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The National Science Foundation (NSF) Advanced Technological Education (ATE) program is effective in assisting two-year college (2YC) institutions of higher education to improve the education of technicians in science and engineering, yet grant proposals from 2YCs to ATE (and NSF as a whole) have declined in number over the past decade. The problem of NSF proposals declining in numbers is multifaceted, though data demonstrates that both 2YCs and NSF can reverse or mitigate the decline in ATE proposals through identified measures; 2YCs can change their grants culture through specific institutional changes, and NSF can aid 2YCs to build their capacity to develop competitive proposals through mentoring and professional development sustainably. This article discusses data, insights, and solutions through the lens of two NSF ATE projects: Project Vision (a mentoring project) and Grant Insights (an applied research project).
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glupyter: Enabling multi-dimensional linked data visualization with glue in the browser.. https://doi.org/10.5281/zenodo.6875831
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The National Science Foundation (NSF) Advanced Technological Education (ATE) program is effective in assisting two-year college (2YC) institutions of higher education to improve the education of technicians in science and engineering, yet grant proposals from 2YCs to ATE (and NSF as a whole) have declined in number over the past decade. The problem of NSF proposals declining in numbers is multifaceted, though data demonstrates that both 2YCs and NSF can reverse or mitigate the decline in ATE proposals through identified measures; 2YCs can change their grants culture through specific institutional changes, and NSF can aid 2YCs to build their capacity to develop competitive proposals through mentoring and professional development sustainably. This article discusses data, insights, and solutions through the lens of two NSF ATE projects: Project Vision (a mentoring project) and Grant Insights (an applied research project).more » « less
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The National Science Foundation Advanced Technological Education (NSF-ATE) program has grant funding opportunities available to support CTE and STEM technician program development. NSF-ATE grant funding opportunities are intended to help educators develop or improve their 2-year technician programs. Proposals may focus on program, curriculum, and educational materials development, program improvement, faculty professional development, teacher preparation, career pathways, outreach activities, undergraduate research experiences, internships, apprenticeships, and more. Partnerships with universities, colleges, and 7-12 institutions in support of workforce development are encouraged. Industry partnerships are essential for NSF-ATE projects. NSF-ATE supports Emerging Technologies and technologies such as Biotechnology, Engineering, Energy, Environmental, Agricultural, Advanced Manufacturing, Micro/Nano Technologies, Information, Security, and Geospatial. Multiple categories of NSF-ATE grant funding are available including Projects, Small Projects for Institutions New to ATE, Applied Research on Technician Education, National Centers, and Resource Centers. The new NSF-ATE solicitation (NSF 21-598) was released in 2021 and includes higher funding levels and multiple categories of grant funding opportunities, including a new Consortia for Innovations in Technician Education. NSF-ATE has some helpful resources for educators planning to develop or improve their courses or programs. Mentoring opportunities for grant proposal development are available through multiple projects such as Mentor-Connect, MNT-EC (Micro Nano Technology Education Center), Mentor Up, Project Vision, Pathways to Innovation, CCPISTEM, and FORCCE-ATE. Each of these projects has a unique approach and a different focus to help their mentees successfully submit NSF-ATE grant proposals.more » « less
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The National Science Foundation Advanced Technological Education (NSF-ATE) program has grant funding opportunities available to support CTE and STEM technician program development. NSF-ATE grant funding opportunities are intended to help educators develop or improve their 2-year technician programs. Proposals may focus on program, curriculum, and educational materials development, program improvement, faculty professional development, teacher preparation, career pathways, outreach activities, undergraduate research experiences, internships, apprenticeships, and more. Partnerships with universities, colleges, and 7-12 institutions in support of workforce development are encouraged. Industry partnerships are essential for NSF-ATE projects. NSF-ATE supports Emerging Technologies and technologies such as Biotechnology, Engineering, Energy, Environmental, Agricultural, Advanced Manufacturing, Micro/Nano Technologies, Information, Security, and Geospatial. Multiple categories of NSF-ATE grant funding are available including Projects, Small Projects for Institutions New to ATE, Applied Research on Technician Education, National Centers, and Resource Centers. The new NSF-ATE solicitation (NSF 21-598) was released in 2021 and includes higher funding levels and multiple categories of grant funding opportunities, including a new Consortia for Innovations in Technician Education. NSF-ATE has some helpful resources for educators planning to develop or improve their courses or programs. Mentoring opportunities for grant proposal development are available through multiple projects such as Mentor-Connect, MNT-EC (Micro Nano Technology Education Center), Mentor Up, Project Vision, Pathways to Innovation, CCPISTEM, and FORCCE-ATE. Each of these projects has a unique approach and a different focus to help their mentees successfully submit NSF-ATE grant proposals.more » « less
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Abstract Nonnegative matrix factorization (NMF) is widely used to analyze high-dimensional count data because, in contrast to real-valued alternatives such as factor analysis, it produces an interpretable parts-based representation. However, in applications such as spatial transcriptomics, NMF fails to incorporate known structure between observations. Here, we present nonnegative spatial factorization (NSF), a spatially-aware probabilistic dimension reduction model based on transformed Gaussian processes that naturally encourages sparsity and scales to tens of thousands of observations. NSF recovers ground truth factors more accurately than real-valued alternatives such as MEFISTO in simulations, and has lower out-of-sample prediction error than probabilistic NMF on three spatial transcriptomics datasets from mouse brain and liver. Since not all patterns of gene expression have spatial correlations, we also propose a hybrid extension of NSF that combines spatial and nonspatial components, enabling quantification of spatial importance for both observations and features. A TensorFlow implementation of NSF is available fromhttps://github.com/willtownes/nsf-paper.more » « less
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