cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.6900422.v1
title: Figure 12

description: (a) BSISO1 phase diagram showing the eight numbered phases defined by Lee et al. (2013) and with labels indicating the regions of active convection in each. (b-e) Amplitudes <i>A</i> of 15-day composites of BSISO1 events starting in either of the stated phases, divided by the amplitude on the first day <i>A<sub>1</sub></i>. An event is defined as beginning on a day when the amplitude sqrt(<i>PC1</i><sup>2</sup> + <i>PC2</i><sup>2</sup>) ≥ 1.5 (i.e., not inside the centre circle in panel (a)). The horizontal lines indicate <i>A</i> = 1.5 for observations.<br>
DOI: 10.7910/dvn/jdfd2b/bntndt
title: 1-10.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6900365
title: Figure 09

description: 30-day smoothed annual cycle of the variance of the leading four pseudo principal components for (a) AVHRR OLR and ERA-Interim u850, (b) GL<sub>90</sub>. Note that year-round data have been used for the projections, even though the EOFs were computed from boreal summer data only. cf. Lee et al. (2013) figure 4.<br>
DOI: 10.6084/m9.figshare.6900377.v1
title: Figure 10

description: MJJASO anomalies in each BSISO1 phase of precipitation (shading) and 850 hPa wind (vectors; anomalies with amplitude<br>greater than 0.5 m s<sup>-1</sup> only), for (a) observations, (b) GL<sub>90</sub>. Phase 1 is in the bottom-left corner and phases continue anti-clockwise round the diagram.<br>
DOI: 10.7910/dvn/jdfd2b/kywjws
title: 1-38.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6900365.v1
title: Figure 09

description: 30-day smoothed annual cycle of the variance of the leading four pseudo principal components for (a) AVHRR OLR and ERA-Interim u850, (b) GL<sub>90</sub>. Note that year-round data have been used for the projections, even though the EOFs were computed from boreal summer data only. cf. Lee et al. (2013) figure 4.<br>
DOI: 10.6084/m9.figshare.6900323
title: Figure 07

description: (a) Variance of 24–70-day bandpass filtered ERA-Interim SST, for May to October only. Ratio to ERA-Interim of (b) GL<sub>90</sub>, (c) ATM<sub>90</sub>[~GL].<br>
DOI: 10.7910/dvn/jdfd2b/5qdcib
title: 1-49.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6900323.v1
title: Figure 07

description: (a) Variance of 24–70-day bandpass filtered ERA-Interim SST, for May to October only. Ratio to ERA-Interim of (b) GL<sub>90</sub>, (c) ATM<sub>90</sub>[~GL].<br>
DOI: 10.6084/m9.figshare.6900341
title: Figure 08

description: (a-d) MV-EOFs of OLR and u850, calculated from AVHRR OLR and ERA-Interim u850 using the method of Lee et al. (2013)<br>(cf. their figures 2a,b and 3a,b). The meridional component of the wind plotted here was computed by regression onto the respective principal components. Figures in parentheses indicate the percentage of variance explained. (e-f) MV-EOFs calculated by the same method from GL<sub>90</sub>.<br>
DOI: 10.6084/m9.figshare.6900269
title: Figure 06

description: Intraseasonal propagation in the region 70<sup>o</sup>–100<sup>o</sup>E, 20<sup>o</sup>S–30<sup>o</sup>N using 24–70-day bandpass filtered data, for May to October only, illustrated by lag correlations at each latitude between zonally-averaged data and the OLR time series (also filtered) at a base point 85<sup>o</sup>E, 12.5<sup>o</sup>N. Coloured shading is for zonally-averaged OLR. Green lines show propagation from 0<sup>o</sup> to 12.5<sup>o</sup>N, and 12.5<sup>o</sup> to 20<sup>o</sup>N. Black<br>contours are for zonally-averaged SST, with solid (dashed) contours indicating negative (positive) correlation (remembering that negative OLR anomalies correspond to increased convection) and the contour interval being the same as for the coloured shading. SST is shown up to 17.5<sup>o</sup>N only, where half of the longitude domain is occupied by land. The vertical dotted line shows the latitude of the base point. (a) Observations/reanalysis (AVHRR OLR and ERA-Interim SST), (b) GL<sub>90</sub>, (c) AO_PO<sub>90</sub>, (d) ATM<sub>90</sub>[~GL] (n.b., SST correlations are too small to appear), (e) IO<sub>90</sub>, (f) ATM<sub>90</sub>[–GL], (g) ATM<sub>90</sub>[~IO].<br>
DOI: 10.6084/m9.figshare.6900341.v1
title: Figure 08

description: (a-d) MV-EOFs of OLR and u850, calculated from AVHRR OLR and ERA-Interim u850 using the method of Lee et al. (2013)<br>(cf. their figures 2a,b and 3a,b). The meridional component of the wind plotted here was computed by regression onto the respective principal components. Figures in parentheses indicate the percentage of variance explained. (e-f) MV-EOFs calculated by the same method from GL<sub>90</sub>.<br>
DOI: 10.7910/dvn/jdfd2b/bneyeb
title: 1-12.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/p7csbr
title: 1-6.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6900257
title: Figure 05

description: (a) Variance of 24–70-day bandpass filtered GPCP precipitation, for May to October only. (b) Ratio of filtered variance of precipitation from GL<sub>90</sub> to that from GPCP. (c,d) As for (a,b) but for ERA-Interim u850, with grey shading used to mask out regions where the 850 hPa surface intersects the ground in GL<sub>90</sub>.<br>
DOI: 10.6084/m9.figshare.6900269.v1
title: Figure 06

description: Intraseasonal propagation in the region 70<sup>o</sup>–100<sup>o</sup>E, 20<sup>o</sup>S–30<sup>o</sup>N using 24–70-day bandpass filtered data, for May to October only, illustrated by lag correlations at each latitude between zonally-averaged data and the OLR time series (also filtered) at a base point 85<sup>o</sup>E, 12.5<sup>o</sup>N. Coloured shading is for zonally-averaged OLR. Green lines show propagation from 0<sup>o</sup> to 12.5<sup>o</sup>N, and 12.5<sup>o</sup> to 20<sup>o</sup>N. Black<br>contours are for zonally-averaged SST, with solid (dashed) contours indicating negative (positive) correlation (remembering that negative OLR anomalies correspond to increased convection) and the contour interval being the same as for the coloured shading. SST is shown up to 17.5<sup>o</sup>N only, where half of the longitude domain is occupied by land. The vertical dotted line shows the latitude of the base point. (a) Observations/reanalysis (AVHRR OLR and ERA-Interim SST), (b) GL<sub>90</sub>, (c) AO_PO<sub>90</sub>, (d) ATM<sub>90</sub>[~GL] (n.b., SST correlations are too small to appear), (e) IO<sub>90</sub>, (f) ATM<sub>90</sub>[–GL], (g) ATM<sub>90</sub>[~IO].<br>
DOI: 10.6084/m9.figshare.6900230
title: Figure 04

description: JJAS SST bias for GL<sub>90</sub> relative to SM07. Land and coastally tiled points (i.e., non-zero land fraction) are in grey.<br>
DOI: 10.6084/m9.figshare.6900257.v1
title: Figure 05

description: (a) Variance of 24–70-day bandpass filtered GPCP precipitation, for May to October only. (b) Ratio of filtered variance of precipitation from GL<sub>90</sub> to that from GPCP. (c,d) As for (a,b) but for ERA-Interim u850, with grey shading used to mask out regions where the 850 hPa surface intersects the ground in GL<sub>90</sub>.<br>
DOI: 10.7910/dvn/jdfd2b/sykvyk
title: 1-29.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6900230.v1
title: Figure 04

description: JJAS SST bias for GL<sub>90</sub> relative to SM07. Land and coastally tiled points (i.e., non-zero land fraction) are in grey.<br>
DOI: 10.7910/dvn/jdfd2b/rt34ze
title: 1-20.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6900206
title: Figure 02

description: (a) JJAS mean precipitation from GPCP. (b) Bias in JJAS mean precipitation in GL<sub>90</sub>. (c) Percentage excess (blue) and deficit (red) JJAS mean precipitation in GL<sub>90</sub> relative to GPCP. Regions in which the GPCP climatology is below 3 mm day<sup>-1</sup> are greyed out. (d) JJAS mean 850 hPa wind (vectors) and wind speed (shading) from ERA-Interim. (e) Bias in JJAS mean 850 hPa wind (vectors) and wind speed (shading; bias of the wind speed, not magnitude of the bias vectors) in GL<sub>90</sub>.<br>
DOI: 10.6084/m9.figshare.6900206.v1
title: Figure 02

description: (a) JJAS mean precipitation from GPCP. (b) Bias in JJAS mean precipitation in GL<sub>90</sub>. (c) Percentage excess (blue) and deficit (red) JJAS mean precipitation in GL<sub>90</sub> relative to GPCP. Regions in which the GPCP climatology is below 3 mm day<sup>-1</sup> are greyed out. (d) JJAS mean 850 hPa wind (vectors) and wind speed (shading) from ERA-Interim. (e) Bias in JJAS mean 850 hPa wind (vectors) and wind speed (shading; bias of the wind speed, not magnitude of the bias vectors) in GL<sub>90</sub>.<br>
DOI: 10.6084/m9.figshare.6900221
title: Figure 03

description: (a,b) JJAS mean precipitation (mm day<sup>-1</sup>) in GL<sub>90</sub> minus that from (a) AO_PO<sub>90</sub>, (b) IO<sub>90</sub>. (c,d) Similarly but for 850 hPa wind (m s<sup>-1</sup>), with vectors and coloured shading as defined in figure 2e.<br><br>
DOI: 10.7910/dvn/jdfd2b/lfezxg
title: 1-2.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6900200
title: Figure 01

description: Coupling weights for each coupling region used in the experiments: (a) GL<sub>90</sub>; (b) AO_PO<sub>90</sub>; (c) IO<sub>90</sub>. The MC-KPP SST is multiplied by the weight shown and the prescribed SST by (1−weight), and the sum of the two is the boundary condition to the atmospheric grid box. A 10<sup>o</sup> width (12 grid points) blending is applied at any boundary that is not a coastline. The weights for 200 km and 40 km experiments are<br>the same but with 5- and 30-point blending respectively.<br>
DOI: 10.6084/m9.figshare.6900221.v1
title: Figure 03

description: (a,b) JJAS mean precipitation (mm day<sup>-1</sup>) in GL<sub>90</sub> minus that from (a) AO_PO<sub>90</sub>, (b) IO<sub>90</sub>. (c,d) Similarly but for 850 hPa wind (m s<sup>-1</sup>), with vectors and coloured shading as defined in figure 2e.<br><br>
DOI: 10.7910/dvn/jdfd2b/lyeeof
title: 1-4.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6900200.v1
title: Figure 01

description: Coupling weights for each coupling region used in the experiments: (a) GL<sub>90</sub>; (b) AO_PO<sub>90</sub>; (c) IO<sub>90</sub>. The MC-KPP SST is multiplied by the weight shown and the prescribed SST by (1−weight), and the sum of the two is the boundary condition to the atmospheric grid box. A 10<sup>o</sup> width (12 grid points) blending is applied at any boundary that is not a coastline. The weights for 200 km and 40 km experiments are<br>the same but with 5- and 30-point blending respectively.<br>
DOI: 10.7910/dvn/jdfd2b/zpb48b
title: 1-26.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/bt77k7
title: 1-31.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/zcxc6f
title: 1-45.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/7iuiko
title: 1-24.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/94b4xp
title: 1-50.25V

description: sigma=1.25Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/odxp25
title: 1-35.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/nm05zm
title: 1-29.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6898325
title: Developmental psychology_Raw data_code

description: Developmental Psychology data share
DOI: 10.7910/dvn/jdfd2b/vkmati
title: 1-10.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6897035
title: Raw data on Linear Enamel Hypoplasia timing in Archaic Texan hunter-gatherers

description: Data on Linear Enamel Hypoplasias (as a sign of stress) in an Early Archaic Texan hunter-gatherer population for open access paper in PeerJ: https://peerj.com/articles/4367/?td=bl.<br>For further questions contact Berbesque at University of Roehampton.<br><br><br><br><br>
DOI: 10.6084/m9.figshare.6897035.v1
title: Raw data on Linear Enamel Hypoplasia timing in Archaic Texan hunter-gatherers

description: Data on Linear Enamel Hypoplasias (as a sign of stress) in an Early Archaic Texan hunter-gatherer population for open access paper in PeerJ: https://peerj.com/articles/4367/?td=bl.<br>For further questions contact Berbesque at University of Roehampton.<br><br><br><br><br>
DOI: 10.7910/dvn/jdfd2b/m7qjpa
title: 1-25.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6898325.v1
title: Developmental psychology_Raw data_code

description: Developmental Psychology data share
DOI: 10.7910/dvn/jdfd2b/mbr6n5
title: 1-41.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.7910/dvn/jdfd2b/yyminx
title: 1-3.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6895898
title: Supplementary Material for: Continuous Monitoring of Patient Mobility for 18 Months Using Inertial Sensors following Traumatic Knee Injury: A Case Study

description: Continuous patient activity monitoring during rehabilitation, enabled by digital technologies, will allow the objective capture of real-world mobility and aligning treatment to each individual’s recovery trajectory in real time. To explore the feasibility and added value of such approaches, we present a case study of a 36-year-old male participant monitored continuously for activity levels and gait parameters using a waist-worn inertial sensor following a tibial plateau fracture on the right side, sustained as a result of a high-energy trauma during a sporting accident. During rehabilitation, data were collected for a period of 553 days, with &gt; 80% daytime compliance, until the participant returned to near full mobility. The participant completed a daily diary with the annotation of major events (falls, near falls, cycling periods, or physiotherapy sessions) and key dates in the patient’s recovery, including medical interventions, transitioning off crutches, and returning to work. We demonstrate the feasibility of collecting, storing, and mining of continuous digital mobility data and show that such data can detect changes in mobility and provide insights into long-term rehabilitation. We make both raw data and annotations available as a resource with the aspiration that further methods and insights will be built on this initial exploration of added value and continue to demonstrate that continuous monitoring can be deployed to aid rehabilitation.
DOI: 10.7910/dvn/jdfd2b/rlsj7u
title: 1-52.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6895898.v1
title: Supplementary Material for: Continuous Monitoring of Patient Mobility for 18 Months Using Inertial Sensors following Traumatic Knee Injury: A Case Study

description: Continuous patient activity monitoring during rehabilitation, enabled by digital technologies, will allow the objective capture of real-world mobility and aligning treatment to each individual’s recovery trajectory in real time. To explore the feasibility and added value of such approaches, we present a case study of a 36-year-old male participant monitored continuously for activity levels and gait parameters using a waist-worn inertial sensor following a tibial plateau fracture on the right side, sustained as a result of a high-energy trauma during a sporting accident. During rehabilitation, data were collected for a period of 553 days, with &gt; 80% daytime compliance, until the participant returned to near full mobility. The participant completed a daily diary with the annotation of major events (falls, near falls, cycling periods, or physiotherapy sessions) and key dates in the patient’s recovery, including medical interventions, transitioning off crutches, and returning to work. We demonstrate the feasibility of collecting, storing, and mining of continuous digital mobility data and show that such data can detect changes in mobility and provide insights into long-term rehabilitation. We make both raw data and annotations available as a resource with the aspiration that further methods and insights will be built on this initial exploration of added value and continue to demonstrate that continuous monitoring can be deployed to aid rehabilitation.
DOI: 10.7910/dvn/jdfd2b/yxuuzp
title: 1-47.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.6084/m9.figshare.6895700
title: Spontaneous gait transition to high-speed galloping by reconciliation between body support and propulsion

description: Quadrupeds change their gait patterns in response to locomotion speed to achieve low cost of transport over a wide range of speeds. Understanding the underlying control mechanism is essential to establish a design principle for legged robots that can adaptively generate energy-efficient locomotion patterns. Even decerebrate cats exhibit spontaneous gait transition, suggesting that adaptive gait patterns are generated via decentralized control systems, i.e. central pattern generators and reflexes. Several studies address this issue; however, the essential control mechanism that enables spontaneous transition from low- to high-speed gait is still poorly understood. To address this issue, this work reconsiders the interlimb coordination mechanism by focusing on two fundamental roles of limbs: body support and propulsion. To verify the proposed model, 2D simulations and 3D hardware experiments were conducted. The results indicate that the proposed model enables the robot to spontaneously exhibit gait transition to high-speed galloping and to achieve faster and more energy-efficient locomotion than a bounding gait in 3D hardware experiments.
DOI: 10.6084/m9.figshare.6895682
title: Microsatellite dataset on Sinanodonta woodiana

description: Microsatellite marker dataset of Sinanodonta woodiana from Europe and China in GenAlEx format
DOI: 10.7910/dvn/jdfd2b/scu9a1
title: 1-2.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.5281/zenodo.4006118
title: SGB.Oct19

description: PhyloPhlAn metagenomic database "SGB.Oct19"
DOI: 10.6084/m9.figshare.6895700.v1
title: Spontaneous gait transition to high-speed galloping by reconciliation between body support and propulsion

description: Quadrupeds change their gait patterns in response to locomotion speed to achieve low cost of transport over a wide range of speeds. Understanding the underlying control mechanism is essential to establish a design principle for legged robots that can adaptively generate energy-efficient locomotion patterns. Even decerebrate cats exhibit spontaneous gait transition, suggesting that adaptive gait patterns are generated via decentralized control systems, i.e. central pattern generators and reflexes. Several studies address this issue; however, the essential control mechanism that enables spontaneous transition from low- to high-speed gait is still poorly understood. To address this issue, this work reconsiders the interlimb coordination mechanism by focusing on two fundamental roles of limbs: body support and propulsion. To verify the proposed model, 2D simulations and 3D hardware experiments were conducted. The results indicate that the proposed model enables the robot to spontaneously exhibit gait transition to high-speed galloping and to achieve faster and more energy-efficient locomotion than a bounding gait in 3D hardware experiments.
DOI: 10.7910/dvn/jdfd2b/uhzixc
title: 1-5.5V

description: sigma=1.5Volts; This is a FITS file with 1 (primary) HDU. The following recognized metadata keys have been found in the FITS file: NAXIS2; NAXIS; NAXIS1; NAXIS0;
DOI: 10.5281/zenodo.4006117
title: SGB.Oct19

description: PhyloPhlAn metagenomic database "SGB.Oct19"
DOI: 10.6084/m9.figshare.6895424
title: Supplementary Material for: Validation of a Wearable Sensor for Measuring Running Biomechanics

description: <b><i>Background:</i></b> Running biomechanics have traditionally been analyzed in laboratory settings, but this may not reflect natural running gait. Wearable sensors may offer an alternative. <b><i>Methods:</i></b> A concurrent validation study to determine agreement between the RunScribe<sup>TM</sup> wearable sensor (triaxial accelerometer and gyroscope) and the 3D motion capture system was conducted. Twelve injury-free participants (6 males, 6 females; age = 23.1 ± 5.5 years, weekly mileage = 16.1 ± 9.3) ran 1.5 miles on a treadmill. Ten consecutive strides from each limb were collected, and the mean values were analyzed. Pronation excursion, maximum pronation velocity, contact time, and cycle time were compared between measurement platforms using intraclass correlation coefficients (ICC) and Bland-Altman analyses. <b><i>Results:</i></b> Excellent ICC estimates were found for maximum pronation velocity, contact time, and cycle time. Pronation excursion demonstrated fair ICC estimates. The mean differences between platforms were small with limits of agreement clustered around zero, except for contact time measures which were consistently higher with the RunScribe compared to the camera-based system. <b><i>Conclusion:</i></b> Our study revealed that the RunScribe wearable device showed good to excellent concurrent validity for maximum pronation velocity, contact time, and cycle time; however, direct comparisons or results between the two platforms should not be used.
DOI: 10.6084/m9.figshare.6895325
title: Untitled Item

description: ашлаглашщ