cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1054786.v2
title: Influence of a Combo Side Airbag on the Risk for Basilar Skull Fracture in a Far-Side Occupant

description: <b>Objective:</b> The impact force to the head and neck were measured in sled tests with and without inflation of a combo airbag for a far-side occupant to determine the risk for basilar skull fracture.<b>Methods:</b> Sled tests were run at 24 and 32 km/h (15 and 20 mph) with and without inflation of a combo side airbag to analyze the effect of cross-car diving into the side interior. The matched tests involved one pair at 24 km/h and another at 32 km/h. The 24 km/h pair involved a lap–shoulder-belted 5th percentile female Hybrid III and the series at 32 km/h involved an unbelted 5th percentile Hybrid III. The dummy was ballasted to 69.5 kg (153 lb) and laid on the right side. The dummy was positioned 30.5 cm (12<sup>″</sup>) from the far-side interior to ensure the full sled delta <i>V</i> occurred before head impact. The buck consisted of a 2001 Ford Taurus. The combo thorax–head side airbag was stored in the seatback. The airbag was triggered about 120 ms before the head impact. The head, chest, and pelvis were instrumented with triaxial accelerometers and the upper and lower neck, thoracic spine, and lumbar spine had transducers measuring triaxial loads and moments. High-speed video recorded different views of the dummy motion. Dummy kinematics and biomechanical responses were compared to study the influence of inflating the side airbag on the head and neck.<b>Results:</b> The top of the head impacted the far side. The force of impact was similar with and without the airbag as the head compressed the airbag and loaded the vehicle interior trim behind the airbag. The peak force on the head was primarily from neck load as torso augmentation occurred. For the 24 km/h (15 mph) tests, the peak force was 4.7 kN (1055 lb) without and 4.8 kN (1088 lb) with the airbag and there was over 2.67 kN (600 lb) of lap belt load. The peak head acceleration was 93 <i>g</i> without and 72 <i>g</i> with the airbag. For the 32 km/h (20 mph) tests, the force on the head was 15.3 kN (3433 lb) without and 15.2 kN (3406 lb) with the airbag, although the instrumentation saturated. The peak head acceleration was 236 <i>g</i> without and 262 <i>g</i> with the airbag.<b>Conclusion:</b> The airbag reduced head acceleration in the belted test but did not influence the diving forces from torso augmentation through the neck of the far-side occupant. The side airbag did not reduce the risk for basilar skull fracture due to high neck compression loads in either the belted or unbelted tests.
DOI: 10.6084/m9.figshare.1054789.v1
title: Comprehensive Target Populations for Current Active Safety Systems Using National Crash Databases

description: <b>Objective:</b> The objective of active safety systems is to prevent or mitigate collisions. A critical component in the design of active safety systems is the identification of the target population for a proposed system. The target population for an active safety system is that set of crashes that a proposed system could prevent or mitigate. Target crashes have scenarios in which the sensors and algorithms would likely activate. For example, the rear-end crash scenario, where the front of one vehicle contacts another vehicle traveling in the same direction and in the same lane as the striking vehicle, is one scenario for which forward collision warning (FCW) would be most effective in mitigating or preventing. This article presents a novel set of precrash scenarios based on coded variables from NHTSA's nationally representative crash databases in the United States.<b>Methods:</b> Using 4 databases (National Automotive Sampling System–General Estimates System [NASS-GES], NASS Crashworthiness Data System [NASS-CDS], Fatality Analysis Reporting System [FARS], and National Motor Vehicle Crash Causation Survey [NMVCCS]) the scenarios developed in this study can be used to quantify the number of police-reported crashes, seriously injured occupants, and fatalities that are applicable to proposed active safety systems. In this article, we use the precrash scenarios to identify the target populations for FCW, pedestrian crash avoidance systems (PCAS), lane departure warning (LDW), and vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) systems. Crash scenarios were derived using precrash variables (critical event, accident type, precrash movement) present in all 4 data sources.<b>Results and Conclusions:</b> This study found that these active safety systems could potentially mitigate approximately 1 in 5 of all severity and serious injury crashes in the United States and 26 percent of fatal crashes. Annually, this corresponds to 1.2 million all severity, 14,353 serious injury (MAIS 3+), and 7412 fatal crashes. In addition, we provide the source code for the crash scenarios as an appendix (see online supplement) to this article so that researchers can use the crash scenarios in future research.
DOI: 10.6084/m9.figshare.1054782
title: Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models

description: We propose a general likelihood evaluation method for nonlinear non-Gaussian state-space models using the simulation-based method of efficient importance sampling. We minimize the simulation effort by replacing some key steps of the likelihood estimation procedure by numerical integration. We refer to this method as numerically accelerated importance sampling. We show that the likelihood function for models with a high-dimensional state vector and a low-dimensional signal can be evaluated more efficiently using the new method. We report many efficiency gains in an extensive Monte Carlo study as well as in an empirical application using a stochastic volatility model for U.S. stock returns with multiple volatility factors. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1054786.v1
title: Influence of a Combo Side Airbag on the Risk for Basilar Skull Fracture in a Far-Side Occupant

description: <b>Objective:</b> The impact force to the head and neck were measured in sled tests with and without inflation of a combo airbag for a far-side occupant to determine the risk for basilar skull fracture.<b>Methods:</b> Sled tests were run at 24 and 32 km/h (15 and 20 mph) with and without inflation of a combo side airbag to analyze the effect of cross-car diving into the side interior. The matched tests involved one pair at 24 km/h and another at 32 km/h. The 24 km/h pair involved a lap–shoulder-belted 5th percentile female Hybrid III and the series at 32 km/h involved an unbelted 5th percentile Hybrid III. The dummy was ballasted to 69.5 kg (153 lb) and laid on the right side. The dummy was positioned 30.5 cm (12<sup>″</sup>) from the far-side interior to ensure the full sled delta <i>V</i> occurred before head impact. The buck consisted of a 2001 Ford Taurus. The combo thorax–head side airbag was stored in the seatback. The airbag was triggered about 120 ms before the head impact. The head, chest, and pelvis were instrumented with triaxial accelerometers and the upper and lower neck, thoracic spine, and lumbar spine had transducers measuring triaxial loads and moments. High-speed video recorded different views of the dummy motion. Dummy kinematics and biomechanical responses were compared to study the influence of inflating the side airbag on the head and neck.<b>Results:</b> The top of the head impacted the far side. The force of impact was similar with and without the airbag as the head compressed the airbag and loaded the vehicle interior trim behind the airbag. The peak force on the head was primarily from neck load as torso augmentation occurred. For the 24 km/h (15 mph) tests, the peak force was 4.7 kN (1055 lb) without and 4.8 kN (1088 lb) with the airbag and there was over 2.67 kN (600 lb) of lap belt load. The peak head acceleration was 93 <i>g</i> without and 72 <i>g</i> with the airbag. For the 32 km/h (20 mph) tests, the force on the head was 15.3 kN (3433 lb) without and 15.2 kN (3406 lb) with the airbag, although the instrumentation saturated. The peak head acceleration was 236 <i>g</i> without and 262 <i>g</i> with the airbag.<b>Conclusion:</b> The airbag reduced head acceleration in the belted test but did not influence the diving forces from torso augmentation through the neck of the far-side occupant. The side airbag did not reduce the risk for basilar skull fracture due to high neck compression loads in either the belted or unbelted tests.
DOI: 10.6084/m9.figshare.1054781
title: Implied Volatility Spreads and Expected Market Returns

description: This article investigates the intertemporal relation between volatility spreads and expected returns on the aggregate stock market. We provide evidence for a significantly negative link between volatility spreads and expected returns at the daily and weekly frequencies. We argue that this link is driven by the information flow from option markets to stock markets. The documented relation is significantly stronger for the periods during which (i) S&P 500 constituent firms announce their earnings; (ii) cash flow and discount rate news are large in magnitude; and (iii) consumer sentiment index takes extreme values. The intertemporal relation remains strongly negative after controlling for conditional volatility, variance risk premium, and macroeconomic variables. Moreover, a trading strategy based on the intertemporal relation with volatility spreads has higher portfolio returns compared to a passive strategy of investing in the S&P 500 index, after transaction costs are taken into account.
DOI: 10.6084/m9.figshare.1054782.v2
title: Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State-Space Models

description: We propose a general likelihood evaluation method for nonlinear non-Gaussian state-space models using the simulation-based method of efficient importance sampling. We minimize the simulation effort by replacing some key steps of the likelihood estimation procedure by numerical integration. We refer to this method as numerically accelerated importance sampling. We show that the likelihood function for models with a high-dimensional state vector and a low-dimensional signal can be evaluated more efficiently using the new method. We report many efficiency gains in an extensive Monte Carlo study as well as in an empirical application using a stochastic volatility model for U.S. stock returns with multiple volatility factors. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.1054781.v2
title: Implied Volatility Spreads and Expected Market Returns

description: This article investigates the intertemporal relation between volatility spreads and expected returns on the aggregate stock market. We provide evidence for a significantly negative link between volatility spreads and expected returns at the daily and weekly frequencies. We argue that this link is driven by the information flow from option markets to stock markets. The documented relation is significantly stronger for the periods during which (i) S&P 500 constituent firms announce their earnings; (ii) cash flow and discount rate news are large in magnitude; and (iii) consumer sentiment index takes extreme values. The intertemporal relation remains strongly negative after controlling for conditional volatility, variance risk premium, and macroeconomic variables. Moreover, a trading strategy based on the intertemporal relation with volatility spreads has higher portfolio returns compared to a passive strategy of investing in the S&P 500 index, after transaction costs are taken into account.
DOI: 10.6084/m9.figshare.1054782.v1
title: Numerically Accelerated Importance Sampling for Nonlinear Non-Gaussian State Space Models

description: We propose a general likelihood evaluation method for nonlinear non-Gaussian state space models using the simulation based method of efficient importance sampling. We minimise the simulation effort by replacing some key steps of the likelihood estimation procedure by numerical integration. We refer to this method as numerically accelerated importance sampling. We show that the likelihood function for models with a high-dimensional state vector and a low-dimensional signal can be evaluated more efficiently using the new method. We report many efficiency gains in an extensive Monte Carlo study as well as in an empirical application using a stochastic volatility model for U.S. stock returns with multiple volatility factors.
DOI: 10.6084/m9.figshare.1054751
title: Multilanguage Presentations on Altmetrics

description: This set of powerpoint slides summarizes our pilot study examining two altmetric gathering products <em>PlumX</em> (Plum Analytics) with additional information on <em>Altmetric.com</em> (MacMillan). We had Plum Analytics create profiles for several University of Colorado faculty. The faculty provided us with feedback on their social media visibility, or lack of it. The original English presentation is translated into three languages: Russian, Chinese and Japanese.
DOI: 10.6084/m9.figshare.1054751.v2
title: Multilanguage Presentations on Altmetrics

description: This set of powerpoint slides summarizes our pilot study examining two altmetric gathering products <em>PlumX</em> (Plum Analytics) with additional information on <em>Altmetric.com</em> (MacMillan). We had Plum Analytics create profiles for several University of Colorado faculty. The faculty provided us with feedback on their social media visibility, or lack of it. The original English presentation is translated into three languages: Russian, Chinese and Japanese.
DOI: 10.6084/m9.figshare.1054733
title: Wind speed data for shore and forest on an island in French Polynesia

description: Wind data were collected on three days during the second and third weeks of June in 2010 on Motu Tiahura when the weather was clear or partly cloudy and calm. The two sites for collection were the "shore" on open sand approximately 3 meters inland from the high-tide mark and at least 3 meters from the beginning of the tree line and "forest" on the leaf-litter under tree canopy at least 10 meters from the tree line. Hermit crabs were collected from both sites where wind data were collected. Wind speed measurements were collected using a hot-wire anemometer (Kurz 443M, Kurz Instruments, Inc, Monterey, CA) in a water-proof casing. Data was routed through a DAQ card (National Instruments, Inc., Austin, TX) to a Matlab software acquisition GUI on an IBM Thinkpad laptop. The anemometer was calibrated in a wind tunnel across a wide range of wind speeds prior to being taken into the field. Data were collected at 12 heights above the substratum from 5 mm to 0.68 m, covering the height range which hermit crabs would likely capture odors. Wind speeds were sampled at data were taken for three-minute intervals and sampled at 100 Hz. Measurements were repeated three times (on three different days) and averaged for each position above the substratum. Turbulence intensity was calcuated by dividing the standard deviation of wind speed in the sample by the mean speed of the sample. Statistics were performed with <em>R</em> and values are reported with standard error. Mean_vel.pdf reports wind speeds (in meters per second) graphed against height above substratum (in meters). Red dots indicate the "forest" location and blue dots indicate "shore" location. In a linear regression model (speed~height*location), there is a significant correlation between speed and height above substratum (slope = 0.18 ±0.04, t-value = 4.477, dof = 20, p = 2e-4) but only a slight correlation between speed and location (coef = 0.0506 ±0.02, t-value = 2.252, dof = 20, p = 0.03). turbint_regression.pdf reports turbulence intensities graphed against height above substratum (in meters). In a linear regression model (turbulence intensity~height*location), turbulence intensity has a significant correlation with height above substratum (slope = 0.0845 ±0.01, t-value = 5.712, dof = 20, p = 1e-5) but no relationship with location (coef = -0.00851 ±0.008, t-value = -1.07, dof = 20, p = 0.30).
DOI: 10.6084/m9.figshare.1054736
title: Arctic 2k v1.1.1

description: These data are well described in the Nature Scientific Data data descriptor: "An extended Arctic proxy temperature database for the past 2,000 years". ***Version 1.1.1 update**** In addition to the revised datasets, three tables are also included 1) MetadataTablev1.1.1.xlsx Which lists the site information and metadata for each site as in table 1 of the Scientific Data articles 2) Revisions_v1.1.1.xlsx Which lists and describes the changes between versions 3) Reconstruction_Arc2kv1.1.1.xlsx Which includes the data for the annual, 30-year and 100-year mean Arctic temperature reconstruction based on the v1.1.1 data. Lastly, the text files were updated to reflect the publication date and DOI of the Scientific Data publication, and the former convention of inverting values in the records with negative temperature was changed to the much more natural approach of showing the original data and relying on the metadata to identify records with inverse temperature relations.
DOI: 10.6084/m9.figshare.1054781.v1
title: Implied Volatility Spreads and Expected Market Returns

description: This paper investigates the intertemporal relation between volatility spreads and expected returns on the aggregate stock market. We provide evidence for a significantly negative link between volatility spreads and expected returns at the daily and weekly frequencies. We argue that this link is driven by the information flow from option markets to stock markets. The documented relation is significantly stronger for the periods during which (i) S&P 500 constituent firms announce their earnings; (ii) cash flow and discount rate news are large in magnitude; and (iii) consumer sentiment index takes extreme values. The intertemporal relation remains strongly negative after controlling for conditional volatility, variance risk premium and macroeconomic variables. Moreover, a trading strategy based on the intertemporal relation with volatility spreads has higher portfolio returns compared to a passive strategy of investing in the S&P 500 index, after transaction costs are taken into account.
DOI: 10.6084/m9.figshare.1054705
title: Data on geographical range size and climatic niche variables for 72 species of western North American Mimulus

description: This file contains all variables used in Fig. 2b of “Identifying the paths leading to variation in geographical range size in western North American monkeyflowers” by Sheth et al. 2014 in Journal of Biogeography. Please see publication for more details. NOTE (4 December 2014): The original file that was uploaded included incorrect estimates of extent of occurrence, number of pixels occupied, latitudinal extent, and longitudinal extent for Mimulus clevelandii. The file uploaded on 4 December 2014 has been updated to correct estimates of these variables for M. clevelandii. Please note that incorrect estimates for extent of occurrence, latitudinal extent, and longitudinal extent were used for M. clevelandii in all analyses in Sheth et al. 2014, but regression and path coefficients based on the correct estimates change only slightly. Please contact S. Sheth (seema.n.sheth@gmail.com) for corrected tables and figures. The columns are as follows: species: species of Mimulus extent_of_occurrence: geographic range size measured by minimum convex polygon (in units of kilometers squared) encompassing the occurrence points of each species number_of_pixels_occupied: number of 5-arc minute pixels in which each species is known to occur latitudinal_extent: latitudinal extent (in decimal degrees) encompassed by the occurrence points of each species longitudinal_extent: longitudinal extent (in decimal degrees) encompassed by the occurrence points of each species niche_breadth: multivariate climatic niche breadth of each species niche_position: multivariate climatic niche position of each species amount_of_suitable_habitat: number of 5-arc minute pixels defined as climatically suitable for each species connectivity: connectivity of climatically suitable habitat (in km multiplied by -1) occupancy_of_suitable_habitat: number of climatically suitable grid cells in which each species is known to occur (number_of_pixels_occupied/amount_of_suitable_habitat)
DOI: 10.6084/m9.figshare.1054706
title: Locality data for 82 species of western North American Mimulus

description: This file contains the western North American Mimulus locality data used in “Identifying the paths leading to variation in geographical range size in western North American monkeyflowers” by Sheth et al. (2014) in Journal of Biogeography. The file includes primary occurrence data for 82 species of Mimulus in western North America. Please see Table S1 in Appendix S3 of “Identifying the paths leading to variation in geographical range size in western North American monkeyflowers” (Sheth et al. 2014; Journal of Biogeography) for information on data sources. Coordinates are based on Albers equal area conic projection of North America. In the publication, only species occupying at least three 5-arc minute pixels were included in the analyses, resulting in a final dataset of 72 Mimulus species.
DOI: 10.6084/m9.figshare.1054733.v1
title: Wind speed data for shore and forest on an island in French Polynesia

description: Wind data were collected on three days during the second and third weeks of June in 2010 on Motu Tiahura when the weather was clear or partly cloudy and calm. The two sites for collection were the "shore" on open sand approximately 3 meters inland from the high-tide mark and at least 3 meters from the beginning of the tree line and "forest" on the leaf-litter under tree canopy at least 10 meters from the tree line. Hermit crabs were collected from both sites where wind data were collected. Wind speed measurements were collected using a hot-wire anemometer (Kurz 443M, Kurz Instruments, Inc, Monterey, CA) in a water-proof casing. Data was routed through a DAQ card (National Instruments, Inc., Austin, TX) to a Matlab software acquisition GUI on an IBM Thinkpad laptop. The anemometer was calibrated in a wind tunnel across a wide range of wind speeds prior to being taken into the field. Data were collected at 12 heights above the substratum from 5 mm to 0.68 m, covering the height range which hermit crabs would likely capture odors. Wind speeds were sampled at data were taken for three-minute intervals and sampled at 100 Hz. Measurements were repeated three times (on three different days) and averaged for each position above the substratum. Turbulence intensity was calcuated by dividing the standard deviation of wind speed in the sample by the mean speed of the sample. Statistics were performed with <em>R</em> and values are reported with standard error. Mean_vel.pdf reports wind speeds (in meters per second) graphed against height above substratum (in meters). Red dots indicate the "forest" location and blue dots indicate "shore" location. In a linear regression model (speed~height*location), there is a significant correlation between speed and height above substratum (slope = 0.18 ±0.04, t-value = 4.477, dof = 20, p = 2e-4) but only a slight correlation between speed and location (coef = 0.0506 ±0.02, t-value = 2.252, dof = 20, p = 0.03). turbint_regression.pdf reports turbulence intensities graphed against height above substratum (in meters). In a linear regression model (turbulence intensity~height*location), turbulence intensity has a significant correlation with height above substratum (slope = 0.0845 ±0.01, t-value = 5.712, dof = 20, p = 1e-5) but no relationship with location (coef = -0.00851 ±0.008, t-value = -1.07, dof = 20, p = 0.30).