cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25452/figshare.plus.24679662
title: File 26_mox files comfortable speed.

description: This data belongs to a manuscript submitted to Data in Brief, in which the content and lay-out of this data is described in detail. Data overviews (including figures and tables for age and gender groups) can be found at OSF | Normative 3D gait data of healthy subjects walking at three different speeds on an instrumented treadmill in virtual reality.A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m<sup>2</sup>) is presented and publicly shared for three walking speed conditions.Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model.Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5 m/s and every second the speed was increased wit 0.01 m/s until comfortable speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded.The 3D gait data was collected using the D-flow CAREN software. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to xls. Processed data includes spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of both legs.The attached files include he raw data presented as .mox files for each adult for walking at comfortable speed. The .mox files contain subject data (e.g. gender, body mass, knee and ankle width), marker position and force plate data, kinematic data (joint angles), kinetic data (GRF, joint moment, joint power) generated by CAREN software (D-flow).The title of this file (26_mox files) corresponds to the associated manuscript (submitted to Data in Brief).
DOI: 10.21942/uva.25251421
title: Review Data - Included Papers

description: This database holds the papers that were included in the literature review after performing a keyword search in Web of Science and Google Scholar. Inclusion criteria were used to select the papers into the database.
DOI: 10.21942/uva.25251421.v1
title: Review Data - Included Papers

description: This database holds the papers that were included in the literature review after performing a keyword search in Web of Science and Google Scholar. Inclusion criteria were used to select the papers into the database.
DOI: 10.25452/figshare.plus.24681339
title: File 26_mox files slow speed.

description: This data belongs to a manuscript submitted to Data in Brief, in which the content and lay-out of this data is described in detail. Data overviews (including figures and tables for age and gender groups) can be found at OSF | Normative 3D gait data of healthy subjects walking at three different speeds on an instrumented treadmill in virtual reality.A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m<sup>2</sup>) is presented and publicly shared for three walking speed conditions.Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model.Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5 m/s and every second the speed was increased wit 0.01 m/s until comfortable speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded.The 3D gait data was collected using the D-flow CAREN software. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to xls. Processed data includes spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of both legs.The attached files include he raw data presented as .mox files for each adult for walking at slow (comfortable -30%) speed. The .mox files contain subject data (e.g. gender, body mass, knee and ankle width), marker position and force plate data, kinematic data (joint angles), kinetic data (GRF, joint moment, joint power) generated by CAREN software (D-flow).The title of this file (26_mox files) corresponds to the associated manuscript (submitted to Data in Brief).
DOI: 10.25452/figshare.plus.24688476
title: File 27_Individual excel files comfortable speed.

description: This data belongs to a manuscript submitted to Data in Brief, in which the content and lay-out of this data is described in detail. Data overviews (including figures and tables for age and gender groups) can be found at OSF | Normative 3D gait data of healthy subjects walking at three different speeds on an instrumented treadmill in virtual reality.A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m<sup>2</sup>) is presented and publicly shared for three walking speed conditions.Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model.Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5 m/s and every second the speed was increased wit 0.01 m/s until comfortable speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded.The 3D gait data was collected using the D-flow CAREN software. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to xls. Processed data includes spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of both legs.The attached files include the processed data for each adult for walking at comfortable speed containing spatiotemporal parameters, MoS, joint angles, GRF, joint moments, joint power including every valid step of both legs.The title of this file (27_individual excel files) corresponds to the associated manuscript (submitted to Data in Brief)<br>
DOI: 10.25452/figshare.plus.24690525
title: File 27_Individual excel files fast speed.

description: This data belongs to a manuscript submitted to Data in Brief, in which the content and lay-out of this data is described in detail. Data overviews (including figures and tables for age and gender groups) can be found at OSF | Normative 3D gait data of healthy subjects walking at three different speeds on an instrumented treadmill in virtual reality.A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m<sup>2</sup>) is presented and publicly shared for three walking speed conditions.Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model.Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5 m/s and every second the speed was increased wit 0.01 m/s until comfortable speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded.The 3D gait data was collected using the D-flow CAREN software. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to xls. Processed data includes spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of both legs.The attached files include the processed data for each adult for walking at fast (comfortable + 30%) speed containing spatiotemporal parameters, MoS, joint angles, GRF, joint moments, joint power including every valid step of both legs.The title of this file (27_individual excel files) corresponds to the associated manuscript (submitted to Data in Brief)<br>
DOI: 10.25452/figshare.plus.24690525.v1
title: File 27_Individual excel files fast speed.

description: This data belongs to a manuscript submitted to Data in Brief, in which the content and lay-out of this data is described in detail. Data overviews (including figures and tables for age and gender groups) can be found at OSF | Normative 3D gait data of healthy subjects walking at three different speeds on an instrumented treadmill in virtual reality.A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m<sup>2</sup>) is presented and publicly shared for three walking speed conditions.Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model.Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5 m/s and every second the speed was increased wit 0.01 m/s until comfortable speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded.The 3D gait data was collected using the D-flow CAREN software. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to xls. Processed data includes spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of both legs.The attached files include the processed data for each adult for walking at fast (comfortable + 30%) speed containing spatiotemporal parameters, MoS, joint angles, GRF, joint moments, joint power including every valid step of both legs.The title of this file (27_individual excel files) corresponds to the associated manuscript (submitted to Data in Brief)<br>
DOI: 10.25452/figshare.plus.24693249
title: File 27_Individual excel files slow speed.

description: This data belongs to a manuscript submitted to Data in Brief, in which the content and lay-out of this data is described in detail. Data overviews (including figures and tables for age and gender groups) can be found at OSF | Normative 3D gait data of healthy subjects walking at three different speeds on an instrumented treadmill in virtual reality.A normative gait dataset of 246 healthy adults (122 men / 124 women, range in age 18-91 years, body weight 46.80-116.10 kg, height 1.53-1.97 m and BMI 18.25-35.63 kg/m<sup>2</sup>) is presented and publicly shared for three walking speed conditions (comfortable, slow and fast speed).Three dimensional gait analysis was performed at the Computer Assisted Rehabilitation Environment (CAREN) at the Maastricht University Medical Centre (MUMC+). Subjects walked on the instrumented treadmill surrounded with twelve 3D cameras, three 2D cameras and a virtual environment projected on a 180° screen using the Human Body Lower Limb Model with trunk markers (HBM-II) as biomechanical model.Subjects walked at comfortable walking speed, 30% slower and 30% faster. These walking speed conditions were applied in a random sequence. Comfortable walking speed was determined using a RAMP protocol: subjects started to walk at 0.5 m/s and every second the speed was increased wit 0.01 m/s until comfortable speed was reached. The average of three repetitions was considered the comfortable speed. For each walking speed condition, 250 steps were recorded.The 3D gait data was collected using the D-flow CAREN software. Raw data were processed in Matlab (Mathworks 2016), including quality check, step determination and the exportation of data to xls. Processed data includes spatiotemporal parameters, medio-lateral (ML) and back-forward (BF) margins of stability (MoS), 3D joint angles, anterior-posterior (AP) and vertical GRFs, 3D joint moments and 3D joint power of both legs.The attached files include the processed data for each adult for walking at slow (comfortable -30%) speed containing spatiotemporal parameters, MoS, joint angles, GRF, joint moments, joint power including every valid step of both legs.The title of this file (27_individual excel files) corresponds to the associated manuscript (submitted to Data in Brief)<br>
DOI: 10.6084/m9.figshare.19254550.v1
title: Additional file 10 of Molecular phylogeography and species distribution modelling evidence of ‘oceanic’ adaptation for Actinidia eriantha with a refugium along the oceanic–continental gradient in a biodiversity hotspot

description: Additional file 10. Estimations of posterior distributions of parameters revealed by DIY-ABC for the best scenarios of set A, B, C, D and E, respectively.
DOI: 10.6084/m9.figshare.19254556.v1
title: Additional file 12 of Molecular phylogeography and species distribution modelling evidence of ‘oceanic’ adaptation for Actinidia eriantha with a refugium along the oceanic–continental gradient in a biodiversity hotspot

description: Additional file 12. Information of 134 herbaria specimens of Actinidia eriantha.
DOI: 10.6084/m9.figshare.19254550
title: Additional file 10 of Molecular phylogeography and species distribution modelling evidence of ‘oceanic’ adaptation for Actinidia eriantha with a refugium along the oceanic–continental gradient in a biodiversity hotspot

description: Additional file 10. Estimations of posterior distributions of parameters revealed by DIY-ABC for the best scenarios of set A, B, C, D and E, respectively.
DOI: 10.6084/m9.figshare.6671618.v1
title: Methods for investigating crash risk: Comparing case control with responsibility analysis

description: Objective: There are two primary methods for establishing relative risk: <i>case-control studies</i> where crash and matched control data are collected separately, and <i>responsibility analysis,</i> which exploits a single existing crash database by using non-responsible drivers as an “induced exposure” control group (which is less expensive and therefore more feasible for examining the large number of substances that can impact driving behavior). Though both approaches are scientifically sound and methodologically valid, each approach has its own inherent obstacles to overcome. In this effort, we examine in detail how different criteria for the development of control cases influence the accuracy of crash-risk estimates for drivers with positive BACs. Methods: We applied responsibility analysis to crash-involved drivers in a recent crash case-control study, thereby providing two sets of control cases: those from responsibility analysis and those from the case control study. Results: Case control and responsibility analysis crash risk curves did not differ significantly, indicating that both systems generate valid estimates of the relative crash risk of drivers on the road. Conclusions: The results suggest that when researchers are faced with finance or time constraints that make case-control studies infeasible, responsibility analysis should be considered a viable alternate methodological approach.
DOI: 10.6084/m9.figshare.6671618
title: Methods for investigating crash risk: Comparing case–control with responsibility analysis

description: <b>Objective:</b> There are 2 primary methods for establishing relative risk: <i>case–control studies</i>, in which crash and matched control data are collected separately, and <i>responsibility analysis</i>, which exploits a single existing crash database by using nonresponsible drivers as an “induced exposure” control group (which is less expensive and therefore more feasible for examining the large number of substances that can impact driving behavior). Though both approaches are scientifically sound and methodologically valid, each approach has its own inherent obstacles to overcome. In this article, we examine in detail how different criteria for the development of control cases influence the accuracy of crash risk estimates for drivers with positive blood alcohol concentrations (BACs). <b>Methods:</b> We applied responsibility analysis to crash-involved drivers in a recent crash case–control study, thereby providing 2 sets of control cases: Those from responsibility analysis and those from the case control study. <b>Results:</b> Case–control and responsibility analysis crash risk curves did not differ significantly, indicating that both systems generate valid estimates of the relative crash risk of drivers on the road. <b>Conclusions:</b> The results suggest that when researchers are faced with finance or time constraints that make case–control studies infeasible, responsibility analysis should be considered a viable alternate methodological approach.
DOI: 10.6084/m9.figshare.6671618.v2
title: Methods for investigating crash risk: Comparing case–control with responsibility analysis

description: <b>Objective:</b> There are 2 primary methods for establishing relative risk: <i>case–control studies</i>, in which crash and matched control data are collected separately, and <i>responsibility analysis</i>, which exploits a single existing crash database by using nonresponsible drivers as an “induced exposure” control group (which is less expensive and therefore more feasible for examining the large number of substances that can impact driving behavior). Though both approaches are scientifically sound and methodologically valid, each approach has its own inherent obstacles to overcome. In this article, we examine in detail how different criteria for the development of control cases influence the accuracy of crash risk estimates for drivers with positive blood alcohol concentrations (BACs). <b>Methods:</b> We applied responsibility analysis to crash-involved drivers in a recent crash case–control study, thereby providing 2 sets of control cases: Those from responsibility analysis and those from the case control study. <b>Results:</b> Case–control and responsibility analysis crash risk curves did not differ significantly, indicating that both systems generate valid estimates of the relative crash risk of drivers on the road. <b>Conclusions:</b> The results suggest that when researchers are faced with finance or time constraints that make case–control studies infeasible, responsibility analysis should be considered a viable alternate methodological approach.
DOI: 10.6084/m9.figshare.19254568.v1
title: Additional file 2 of Molecular phylogeography and species distribution modelling evidence of ‘oceanic’ adaptation for Actinidia eriantha with a refugium along the oceanic–continental gradient in a biodiversity hotspot

description: Additional file 2. Analysis of molecular variance (AMOVA) for 28 sampled populations of Actinidia eriantha based on cpDNA.
DOI: 10.6084/m9.figshare.19254556
title: Additional file 12 of Molecular phylogeography and species distribution modelling evidence of ‘oceanic’ adaptation for Actinidia eriantha with a refugium along the oceanic–continental gradient in a biodiversity hotspot

description: Additional file 12. Information of 134 herbaria specimens of Actinidia eriantha.
DOI: 10.6084/m9.figshare.19254586
title: Additional file 8 of Molecular phylogeography and species distribution modelling evidence of ‘oceanic’ adaptation for Actinidia eriantha with a refugium along the oceanic–continental gradient in a biodiversity hotspot

description: Additional file 8. Prior distributions of the parameters used in DIYABC.
DOI: 10.6084/m9.figshare.19254568
title: Additional file 2 of Molecular phylogeography and species distribution modelling evidence of ‘oceanic’ adaptation for Actinidia eriantha with a refugium along the oceanic–continental gradient in a biodiversity hotspot

description: Additional file 2. Analysis of molecular variance (AMOVA) for 28 sampled populations of Actinidia eriantha based on cpDNA.
DOI: 10.6084/m9.figshare.25251427
title: Newborn Preemie MRI (Control)

description: Normal brain incl dMRI of prematurely born infant of the age of 2 months & 10 days.~42 wk gest.
DOI: 10.6084/m9.figshare.25251427.v1
title: Newborn Preemie MRI (Control)

description: Normal brain incl dMRI of prematurely born infant of the age of 2 months & 10 days.~42 wk gest.
DOI: 10.6084/m9.figshare.19254586.v1
title: Additional file 8 of Molecular phylogeography and species distribution modelling evidence of ‘oceanic’ adaptation for Actinidia eriantha with a refugium along the oceanic–continental gradient in a biodiversity hotspot

description: Additional file 8. Prior distributions of the parameters used in DIYABC.
DOI: 10.6084/m9.figshare.13487381.v1
title: Global insights to drought stress perturbed genes in oat (<i>Avena sativa L</i>.) seedlings using RNA sequencing

description: Oat (<i>Avena sativa</i> L.) is an important crop in northwestern China. Drought stress is the most significant factor affecting oat yield. In the present study, we explored the changes that occur in oats under drought stress conditions at a global genomic level. RNA sequencing was performed using 15-day-old oat seedlings. The differentially expressed transcripts were identified, and their related functions and pathways were investigated. In total, 1,065 unigenes were differentially expressed in oats under drought stress conditions. Of these, 386 unigenes were upregulated and 679 were downregulated. The perturbed transcripts were closely related to the biosynthesis of secondary metabolites, plant hormone signal transduction, and biosynthesis of antibiotics. DN50483_c0_g1_i3, which was annotated as acetyl-CoA carboxylase, was a significant node in the protein-protein interaction network. Biosynthesis of antibiotics and secondary metabolites may be involved in the drought stress response mechanisms of oats. The perturbed transcripts may provide targets for improving plant stress responses.