540, "first_name": "Eva", "last_name": "Zanzerkia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2021-07-31", "award_amount": 10417, "principal_investigator": { "id": 3542, "first_name": "Margarete", "last_name": "Jadamec", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 422, "ror": "", "name": "SUNY at Buffalo", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [ { "id": 3541, "first_name": "Erasmus K", "last_name": "Oware", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "awardee_organization": { "id": 422, "ror": "", "name": "SUNY at Buffalo", "address": "", "city": "", "state": "NY", "zip": "", "country": "United States", "approved": true }, "abstract": "Today, classrooms are challenged by the need for flexible learning due to the COVID-19 pandemic and for dynamic course materials that attract students from diverse backgrounds and allow them to thrive. This EAGER initiates a pilot effort to develop research-based learning modules for the upper level geophysics classes that can be used by geophysics programs across the country. At the upper level there are fewer resources for teaching and course development guides, and there is a notable drop off in students from underrepresented groups. This EAGER will explore how to implement high quality teaching materials to create a more equitable and inclusive learning environment that can build student confidence and allow students from a variety of diverse backgrounds to succeed. The PI team brings together complementary academic expertise facilitating the creation of a broad range of teaching modules and the convergence between upper level geophysics students and students in core STEM disciplines such as physics, mathematics, and computational science, which are fundamental disciplines on which global seismology relies.The project will design multimodal pilot modules on (1) Elastodynamics, (2) Seismology and Plate Tectonics, and (3) Near surface seismological methods and applications to the developed environment. The work has the potential to transform the traditional teaching mechanisms for upper level geophysics courses, and be used as a means to attract underrepresented groups to geophysics who may not have otherwise considered Earth science as a research focus. The project will incorporate flipped course design and mastery learning to help build student confidence, with the materials created in such a way that they are adaptable to both on-line and in-person teaching. In addition, the team will further develop inclusivity through the creation of biographies featuring scientists from underrepresented groups that will be incorporated directly into the learning modules. The pilot modules will specifically focus on upper level geophysics courses to address and support students transitioning from undergraduate training to graduate school or to professional work in the geosciences, thus extending the pedagogical support from lower level to upper level geophysics teaching.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1375", "attributes": { "award_id": "2042011", "title": "Collaborative Research: EAGER: Advancing Pedagogy and Inclusivity through Multimodal Upper Level Geophysics Education", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [ "7916" ], "program_officials": [ { "id": 3543, "first_name": "Eva", "last_name": "Zanzerkia", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-15", "end_date": "2021-07-31", "award_amount": 5556, "principal_investigator": { "id": 3544, "first_name": "Stefany", "last_name": "Sit", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 163, "ror": "https://ror.org/02mpq6x41", "name": "University of Illinois at Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 163, "ror": "https://ror.org/02mpq6x41", "name": "University of Illinois at Chicago", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Today, classrooms are challenged by the need for flexible learning due to the COVID-19 pandemic and for dynamic course materials that attract students from diverse backgrounds and allow them to thrive. This EAGER initiates a pilot effort to develop research-based learning modules for the upper level geophysics classes that can be used by geophysics programs across the country. At the upper level there are fewer resources for teaching and course development guides, and there is a notable drop off in students from underrepresented groups. This EAGER will explore how to implement high quality teaching materials to create a more equitable and inclusive learning environment that can build student confidence and allow students from a variety of diverse backgrounds to succeed. The PI team brings together complementary academic expertise facilitating the creation of a broad range of teaching modules and the convergence between upper level geophysics students and students in core STEM disciplines such as physics, mathematics, and computational science, which are fundamental disciplines on which global seismology relies.The project will design multimodal pilot modules on (1) Elastodynamics, (2) Seismology and Plate Tectonics, and (3) Near surface seismological methods and applications to the developed environment. The work has the potential to transform the traditional teaching mechanisms for upper level geophysics courses, and be used as a means to attract underrepresented groups to geophysics who may not have otherwise considered Earth science as a research focus. The project will incorporate flipped course design and mastery learning to help build student confidence, with the materials created in such a way that they are adaptable to both on-line and in-person teaching. In addition, the team will further develop inclusivity through the creation of biographies featuring scientists from underrepresented groups that will be incorporated directly into the learning modules. The pilot modules will specifically focus on upper level geophysics courses to address and support students transitioning from undergraduate training to graduate school or to professional work in the geosciences, thus extending the pedagogical support from lower level to upper level geophysics teaching.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1366", "attributes": { "award_id": "2029053", "title": "EAGER: STEM Illinois: The Land-Grant Model of Outreach and Education to Nurture Future Underrepresented Computer Scientists", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Computer and Information Science and Engineering (CISE)" ], "program_reference_codes": [ "7916" ], "program_officials": [ { "id": 3523, "first_name": "Jeffrey", "last_name": "Forbes", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-05-01", "end_date": "2023-04-30", "award_amount": 300000, "principal_investigator": { "id": 3524, "first_name": "Ruby", "last_name": "Mendenhall", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 281, "ror": "", "name": "University of Illinois at Urbana-Champaign", "address": "", "city": "", "state": "IL", "zip": "", "country": "United States", "approved": true }, "abstract": "Despite decades of computer science pipeline programming, participation of students of color in computer science and information technology disciplines remains alarmingly low. STEM Illinois is a project deeply rooted in the historic mission of land-grant institutions, which is to democratize higher education and to address the world?s most pressing societal challenges. This project will specifically work with 50 to 100 students whose parents do not have a college degree and who are at risk for dropping out of school. Computer scientists from these backgrounds represent a small number of faculty and industry professionals. The STEM Illinois project seeks to evaluate and analyze the impact of COVID-19 on their educational outcomes and pursuits by using a radical model of intergenerational outreach and education. This model seeks to create a culture of innovation where all members of the community see, understand, and feel that they can support their youth in computer science innovation. It is importance to understand which aspects of the culturally engaging project foster a computer science identity and persistence among the youth.The goal of the STEM Illinois project is to create a unique ecosystem that will nurture future computer scientists in industry and the academy. This project will provide underrepresented students with computer science activities that include using spatial analysis to map communities vulnerable to COVID-19, coding, big data analysis, artificial intelligence, data visualization, math, algorithms, and access to a recording studio to represent computer science concepts using art and music. Students will receive unprecedented access to mentors and professional experiences that include interviewing industry/academic computing scientists for podcasts, giving TEDxYouth talks about their experiences with computer science, questions and answer sessions with Nobel Prize Winners, etc. This project will increase the number of marginalized students majoring in Computer Science and Information Technology at the University of Illinois and around the country. The goal is also to increase the number of students in computer science internships and jobs at large companies such as State Farm, American Association of Retired Persons (AARP) and Microsoft (workforce development). The project will be evaluated using pre- and post-tests surveys, interviews, and participant observation. The project will use the Computer Science Attitude and Identity Survey (or a similar instrument) that includes questions about how confident a person feels about their computer science skills and how much interest they have in computer science as a profession.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1547", "attributes": { "award_id": "2035235", "title": "RAPID: Collaborative Proposal: Development of Digital Models of Minerals and Rocks for Online Geoscience Classes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [ "7914", "9150" ], "program_officials": [ { "id": 4037, "first_name": "Dennis", "last_name": "Geist", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2022-12-31", "award_amount": 16864, "principal_investigator": { "id": 4038, "first_name": "Matthew E", "last_name": "Brueseke", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 197, "ror": "https://ror.org/05p1j8758", "name": "Kansas State University", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 197, "ror": "https://ror.org/05p1j8758", "name": "Kansas State University", "address": "", "city": "", "state": "KS", "zip": "", "country": "United States", "approved": true }, "abstract": "The sudden shift to remote online teaching due to the COVID-19 crisis has especially impacted laboratory classes. Petrologists and mineralogists have created three-dimensional models of rock and mineral specimens, which are available online. The commercial site that hosts these does not include a searchable database. This project will create a searchable database and copy the models to a university-hosted server. Additional specimens will be added. Image-processing has become routine, which has made it practical to create visually and topologically accurate representation of geologic specimens. Teachers have been creating these images and putting them on a commercial website for the use of the wider community. The problem is that this collection of hundreds of virtual specimens is not searchable. The PIs will create a searchable database with the specific metadata that will be useful to science teachers. Moreover, the virtual specimens will also be copied to a university-hosted server, which will assure access for years. Additional specimens will be imaged. This virtual collection has become vital in the time of COVID-19 and a shift to widespread online instruction.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.", "keywords": [], "approved": true } }, { "type": "Grant", "id": "1548", "attributes": { "award_id": "2035243", "title": "RAPID: Collaborative Proposal: Development of Digital Models of Minerals and Rocks for Online Geoscience Classes", "funder": { "id": 3, "ror": "https://ror.org/021nxhr62", "name": "National Science Foundation", "approved": true }, "funder_divisions": [ "Geosciences (GEO)" ], "program_reference_codes": [ "7914", "9150" ], "program_officials": [ { "id": 4039, "first_name": "Dennis", "last_name": "Geist", "orcid": null, "emails": "", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [] } ], "start_date": "2020-08-01", "end_date": "2021-07-31", "award_amount": 33227, "principal_investigator": { "id": 4040, "first_name": "Graham D", "last_name": "Andrews", "orcid": null, "emails": "[email protected]", "private_emails": "", "keywords": null, "approved": true, "websites": null, "desired_collaboration": null, "comments": null, "affiliations": [ { "id": 385, "ror": "", "name": "West Virginia University Research Corporation", "address": "", "city": "", "state": "WV", "zip": "", "country": "United States", "approved": true } ] }, "other_investigators": [], "awardee_organization": { "id": 385, "ror": "", "name": "West Virginia University Research Corporation", "address": "", "city": "", "state": "WV", "zip": "", "country": "United States", "approved": true }, "abstract": "The sudden shift to remote online teaching due to the COVID-19 crisis has especially impacted laboratory classes. Petrologists and mineralogists have created three-dimensional models of rock and mineral specimens, which are available online. The commercial site that hosts these does not include a searchable database. This project will create a searchable database and copy the models to a university-hosted server. Additional specimens will be added.Image-processing has become routine, which has made it practical to create visually and topologically accurate representation of geologic specimens. Teachers have been creating these images and putting them on a commercial website for the use of the wider community. The problem is that this collection of hundreds of virtual specimens is not searchable. The PIs will create a searchable database with the specific metadata that will be useful to science teachers. Moreover, the virtual specimens will also be copied to a university-hosted server, which will assure access for years. Additional specimens will be imaged. This virtual collection has become vital in the time of COVID-19 and a shift to widespread online instruction.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.", "keywords": [], "approved": true } } ], "meta": { "pagination": { "page": 1, "pages": 1392, "count": 13920 } } }