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1. The United States COVID-19 Forecast Hub dataset

   https://doi.org/10.1038/s41597-022-01517-w  

   Cramer, Estee Y.; Huang, Yuxin; Wang, Yijin; Ray, Evan L.; Cornell, Matthew; Bracher, Johannes; Brennen, Andrea; Rivadeneira, Alvaro J.; Gerding, Aaron; House, Katie; et al (December 2022, Scientific Data)

   Abstract Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages. 

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2. HUB: a method to model and extract the distribution of ice nucleation temperatures from drop-freezing experiments

   https://doi.org/10.5194/acp-23-5623-2023  

   de_Almeida_Ribeiro, Ingrid; Meister, Konrad; Molinero, Valeria (January 2023, Atmospheric Chemistry and Physics)

   The heterogeneous nucleation of ice is an importantatmospheric process facilitated by a wide range of aerosols. Drop-freezingexperiments are key for the determination of the ice nucleation activity ofbiotic and abiotic ice nucleators (INs). The results of these experimentsare reported as the fraction of frozen droplets fice(T) as a functionof decreasing temperature and the corresponding cumulative freezing spectraNm(T) computed using Gabor Vali's methodology. The differential freezingspectrum nm(T) is an approximant to the underlying distribution ofheterogeneous ice nucleation temperatures Pu(T) that represents thecharacteristic freezing temperatures of all INs in the sample. However,Nm(T) can be noisy, resulting in a differential form nm(T) that is challenging to interpret. Furthermore, there is no rigorousstatistical analysis of how many droplets and dilutions are needed to obtaina well-converged nm(T) that represents the underlying distributionPu(T). Here, we present the HUB (heterogeneousunderlying-based) method and associated Python codes thatmodel (HUB-forward code) and interpret (HUB-backward code) the results ofdrop-freezing experiments. HUB-forward predicts fice(T) and Nm(T)from a proposed distribution Pu(T) of IN temperatures, allowing itsusers to test hypotheses regarding the role of subpopulations of nuclei infreezing spectra and providing a guide for a more efficient collection offreezing data. HUB-backward uses a stochastic optimization method to computenm(T) from either Nm(T) or fice(T). The differential spectrumcomputed with HUB-backward is an analytical function that can be used toreveal and characterize the underlying number of IN subpopulations ofcomplex biological samples (e.g., ice-nucleating bacteria, fungi, pollen)and to quantify the dependence of these subpopulations on environmentalvariables. By delivering a way to compute the differential spectrum fromdrop-freezing data, and vice versa, the HUB-forward and HUB-backward codesprovide a hub to connect experiments and interpretative physical quantitiesthat can be analyzed with kinetic models and nucleation theory. 

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3. Emerging Role of 2-year Hispanic-Serving Institution (HSIs) in Advanced Technological Education (ATE): Challenges, Opportunities, and Impacts for Growing the United States Technical Workforce

   Pickering, Cynthia; Craft, Elaine; VanIngen-Dunn, Caroline; Tanguma-Gallegos, Anna; DeWitt, Emery (July 2020, ASEE annual conference)

   This paper is the second in a series of annual papers about the role 2-year Hispanic Serving Institutions (HSIs) have in educating technicians from underrepresented groups and how the National Science Foundation (NSF) sponsored HSI Advanced Technological Education (ATE) Hub program supports faculty at HSIs in improving Hispanic/Latinx student success. The goal of the HSI ATE Hub project is to build capacity and leadership at 2-year HSIs for developing competitive ATE proposals to NSF to prepare technicians in advanced technologies that drive the American economy. Funded by the NSF ATE Program, the HSI ATE Hub is a three-year collaborative project implemented by Florence Darlington Technical College in South Carolina and the Science Foundation Arizona Center for STEM at Arizona State University. Last year’s paper described the research need, provided a project overview, included baseline and initial data, and discussed early lessons learned and their implications for future research. This paper describes continued fostering of the HSI ATE community (2-year HSIs with grant prospects and awards from the NSF ATE Program), resource dissemination, usage, perceived value to the community, and additional data gathered during the first and second cohorts of HSI ATE Hub, including adjustments based on learnings from year 1. Emphasis will be placed on HSI ATE Community building and resources. Lessons learned and implications for future research are also described in the paper. 

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4. Emerging Role of 2-year Hispanic-Serving Institution (HSIs) in Advanced Technological Education (ATE): Challenges, Opportunities, and Impacts for Growing the United States Technical Workforce

   Pickering, Cynthia; Craft, Elaine; VanIngen-Dunn, Caroline; Tanguma-Gallegos, Anna; DeWitt, Emery (July 2020, ASEE annual conference)

   This paper is the second in a series of annual papers about the role 2-year Hispanic Serving Institutions (HSIs) have in educating technicians from underrepresented groups and how the National Science Foundation (NSF) sponsored HSI Advanced Technological Education (ATE) Hub program supports faculty at HSIs in improving Hispanic/Latinx student success. The goal of the HSI ATE Hub project is to build capacity and leadership at 2-year HSIs for developing competitive ATE proposals to NSF to prepare technicians in advanced technologies that drive the American economy. Funded by the NSF ATE Program, the HSI ATE Hub is a three-year collaborative project implemented by Florence Darlington Technical College in South Carolina and the Science Foundation Arizona Center for STEM at Arizona State University. Last year’s paper described the research need, provided a project overview, included baseline and initial data, and discussed early lessons learned and their implications for future research. This paper describes continued fostering of the HSI ATE community (2-year HSIs with grant prospects and awards from the NSF ATE Program), resource dissemination, usage, perceived value to the community, and additional data gathered during the first and second cohorts of HSI ATE Hub, including adjustments based on learnings from year 1. Emphasis will be placed on HSI ATE Community building and resources. Lessons learned and implications for future research are also described in the paper. 

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5. PIOT-Hub - A collaborative cloud tool for generation of physical input–output tables using mechanistic engineering models

   Venkata Sai Gargeya Vunnava; Jaewoo Shin; Lan Zhao; Shweta Singh (February 2022, Journal of industrial ecology)

   Lenzen, Manfred (Ed.)

   Mapping material flows in an economy is crucial to identifying strategies for resource management toward lowering the waste and environmental impacts of society, a key objective of research in industrial ecology. However, constructing models for mapping material flows at a sectoral level, such as in physical input–output tables (PIOTs) at highly disaggregated levels, is tedious and relies on a large amount of empirical data. To overcome this challenge, a novel collaborative cloud platform PIOT-Hub is developed in this work. This platform utilizes a Python-based simulation system for extracting material flow data from mechanistic models, thus semi-automating the generation of PIOTs. The simulation system implements a bottom-up approach of utilizing scaled engineering models to generate physical supply tables (PSTs) and physical use tables (PUTs) which are converted to PIOTs (described in (Vunnava & Singh, 2021)). Mechanistic models can be uploaded by users for sectors on PIOT-Hub to develop PIOTs for any region. Both models and resulting PST/PUT/PIOTs can be shared with other users utilizing the collaborative platform. The automation and sharing features provided by PIOT-Hub will help to significantly reduce the time required to develop PIOT and improve the reproducibility/continuity of PIOT generation, thus allowing the study of the changing nature of material flows in regional economy. In this paper, we describe the simulation system MFDES and PIOT-Hub architecture/functionality through a demo example for creating PIOT in agro-based sectors for Illinois. Future work includes scaling up the cloud infrastructure for large scale PIOT generation and enhancing the tool compatibility for different sectors in economy. 

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