cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4873325
title: Palythoa grandis SPAdes preassembly

description: .
DOI: 10.5281/zenodo.4873324
title: Palythoa grandis SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.14703426
title: PRISMA Checklist for "What do we know about ancillary care practices in East and Southern Africa? A systematic review and meta-synthesis."

description: Despite growing calls for the provision of ancillary care to study participants during medical research, there remains a noticeable gap in ethical guidelines for medical researchers in resource-constrained settings (RCS). We reviewed recent studies to determine the extent to which ancillary care is provided in East and Southern Africa and to examine the ethical justifications researchers provide to support their views on ancillary care obligations. <br>A systematic search for qualitative and mixed methods studies on ancillary care was conducted across MEDLINE, Embase, African Wide Information, PubMed, CINAHL Plus, and Scopus. The NIH Department of Bioethics and H3 Africa websites and Google Scholar were further searched. Studies conducted in East and Southern Africa between 2004 and 2020, as well as those that reported on ancillary care provided to study participants were included. All studies included in this review were evaluated for methodological quality as well as bias risk. NVivo version 12 was used for thematic analysis.Since this study utilised a secondary synthesis of data, which is already in the public domain, ethical approvals, and consent to participate were not necessary.<br>
DOI: 10.6084/m9.figshare.14703426.v1
title: PRISMA Checklist for "What do we know about ancillary care practices in East and Southern Africa? A systematic review and meta-synthesis."

description: Despite growing calls for the provision of ancillary care to study participants during medical research, there remains a noticeable gap in ethical guidelines for medical researchers in resource-constrained settings (RCS). We reviewed recent studies to determine the extent to which ancillary care is provided in East and Southern Africa and to examine the ethical justifications researchers provide to support their views on ancillary care obligations. <br>A systematic search for qualitative and mixed methods studies on ancillary care was conducted across MEDLINE, Embase, African Wide Information, PubMed, CINAHL Plus, and Scopus. The NIH Department of Bioethics and H3 Africa websites and Google Scholar were further searched. Studies conducted in East and Southern Africa between 2004 and 2020, as well as those that reported on ancillary care provided to study participants were included. All studies included in this review were evaluated for methodological quality as well as bias risk. NVivo version 12 was used for thematic analysis.Since this study utilised a secondary synthesis of data, which is already in the public domain, ethical approvals, and consent to participate were not necessary.<br>
DOI: 10.5281/zenodo.4873603
title: Calvadosia cruxmelitensis SPAdes preassembly

description: .
DOI: 10.5281/zenodo.4873602
title: Calvadosia cruxmelitensis SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.14564700.v2
title: Osteogenic Nanofibrous coated implants.rar

description: Anchoring of endosseous implant through osseointegration continues to be an important clinical need. Here, we describe the development of superior endosseous implant demonstrating enhance osseointegration, achieved through surface modification via coating of osteogenic nanofibres. The randomized bio-composite osteogenic nanofibres incorporating polycaprolactone, gelatin, hydroxyapatite, dexamethasone, beta-glycerophosphate and ascorbic acid were electrospun on titanium implants mimicking bone extracellular matrix and subsequently induced osteogenesis by targeting undifferentiated mesenchymal stem cells present in the peri-implant niche to regenerate osseous tissue. In proof-of-concept experiment on rabbit study models (n = 6), micro-computed tomography (Micro-CT), histomorphometric analysis and biomechanical testing in relation to our novel osteogenic nanofibrous coated implants showed improved results when compared to uncoated controls. Further, no pathological changes were detected during gross examination and necropsy on peri-implant osseous tissues regenerated in response to such coated implants. The findings of the present study confirm that osteogenic nanofibrous coating significantly increases the magnitude of osteogenesis in the peri-implant zone and favours the dynamics of osseointegration.
DOI: 10.3886/e139461v1
title: Messaging to boost COVID-19 vaccinations: emphasize personal health benefits

description: The rapid development of COVID-19 vaccines is a tremendous scientific response to the current global pandemic. However, vaccines per se do not save lives and restart economies. The success of the vaccines depends on the number of people getting vaccinated. We use a survey experiment to examine the impact on vaccine intentions of a variety of public health messages identified as particularly promising: three messages that emphasize different benefits from the vaccines (personal health, the health of others, and the recovery of local and national economies), and one message that emphasizes vaccine safety. Because people will likely be exposed to multiple messages in the real world, we also examine the effect of these messages in combinations. Based on a nationally quota representative sample of 3,048 adults in the United States, our findings suggest that several forms of public messages can increase vaccine intentions, but messaging that emphasizes personal health benefits had the largest impact.
DOI: 10.6084/m9.figshare.14703108
title: Table 2. Characteristics of Included Reviews

description: Characteristics of Included Reviews<br>
DOI: 10.6084/m9.figshare.14703108.v2
title: Table 2. Characteristics of Included Reviews

description: Characteristics of Included Reviews<br>
DOI: 10.6084/m9.figshare.14703132
title: Table 3. Presentation of Phenomenon of Interest, SynthesizedFindings and Implications for Research and Practice

description: Presentation of Phenomenon of Interest, Synthesized Findings and Implications for Research and Practice
DOI: 10.6084/m9.figshare.14703132.v2
title: Table 3. Presentation of Phenomenon of Interest, SynthesizedFindings and Implications for Research and Practice

description: Presentation of Phenomenon of Interest, Synthesized Findings and Implications for Research and Practice
DOI: 10.5281/zenodo.4875738
title: Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data

description: <strong>Background:</strong> The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. For quantitative assessment and disease monitoring medical imaging like computed tomography offers great potential as alternative to RT-PCR methods. For this reason, automated image segmentation is highly desired as clinical decision support. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches. <strong>Methods:</strong> To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U-Net architecture instead of new or computational complex neural network architectures. <strong>Results:</strong> Through a k-fold cross-validation on 20 CT scans as training and validation of COVID-19, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID-19 infected regions without overfitting on limited data. We performed an in-detail analysis and discussion on the robustness of our pipeline through a sensitivity analysis based on the cross-validation and impact on model generalizability of applied preprocessing techniques. Our method achieved Dice similarity coefficients for COVID-19 infection between predicted and annotated segmentation from radiologists of 0.804 on validation and 0.661 on a separate testing set consisting of 100 patients. <strong>Conclusions:</strong> We demonstrated that the proposed method outperforms related approaches, advances the state-of-the-art for COVID-19 segmentation and improves robust medical image analysis based on limited data. The code and model are available under the following link:<br> https://github.com/frankkramer-lab/covid19.MIScnn
description: Code: https://github.com/frankkramer-lab/covid19.MIScnn
DOI: 10.5281/zenodo.3902292
title: Robust Chest CT Image Segmentation of COVID-19 Lung Infection based on limited data

description: <strong>Background:</strong> The coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. For quantitative assessment and disease monitoring medical imaging like computed tomography offers great potential as alternative to RT-PCR methods. For this reason, automated image segmentation is highly desired as clinical decision support. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches. <strong>Methods:</strong> To address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U-Net architecture instead of new or computational complex neural network architectures. <strong>Results:</strong> Through a k-fold cross-validation on 20 CT scans as training and validation of COVID-19, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID-19 infected regions without overfitting on limited data. We performed an in-detail analysis and discussion on the robustness of our pipeline through a sensitivity analysis based on the cross-validation and impact on model generalizability of applied preprocessing techniques. Our method achieved Dice similarity coefficients for COVID-19 infection between predicted and annotated segmentation from radiologists of 0.804 on validation and 0.661 on a separate testing set consisting of 100 patients. <strong>Conclusions:</strong> We demonstrated that the proposed method outperforms related approaches, advances the state-of-the-art for COVID-19 segmentation and improves robust medical image analysis based on limited data. The code and model are available under the following link:<br> https://github.com/frankkramer-lab/covid19.MIScnn
description: Code: https://github.com/frankkramer-lab/covid19.MIScnn
DOI: 10.3886/e139461v2
title: Messaging to boost COVID-19 vaccinations: emphasize personal health benefits

description: The rapid development of COVID-19 vaccines is a tremendous scientific response to the current global pandemic. However, vaccines per se do not save lives and restart economies. The success of the vaccines depends on the number of people getting vaccinated. We use a survey experiment to examine the impact on vaccine intentions of a variety of public health messages identified as particularly promising: three messages that emphasize different benefits from the vaccines (personal health, the health of others, and the recovery of local and national economies), and one message that emphasizes vaccine safety. Because people will likely be exposed to multiple messages in the real world, we also examine the effect of these messages in combinations. Based on a nationally quota representative sample of 3,048 adults in the United States, our findings suggest that several forms of public messages can increase vaccine intentions, but messaging that emphasizes personal health benefits had the largest impact.
DOI: 10.7910/dvn/hatfqa
title: Supplementary Data

description: Responses to childhood trauma and early life adversity differ greatly across individuals. Elucidating which factors underlie this variation can help us better understand how to improve health trajectories. We used a case:control study of refugee and non-refugee youth, differentially exposed to war-related trauma, to investigate the effects of genetics and psychosocial environment on response to trauma. We investigated genetic variants in two genes (serotonin transporter, 5-HTT , and catechol-Omethyltransferase, COMT ) that have been implicated in response to early life adversity . We collected buccal samples and survey data from 417 Syrian refugee and 306 Jordanian non-refugee youth who were enrolled in a randomized controlled trial to evaluate a mental health-focused intervention. Measures of lifetime trauma exposure, resilience, and six mental health and psychosocial stress outcomes were collected at three time points: baseline, ~13 weeks, and ~48 weeks. We used multilevel models to identify gene x environment (GxE) interactions and direct effects of the genetic variants in association with the six outcome measures over time. We did not identify any interactions with trauma exposure, but we did identify GxE interactions with both genes and resilience; 1) individuals with high expression (HE) variants of 5- HTTLPR and high levels of resilience had the lowest levels of perceived stress and 2) individuals homozygous for the Val variant of COMT with high levels of resilience showed stable levels of post-traumatic stress symptoms. We also identified a direct protective effect of 5-HTTLPR HE homozygotes on perceived insecurity. Our results point to novel interactions between the protective effects of genetic variants and resilience, lending support to ideas of differential susceptibility and altered stress reactivity in a cohort of war-affected adolescents.
DOI: 10.34740/kaggle/dsv/2286690
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5255/ukda-sn-854944
title: Changing Socio-spatial Inequalities: Population Change and the Lived Experience of Inequality in Urban South Africa, 2011-2017

description: The data deposited include: 1. South Africa wards on consistent spatial boundaries for 2001 and 2011 with Index of Multiple deprivation and domain scores (ArcGIS TM shapefile format ward boundaries, attributes in comma separated values (csv) format) 2. Cape Town population grid for 2001: unemployment rates (ArcGIS TM shapefile format grid square boundaries with linked atrributes) 3. Cape Town population grid for 2011: unemployment rates (ArcGIS TM shapefile format grid square boundaries with linked atrributes) In each case, a metadata file is provided in comma separated values (csv) format. The project also funded inclusion of a bespoke question on inequalities in the 2017 round of the South African Social Attitudes Survey (SASAS). The 2017 SASAS data are available through the Human Sciences Research Council: http://www.hsrc.ac.za/en/departments/sasas
DOI: 10.5255/ukda-sn-853750
title: Photographic Data of Urban Gardening and Making Zimnina in Sofia, 2017-2018

description: The project was designed with societal, policy and research impact in mind. The target audiences of the project included local authorities, the urban poor and other practitioners (such as engineers and NGOs) and researchers. Societally, the primary beneficiaries of the project are expected to be urban practitioners (policy-makers/planners, engineers/designers, service providers, NGOs, and poor end-users). This photographic data on urban gardening and making zimnina in Sofia, Bulgaria collected from June 2017 until March 2018.
DOI: 10.6084/m9.figshare.14703537
title: Data base HL and OAC.xlsx

description: The database includes all the information concerning the patients including socio-demographic data, pathologies and the score obtained in the questionnaire.
DOI: 10.18419/darus-1410/216
title: lowfield_sim.txt

description: :unav
DOI: 10.18419/darus-1410/215
title: Hys3fft.m

description: :unav
DOI: 10.18419/darus-1410/214
title: Fe1dpbz.txt

description: :unav
DOI: 10.18419/darus-1410/213
title: diFe_AC_12500Oe.dat

description: :unav
DOI: 10.18419/darus-1410/212
title: dppfFeCO3BArF4.txt

description: :unav
DOI: 10.18419/darus-1410/211
title: 13_MW_20180904_DiFeox1sol_XB_SP_140K_probeweiterdraussen.spc

description: :unav
DOI: 10.18419/darus-1410/210
title: ox-3K-MossWin_Versuch5.txt

description: :unav
DOI: 10.18419/darus-1410/209
title: DiFeOx3 Prob1 MCD 20190425 FE NIR 10T 1p5K SUB.dat

description: :unav
DOI: 10.18419/darus-1410/208
title: FcBArF4.txt

description: :unav
DOI: 10.18419/darus-1410/207
title: DiFeplus_3K_Fit.txt

description: :unav
DOI: 10.18419/darus-1410/206
title: 4equiv

description: :unav
DOI: 10.18419/darus-1410/205
title: Fe2_DCM_Toluol_1mM_T1_40K_12279_3500_2ms.dat

description: :unav
DOI: 10.18419/darus-1410/204
title: HFEPR_noTdep.py

description: :unav
DOI: 10.18419/darus-1410/203
title: Devis_9751_6gl_diFe_plus_256_128_3515_10K_large_sweep.DTA

description: :unav
DOI: 10.18419/darus-1410/202
title: ox-RT-MossWin_Versuch4.txt

description: :unav
DOI: 10.18419/darus-1410/201
title: simulation_90GHz.dat

description: :unav
DOI: 10.18419/darus-1410/200
title: Fe2_DCM_Toluol_1mM_He2_100K_12468_3500_2ms.dat

description: :unav
DOI: 10.18419/darus-1410/199
title: Fe2_DCM_Toluol_1mM_S7_40K_12to13kG_3500_2ms.dat

description: :unav
DOI: 10.18419/darus-1410/198
title: 5T subtracted.dat

description: :unav
DOI: 10.18419/darus-1410/196
title: S17_Simulation_ESE_Xband_2.m

description: :unav
DOI: 10.18419/darus-1410/195
title: SI_21_HYSCORE_DiFeplus_13ChighfieldSim.png

description: :unav
DOI: 10.18419/darus-1410/194
title: bruker_hamming.m

description: :unav
DOI: 10.18419/darus-1410/193
title: DiFe_Xband_TempDep_Solution.py

description: :unav
DOI: 10.18419/darus-1410/192
title: HYSCOREplot.m

description: :unav
DOI: 10.18419/darus-1410/191
title: 14_MW_20180904_DiFeox1sol_XB_SP_140K_probeweiterdraussen_5reps.par

description: :unav
DOI: 10.18419/darus-1410/190
title: Fe2_DCM_Toluol_1mM_He2_15K_12468_3500_20ms.dat

description: :unav
DOI: 10.18419/darus-1410/189
title: QBandENDOR.py

description: :unav
DOI: 10.18419/darus-1410/188
title: 11_MW_20180904_DiFeox1sol_XB_SP_120K.par

description: :unav
DOI: 10.18419/darus-1410/187
title: DiFe0av20.txt

description: :unav
DOI: 10.18419/darus-1410/186
title: 03_9751_DiFe_DCMTol_50K_EDFS_20-400-40_12007G_33-596397GHz.DSC

description: :unav
DOI: 10.18419/darus-1410/185
title: Fe2_DCM_Toluol_1mM_T1_50K_12279_3500_2ms.dat

description: :unav
DOI: 10.18419/darus-1410/184
title: Fe2_DCM_Toluol_1mM_S1_5.5K_3500_cuSignal.dat

description: :unav
DOI: 10.18419/darus-1410/183
title: Devis_9751_6gl_diFe_plus_256_128_3448_10K.DTA

description: :unav
DOI: 10.18419/darus-1410/182
title: Simulation_ESE_Qband.m

description: :unav
DOI: 10.18419/darus-1410/181
title: XBandENDOR.py

description: :unav
DOI: 10.18419/darus-1410/180
title: higfield_mess.txt

description: :unav
DOI: 10.18419/darus-1410/179
title: 9739_DiFe_plus_3gperl_HS_136_16_30_inc20_T1_T2_100_6db_3439.DSC

description: :unav
DOI: 10.18419/darus-1410/178
title: 9739_DiFe_plus_3gperl_HS_136_16_30_inc20_T1_T2_100_5db_3544.DSC

description: :unav
DOI: 10.18419/darus-1410/177
title: Fe2_DCM_Toluol_1mM_T1_30K_12279_3500_2ms.dat

description: :unav
DOI: 10.18419/darus-1410/176
title: simulation_gStrain_lwpp0.dat

description: :unav
DOI: 10.18419/darus-1410/175
title: 8equiv

description: :unav
DOI: 10.18419/darus-1410/174
title: 182 DiFe P2 MCD 20190528 FE 380nm 0pm10to0T 1p5K 2nmBW 1sAg 1Tm BvsT .dat

description: :unav
DOI: 10.18419/darus-1410/173
title: Fe2_DCM_Toluol_1mM_T1_25K_12279_3500_6ms.dat

description: :unav
DOI: 10.18419/darus-1410/172
title: DiFe_Xband_TempDep_solution.txt

description: :unav
DOI: 10.18419/darus-1410/171
title: Hys3fft.m

description: :unav
DOI: 10.18419/darus-1410/170
title: Devis_9751_6gl_diFe_plus_256_128_3515_10K_large_sweep.DSC

description: :unav
DOI: 10.18419/darus-1410/169
title: Fe2_DCM_Toluol_1mM_S8_100K_12to13kG_3500_2ms.dat

description: :unav
DOI: 10.18419/darus-1410/168
title: DiFecatav5.txt

description: :unav
DOI: 10.18419/darus-1410/167
title: bdaFeCO3.txt

description: :unav
DOI: 10.18419/darus-1410/166
title: 9739_DiFe_plus_3gperl_HS_136_16_30_inc20_T1_T2_100_3.10db_3505.DTA

description: :unav
DOI: 10.18419/darus-1410/165
title: 10_MW_20180904_DiFeox1sol_XB_SP_100K.par

description: :unav
DOI: 10.18419/darus-1410/164
title: lowfield_mess.txt

description: :unav
DOI: 10.18419/darus-1410/163
title: 9739_DiFe_plus_3gperl_HS_200_16_30_inc16_T1_T2_100_6db_3504.7.DSC

description: :unav
DOI: 10.18419/darus-1410/162
title: Devis_9751_6gl_diFe_plus_32_16_3515_10K.DTA

description: :unav
DOI: 10.18419/darus-1410/161
title: FcBArF4 P2 MCD 20190516 FE VIS 0p1T 1p5K SUB.txt

description: :unav
DOI: 10.18419/darus-1410/160
title: DiFeOx3 Prob1 MCD 20190425 FE NIR 5T 1p5K SUB.dat

description: :unav
DOI: 10.18419/darus-1410/159
title: Mossbauer_3K_7K.py

description: :unav
DOI: 10.18419/darus-1410/158
title: Devis_ENDOR_9751_200_400_1000_15Kdriftup_srt_10000_60MHz_12149.DTA

description: :unav
DOI: 10.18419/darus-1410/157
title: HYSCOREload.m

description: :unav
DOI: 10.18419/darus-1410/156
title: QBand.py

description: :unav
DOI: 10.18419/darus-1410/155
title: DiFe2 P2 MCD 20190528 FE NIR 5T 1p5K SUB.txt

description: :unav
DOI: 10.18419/darus-1410/154
title: Devis_9751_6gl_diFe_plus_256_128_3448_10K.DSC

description: :unav
DOI: 10.18419/darus-1410/153
title: Plotting_Xband_solution_TempDep_Low2High.m

description: :unav
DOI: 10.18419/darus-1410/152
title: Hys3fft.m

description: :unav
DOI: 10.18419/darus-1410/151
title: DiFeplus_7K_Fit.txt

description: :unav
DOI: 10.18419/darus-1410/150
title: Fe2_DCM_Toluol_1mM_He2_30K_12279_3500_2ms.dat

description: :unav
DOI: 10.18419/darus-1410/149
title: Devis_9751_6gl_diFe_plus_32_16_3559_10K.DSC

description: :unav
DOI: 10.18419/darus-1410/148
title: DiFe_Xband_TempDep.py

description: :unav
DOI: 10.18419/darus-1410/147
title: S9_DiFe_Xband_Simulation_Solution_9K.py

description: :unav
DOI: 10.18419/darus-1410/146
title: simulation_125GHz.dat

description: :unav
DOI: 10.18419/darus-1410/145
title: Fc+_35K.txt

description: :unav
DOI: 10.18419/darus-1410/144
title: Devis_ENDOR_9751_200_400_1000_15Kdriftup_srt_10000_60MHz_12149.DSC

description: :unav
DOI: 10.18419/darus-1410/143
title: higfield_sim.txt

description: :unav
DOI: 10.6084/m9.figshare.14703594.v1
title: Data_Agouti_Observations

description: In this folder the raw output of the data annotation in Agouti, for all camera trap observations in the data collection period in Flevopark in Amsterdam, the Netherlands (24/03/2021 - 27/04/2021),is stored.Agouti processed and obtained standardized summaries of all camera trap observations and displayed the results in an Excel sheet. <br>
DOI: 10.18419/darus-1410/142
title: Ferrocen.txt

description: :unav
DOI: 10.6084/m9.figshare.14703594
title: Data_Agouti_Observations

description: In this folder the raw output of the data annotation in Agouti, for all camera trap observations in the data collection period in Flevopark in Amsterdam, the Netherlands (24/03/2021 - 27/04/2021),is stored.Agouti processed and obtained standardized summaries of all camera trap observations and displayed the results in an Excel sheet. <br>
DOI: 10.18419/darus-1410/141
title: 15_(dppf)Fe(CO)3_000K_375GHz_15-00T_000mA_phased.dat

description: :unav
DOI: 10.18419/darus-1410/140
title: Fe2_DCM_Toluol_1mM_He2_50K_12468_3500_2ms.dat

description: :unav
DOI: 10.18419/darus-1410/139
title: Fcplus.txt

description: :unav
DOI: 10.18419/darus-1410/138
title: Fe2_DCM_Toluol_1mM_T1_10K_12468_3500_250ms.dat

description: :unav
DOI: 10.18419/darus-1410/137
title: Fe2_DCM_Toluol_1mM_He2_100K_12279_3500_2ms.dat

description: :unav
DOI: 10.18419/darus-1410/136
title: Ferrocenium.txt

description: :unav
DOI: 10.18419/darus-1410/135
title: HFEPR.py

description: :unav