cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25909/22875620.v1
title: APPF TPA phenotyping dataset: UA ACPFG (Roy) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Roy) ending 2016-08-21
DOI: 10.25909/22875626
title: APPF TPA phenotyping dataset: UA ACPFG (Roy) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Roy) ending 2016-07-24
DOI: 10.25909/22875626.v1
title: APPF TPA phenotyping dataset: UA ACPFG (Roy) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Roy) ending 2016-07-24
DOI: 10.25909/22875632
title: APPF TPA phenotyping dataset: UA (Roy, Garnett) - Rice (USAID)

description: Images and data from rice phenotyping studies (USAID) performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy, Garnett)
DOI: 10.25909/22875632.v1
title: APPF TPA phenotyping dataset: UA (Roy, Garnett) - Rice (USAID)

description: Images and data from rice phenotyping studies (USAID) performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy, Garnett)
DOI: 10.25909/22875638
title: APPF TPA phenotyping dataset: UA (Roy, Garnett) - Wheat (USAID)

description: Images and data from wheat phenotyping studies (USAID) performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy, Garnett)
DOI: 10.25909/22875638.v1
title: APPF TPA phenotyping dataset: UA (Roy, Garnett) - Wheat (USAID)

description: Images and data from wheat phenotyping studies (USAID) performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy, Garnett)
DOI: 10.25909/22875644
title: APPF TPA phenotyping dataset: UA (Roy, Menadue) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy, Menadue) ending 2017-07-30
DOI: 10.25909/22875644.v1
title: APPF TPA phenotyping dataset: UA (Roy, Menadue) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy, Menadue) ending 2017-07-30
DOI: 10.25909/22875650
title: APPF TPA phenotyping dataset: UA (Roy) - Rice

description: Images and data from rice phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy)
DOI: 10.25909/22875650.v1
title: APPF TPA phenotyping dataset: UA (Roy) - Rice

description: Images and data from rice phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy)
DOI: 10.25909/22875656
title: APPF TPA phenotyping dataset: UA (Roy) - Rice

description: Images and data from rice phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy)
DOI: 10.25909/22875656.v1
title: APPF TPA phenotyping dataset: UA (Roy) - Rice

description: Images and data from rice phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy)
DOI: 10.25909/22875662
title: APPF TPA phenotyping dataset: UA (Roy) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy)
DOI: 10.25909/22875662.v1
title: APPF TPA phenotyping dataset: UA (Roy) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Roy)
DOI: 10.25909/22875665
title: APPF TPA phenotyping dataset: UA TPA (Roy, Hairmansis) - Rice

description: Images and data from rice phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA TPA (Roy, Hairmansis)
DOI: 10.25909/22875665.v1
title: APPF TPA phenotyping dataset: UA TPA (Roy, Hairmansis) - Rice

description: Images and data from rice phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA TPA (Roy, Hairmansis)
DOI: 10.25909/22875668
title: APPF TPA phenotyping dataset: UA TPA (Roy, Tilbrook) - Barley

description: Images and data from barley phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA TPA (Roy, Tilbrook) ending 2013-08-25
DOI: 10.25909/22875668.v1
title: APPF TPA phenotyping dataset: UA TPA (Roy, Tilbrook) - Barley

description: Images and data from barley phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA TPA (Roy, Tilbrook) ending 2013-08-25
DOI: 10.25909/22875671
title: APPF TPA phenotyping dataset: UA ACPFG (Garcia) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Garcia) ending 2013-10-09
DOI: 10.25909/22875671.v1
title: APPF TPA phenotyping dataset: UA ACPFG (Garcia) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Garcia) ending 2013-10-09
DOI: 10.25909/22875674
title: APPF TPA phenotyping dataset: UA (Shavrukov) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Shavrukov) ending 2017-04-17
DOI: 10.25909/22875674.v1
title: APPF TPA phenotyping dataset: UA (Shavrukov) - Wheat

description: Images and data from wheat phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA (Shavrukov) ending 2017-04-17
DOI: 10.25909/22875680
title: APPF TPA phenotyping dataset: UA ACPFG (Sutton, Atieno) - Chickpea

description: Images and data from chickpea phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Sutton, Atieno) ending 2014-07-29
DOI: 10.25909/22875680.v1
title: APPF TPA phenotyping dataset: UA ACPFG (Sutton, Atieno) - Chickpea

description: Images and data from chickpea phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Sutton, Atieno) ending 2014-07-29
DOI: 10.25909/22875683
title: APPF TPA phenotyping dataset: UA ACPFG (Sutton, Atieno) - Chickpea

description: Images and data from chickpea phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Sutton, Atieno) ending 2015-07-26
DOI: 10.25909/22875683.v1
title: APPF TPA phenotyping dataset: UA ACPFG (Sutton, Atieno) - Chickpea

description: Images and data from chickpea phenotyping studies performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Sutton, Atieno) ending 2015-07-26
DOI: 10.25909/22875686
title: APPF TPA phenotyping dataset: UA ACPFG (Sutton, Atieno) - Chickpea (pilot 1)

description: Images and data from chickpea phenotyping studies (pilot 1) performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Sutton, Atieno) ending 2014-01-28
DOI: 10.25909/22875686.v1
title: APPF TPA phenotyping dataset: UA ACPFG (Sutton, Atieno) - Chickpea (pilot 1)

description: Images and data from chickpea phenotyping studies (pilot 1) performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Sutton, Atieno) ending 2014-01-28
DOI: 10.25909/22875689
title: APPF TPA phenotyping dataset: UA ACPFG (Sutton, Atieno) - Chickpea (pilot 2)

description: Images and data from chickpea phenotyping studies (pilot 2) performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Sutton, Atieno) ending 2014-03-19
DOI: 10.25909/22875689.v1
title: APPF TPA phenotyping dataset: UA ACPFG (Sutton, Atieno) - Chickpea (pilot 2)

description: Images and data from chickpea phenotyping studies (pilot 2) performed at the APPF Plant Accelerator (TPA), University of Adelaide, on behalf of UA ACPFG (Sutton, Atieno) ending 2014-03-19
DOI: 10.5281/zenodo.17947010
title: Depression and Death Anxiety Dataset
DOI: 10.5281/zenodo.17947011
title: Depression and Death Anxiety Dataset
DOI: 10.34740/kaggle/dsv/13966906
title: Pratical_Durian
DOI: 10.25340/r4/crgjle
title: Related data for: Global fall and rise of digital learning inequality

description: Related data for: Global fall and rise of digital learning inequality
DOI: 10.5281/zenodo.17254590
title: The UAH Predictions of the Solar Wind and Magnetic Field at the Parker Solar Probe during Solar Encounter 22 (2024-12-21)

description: 1. Model

The MS-FLUKSS heliospheric MHD model is coupled with the WSA model (Kim et al. 2020), which used ADAPT-GONG input magnetograms, with the PFSS source surface height and the WSA outer boundary at 2.5 and 9 solar radii, respectively. The MHD model propagates the solar wind from 9 solar radii to 236 solar radii (1.1 AU) to fully enclose the PSP trajectory. Hence, field line tracing from PSP to the solar surface is performed through the MHD domain down to 9 solar radii instantaneously at approximately 1 hour cadence, where the origin of the field line on the photosphere is already known in the WSA model. See Badman et al. (2023) for details.

MS-FLUKSS: Multi-Scale Fluid-Kinetic Simulation Suite

MHD: Magnetohydrodynamic

WSA: Wang-Sheeley-Arge

ADAPT: Air Force Data Assimilative Photospheric flux Transport

GONG: Global Oscillation Network Group

PFSS: Potential Field Source Surface

PSP: Parker Solar Probe

 

2. Data

There are two sets of data files as described below:

(A) "PSP_AGONG_R000.dat" - "PSP_AGONG_R011.dat": MS-FLUKSS simulation results extracted along the PSP trajectory labeled by the input magnetogram (each of the 12 realizations of ADAPT-GONG) for the WSA model. 

Each data file consist of a header and 16 columns of numerical values in the following order:

(1) Time (fractional year)

(2) Heliocentric distance (AU)

(3) MS-FLUKSS longitude (rotated by ~180 degrees from inertial heliographic longitude in degrees)

(4) MS-FLUKSS latitude (same as heliographic latitude in degrees)

(5) Solar wind proton number density (cm^-3)

(6) Solar wind Vr (km/s)

(7) Solar wind Vphi (km/s)

(8) Solar wind Vtheta (km/s)

(9) Thermal pressure of solar wind and interstellar pickup ion mixture (picodyne/cm^2)

(10) Br (microgauss)

(11) Bphi (microgauss)

(12) Btheta (microgauss)

(13) Level set value for tracking the heliospheric current sheet

(14) Level set value for tracking the initial MS-FLUKSS longitude at the inner boundary

(15) Level set value for tracking the initial MS-FLUKSS latitude at the inner boundary

(16) Solar wind temperature.

 

(B) "UAH_psp_e22_20241221*_agongWSAmhd.csv": Carrington longitude and latitude of the predicted magnetic footpoint from 2024-12-21 to 2025-01-01.

 

Each data file consists of 4 columns of numeric values in the following order:

(1) Date (YYYY-MM-DD)

(2) Time (hh:mm:ss in UT)

(3) Carrington longitude (degree)

(4) Carrington latitude (degree)

 

3. Figures

There is one figure showing plots of predicted Br, Vr, density, and temperature at PSP: uah_psp_e22_20241221_agongWSAmhd.pdf.

A plot showing the footpoint prediction results projected onto the solar disk is available at the PSP science gateway, at https://psp-gateway.jhuapl.edu/website/SciencePlanning/MissionEvents/, under “Encounter 22”.
DOI: 10.5281/zenodo.17254591
title: The UAH Predictions of the Solar Wind and Magnetic Field at the Parker Solar Probe during Solar Encounter 22 (2024-12-21)

description: 1. Model

The MS-FLUKSS heliospheric MHD model is coupled with the WSA model (Kim et al. 2020), which used ADAPT-GONG input magnetograms, with the PFSS source surface height and the WSA outer boundary at 2.5 and 9 solar radii, respectively. The MHD model propagates the solar wind from 9 solar radii to 236 solar radii (1.1 AU) to fully enclose the PSP trajectory. Hence, field line tracing from PSP to the solar surface is performed through the MHD domain down to 9 solar radii instantaneously at approximately 1 hour cadence, where the origin of the field line on the photosphere is already known in the WSA model. See Badman et al. (2023) for details.

MS-FLUKSS: Multi-Scale Fluid-Kinetic Simulation Suite

MHD: Magnetohydrodynamic

WSA: Wang-Sheeley-Arge

ADAPT: Air Force Data Assimilative Photospheric flux Transport

GONG: Global Oscillation Network Group

PFSS: Potential Field Source Surface

PSP: Parker Solar Probe

 

2. Data

There are two sets of data files as described below:

(A) "PSP_AGONG_R000.dat" - "PSP_AGONG_R011.dat": MS-FLUKSS simulation results extracted along the PSP trajectory labeled by the input magnetogram (each of the 12 realizations of ADAPT-GONG) for the WSA model. 

Each data file consist of a header and 16 columns of numerical values in the following order:

(1) Time (fractional year)

(2) Heliocentric distance (AU)

(3) MS-FLUKSS longitude (rotated by ~180 degrees from inertial heliographic longitude in degrees)

(4) MS-FLUKSS latitude (same as heliographic latitude in degrees)

(5) Solar wind proton number density (cm^-3)

(6) Solar wind Vr (km/s)

(7) Solar wind Vphi (km/s)

(8) Solar wind Vtheta (km/s)

(9) Thermal pressure of solar wind and interstellar pickup ion mixture (picodyne/cm^2)

(10) Br (microgauss)

(11) Bphi (microgauss)

(12) Btheta (microgauss)

(13) Level set value for tracking the heliospheric current sheet

(14) Level set value for tracking the initial MS-FLUKSS longitude at the inner boundary

(15) Level set value for tracking the initial MS-FLUKSS latitude at the inner boundary

(16) Solar wind temperature.

 

(B) "UAH_psp_e22_20241221*_agongWSAmhd.csv": Carrington longitude and latitude of the predicted magnetic footpoint from 2024-12-21 to 2025-01-01.

 

Each data file consists of 4 columns of numeric values in the following order:

(1) Date (YYYY-MM-DD)

(2) Time (hh:mm:ss in UT)

(3) Carrington longitude (degree)

(4) Carrington latitude (degree)

 

3. Figures

There is one figure showing plots of predicted Br, Vr, density, and temperature at PSP: uah_psp_e22_20241221_agongWSAmhd.pdf.

A plot showing the footpoint prediction results projected onto the solar disk is available at the PSP science gateway, at https://psp-gateway.jhuapl.edu/website/SciencePlanning/MissionEvents/, under “Encounter 22”.
DOI: 10.17605/osf.io/uczs8
title: Paper: Measuring Muslim Religiosity Using a Halal-Scale

description: The background and motivation of the research presented in this article is the obligation of Public Theology to do justice to young Muslim refugees as a minority group in German society regarding the role of religiosity for the way they are coping with life. In the research process, the authors be-came increasingly aware that most instruments to measure religiosity have a Western and/or Protestant bias in that they are more interested in religious attitude than in religious practice and/or religious lifestyle which is very important for Muslim religiosity. Therefore, this article fo-cuses on the distinction between halal and haram as indicators for religious practice according to Muslim benchmarks. Both the concept and the operationalization of a two-dimensional instru-ment of living a halal life are described. The instrument distinguishes between the individual im-portance of halal goods (food, medicine, cosmetics) and services (doctors) and their availability in the local environment. Each of the two dimensions comprises four items. Construct validity is shown by confirmatory factor analysis (CFIrobust = .934, TLIrobust = .902, RMSEArobust = .114 [.073; .156]) of a sample of N = 155 Muslim adolescents who have fled to Germany from Syria, Iraq and Afghanistan. External validity is tested by analyzing the correlation of the measurement instru-ment developed by the authors with the Centrality of Religiosity Scale. The presented halal in-strument offers an approach to Muslim religiosity that meets the orthopractic character of this re-ligion. At the same time, it addresses the consequential dimension of religion within quantitative research.
DOI: 10.6084/m9.figshare.28050484
title: The efficacy of colchicine compared to placebo for preventing ischemic stroke among individuals with established atherosclerotic cardiovascular diseases: a systematic review and meta-analysis

description: <i>Background.</i> Colchicine is an anti-inflammatory drug with promising efficacy for preventing cardiovascular events. We aimed to assess the pooled effect of colchicine on ischemic stroke among patients with established atherosclerotic cardiovascular diseases. <i>Methods</i>. PubMed, Scopus, Web of Science, and the Cochrane Library were systematically searched from the inception to August 5, 2024. A random-effects (DerSimonian–Laird) model was used to conduct this meta-analysis. The inclusion criteria were as follows: (I) being a randomized controlled trial; and (II) measuring the efficacy of colchicine compared to placebo for preventing ischemic stroke among those with established atherosclerotic cardiovascular diseases. <i>Results</i>. We identified 13 eligible clinical trials with 24900 participants. Colchicine significantly decreased the risk of ischemic stroke (relative risk (RR) 0.85, 95% confidence interval (CI) (0.72, 0.99), I<sup>2</sup>=2.92%) among those with established atherosclerotic cardiovascular diseases. Colchicine was more effective when used at 0.5 mg/day (RR 0.86, 95% CI (0.75, 0.99)), prescribed for more than 30 days (RR 0.86, 95% CI (0.75, 1.00)) or for more than 90 days (RR 0.65, 95% CI (0.46, 0.92)), or administered for patients with acute coronary syndrome (RR 0.46, 95% CI (0.23, 0.92)). In addition, colchicine was more effective in studies with a sample size of more than 500 patients, consistent with sensitivity analysis, which indicated that the results relied on large-sized clinical trials. <i>Conclusion</i>. Colchicine may decrease the risk of ischemic stroke among patients with established atherosclerotic cardiovascular diseases, particularly after long-term use; however, future studies are needed due to inconsistencies between existing trials.
DOI: 10.6084/m9.figshare.28050484.v1
title: The efficacy of colchicine compared to placebo for preventing ischemic stroke among individuals with established atherosclerotic cardiovascular diseases: a systematic review and meta-analysis

description: <i>Background.</i> Colchicine is an anti-inflammatory drug with promising efficacy for preventing cardiovascular events. We aimed to assess the pooled effect of colchicine on ischemic stroke among patients with established atherosclerotic cardiovascular diseases. <i>Methods</i>. PubMed, Scopus, Web of Science, and the Cochrane Library were systematically searched from the inception to August 5, 2024. A random-effects (DerSimonian–Laird) model was used to conduct this meta-analysis. The inclusion criteria were as follows: (I) being a randomized controlled trial; and (II) measuring the efficacy of colchicine compared to placebo for preventing ischemic stroke among those with established atherosclerotic cardiovascular diseases. <i>Results</i>. We identified 13 eligible clinical trials with 24900 participants. Colchicine significantly decreased the risk of ischemic stroke (relative risk (RR) 0.85, 95% confidence interval (CI) (0.72, 0.99), I<sup>2</sup>=2.92%) among those with established atherosclerotic cardiovascular diseases. Colchicine was more effective when used at 0.5 mg/day (RR 0.86, 95% CI (0.75, 0.99)), prescribed for more than 30 days (RR 0.86, 95% CI (0.75, 1.00)) or for more than 90 days (RR 0.65, 95% CI (0.46, 0.92)), or administered for patients with acute coronary syndrome (RR 0.46, 95% CI (0.23, 0.92)). In addition, colchicine was more effective in studies with a sample size of more than 500 patients, consistent with sensitivity analysis, which indicated that the results relied on large-sized clinical trials. <i>Conclusion</i>. Colchicine may decrease the risk of ischemic stroke among patients with established atherosclerotic cardiovascular diseases, particularly after long-term use; however, future studies are needed due to inconsistencies between existing trials.
DOI: 10.6084/m9.figshare.29207704
title: Aortic valve phenotype and distal aortic outcome after ascending aortic surgery

description: Recent guidelines advocate postoperative aortic surveillance without clear distinction between aortic valve phenotypes. We sought to determine the long-term occurrence and location of distal aortic complications and aortic growth rates postoperatively in patients with bicuspid and tricuspid aortic valves, respectively. Patients underwent elective ascending aorta replacement, with or without concomitant aortic valve surgery, and computed tomography of the aorta preoperatively. Repeat imaging was performed 10 years after surgery and medical records were reviewed to identify aortic events. Overall, 127 patients (bicuspid <i>n</i> = 85 [66.9%], tricuspid <i>n</i> = 42 [33.1%]; male <i>n</i> = 88 [69.3%]) were included at baseline. Ten patients (23.8%) in the tricuspid group developed an acute aortic event, indication for surgery at a distal segment, or underwent aortic intervention, compared with two (2.4%) in the bicuspid group (<i>p</i> &lt; 0.001). 96 patients (75.6%, bicuspid <i>n</i> = 67, tricuspid <i>n</i> = 29) returned for repeat aortic imaging, with a median follow-up of 10.8 years. The frequency of arch complications was significantly higher in the tricuspid group (<i>n</i> = 4 vs. <i>n</i> = 0, <i>p</i> = 0.007). Multivariable logistic regression identified tricuspid aortic valve (OR 8.52, <i>p</i> = 0.012) and baseline distal aortic diameter (OR 1.27 per mm increment, <i>p</i> &lt; 0.001) as risk factors for a distal aortic complication. Patients with a tricuspid aortic valve are at high risk of a distal aortic complication, whereas patients with a bicuspid aortic valve are at low risk. Long-term surveillance should be individualized accordingly.
DOI: 10.6084/m9.figshare.29207704.v1
title: Aortic Valve Phenotype and Distal Aortic Outcome after Ascending Aortic Surgery

description: Recent guidelines advocate postoperative aortic surveillance without clear distinction between aortic valve phenotypes. We sought to determine the long-term occurrence and location of distal aortic complications and aortic growth rates postoperatively in patients with bicuspid and tricuspid aortic valves, respectively. Patients underwent elective ascending aorta replacement, with or without concomitant aortic valve surgery, and computed tomography of the aorta preoperatively. Repeat imaging was performed ten years after surgery and medical records were reviewed to identify aortic events. Overall, 127 patients (bicuspid n = 85 [66.9%], tricuspid n = 42 [33.1%]; male n = 88 [69.3%]) were included at baseline. Ten patients (23.8%) in the tricuspid group developed an acute aortic event, indication for surgery at a distal segment, or underwent aortic intervention, compared with two (2.4%) in the bicuspid group (p &lt; 0.001). 96 patients (75.6%, bicuspid n = 67, tricuspid n = 29) returned for repeat aortic imaging, with a median follow-up of 10.8 years. The frequency of arch complications was significantly higher in the tricuspid group (n = 4 vs. n = 0, p = 0.007). Multivariable logistic regression identified tricuspid aortic valve (OR 8.52, p = 0.012) and baseline distal aortic diameter (OR 1.27 per mm increment, p &lt; 0.001) as risk factors for a distal aortic complication. Patients with a tricuspid aortic valve are at high risk of a distal aortic complication, whereas patients with a bicuspid aortic valve are at low risk. Long-term surveillance should be individualized accordingly.