cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6483761.v1
title: Bayesian Inference for the One-Factor Copula Model

description: We develop efficient Bayesian inference for the one-factor copula model with two significant contributions over existing methodologies. First, our approach leads to straightforward inference on dependence parameters and the latent factor; only inference on the former is available under frequentist alternatives. Second, we develop a reversible jump Markov chain Monte Carlo algorithm that averages over models constructed from different bivariate copula building blocks. Our approach accommodates any combination of discrete and continuous margins. Through extensive simulations, we compare the computational and Monte Carlo efficiency of alternative proposed sampling schemes. The preferred algorithm provides reliable inference on parameters, the latent factor, and model space. The potential of the methodology is highlighted in an empirical study of 10 binary measures of socio-economic deprivation collected for 11,463 East Timorese households. The importance of conducting inference on the latent factor is motivated by constructing a poverty index using estimates of the factor. Compared to a linear Gaussian factor model, our model average improves out-of-sample fit. The relationships between the poverty index and observed variables uncovered by our approach are diverse and allow for a richer and more precise understanding of the dependence between overall deprivation and individual measures of well-being.
DOI: 10.6084/m9.figshare.6483758.v2
title: Bayesian Model Selection for Exponential Random Graph Models via Adjusted Pseudolikelihoods

description: Models with intractable likelihood functions arise in areas including network analysis and spatial statistics, especially those involving Gibbs random fields. Posterior parameter estimation in these settings is termed a doubly intractable problem because both the likelihood function and the posterior distribution are intractable. The comparison of Bayesian models is often based on the statistical evidence, the integral of the un-normalized posterior distribution over the model parameters which is rarely available in closed form. For doubly intractable models, estimating the evidence adds another layer of difficulty. Consequently, the selection of the model that best describes an observed network among a collection of exponential random graph models for network analysis is a daunting task. Pseudolikelihoods offer a tractable approximation to the likelihood but should be treated with caution because they can lead to an unreasonable inference. This article specifies a method to adjust pseudolikelihoods to obtain a reasonable, yet tractable, approximation to the likelihood. This allows implementation of widely used computational methods for evidence estimation and pursuit of Bayesian model selection of exponential random graph models for the analysis of social networks. Empirical comparisons to existing methods show that our procedure yields similar evidence estimates, but at a lower computational cost. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.6483758.v1
title: Bayesian Model Selection for Exponential Random Graph Models via Adjusted Pseudolikelihoods

description: Models with intractable likelihood functions arise in areas including network analysis and spatial statistics, especially those involving Gibbs random fields. Posterior parameter estimation in these settings is termed a doubly intractable problem because both the likelihood function and the posterior distribution are intractable. The comparison of Bayesian models is often based on the statistical evidence, the integral of the un-normalized posterior distribution over the model parameters which is rarely available in closed form. For doubly intractable models, estimating the evidence adds another layer of difficulty. Consequently, the selection of the model that best describes an observed network among a collection of exponential random graph models for network analysis is a daunting task. Pseudolikelihoods offer a tractable approximation to the likelihood but should be treated with caution because they can lead to an unreasonable inference. This article specifies a method to adjust pseudolikelihoods to obtain a reasonable, yet tractable, approximation to the likelihood. This allows implementation of widely used computational methods for evidence estimation and pursuit of Bayesian model selection of exponential random graph models for the analysis of social networks. Empirical comparisons to existing methods show that our procedure yields similar evidence estimates, but at a lower computational cost. Supplementary material for this article is available online.
DOI: 10.6084/m9.figshare.6483689
title: Electroacoustic-Propulsion

description: Present and future spaceflight missions depend on the ability to produce high exhaust velocities while reducing the dependence on chemical fuel and its mass onboard a spaceflight vehicle. Oscillation of gaseous molecules during pre-ejection stages via an embedded wave driver allows for ejection at higher velocities, increasing chemical fuel efficiency. Oscillation of granulate and liquid reagents using simple harmonic motion has been shown to excite particles, forming geometric patterns when using calibrated frequencies. Methods shown to induce geometric patterns were used to attain similar formations in the reagents Lycopodium, CO2(g) and SF6(g). Oscillation of Lycopodium was used as a proven method to target, observe, and calibrate specific sound formations for experimentation with gases. SF6 was used to simulate xenon, a dense gas used in modern electronic propulsion devices. Ten-millimeter polypropylene, air-filled mass objects were used to observe acceleration, force, and velocity for a dense gas during oscillation and resulting formations. Observation of non-zero forces within gas formations during oscillation shows that additional thrust velocity can be achieved through the oscillation of propellant gas via wave drivers embedded within experimental electronic propulsion systems. Force and velocity calculations taken during oscillation of SF6 demonstrate proof of concept for future experimentation using xenon as an oscillation and ionization medium for ejection at velocities which can be used for spaceflight. Results of this experiment introduce a novel method for achieving increased velocity during space flight using sound as a performance enhancer.
DOI: 10.6084/m9.figshare.6483689.v2
title: Electroacoustic-Propulsion

description: Present and future spaceflight missions depend on the ability to produce high exhaust velocities while reducing the dependence on chemical fuel and its mass onboard a spaceflight vehicle. Oscillation of gaseous molecules during pre-ejection stages via an embedded wave driver allows for ejection at higher velocities, increasing chemical fuel efficiency. Oscillation of granulate and liquid reagents using simple harmonic motion has been shown to excite particles, forming geometric patterns when using calibrated frequencies. Methods shown to induce geometric patterns were used to attain similar formations in the reagents Lycopodium, CO2(g) and SF6(g). Oscillation of Lycopodium was used as a proven method to target, observe, and calibrate specific sound formations for experimentation with gases. SF6 was used to simulate xenon, a dense gas used in modern electronic propulsion devices. Ten-millimeter polypropylene, air-filled mass objects were used to observe acceleration, force, and velocity for a dense gas during oscillation and resulting formations. Observation of non-zero forces within gas formations during oscillation shows that additional thrust velocity can be achieved through the oscillation of propellant gas via wave drivers embedded within experimental electronic propulsion systems. Force and velocity calculations taken during oscillation of SF6 demonstrate proof of concept for future experimentation using xenon as an oscillation and ionization medium for ejection at velocities which can be used for spaceflight. Results of this experiment introduce a novel method for achieving increased velocity during space flight using sound as a performance enhancer.
DOI: 10.6084/m9.figshare.6483689.v1
title: Electroacoustic-Propulsion

description: Present and future spaceflight missions depend on the ability to produce high exhaust velocities while reducing the dependence on chemical fuel and its mass onboard a spaceflight vehicle. Oscillation of gaseous molecules during pre-ejection stages via an embedded wave driver allows for ejection at higher velocities, increasing chemical fuel efficiency. Oscillation of granulate and liquid reagents using simple harmonic motion has been shown to excite particles, forming geometric patterns when using calibrated frequencies. Methods shown to induce geometric patterns were used to attain similar formations in the reagents Lycopodium, CO2(g) and SF6(g). Oscillation of Lycopodium was used as a proven method to target, observe, and calibrate specific sound formations for experimentation with gases. SF6 was used to simulate xenon, a dense gas used in modern electronic propulsion devices. Ten-millimeter polypropylene, air-filled mass objects were used to observe acceleration, force, and velocity for a dense gas during oscillation and resulting formations. Observation of non-zero forces within gas formations during oscillation shows that additional thrust velocity can be achieved through the oscillation of propellant gas via wave drivers embedded within experimental electronic propulsion systems. Force and velocity calculations taken during oscillation of SF6 demonstrate proof of concept for future experimentation using xenon as an oscillation and ionization medium for ejection at velocities which can be used for spaceflight. Results of this experiment introduce a novel method for achieving increased velocity during space flight using sound as a performance enhancer.
DOI: 10.6084/m9.figshare.6483683
title: electroacoustic-solid-fuel-reactor

description: Using a speaker coil and a function generator to oscillate fine-medium metal flake which releases energy as friction / ignition.
DOI: 10.6084/m9.figshare.6483683.v1
title: electroacoustic-solid-fuel-reactor

description: Using a speaker coil and a function generator to oscillate fine-medium metal flake which releases energy as friction / ignition.
DOI: 10.6084/m9.figshare.6483680
title: DEFENSIVE-MECHANISMS-FOR-ORBITAL-SATELLITES

description: COPYRIGHT 2018 GUNDAM. PUBLISHED BY CORY ANDREW HOFSTAD
DOI: 10.6084/m9.figshare.6483680.v1
title: DEFENSIVE-MECHANISMS-FOR-ORBITAL-SATELLITES

description: COPYRIGHT 2018 GUNDAM. PUBLISHED BY CORY ANDREW HOFSTAD
DOI: 10.6084/m9.figshare.6483671
title: CORSAIR-CX750-DC-POWER-SUPPLY-LAB-CONVERSION

description: Converting Corsair CX750 Desktop Power Supply for use in WAPS Mark I testing environment.
DOI: 10.6084/m9.figshare.6483671.v1
title: CORSAIR-CX750-DC-POWER-SUPPLY-LAB-CONVERSION

description: Converting Corsair CX750 Desktop Power Supply for use in WAPS Mark I testing environment.
DOI: 10.6084/m9.figshare.6483614
title: Simple model bundle atlas for RecoBundles

description: This is a dataset to be used by the RecoBundles algorithm available in DIPY (http://dipy.org). The algorithm is explained in the paper:<br>Garyfallidis, Eleftherios, et al. "Recognition of white matter bundles using local and global streamline-based registration and clustering." NeuroImage 170 (2017): 283-297.<br>https://www.ncbi.nlm.nih.gov/pubmed/28712994<br><br>This dataset is curated from the dataset explained in the following paper <br>Yeh, Fang-Cheng, et al. "Population-averaged atlas of the macroscale human structural connectome and its network topology." NeuroImage 178 (2018): 57-68.<br><br>https://www.sciencedirect.com/science/article/pii/S1053811918304324<br>All the bundles and trk (Trackvis) files are in MNI space ICBM 2009a.<br>
DOI: 10.6084/m9.figshare.6483476
title: 3HA Nanoparticle Stability Data

description: Vaccine distribution infrastructure remains inadequate in many parts of the world, and it is estimated that up to 40-50% of all vaccine doses are wasted in certain countries. Vaccines that can maintain viability outside of the cold chain would decrease vaccine wastage and increase immunization rates in regions of the world with underdeveloped vaccine distribution infrastructure. We examined the potential of crosslinked protein nanoparticles, made from trimerized influenza hemagglutinin (3HA), to maintain immunogenicity after cold-chain-independent storage. We found that the nanoparticles could be stored for 112 days at room temperature without any loss in hemagglutinating activity or immunogenicity, and that nanoparticles could be stored at 37°C for 2 weeks without any loss in hemagglutinating activity. As vaccine development moves towards the use of recombinant subunit antigens, our results demonstrate the potential of crosslinked antigen nanoparticles as an immunogenic vehicle for bringing effective vaccines to underdeveloped regions outside of the cold chain.<b></b>
DOI: 10.6084/m9.figshare.6483476.v1
title: 3HA Nanoparticle Stability Data

description: Vaccine distribution infrastructure remains inadequate in many parts of the world, and it is estimated that up to 40-50% of all vaccine doses are wasted in certain countries. Vaccines that can maintain viability outside of the cold chain would decrease vaccine wastage and increase immunization rates in regions of the world with underdeveloped vaccine distribution infrastructure. We examined the potential of crosslinked protein nanoparticles, made from trimerized influenza hemagglutinin (3HA), to maintain immunogenicity after cold-chain-independent storage. We found that the nanoparticles could be stored for 112 days at room temperature without any loss in hemagglutinating activity or immunogenicity, and that nanoparticles could be stored at 37°C for 2 weeks without any loss in hemagglutinating activity. As vaccine development moves towards the use of recombinant subunit antigens, our results demonstrate the potential of crosslinked antigen nanoparticles as an immunogenic vehicle for bringing effective vaccines to underdeveloped regions outside of the cold chain.<b></b>
DOI: 10.6084/m9.figshare.6483437
title: Graham_et_al_STOTEN_2018

description: Data for Graham et al. 2018 Science of the Total Environment
DOI: 10.6084/m9.figshare.6483437.v1
title: Graham_et_al_STOTEN_2018

description: Data for Graham et al. 2018 Science of the Total Environment
DOI: 10.6084/m9.figshare.6483413
title: Reyes-Contreras_et_al_2018_DATA.xlsx

description: original paper of an experiment described in a manuscript<br>
DOI: 10.6084/m9.figshare.6483413.v1
title: Reyes-Contreras_et_al_2018_DATA.xlsx

description: original paper of an experiment described in a manuscript<br>
DOI: 10.6084/m9.figshare.6483392
title: The Freshening Power of Centennial Hops

description: The addition of hops to fermented beer in the presence of yeast may result in a secondary fermentation commonly referred to as “after-fermentation,” “dry-hop creep,” “ABV creep,” and “the freshening power of the hop” (FPH). The effect is most likely caused by glycolytic enzyme(s) in hops converting residual nonfermentable polysaccharides from malt into simple sugars that are then fermented by yeast. The FPH must be considered because the ethanol increase can exceed ±0.3% ABV tolerance set out in the U.S. Code of Federal Regulations. Furthermore, with unpasteurized and unfiltered beers, the commensurate rise in carbonation levels may lead to high pressures and potential consumer hazard. Development of a quantitative measurement system for FPH will be a critical component in an ongoing challenge in the industry to consistently ‘brew to final gravity’ while minimizing handling and blending prior to packaging and to ensure ABV compliance. The research presented herein was carried out to characterize the FPH of Centennial hops, identify key factors involved, compare the magnitude of hop-induced secondary fermentation in common dry-hopping processes, and provide a roadmap to develop a quantitative protocol to predict FPH potential in any given sample of hops. In this paper, we review the current understanding of FPH and present a relatively simple and affordable method to measure FPH impact to better understand and control ABV, CO<sub>2</sub> output, and diacetyl in beers that are dry-hopped in the presence of live yeast and brewed to final gravity.
DOI: 10.6084/m9.figshare.6483392.v1
title: The Freshening Power of Centennial Hops

description: The addition of hops to fermented beer in the presence of yeast may result in a secondary fermentation commonly referred to as “after-fermentation,” “dry-hop creep,” “ABV creep,” and “the freshening power of the hop” (FPH). The effect is most likely caused by glycolytic enzyme(s) in hops converting residual nonfermentable polysaccharides from malt into simple sugars that are then fermented by yeast. The FPH must be considered because the ethanol increase can exceed ±0.3% ABV tolerance set out in the U.S. Code of Federal Regulations. Furthermore, with unpasteurized and unfiltered beers, the commensurate rise in carbonation levels may lead to high pressures and potential consumer hazard. Development of a quantitative measurement system for FPH will be a critical component in an ongoing challenge in the industry to consistently ‘brew to final gravity’ while minimizing handling and blending prior to packaging and to ensure ABV compliance. The research presented herein was carried out to characterize the FPH of Centennial hops, identify key factors involved, compare the magnitude of hop-induced secondary fermentation in common dry-hopping processes, and provide a roadmap to develop a quantitative protocol to predict FPH potential in any given sample of hops. In this paper, we review the current understanding of FPH and present a relatively simple and affordable method to measure FPH impact to better understand and control ABV, CO<sub>2</sub> output, and diacetyl in beers that are dry-hopped in the presence of live yeast and brewed to final gravity.
DOI: 10.6084/m9.figshare.6481226
title: Supplemental Materials

description: Stimuli, data, and model predictions for the paper titled "Modeling human intuitions about liquid flow with particle-based simulation" (Bates, Yildirim, Battaglia, Tenenbaum) forthcoming in PLOS Computational Biology.<br>Content Description<br><b>Stimuli:</b>Exp1_images: Images for all stimuli in Experiment 1Exp2_images: Images for all stimuli in Experiment 2<br>Exp1_movies: Videos used for practice trials in Experiment 1Exp2_movies: Videos used for practice trials in Experiment 2<br><b>Data:</b>responses.zip: Subject responses, indexed by stimulus name, in Pickle format. These can be loaded in Python using:<br>import picklewith open([file name], 'rb') as fid: data = pickle.load(fid)<br>The resulting format will be a Python dictionary.<br><b>Model Predictions:</b>(Note: load using Python NumPy, e.g.<br>import numpy as npdata = np.load('path_to_file')<br>All files are simple, numpy arrays containing model predictions for each set of parameter values. See notes below for description of each dimension. Parameter values along a dimension always go from low to high.)<br><i>SPH ground truth:</i>exp1_grnd_water_200.npy<br>exp1_grnd_stickyhoney_100.npyexp2_grnd_water_200.npyexp2_grnd_stickyhoney_100.npyDimensions: (scenes)<br><i>SPH Intuitive Fluids Engine:<br></i>exp1_damping_alpha_0.01_0.2_2.0_sticky_N_1_5_10_15_25_50_75_100.npyexp2_damping_alpha_0.01_0.2_2.0_sticky_N_1_5_10_15_25_50_75_100.npy<br>Dimensions: (scenes, alpha, zeta, # particles)<br><i>SPH Intuitive Fluids Engine (Position Noise, from Bates et al. 2015, referenced in text):</i>exp1_position_noise_alpha_0.01_0.2_2.0_sticky_noiselevels_5_N_1_5_10_15_25_50_75_100_samples_16.npy<br>exp2_position_noise_alpha_0.01_0.2_2.0_sticky_N_noiselevels_5_1_5_10_15_25_50_75_100_samples_16.npyDimensions: (scenes, alpha, noise magnitudes, # particles, noisy samples)<br><i>SimpleSim and Gravity Heuristic:</i>exp1_heuristic.npyexp2_heuristic.npyDimensions: (scenes, g, m)<br><i>Convolutional Neural Networks:</i>convnet_exp1_water.npyconvnet_exp1_honey.npyconvnet_exp2_water.npyconvnet_exp2_honey.npyDimensions: (scenes)<br>
DOI: 10.6084/m9.figshare.6481226.v2
title: Supplemental Materials

description: Stimuli, data, and model predictions for the paper titled "Modeling human intuitions about liquid flow with particle-based simulation" (Bates, Yildirim, Battaglia, Tenenbaum) forthcoming in PLOS Computational Biology.<br>Content Description<br><b>Stimuli:</b>Exp1_images: Images for all stimuli in Experiment 1Exp2_images: Images for all stimuli in Experiment 2<br>Exp1_movies: Videos used for practice trials in Experiment 1Exp2_movies: Videos used for practice trials in Experiment 2<br><b>Data:</b>responses.zip: Subject responses, indexed by stimulus name, in Pickle format. These can be loaded in Python using:<br>import picklewith open([file name], 'rb') as fid: data = pickle.load(fid)<br>The resulting format will be a Python dictionary.<br><b>Model Predictions:</b>(Note: load using Python NumPy, e.g.<br>import numpy as npdata = np.load('path_to_file')<br>All files are simple, numpy arrays containing model predictions for each set of parameter values. See notes below for description of each dimension. Parameter values along a dimension always go from low to high.)<br><i>SPH ground truth:</i>exp1_grnd_water_200.npy<br>exp1_grnd_stickyhoney_100.npyexp2_grnd_water_200.npyexp2_grnd_stickyhoney_100.npyDimensions: (scenes)<br><i>SPH Intuitive Fluids Engine:<br></i>exp1_damping_alpha_0.01_0.2_2.0_sticky_N_1_5_10_15_25_50_75_100.npyexp2_damping_alpha_0.01_0.2_2.0_sticky_N_1_5_10_15_25_50_75_100.npy<br>Dimensions: (scenes, alpha, zeta, # particles)<br><i>SPH Intuitive Fluids Engine (Position Noise, from Bates et al. 2015, referenced in text):</i>exp1_position_noise_alpha_0.01_0.2_2.0_sticky_noiselevels_5_N_1_5_10_15_25_50_75_100_samples_16.npy<br>exp2_position_noise_alpha_0.01_0.2_2.0_sticky_N_noiselevels_5_1_5_10_15_25_50_75_100_samples_16.npyDimensions: (scenes, alpha, noise magnitudes, # particles, noisy samples)<br><i>SimpleSim and Gravity Heuristic:</i>exp1_heuristic.npyexp2_heuristic.npyDimensions: (scenes, g, m)<br><i>Convolutional Neural Networks:</i>convnet_exp1_water.npyconvnet_exp1_honey.npyconvnet_exp2_water.npyconvnet_exp2_honey.npyDimensions: (scenes)<br>
DOI: 10.6084/m9.figshare.6481226.v1
title: Supplemental Materials

description: Stimuli and data for the paper titled "Modeling human intuitions about liquid flow with particle-based simulation" (Bates, Yildirim, Battaglia, Tenenbaum) forthcoming in PLOS Computational Biology.<br>Content DescriptionExp1_images: Images for all stimuli in Experiment 1Exp2_images: Images for all stimuli in Experiment 2<br>Exp1_movies: Videos used for practice trials in Experiment 1Exp2_movies: Videos used for practice trials in Experiment 2responses.zip: Subject responses, indexed by stimulus name, in Pickle format. These can be loaded in Python using:<br>import picklewith open([file name], 'rb') as fid: data = pickle.load(fid)<br>The resulting format will be a Python dictionary.
DOI: 10.6084/m9.figshare.6480854
title: output of the expertiment.pdf

description: This presents the relation between Number of Unemployment people in New York and Number of DNA Structures Released by protein data bank from 2000 to 2018, It contains a data sets from two different open data repository.
DOI: 10.6084/m9.figshare.6480617
title: Follow up time from The relative fitness of drug-resistant <i>Mycobacterium tuberculosis</i>: a modelling study of household transmission in Peru

description: Original raw data used to parameterise follow up times
DOI: 10.6084/m9.figshare.6480617.v1
title: Follow up time from The relative fitness of drug-resistant <i>Mycobacterium tuberculosis</i>: a modelling study of household transmission in Peru

description: Original raw data used to parameterise follow up times