cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.1201780.v1
title: A Book is Not a Story: Professional Development, session notes

description: Professional Development session developed as part of the research project, <em>Addressing Australian Curriculum Outcomes for a Diverse Range of Learners through Storytelling</em><br>Principal investigators – Simon Ellaby and Kim Shaw
DOI: 10.6084/m9.figshare.1201772
title: Thoughts on Excellence - John W. Gardner

description: Thoughts on Excellence - John W. Gardner by Jeremy L. McLaughlin is a presentation prepared for LIBR 282-11 (Leadership) at San Jose State University.
DOI: 10.6084/m9.figshare.1202060.v1
title: Web API Responses - SANER 2015

description: Web API responses for the SANER 2015 paper.
DOI: 10.6084/m9.figshare.1201772.v1
title: Thoughts on Excellence - John W. Gardner

description: Thoughts on Excellence - John W. Gardner by Jeremy L. McLaughlin is a presentation prepared for LIBR 282-11 (Leadership) at San Jose State University.
DOI: 10.6084/m9.figshare.1202060.v3
title: Web API Responses - MOBILESoft 2015

description: Web API responses for the MOBILESoft 2015 paper.
DOI: 10.6084/m9.figshare.1202060.v2
title: Web API Responses - SANER 2015

description: Web API responses for the SANER 2015 paper.
DOI: 10.6084/m9.figshare.1201734
title: Autonomous Docking station telsa Electric Vehicles

description: Autonomous Docking station telsa Electric Vehicles
DOI: 10.6084/m9.figshare.1201734.v2
title: Autonomous Docking station telsa

description: Autonomous Docking station telsa Electric Vehicles
DOI: 10.6084/m9.figshare.1201691
title: ADNI 62

description: 62 MR brain images selected from ADNI dataset.
DOI: 10.6084/m9.figshare.1201726
title: LTSTM data from 2003-09-04

description: This is low temperature STM data from the 4th September 2003.
DOI: 10.6084/m9.figshare.1201726.v1
title: LTSTM data from 2003-09-04

description: This is low temperature STM data from the 4th September 2003.
DOI: 10.6084/m9.figshare.1201509
title: LTSTM data from 1st & 2nd Sept 2003

description: This data set is low temperature STM data from 1st & 2nd September 2003.
DOI: 10.6084/m9.figshare.1201512
title: Parameter files, model files, and example scripts

description: Parameter files, model files, and example scripts for running simulations in CARP.
DOI: 10.6084/m9.figshare.1201508
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD. Much of the data will require a special tool to read it. You can use WSxM from www.nanotec.es for this purpose.
DOI: 10.6084/m9.figshare.1201509.v1
title: LTSTM data from 1st & 2nd Sept 2003

description: This data set is low temperature STM data from 1st & 2nd September 2003.
DOI: 10.6084/m9.figshare.1201490
title: LTSTM data from July & August 2003

description: Low temperature STM data from July & August 2003
DOI: 10.6084/m9.figshare.1201412
title: Optimized Lower Leg Injury Probability Curves From Postmortem Human Subject Tests Under Axial Impacts

description: <b>Objective:</b> Derive optimum injury probability curves to describe human tolerance of the lower leg using parametric survival analysis.<b>Methods:</b> The study reexamined lower leg postmortem human subjects (PMHS) data from a large group of specimens. Briefly, axial loading experiments were conducted by impacting the plantar surface of the foot. Both injury and noninjury tests were included in the testing process. They were identified by pre- and posttest radiographic images and detailed dissection following the impact test. Fractures included injuries to the calcaneus and distal tibia–fibula complex (including pylon), representing severities at the Abbreviated Injury Score (AIS) level 2+. For the statistical analysis, peak force was chosen as the main explanatory variable and the age was chosen as the covariable. Censoring statuses depended on experimental outcomes. Parameters from the parametric survival analysis were estimated using the maximum likelihood approach and the dfbetas statistic was used to identify overly influential samples. The best fit from the Weibull, log-normal, and log-logistic distributions was based on the Akaike information criterion. Plus and minus 95% confidence intervals were obtained for the optimum injury probability distribution. The relative sizes of the interval were determined at predetermined risk levels. Quality indices were described at each of the selected probability levels.<b>Results:</b> The mean age, stature, and weight were 58.2 ± 15.1 years, 1.74 ± 0.08 m, and 74.9 ± 13.8 kg, respectively. Excluding all overly influential tests resulted in the tightest confidence intervals. The Weibull distribution was the most optimum function compared to the other 2 distributions. A majority of quality indices were in the good category for this optimum distribution when results were extracted for 25-, 45- and 65-year-olds at 5, 25, and 50% risk levels age groups for lower leg fracture. For 25, 45, and 65 years, peak forces were 8.1, 6.5, and 5.1 kN at 5% risk; 9.6, 7.7, and 6.1 kN at 25% risk; and 10.4, 8.3, and 6.6 kN at 50% risk, respectively.<b>Conclusions:</b> This study derived axial loading-induced injury risk curves based on survival analysis using peak force and specimen age; adopting different censoring schemes; considering overly influential samples in the analysis; and assessing the quality of the distribution at discrete probability levels. Because procedures used in the present survival analysis are accepted by international automotive communities, current optimum human injury probability distributions can be used at all risk levels with more confidence in future crashworthiness applications for automotive and other disciplines.
DOI: 10.6084/m9.figshare.1201490.v1
title: LTSTM data from July & August 2003

description: Low temperature STM data from July & August 2003
DOI: 10.6084/m9.figshare.1201508.v1
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD.
DOI: 10.6084/m9.figshare.1201508.v2
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD.
DOI: 10.6084/m9.figshare.1201412.v2
title: Optimized Lower Leg Injury Probability Curves From Postmortem Human Subject Tests Under Axial Impacts

description: <b>Objective:</b> Derive optimum injury probability curves to describe human tolerance of the lower leg using parametric survival analysis.<b>Methods:</b> The study reexamined lower leg postmortem human subjects (PMHS) data from a large group of specimens. Briefly, axial loading experiments were conducted by impacting the plantar surface of the foot. Both injury and noninjury tests were included in the testing process. They were identified by pre- and posttest radiographic images and detailed dissection following the impact test. Fractures included injuries to the calcaneus and distal tibia–fibula complex (including pylon), representing severities at the Abbreviated Injury Score (AIS) level 2+. For the statistical analysis, peak force was chosen as the main explanatory variable and the age was chosen as the covariable. Censoring statuses depended on experimental outcomes. Parameters from the parametric survival analysis were estimated using the maximum likelihood approach and the dfbetas statistic was used to identify overly influential samples. The best fit from the Weibull, log-normal, and log-logistic distributions was based on the Akaike information criterion. Plus and minus 95% confidence intervals were obtained for the optimum injury probability distribution. The relative sizes of the interval were determined at predetermined risk levels. Quality indices were described at each of the selected probability levels.<b>Results:</b> The mean age, stature, and weight were 58.2 ± 15.1 years, 1.74 ± 0.08 m, and 74.9 ± 13.8 kg, respectively. Excluding all overly influential tests resulted in the tightest confidence intervals. The Weibull distribution was the most optimum function compared to the other 2 distributions. A majority of quality indices were in the good category for this optimum distribution when results were extracted for 25-, 45- and 65-year-olds at 5, 25, and 50% risk levels age groups for lower leg fracture. For 25, 45, and 65 years, peak forces were 8.1, 6.5, and 5.1 kN at 5% risk; 9.6, 7.7, and 6.1 kN at 25% risk; and 10.4, 8.3, and 6.6 kN at 50% risk, respectively.<b>Conclusions:</b> This study derived axial loading-induced injury risk curves based on survival analysis using peak force and specimen age; adopting different censoring schemes; considering overly influential samples in the analysis; and assessing the quality of the distribution at discrete probability levels. Because procedures used in the present survival analysis are accepted by international automotive communities, current optimum human injury probability distributions can be used at all risk levels with more confidence in future crashworthiness applications for automotive and other disciplines.
DOI: 10.6084/m9.figshare.1201412.v1
title: Optimized Lower Leg Injury Probability Curves From Postmortem Human Subject Tests Under Axial Impacts

description: <b>Objective:</b> Derive optimum injury probability curves to describe human tolerance of the lower leg using parametric survival analysis.<b>Methods:</b> The study reexamined lower leg postmortem human subjects (PMHS) data from a large group of specimens. Briefly, axial loading experiments were conducted by impacting the plantar surface of the foot. Both injury and noninjury tests were included in the testing process. They were identified by pre- and posttest radiographic images and detailed dissection following the impact test. Fractures included injuries to the calcaneus and distal tibia–fibula complex (including pylon), representing severities at the Abbreviated Injury Score (AIS) level 2+. For the statistical analysis, peak force was chosen as the main explanatory variable and the age was chosen as the covariable. Censoring statuses depended on experimental outcomes. Parameters from the parametric survival analysis were estimated using the maximum likelihood approach and the dfbetas statistic was used to identify overly influential samples. The best fit from the Weibull, log-normal, and log-logistic distributions was based on the Akaike information criterion. Plus and minus 95% confidence intervals were obtained for the optimum injury probability distribution. The relative sizes of the interval were determined at predetermined risk levels. Quality indices were described at each of the selected probability levels.<b>Results:</b> The mean age, stature, and weight were 58.2 ± 15.1 years, 1.74 ± 0.08 m, and 74.9 ± 13.8 kg, respectively. Excluding all overly influential tests resulted in the tightest confidence intervals. The Weibull distribution was the most optimum function compared to the other 2 distributions. A majority of quality indices were in the good category for this optimum distribution when results were extracted for 25-, 45- and 65-year-olds at 5, 25, and 50% risk levels age groups for lower leg fracture. For 25, 45, and 65 years, peak forces were 8.1, 6.5, and 5.1 kN at 5% risk; 9.6, 7.7, and 6.1 kN at 25% risk; and 10.4, 8.3, and 6.6 kN at 50% risk, respectively.<b>Conclusions:</b> This study derived axial loading-induced injury risk curves based on survival analysis using peak force and specimen age; adopting different censoring schemes; considering overly influential samples in the analysis; and assessing the quality of the distribution at discrete probability levels. Because procedures used in the present survival analysis are accepted by international automotive communities, current optimum human injury probability distributions can be used at all risk levels with more confidence in future crashworthiness applications for automotive and other disciplines.
DOI: 10.6084/m9.figshare.1201508.v3
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD.
DOI: 10.6084/m9.figshare.1201508.v4
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD.
DOI: 10.6084/m9.figshare.1201508.v5
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD.
DOI: 10.6084/m9.figshare.1201508.v6
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD.
DOI: 10.6084/m9.figshare.1201508.v7
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD.
DOI: 10.6084/m9.figshare.1201399
title: Comparing Handheld and Hands-free Cell Phone Usage Behaviors While Driving

description: <b>Objective:</b> The goal of this study was to compare cell phone usage behaviors while driving across 3 types of cell phones: handheld (HH) cell phones, portable hands-free (PHF) cell phones, and integrated hands-free (IHF) cell phones. Naturalistic driving data were used to observe HH, PHF, and IHF usage behaviors in participants’ own vehicles without any instructions or manipulations by researchers.<b>Methods:</b> In addition to naturalistic driving data, drivers provided their personal cell phone call records. Calls during driving were sampled and observed in naturalistically collected video. Calls were reviewed to identify cell phone type used for, and duration of, cell phone subtasks, non–cell phone secondary tasks, and other use behaviors. Drivers in the study self-identified as HH, PHF, or IHF users if they reported using that cell phone type at least 50% of the time. However, each sampled call was classified as HH, PHF, or IHF if the talking/listening subtask was conducted using that cell phone type, without considering the driver's self-reported group.<b>Results:</b> Drivers with PHF or IHF systems also used HH cell phones (IHF group used HH cell phone in 53.2% of the interactions, PHF group used HH cell phone for 55.5% of interactions). Talking/listening on a PHF phone or an IHF phone was significantly longer than talking/listening on an HH phone (<i>P</i> <b>Conclusions:</b> Hands-free cell phone technologies reduce the duration of cell phone visual–manual tasks compared to handheld cell phones. However, drivers with hands-free cell phone technologies available to them still choose to use handheld cell phones to converse or complete cell phone visual–manual tasks for a noteworthy portion of interactions.
DOI: 10.6084/m9.figshare.1201508.v8
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD.
DOI: 10.6084/m9.figshare.1201399.v2
title: Comparing Handheld and Hands-free Cell Phone Usage Behaviors While Driving

description: <b>Objective:</b> The goal of this study was to compare cell phone usage behaviors while driving across 3 types of cell phones: handheld (HH) cell phones, portable hands-free (PHF) cell phones, and integrated hands-free (IHF) cell phones. Naturalistic driving data were used to observe HH, PHF, and IHF usage behaviors in participants’ own vehicles without any instructions or manipulations by researchers.<b>Methods:</b> In addition to naturalistic driving data, drivers provided their personal cell phone call records. Calls during driving were sampled and observed in naturalistically collected video. Calls were reviewed to identify cell phone type used for, and duration of, cell phone subtasks, non–cell phone secondary tasks, and other use behaviors. Drivers in the study self-identified as HH, PHF, or IHF users if they reported using that cell phone type at least 50% of the time. However, each sampled call was classified as HH, PHF, or IHF if the talking/listening subtask was conducted using that cell phone type, without considering the driver's self-reported group.<b>Results:</b> Drivers with PHF or IHF systems also used HH cell phones (IHF group used HH cell phone in 53.2% of the interactions, PHF group used HH cell phone for 55.5% of interactions). Talking/listening on a PHF phone or an IHF phone was significantly longer than talking/listening on an HH phone (<i>P</i> <b>Conclusions:</b> Hands-free cell phone technologies reduce the duration of cell phone visual–manual tasks compared to handheld cell phones. However, drivers with hands-free cell phone technologies available to them still choose to use handheld cell phones to converse or complete cell phone visual–manual tasks for a noteworthy portion of interactions.
DOI: 10.6084/m9.figshare.1201399.v1
title: Comparing Handheld and Hands-free Cell Phone Usage Behaviors While Driving

description: <b>Objective:</b> The goal of this study was to compare cell phone usage behaviors while driving across 3 types of cell phones: handheld (HH) cell phones, portable hands-free (PHF) cell phones, and integrated hands-free (IHF) cell phones. Naturalistic driving data were used to observe HH, PHF, and IHF usage behaviors in participants’ own vehicles without any instructions or manipulations by researchers.<b>Methods:</b> In addition to naturalistic driving data, drivers provided their personal cell phone call records. Calls during driving were sampled and observed in naturalistically collected video. Calls were reviewed to identify cell phone type used for, and duration of, cell phone subtasks, non–cell phone secondary tasks, and other use behaviors. Drivers in the study self-identified as HH, PHF, or IHF users if they reported using that cell phone type at least 50% of the time. However, each sampled call was classified as HH, PHF, or IHF if the talking/listening subtask was conducted using that cell phone type, without considering the driver's self-reported group.<b>Results:</b> Drivers with PHF or IHF systems also used HH cell phones (IHF group used HH cell phone in 53.2% of the interactions, PHF group used HH cell phone for 55.5% of interactions). Talking/listening on a PHF phone or an IHF phone was significantly longer than talking/listening on an HH phone (<i>P</i> <b>Conclusions:</b> Hands-free cell phone technologies reduce the duration of cell phone visual–manual tasks compared to handheld cell phones. However, drivers with hands-free cell phone technologies available to them still choose to use handheld cell phones to converse or complete cell phone visual–manual tasks for a noteworthy portion of interactions.
DOI: 10.6084/m9.figshare.1201508.v9
title: LTSTM data

description: This is the collection of low temperature STM data collected for my PhD.
DOI: 10.6084/m9.figshare.1201396
title: Comparison of Q3s ATD Biomechanical Responses to Pediatric Volunteers

description: <b>Objective:</b> The biofidelity of pediatric anthropomorphic test devices (ATDs) continues to be evaluated with scaled-down adult data, a methodology that requires inaccurate assumptions about the likeness of biomechanical properties of children and adults. Recently, evaluation of pediatric ATDs by comparison of pediatric volunteer (PV) data has emerged as a valuable and practical alternative to the use of scaled adult data. This study utilized existing PV data to evaluate a 3-year-old side impact ATD, the Q3s. Though ATDs have been compared to volunteer responses in frontal impacts, this study is the first to extend ATD-PV comparison methods to the Q3s ATD and among the first to extend these methods to side impacts.<b>Methods:</b> Previously conducted experiments were replicated in order to make a direct comparison between the Q3s and PVs. PV data were used from 4- to 7-year-olds (shoulder tests, <i>n</i> = 14) and 6- to 8-year-olds (sled tests, <i>n</i> = 7). Force–deflection data were captured during quasistatic shoulder tests through manual displacement of the shoulder joint. Resulting shoulder stiffness was compared between the Q3s and PVs. Low-speed far-side sled tests were conducted with the Q3s at lateral (90°) and oblique (60°) impacts. Primary outcomes of interest included (1) lateral displacement of the torso, (2) torso rollout angle, and (3) kinematic trajectories of the head and neck.<b>Results:</b> The Q3s exhibited shoulder stiffness values at least 32 N/mm greater than the PVs for all conditions (PV muscle tensed and relaxed, deflection calculated for full- and half-thoracic). In lateral sled tests, the Q3s demonstrated increased coronal torso rollout (Q3s: 49.2°; PVs: 35.7° ± 12.4°) and lateral (Δ<i>Y</i>) movement of the top of the head (Q3s: −389 mm; PVs: −320 ± 23 mm) compared to PVs. In oblique trials, the Q3s achieved significantly decreased lateral torso displacement (Q3s: 153.3 mm; PVs: 193.6 ± 25.6 mm) and top of the head forward (Δ<i>X</i>) motion (Q3s: 68 mm; PVs: 133 ± 20 mm) compared to PVs. In all tests, greater downward (Δ<i>Z</i>) excursions of C4 and T1 were observed in the Q3s relative to PVs.<b>Conclusions:</b> Increased Q3s shoulder stiffness could affect head–neck kinematics as well as thorax responses because unrealistic force can be transmitted to the spine from the shoulder. Q3s and PV trajectories were of similar shape, although Q3s head kinematics displayed rigid body motion followed by independent lateral bending of the head, suggesting cervical and thoracic spine rigidity compared to PVs.
DOI: 10.6084/m9.figshare.1201395
title: Comparison and Validation of Injury Risk Classifiers for Advanced Automated Crash Notification Systems

description: <b>Objective:</b> The odds of death for a seriously injured crash victim are drastically reduced if he or she received care at a trauma center. Advanced automated crash notification (AACN) algorithms are postcrash safety systems that use data measured by the vehicles during the crash to predict the likelihood of occupants being seriously injured. The accuracy of these models are crucial to the success of an AACN. The objective of this study was to compare the predictive performance of competing injury risk models and algorithms: logistic regression, random forest, AdaBoost, naïve Bayes, support vector machine, and classification <i>k</i>-nearest neighbors.<b>Methods:</b> This study compared machine learning algorithms to the widely adopted logistic regression modeling approach. Machine learning algorithms have not been commonly studied in the motor vehicle injury literature. Machine learning algorithms may have higher predictive power than logistic regression, despite the drawback of lacking the ability to perform statistical inference. To evaluate the performance of these algorithms, data on 16,398 vehicles involved in non-rollover collisions were extracted from the NASS-CDS. Vehicles with any occupants having an Injury Severity Score (ISS) of 15 or greater were defined as those requiring victims to be treated at a trauma center. The performance of each model was evaluated using cross-validation. Cross-validation assesses how a model will perform in the future given new data not used for model training. The crash Δ<i>V</i> (change in velocity during the crash), damage side (struck side of the vehicle), seat belt use, vehicle body type, number of events, occupant age, and occupant sex were used as predictors in each model.<b>Results and Conclusions:</b> Logistic regression slightly outperformed the machine learning algorithms based on sensitivity and specificity of the models. Previous studies on AACN risk curves used the same data to train and test the power of the models and as a result had higher sensitivity compared to the cross-validated results from this study. Future studies should account for future data; for example, by using cross-validation or risk presenting optimistic predictions of field performance. Past algorithms have been criticized for relying on age and sex, being difficult to measure by vehicle sensors, and inaccuracies in classifying damage side. The models with accurate damage side and including age/sex did outperform models with less accurate damage side and without age/sex, but the differences were small, suggesting that the success of AACN is not reliant on these predictors.