cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7026127
title: Incidence and predictor of diabetic foot ulcer and its association with change in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia, 2021

description: Abstract <strong>Background</strong> Diabetes mellitus is one of the global public health problems and fasting blood sugar is an important indicator of diabetes management. Uncontrolled diabetes can lead to diabetic foot ulcers, which is a common and disabling complication. The association between fasting blood glucose level and the incidence of diabetic foot ulcers is rarely considered, and knowing its predictors is good for clinical decision-making. Therefore, the aim of this study was to determine the incidence and predictors of diabetic foot ulcers and its association with changes in fasting blood sugar among diabetes mellitus patients at referral hospitals in Northwest Ethiopia. <strong>Methods</strong> A multicenter retrospective follow-up study was conducted at a referral hospital in Northwest Ethiopia. A total of 539 newly diagnosed DM patients who had follow-up from 2010 to 2020 were selected using a computer-generated simple random sampling technique. Data was entered using Epi-Data 4.6 and analyzed in R software version 4.1. A Cox proportional hazard with a linear mixed effect model was jointly modeled and 95% Cl was used to select significant variables. AIC and BIC were used for model comparison. <strong>Result</strong> A total of 539 diabetes patients were followed for a total of 28727.53 person-month observations. Overall, 65 (12.1%) patients developed diabetic foot ulcers with incidence rate of 2.26/1000-person month observation with a 95% CI of [1.77, 2.88]. Being rural (AHR= 2.30, 95%CI: [1.23, 4.29]), being a DM patient with Diabetic Neuropathy (AHR= 2.61, 95%CI: [1.12, 6.06]), and having peripheral arterial disease(PAD) (AHR= 2.96, 95%CI: [1.37, 6.40]) were significant predictors of DFU. The time-dependent lagged value of fasting blood sugar change was significantly associated to the incident of DFU (α = 1.85, AHR=6.35, 95%CI [2.40, 16.79]). <strong>Conclusion and recommendation</strong> In this study, the incidence of DFU was higher than in previous studies and was influenced by multiple factors like rural residence, having neuropathy, and PAD were significant predictors of the incidence of DFU. In addition, longitudinal changes in fasting blood sugar were associated with an increased risk of DFU. Health professionals and DM patients should give greater attention to the identified risk factors for DFU were recommended.
DOI: 10.6084/m9.figshare.20662344
title: buteo_lagopus_decontaminated_FSCR.fasta.gz

description: .........
DOI: 10.6084/m9.figshare.20662344.v1
title: buteo_lagopus_decontaminated_FSCR.fasta.gz

description: .........
DOI: 10.6073/pasta/cf438af53e8f3599052908433cc11d69
title: Nevada Desert FACE Facility Soil Organic Carbon Data

description: This data set is the result of soils analysis from the Nevada Desert Free-Air CO2 Enrichment Facility (NDFF) experiment in the Mojave Desert and reports soil organic carbon (%C) and delta 13C stable isotope values. These soils were collected at the end of the NDFF experiment in 2007 and stored at Cornell University until analysis in 2018. Soils were harvested from 6 cover types (5 perennial vegetation covers and unvegetated interspace soils) from 0-100 cm in the soil profile in 20 cm increments. Soils were pretreated for inorganic carbon removal using an acid fumigation technique with HCl. Bulk density from NDFF plots is provided (kg soil* ha ^ -1) so that SOC stocks may be calculated. These data provide the basis for a publication challenging the prevailing idea that arid ecosystems will increase soil organic carbon stocks under long term elevated CO2.
DOI: 10.34740/kaggle/ds/2438020
title: Tweets about COVID-19 Brazilian PCI
DOI: 10.5281/zenodo.7026459
title: Recurrent patterns behind extreme heatwaves in a warming climate

description: Data generated and used in the 'Recurrent patterns behind extreme heatwaves in a warming climate' by Prashant Neel. Spreadsheets are labelled by region (PNW or Siberia) and Scenario SSP1-2.6 or SSP2-4.5 from the EC-Earth model output. Heatwave days for this study are 28<sup>th</sup> June 2021 in PNW, 20<sup>th</sup> June 2020 in Siberia and 25th July 1988 in Siberia. 1. MaxTemp_ spreadsheets show daily maximum temperature calculated by averaging the maximum 2m surface temperatures over a region from 50°N to 90°N and 60°E to 180°E in Siberia, the area containing the town of Verkhoyansk and bordered by high relief, and from 65°N to 70°N and 130°W to 137°W in the Pacific Northwest (PNW). 2. _corr_ spreadsheets show the generated pattern correlation for daily mean 500 hPa geopotential height (z500) data for June and July days against the z500 data for the two heatwaves. For Siberia, this was performed over the region of 50°N to 90°N and 60°E to 180°E and for the PNW this was 30°N to 70°N and 70°W to 145°W. The latitude-corrected Pearson correlation coefficient was found for each day compared to the heatwave day (28<sup>th</sup> June 2021 in PNW and 20<sup>th</sup> June 2020 in Siberia). Those labelled 1988 were for the Siberian heatwave on 25th July 1988. 3. The 'wavenumber_reanalysis' spreadsheet shows the predominant Rossby wavenumber for the reanalysis data for the week preceding each day. Daily mean June and July ERA5 reanalysis data was averaged over 35°N-70°N to create a single value per longitude. A rolling 7-day mean was then be found, truncating the first 6 days of June each year. Then, the fast fourier transform (FFT) was performed to generate amplitudes of each wavenumber for a given day, based on the average v250 of the week prior. The amplitudes were converted to standard deviations from the mean. 4. Spreadsheets ending in '_all' summarise all this data for each region and scenario.
DOI: 10.5281/zenodo.7026458
title: Recurrent patterns behind extreme heatwaves in a warming climate

description: Data generated and used in the 'Recurrent patterns behind extreme heatwaves in a warming climate' by Prashant Neel. Spreadsheets are labelled by region (PNW or Siberia) and Scenario SSP1-2.6 or SSP2-4.5 from the EC-Earth model output. Heatwave days for this study are 28<sup>th</sup> June 2021 in PNW, 20<sup>th</sup> June 2020 in Siberia and 25th July 1988 in Siberia. 1. MaxTemp_ spreadsheets show daily maximum temperature calculated by averaging the maximum 2m surface temperatures over a region from 50°N to 90°N and 60°E to 180°E in Siberia, the area containing the town of Verkhoyansk and bordered by high relief, and from 65°N to 70°N and 130°W to 137°W in the Pacific Northwest (PNW). 2. _corr_ spreadsheets show the generated pattern correlation for daily mean 500 hPa geopotential height (z500) data for June and July days against the z500 data for the two heatwaves. For Siberia, this was performed over the region of 50°N to 90°N and 60°E to 180°E and for the PNW this was 30°N to 70°N and 70°W to 145°W. The latitude-corrected Pearson correlation coefficient was found for each day compared to the heatwave day (28<sup>th</sup> June 2021 in PNW and 20<sup>th</sup> June 2020 in Siberia). Those labelled 1988 were for the Siberian heatwave on 25th July 1988. 3. The 'wavenumber_reanalysis' spreadsheet shows the predominant Rossby wavenumber for the reanalysis data for the week preceding each day. Daily mean June and July ERA5 reanalysis data was averaged over 35°N-70°N to create a single value per longitude. A rolling 7-day mean was then be found, truncating the first 6 days of June each year. Then, the fast fourier transform (FFT) was performed to generate amplitudes of each wavenumber for a given day, based on the average v250 of the week prior. The amplitudes were converted to standard deviations from the mean. 4. Spreadsheets ending in '_all' summarise all this data for each region and scenario.
DOI: 10.6084/m9.figshare.20539947
title: Genome-wide simulation for SparsePro paper

description: Genome-wide simulation data for SparsePro paper: <br> K100_W0.zip: contains 22 replicates of chromosomes simulated under no functional enricment. K100_W1.zip: contains 22 replicates of chromosomes simulated under W=1 (log-relative enrichment). K100_W2.zip: contains 22 replicates of chromosomes simulated under W=2 (log-relative enrichment). 22.idx.anno: annotation file for running functionally informed fine-mapping methods. 22.dict: dictionary for converting rsid to chr.pos.a1.a2 run_sparsepro_susie_ukb.sh: bash command for running SparsePro-/+ and SuSiE on the simulated data. run_susie_poly.sh: bash command for running PolyFun-informed SuSiE. susie_poly.py: script for running PolyFun-informed SuSiE. plot_gw_simulation.ipynb: scripts for plotting genome-wide simulation results. sparsepro_ukb.py: script for running SparsePro-/+ susie_ukb.py: script for running SuSiE
DOI: 10.6084/m9.figshare.20540376.v1
title: Locus-specific simulation for SparsePro paper

description: Locus-specific simulation data for SparsePro paper: 20_31000000_32000000.zip: contains simulated GWAS summary statistics in the chr20:31000000-32000000 locus with K=1,2,5,10 (number of causal variants) and W=0,1,2 (log-relative enrichment). 21_31000000_32000000.zip: contains simulated GWAS summary statistics in the chr21:31000000-32000000 locus with K=1,2,5,10 (number of causal variants) and W=0,1,2 (log-relative enrichment). 22_31000000_32000000.zip: contains simulated GWAS summary statistics in the chr22:31000000-32000000 locus with K=1,2,5,10 (number of causal variants) and W=0,1,2 (log-relative enrichment). get_master.py: script for preparing master file for FINEMAP. prepare_ss.sh: bash command for preparing summary statistics for running different fine-mapping methods. run_finemap.sh: bash command for running FINEMAP. run_paintor_anno.sh: bash command for running PAINTOR+. run_paintor_no.sh: bash command for running PAINTOR-. run_SP_all.sh: bash command for running SparsePro- and SparsePro+. run_susie_rss.sh: bash command for running SuSiE. sparsepro_zld.py: script for running SparsePro-/+. susie_rss.py: script for running SuSiE. plot_locus_simulation.ipynb: scripts for plotting locus simulation results.
DOI: 10.6084/m9.figshare.20539947.v1
title: Genome-wide simulation for SparsePro paper

description: Genome-wide simulation data for SparsePro paper: <br> K100_W0.zip: contains 22 replicates of chromosomes simulated under no functional enricment. K100_W1.zip: contains 22 replicates of chromosomes simulated under W=1 (log-relative enrichment). K100_W2.zip: contains 22 replicates of chromosomes simulated under W=2 (log-relative enrichment). 22.idx.anno: annotation file for running functionally informed fine-mapping methods. 22.dict: dictionary for converting rsid to chr.pos.a1.a2 run_sparsepro_susie_ukb.sh: bash command for running SparsePro-/+ and SuSiE on the simulated data. run_susie_poly.sh: bash command for running PolyFun-informed SuSiE. susie_poly.py: script for running PolyFun-informed SuSiE. plot_gw_simulation.ipynb: scripts for plotting genome-wide simulation results. sparsepro_ukb.py: script for running SparsePro-/+ susie_ukb.py: script for running SuSiE
DOI: 10.6084/m9.figshare.20540376
title: Locus-specific simulation for SparsePro paper

description: Locus-specific simulation data for SparsePro paper: 20_31000000_32000000.zip: contains simulated GWAS summary statistics in the chr20:31000000-32000000 locus with K=1,2,5,10 (number of causal variants) and W=0,1,2 (log-relative enrichment). 21_31000000_32000000.zip: contains simulated GWAS summary statistics in the chr21:31000000-32000000 locus with K=1,2,5,10 (number of causal variants) and W=0,1,2 (log-relative enrichment). 22_31000000_32000000.zip: contains simulated GWAS summary statistics in the chr22:31000000-32000000 locus with K=1,2,5,10 (number of causal variants) and W=0,1,2 (log-relative enrichment). get_master.py: script for preparing master file for FINEMAP. prepare_ss.sh: bash command for preparing summary statistics for running different fine-mapping methods. run_finemap.sh: bash command for running FINEMAP. run_paintor_anno.sh: bash command for running PAINTOR+. run_paintor_no.sh: bash command for running PAINTOR-. run_SP_all.sh: bash command for running SparsePro- and SparsePro+. run_susie_rss.sh: bash command for running SuSiE. sparsepro_zld.py: script for running SparsePro-/+. susie_rss.py: script for running SuSiE. plot_locus_simulation.ipynb: scripts for plotting locus simulation results.
DOI: 10.6084/m9.figshare.20665581
title: accipiter_gentilis_decontaminated_FSCR.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.20665581.v1
title: accipiter_gentilis_decontaminated_FSCR.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.19412180.v1
title: CreativeDestruction_Supplementary_Files_2-6

description: Multiple Sequence Alignments for: the OB-fold domain of uL2; the SH3 domain of aS4; the OB-fold domain of aS28, the cradle loop barrel domain of bIF2; the cradle loop barrel domain of aIF5B.
DOI: 10.34740/kaggle/dsv/4125586
title: Tales from the Pizza Guy
DOI: 10.6084/m9.figshare.20666060
title: Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials

description: In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach for covariate adjustment to gain credibility and efficiency while producing asymptotically valid inference even when the model is incorrect. In this article we present three considerations for better practice when model-assisted inference is applied to adjust for covariates under simple or covariate-adaptive randomized trials: (a) guaranteed efficiency gain: a model-assisted method should often gain but never hurt efficiency; (b) wide applicability: a valid procedure should be applicable, and preferably universally applicable, to all commonly used randomization schemes; (c) robust standard error: variance estimation should be robust to model misspecification and heteroscedasticity. To achieve these, we recommend a model-assisted estimator under an analysis of heterogeneous covariance working model that includes all covariates used in randomization. Our conclusions are based on an asymptotic theory that provides a clear picture of how covariate-adaptive randomization and regression adjustment alter statistical efficiency. Our theory is more general than the existing ones in terms of studying arbitrary functions of response means (including linear contrasts, ratios, and odds ratios), multiple arms, guaranteed efficiency gain, optimality, and universal applicability. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.20666060.v1
title: Toward Better Practice of Covariate Adjustment in Analyzing Randomized Clinical Trials

description: In randomized clinical trials, adjustments for baseline covariates at both design and analysis stages are highly encouraged by regulatory agencies. A recent trend is to use a model-assisted approach for covariate adjustment to gain credibility and efficiency while producing asymptotically valid inference even when the model is incorrect. In this article we present three considerations for better practice when model-assisted inference is applied to adjust for covariates under simple or covariate-adaptive randomized trials: (a) guaranteed efficiency gain: a model-assisted method should often gain but never hurt efficiency; (b) wide applicability: a valid procedure should be applicable, and preferably universally applicable, to all commonly used randomization schemes; (c) robust standard error: variance estimation should be robust to model misspecification and heteroscedasticity. To achieve these, we recommend a model-assisted estimator under an analysis of heterogeneous covariance working model that includes all covariates used in randomization. Our conclusions are based on an asymptotic theory that provides a clear picture of how covariate-adaptive randomization and regression adjustment alter statistical efficiency. Our theory is more general than the existing ones in terms of studying arbitrary functions of response means (including linear contrasts, ratios, and odds ratios), multiple arms, guaranteed efficiency gain, optimality, and universal applicability. Supplementary materials for this article are available online.
DOI: 10.25574/15753
title: Chandra X-ray Observatory ObsId 15753

description: Precise Localization of Transient Low-Mass X-ray Binaries
DOI: 10.25574/15765
title: Chandra X-ray Observatory ObsId 15765

description: LETGS Spectroscopy of the Ultracompact Binary 4U 1626-67
DOI: 10.25574/15781
title: Chandra X-ray Observatory ObsId 15781

description: SS 433 Jet Formation
DOI: 10.25574/15784
title: Chandra X-ray Observatory ObsId 15784

description: Chandra's study of the slow pulsar SXP1062 associated with a young SNR in the SMC
DOI: 10.25574/15875
title: Chandra X-ray Observatory ObsId 15875

description: PROMPT STUDY OF MAGNETAR OUTBURSTS WITH CHANDRA
DOI: 10.6084/m9.figshare.20665899
title: barnardius_zonarius_decontaminated_FSCR.fasta.gz

description: .......
DOI: 10.6084/m9.figshare.20665899.v1
title: barnardius_zonarius_decontaminated_FSCR.fasta.gz

description: .......
DOI: 10.5281/zenodo.7026499
title: Zenodo Upload for DataFed ID:d/403447813

description: Zenodo Upload for DataFed ID:d/403447813
DOI: 10.5281/zenodo.7026498
title: Zenodo Upload for DataFed ID:d/403447813

description: Zenodo Upload for DataFed ID:d/403447813
DOI: 10.6084/m9.figshare.20666025
title: Supplementary Table 1

description: Supplementary Table 1. Raw lipidoimcs data. Pediatric gastroparesis (PCT) and Pediatric gastroparesis and HαT (PHAT) samples were analyzed using liquid chromatography with tandem mass spectrometry. Lipids were quantified semi-quantitatively using a surrogate internal standard and reported as µM in EVs.
DOI: 10.6084/m9.figshare.20666025.v1
title: Supplementary Table 1

description: Supplementary Table 1. Raw lipidoimcs data. Pediatric gastroparesis (PCT) and Pediatric gastroparesis and HαT (PHAT) samples were analyzed using liquid chromatography with tandem mass spectrometry. Lipids were quantified semi-quantitatively using a surrogate internal standard and reported as µM in EVs.
DOI: 10.5281/zenodo.7026504
title: Zenodo Upload for DataFed ID:d/403447813

description: Zenodo Upload for DataFed ID:d/403447813
DOI: 10.5281/zenodo.7026503
title: Zenodo Upload for DataFed ID:d/403447813

description: Zenodo Upload for DataFed ID:d/403447813
DOI: 10.6084/m9.figshare.20401851
title: The underlying data of Figure 2 and Figure 3

description: The underlying data of Figure 2 and Figure 3.
DOI: 10.6084/m9.figshare.20401851.v1
title: The conservation of human functional variants and their effects across mammals

description: The underlying data of Figure 2 and Figure 3.
DOI: 10.5281/zenodo.7026508
title: Cerebrovascular Function in CKD

description: Dataset for manuscript: Cerebrovascular Pulsatility Index is Increased in Chronic Kidney Disease
DOI: 10.5281/zenodo.7026507
title: Cerebrovascular Function in CKD

description: Dataset for manuscript: Cerebrovascular Pulsatility Index is Increased in Chronic Kidney Disease
DOI: 10.6084/m9.figshare.20666463
title: SoloTE_RawData

description: Raw data used for Figures 1 and 3 of the SoloTE paper
DOI: 10.6084/m9.figshare.20666463.v1
title: SoloTE_RawData

description: Raw data used for Figures 1 and 3 of the SoloTE paper
DOI: 10.5281/zenodo.7026517
title: Zenodo Upload for DataFed ID:YG044_Yichen_Guo_07172022

description: Zenodo Upload for DataFed ID:YG044_Yichen_Guo_07172022
DOI: 10.5281/zenodo.7026516
title: Zenodo Upload for DataFed ID:YG044_Yichen_Guo_07172022

description: Zenodo Upload for DataFed ID:YG044_Yichen_Guo_07172022
DOI: 10.25318/9810012401-eng
title: Census family structure including detailed information on stepfamilies, number of children, average number of children and age of youngest child: Canada, provinces and territories, census metropolitan areas and census agglomerations

description: Data on census family structure, number of children, average number of children and age of youngest child for census families with children, Canada, provinces and territories, census metropolitan areas and census agglomerations, 2021, 2016 and 2011 censuses.
DOI: 10.25318/9810012401-fra
title: Structure de la famille de recensement y compris de l'information détaillée sur les familles recomposées, nombre d'enfants, nombre moyen d'enfants et âge du plus jeune enfant : Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement

description: Données sur la structure de la famille de recensement, nombre d’enfants, nombre moyen d’enfants et âge du plus jeune enfant pour les familles de recensement avec enfants, Canada, provinces et territoires, régions métropolitaines de recensement et agglomérations de recensement, recensements de 2021, 2016 et 2011.
DOI: 10.25318/9810012501-eng
title: Marital status, age group and gender: Canada, provinces and territories and census metropolitan areas

description: Data on marital status, age group and gender for the population aged 15 and over, Canada, provinces and territories and census metropolitan areas, 2021, 2016, 2011 and 2006 censuses.
DOI: 10.25318/9810012501-fra
title: État matrimonial, groupe d’âge et genre : Canada, provinces et territoires et régions métropolitaines de recensement

description: Données sur l’état matrimonial, le groupe d’âge et le genre de la population de 15 ans et plus, Canada, provinces et territoires, et des régions métropolitaines de recensement, recensements de 2021, 2016, 2011 et 2006.
DOI: 10.5281/zenodo.7026536
title: Zenodo Upload for DataFed ID:YG044_Yichen_Guo_07172022

description: Zenodo Upload for DataFed ID:YG044_Yichen_Guo_07172022
DOI: 10.5281/zenodo.7026535
title: Zenodo Upload for DataFed ID:YG044_Yichen_Guo_07172022

description: Zenodo Upload for DataFed ID:YG044_Yichen_Guo_07172022
DOI: 10.5281/zenodo.7026541
title: Zenodo Upload for DataFed record:YG044_Yichen_Guo_07172022

description: Zenodo Upload for DataFed record:YG044_Yichen_Guo_07172022
DOI: 10.5281/zenodo.7026533
title: Selective enamel etching: What level of accuracy is achievable in simulated clinical conditions?

description: Dataset of a laboratory study assessing the accuracy of selective enamel etching
DOI: 10.5281/zenodo.7026532
title: Selective enamel etching: What level of accuracy is achievable in simulated clinical conditions?

description: Dataset of a laboratory study assessing the accuracy of selective enamel etching
DOI: 10.5281/zenodo.7026540
title: Zenodo Upload for DataFed record:YG044_Yichen_Guo_07172022

description: Zenodo Upload for DataFed record:YG044_Yichen_Guo_07172022
DOI: 10.5517/ccdc.csd.cc2c0qbg
title: CCDC 2175435: Experimental Crystal Structure Determination

description: Related Article: Laura A. de las Heras, Miguel A. Esteruelas, Montserrat Oliván, Enrique Oñate|2022|Organometallics|41|2317|doi:10.1021/acs.organomet.2c00270
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c0qlq
title: CCDC 2175443: Experimental Crystal Structure Determination

description: Related Article: Laura A. de las Heras, Miguel A. Esteruelas, Montserrat Oliván, Enrique Oñate|2022|Organometallics|41|2317|doi:10.1021/acs.organomet.2c00270
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.