cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14842674.v1
title: A survey of distracted driving and electronic device use among app-based and taxi drivers

description: The purpose of this study was to identify, characterize and compare attitudes, beliefs, behaviors and other factors related to electronic device (ED) use and distracted driving (DD) among taxi and app-based drivers. A survey among drivers in San Diego and across the United States was used to collect self-reported attitudes and behaviors among taxi and app-based passenger-carrying drivers from October 1, 2016 to January 31, 2017. Chi-square or Fisher’s exact test was used to assess the difference between sociodemographics, ED use, ED use attitudes, and citation and crash history by driver type. Prevalence ratios were assessed for differences in ED use and ED use attitudes by driver type using Poisson regression models with robust error variance and a log link function. The final models adjusted for age, sex, level of experience, education and English fluency. All analyses were performed using SAS version 9.4. Of the 175 drivers that met eligibility criteria, 131 reported driving for app-based services and 44 identified as taxi drivers. Compared to taxi drivers, app-based drivers were more likely to be female, native English speaking, and have fewer than 3 years of experience in the transportation business. All drivers reported at least one type of DD while the car was in motion. App-based drivers were significantly more likely to use a smartphone while driving (adjusted prevalence ratio (APR): 1.42), report that while driving it is safe or very safe to accept a call (APR: 1.73), receive/respond to a passenger request (APR: 3.40), or process a payment (APR: 5.39). Taxi drivers were more likely to either receive a citation for ED use (31.8% v 7.6%, <i>p</i> &lt; 0.001) or be in a collision while using ED (29.6% v 4.6%, <i>p</i> &lt; 0.001). Almost all drivers who received a citation or who were involved in a crash reported becoming somewhat or significantly more cautious about using ED while driving. Drivers in the small passenger-carrying transportation industry engage in DD frequently due to occupational demands. Given the known increased crash risk with DD, effective policies and interventions for app-based and taxi drivers are needed.
DOI: 10.6084/m9.figshare.16879127
title: Brain injury severity due to direct head contact from near-side motor vehicle collisions

description: The objective of this study was to generate functional forms of brain injury risk curves using the National Automotive Sample System Crashworthiness Data System’s (NASS-CDS) database for the years of 2001–2015. The population of interest was near-side occupants who experienced a direct head impact with an injury source located lateral to a typical seated position. Brain injuries were restricted to Abbreviated Injury Scale (AIS) 2005 Update 2008 defined concussions and internal organ injuries of the head. Near-side occupants comprised two major groups, both of which were required to have evidence of head contact (i.e., a head injury with DIRINJ = 1 and SOUCON = 1 or 2): brain injured occupants (MAIS1, MAIS2, MAIS3+) and non-brain injured occupants with some other direct contact head injury (MAIS0). Analyzed cases were required to have an indication of a reasonable crash reconstruction. Injury sources allowed within the final sample consisted of A-pillars, B-pillars, roof/roof rails, impacting vehicles/exterior objects, other components of the vehicle’s side interior, and other occupants or otherwise unspecified interior objects. Risk curves for occupants with brain injury severities of MAIS0, MAIS1+, MAIS2+, and MAIS3+ were generated using multivariate stepwise logistic regressions. Investigated predictors involved vehicle change in velocity, seat belt use, principal direction of force (PDOF), and injury source type (B-pillar and side window). Multivariate stepwise logistic regressions identified significant predictors of lateral change in velocity (<i>dvlat</i>) for all injury severity categories, and side window injury source (INJSOU = 56, 57, 58, 106, and 107) for MAIS0 and MAIS1+ risk curves. Although model sensitivity decreased for more severe injury predictions, risk curves dependent on only <i>dvlat</i> yielded accuracies of 70% for all presented models. Real world crashes are often complex and lack the benefit of real time monitoring; however, NASS-CDS post-crash investigations provide data useful for injury risk prediction. Further analysis is needed to determine the effect of data confidence, injury source, and accident sequence restrictions on NASS-CDS sampling biases. The presented models likely favor a more conservative risk prediction due to the limitations of NASS-CDS data collection, AIS code conversion, and unweighted sample analysis.
DOI: 10.6084/m9.figshare.16567171
title: Quantitative characterization of AEB pulses across the modern fleet

description: Characteristics of specific Automatic Emergency Braking (AEB) pulses can result in increased motion of the occupant, which can lead to the occupant being out-of-position such that when a crash occurs protection may be compromised. Quantifying these variations across the modern fleet is crucial to understand the loading environment to which vehicle occupants are exposed. Therefore, we categorized the AEB pulses based on acceleration pulse features such as deceleration magnitude, jerk, and ramp time. A total of 2278 AEB vehicle tests (years 2013–2019) were extracted from the Insurance Institute for Highway Safety (IIHS) database and analyzed. The following pulse characteristics were extracted: Jerk (g/s), Ramp-time (s), and Maximum deceleration (g). A subset of tests in which the tested vehicle did not contact the foam target (n = 1665) was analyzed further, with the following additional variables extracted: Deceleration time (s), Steady-state deceleration (g), and Duration (s). Other non-pulse related features were also considered: Test speed (20 and 40 km/h), Curb weight (Kg), and Vehicle Model Year. Using machine learning methods, the pulses were categorized into clusters. One-way ANOVAs for continuous variables and X<sup>2</sup> for categorical features were used to assess differences between clusters (p ≤ 0.05). Using the entirety of the AEB vehicle tests extracted (n = 2278), a total of 3 clusters were selected. The three clusters showed significantly different Jerk, Ramp-time, and Maximum deceleration (p &lt; 0.001). Target contact decreased in AEB tests with more recent vehicle model years (rate of contact 66% in 2014 vs 1.7% in 2019). In one cluster, Jerk and Maximum deceleration increased with vehicle model year. Using the subset of tests in which there was no contact with the foam target (n = 1665), 4 categories of pulses were selected. In both sets of clusters, Ramp-time and Jerk showed moderate inverse correlation (r = –0.7), while all other features showed a low correlation. These results show that AEB technology improved over the years in obstacle avoidance. The identification of AEB pulse clusters is important in order to describe distinct approaches to achieving AEB and to be able to reproduce representative AEB pulses in the laboratory and understand the influences of those pulses on occupants’ motion.
DOI: 10.6084/m9.figshare.16567171.v2
title: Quantitative characterization of AEB pulses across the modern fleet

description: Characteristics of specific Automatic Emergency Braking (AEB) pulses can result in increased motion of the occupant, which can lead to the occupant being out-of-position such that when a crash occurs protection may be compromised. Quantifying these variations across the modern fleet is crucial to understand the loading environment to which vehicle occupants are exposed. Therefore, we categorized the AEB pulses based on acceleration pulse features such as deceleration magnitude, jerk, and ramp time. A total of 2278 AEB vehicle tests (years 2013–2019) were extracted from the Insurance Institute for Highway Safety (IIHS) database and analyzed. The following pulse characteristics were extracted: Jerk (g/s), Ramp-time (s), and Maximum deceleration (g). A subset of tests in which the tested vehicle did not contact the foam target (n = 1665) was analyzed further, with the following additional variables extracted: Deceleration time (s), Steady-state deceleration (g), and Duration (s). Other non-pulse related features were also considered: Test speed (20 and 40 km/h), Curb weight (Kg), and Vehicle Model Year. Using machine learning methods, the pulses were categorized into clusters. One-way ANOVAs for continuous variables and X<sup>2</sup> for categorical features were used to assess differences between clusters (p ≤ 0.05). Using the entirety of the AEB vehicle tests extracted (n = 2278), a total of 3 clusters were selected. The three clusters showed significantly different Jerk, Ramp-time, and Maximum deceleration (p &lt; 0.001). Target contact decreased in AEB tests with more recent vehicle model years (rate of contact 66% in 2014 vs 1.7% in 2019). In one cluster, Jerk and Maximum deceleration increased with vehicle model year. Using the subset of tests in which there was no contact with the foam target (n = 1665), 4 categories of pulses were selected. In both sets of clusters, Ramp-time and Jerk showed moderate inverse correlation (r = –0.7), while all other features showed a low correlation. These results show that AEB technology improved over the years in obstacle avoidance. The identification of AEB pulse clusters is important in order to describe distinct approaches to achieving AEB and to be able to reproduce representative AEB pulses in the laboratory and understand the influences of those pulses on occupants’ motion.
DOI: 10.6084/m9.figshare.16879127.v1
title: Brain injury severity due to direct head contact from near-side motor vehicle collisions

description: The objective of this study was to generate functional forms of brain injury risk curves using the National Automotive Sample System Crashworthiness Data System’s (NASS-CDS) database for the years of 2001–2015. The population of interest was near-side occupants who experienced a direct head impact with an injury source located lateral to a typical seated position. Brain injuries were restricted to Abbreviated Injury Scale (AIS) 2005 Update 2008 defined concussions and internal organ injuries of the head. Near-side occupants comprised two major groups, both of which were required to have evidence of head contact (i.e., a head injury with DIRINJ = 1 and SOUCON = 1 or 2): brain injured occupants (MAIS1, MAIS2, MAIS3+) and non-brain injured occupants with some other direct contact head injury (MAIS0). Analyzed cases were required to have an indication of a reasonable crash reconstruction. Injury sources allowed within the final sample consisted of A-pillars, B-pillars, roof/roof rails, impacting vehicles/exterior objects, other components of the vehicle’s side interior, and other occupants or otherwise unspecified interior objects. Risk curves for occupants with brain injury severities of MAIS0, MAIS1+, MAIS2+, and MAIS3+ were generated using multivariate stepwise logistic regressions. Investigated predictors involved vehicle change in velocity, seat belt use, principal direction of force (PDOF), and injury source type (B-pillar and side window). Multivariate stepwise logistic regressions identified significant predictors of lateral change in velocity (<i>dvlat</i>) for all injury severity categories, and side window injury source (INJSOU = 56, 57, 58, 106, and 107) for MAIS0 and MAIS1+ risk curves. Although model sensitivity decreased for more severe injury predictions, risk curves dependent on only <i>dvlat</i> yielded accuracies of 70% for all presented models. Real world crashes are often complex and lack the benefit of real time monitoring; however, NASS-CDS post-crash investigations provide data useful for injury risk prediction. Further analysis is needed to determine the effect of data confidence, injury source, and accident sequence restrictions on NASS-CDS sampling biases. The presented models likely favor a more conservative risk prediction due to the limitations of NASS-CDS data collection, AIS code conversion, and unweighted sample analysis.
DOI: 10.6084/m9.figshare.16567171.v1
title: Quantitative characterization of AEB pulses across the modern fleet

description: Characteristics of specific Automatic Emergency Braking (AEB) pulses can result in increased motion of the occupant, which can lead to the occupant being out-of-position such that when a crash occurs protection may be compromised. Quantifying these variations across the modern fleet is crucial to understand the loading environment to which vehicle occupants are exposed. Therefore, we categorized the AEB pulses based on acceleration pulse features such as deceleration magnitude, jerk, and ramp time. A total of 2278 AEB vehicle tests (years 2013–2019) were extracted from the Insurance Institute for Highway Safety (IIHS) database and analyzed. The following pulse characteristics were extracted: Jerk (g/s), Ramp-time (s), and Maximum deceleration (g). A subset of tests in which the tested vehicle did not contact the foam target (n = 1665) was analyzed further, with the following additional variables extracted: Deceleration time (s), Steady-state deceleration (g), and Duration (s). Other non-pulse related features were also considered: Test speed (20 and 40 km/h), Curb weight (Kg), and Vehicle Model Year. Using machine learning methods, the pulses were categorized into clusters. One-way ANOVAs for continuous variables and X<sup>2</sup> for categorical features were used to assess differences between clusters (p ≤ 0.05). Using the entirety of the AEB vehicle tests extracted (n = 2278), a total of 3 clusters were selected. The three clusters showed significantly different Jerk, Ramp-time, and Maximum deceleration (p &lt; 0.001). Target contact decreased in AEB tests with more recent vehicle model years (rate of contact 66% in 2014 vs 1.7% in 2019). In one cluster, Jerk and Maximum deceleration increased with vehicle model year. Using the subset of tests in which there was no contact with the foam target (n = 1665), 4 categories of pulses were selected. In both sets of clusters, Ramp-time and Jerk showed moderate inverse correlation (r = –0.7), while all other features showed a low correlation. These results show that AEB technology improved over the years in obstacle avoidance. The identification of AEB pulse clusters is important in order to describe distinct approaches to achieving AEB and to be able to reproduce representative AEB pulses in the laboratory and understand the influences of those pulses on occupants’ motion.
DOI: 10.6084/m9.figshare.16879126
title: Deflection-based parametric survival analysis side impact chest injury risk curves AIS 2015

description: The objective of this study was to reanalyze lateral postmortem human surrogate (PMHS) sled test chestband data to construct updated lateral thoracic injury risk curves (IRCs) using survival analysis. Chestband and injury data were gathered from 16 previously conducted PMHS sled tests. Briefly, 2 chestbands were wrapped around the thorax’s circumference at the levels of ribs 4 and 8. Tests were conducted at 6.7 m/s on a rigid and padded load wall fixed to the top of a rebound sled. The injuries were reclassified using the Abbreviated Injury Scale (AIS) 2015 coding scheme. Chestband signals were combined with pretest specimen measurements to calculate the chest deflection contour time history. Deflections were determined using updated processing techniques calculating the change in length of every point on the contour from the impacted side using the thorax’s midpoint as the origin. Four candidate metrics were selected: the deflection from rib 4, the deflection from rib 8, the greater of the deflections from ribs 4 and 8, and the average of the deflections from ribs 4 and 8. AIS 3+ IRCs were developed considering outcomes of AIS ≥3 injuries. All injury data were uncensored, and noninjury data were right-censored. Three specimen mass-based IRCs were determined using the IRC with the lowest Brier score metric (BSM): The first corresponded to the 5th percentile female mass (49 kg), the second to the 50th percentile male mass (77 kg), and the third to the average mass of the PMHS ensemble (65 kg). Sixteen PMHS were used in the current study. Six specimens were right-censored, and 10 were uncensored. The average metric had the lowest BSM, and mass was a significant covariate with 50% risk of AIS3+ injury at 72mm of chest deflection. The 50% risk deflection magnitudes for the 5th percentile female (49 kg), 50th percentile male (77 kg), and PMHS ensemble (PMHS-E) (65 kg) were 59, 81, and 71 mm. IRCs for the 4 metrics and the 3 occupant masses are given. IRCs were developed using survival analysis, and the average of the peak deflections was found to best represent the thoracic chest deflection response. Mass-based side impact IRCs were calculated for occupants representing the WorldSID 5th percentile female and 50th percentile male anthropomorphic test device.
DOI: 10.6084/m9.figshare.16879126.v1
title: Deflection-based parametric survival analysis side impact chest injury risk curves AIS 2015

description: The objective of this study was to reanalyze lateral postmortem human surrogate (PMHS) sled test chestband data to construct updated lateral thoracic injury risk curves (IRCs) using survival analysis. Chestband and injury data were gathered from 16 previously conducted PMHS sled tests. Briefly, 2 chestbands were wrapped around the thorax’s circumference at the levels of ribs 4 and 8. Tests were conducted at 6.7 m/s on a rigid and padded load wall fixed to the top of a rebound sled. The injuries were reclassified using the Abbreviated Injury Scale (AIS) 2015 coding scheme. Chestband signals were combined with pretest specimen measurements to calculate the chest deflection contour time history. Deflections were determined using updated processing techniques calculating the change in length of every point on the contour from the impacted side using the thorax’s midpoint as the origin. Four candidate metrics were selected: the deflection from rib 4, the deflection from rib 8, the greater of the deflections from ribs 4 and 8, and the average of the deflections from ribs 4 and 8. AIS 3+ IRCs were developed considering outcomes of AIS ≥3 injuries. All injury data were uncensored, and noninjury data were right-censored. Three specimen mass-based IRCs were determined using the IRC with the lowest Brier score metric (BSM): The first corresponded to the 5th percentile female mass (49 kg), the second to the 50th percentile male mass (77 kg), and the third to the average mass of the PMHS ensemble (65 kg). Sixteen PMHS were used in the current study. Six specimens were right-censored, and 10 were uncensored. The average metric had the lowest BSM, and mass was a significant covariate with 50% risk of AIS3+ injury at 72mm of chest deflection. The 50% risk deflection magnitudes for the 5th percentile female (49 kg), 50th percentile male (77 kg), and PMHS ensemble (PMHS-E) (65 kg) were 59, 81, and 71 mm. IRCs for the 4 metrics and the 3 occupant masses are given. IRCs were developed using survival analysis, and the average of the peak deflections was found to best represent the thoracic chest deflection response. Mass-based side impact IRCs were calculated for occupants representing the WorldSID 5th percentile female and 50th percentile male anthropomorphic test device.
DOI: 10.15468/dl.3tr9fr
title: Occurrence Download

description: A dataset containing 6233 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Allium drummondii Regel"
 ]
}

 The dataset includes 6233 records from 126 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005449-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pz8wx4
title: Occurrence Download

description: A dataset containing 382 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Type collections of Liliopsida at Herbarium Bogoriense (BO), Indonesia"
 ]
}

 The dataset includes 382 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005450-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14769888
title: MC_PacMan: macaque motor cortex spiking activity during isometric force production

description: This dataset contains sorted spike times from neural recordings (128 electrodes recorded simultaneously from a passive pre-production version of the Neuropixels 1.0-NHP probe) in motor cortex while a monkey generated forward isometric force against an immovable handle. The monkey was presented with a PacMan icon on a screen. PacMan’s vertical position corresponded to the force applied to the handle. A path of scrolling dots moved horizontally across the screen and the monkey was rewarded when PacMan intercepted the dots. The pattern of scrolling dots was used to cue various dynamic force profiles. The task contained eight conditions that all spanned approximately the same range of forces: four ramping force profiles (fast and slow, ascending and descending), three sinusoidal force profiles (0.25 Hz, 1 Hz, 3 Hz), and a chirp profile in which the frequency steadily increased throughout the trial from 0-3 Hz. More information on the task can be found in Marshall et al. 2022.

This dataset has been released as a supplement to Perkins et al. 2025. Contact the authors if guidance on the dataset is needed.
DOI: 10.5281/zenodo.14774224
title: RNA-Seq Primary Mouse Hepatocytes Atorvastatin Treatment

description: RNA-Seq Primary Mouse Hepatocytes Atorvastatin Treatment
DOI: 10.5281/zenodo.14774225
title: RNA-Seq Primary Mouse Hepatocytes Atorvastatin Treatment

description: RNA-Seq Primary Mouse Hepatocytes Atorvastatin Treatment
DOI: 10.5281/zenodo.14774181
title: List of indicators for the integrated impact assessment of Living Lab interventions

description: This files presents a set of indicators that are aimed at assessing the impact of interventions carried out in the framework of the SWEET-Lantern Living Lab. The list has been established following the recommendations detailed in the conceptual framework presented in the paper. It includes social, energy/ environmental, and economic variables to assess the impact of the project. It also proposes a series of indicators that measure expected outcomes of the project, notably in terms of citizens’ level of engagement and their resources. These factors are theorised to be affected by the project activities and are expected by the research teams to lead to the overall impact of the project by influencing individuals’ technological choices and/or their behaviour. These indicators will be measured before as well as after the activities.
DOI: 10.5281/zenodo.14774182
title: List of indicators for the integrated impact assessment of Living Lab interventions

description: This files presents a set of indicators that are aimed at assessing the impact of interventions carried out in the framework of the SWEET-Lantern Living Lab. The list has been established following the recommendations detailed in the conceptual framework presented in the paper. It includes social, energy/ environmental, and economic variables to assess the impact of the project. It also proposes a series of indicators that measure expected outcomes of the project, notably in terms of citizens’ level of engagement and their resources. These factors are theorised to be affected by the project activities and are expected by the research teams to lead to the overall impact of the project by influencing individuals’ technological choices and/or their behaviour. These indicators will be measured before as well as after the activities.