cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/bjx3z5m5pd.2
title: Prediction of CoVid-19 mortality in Iraq-Kurdistan by using Machine learning

description: 
This research analyzed different aspects of coronavirus disease (COVID-19) for patients who have coronavirus, for find out which aspects have an effect to patient death. First, a literature has been made with the previous research that has been done on the analysis dataset of coronavirus using Machine learning (ML) algorithm. Second, data analytics is applied on a dataset of Sulaymaniyah, Iraq, to find factors that affect the mortality rate of coronavirus patients. Third, classification algorithms are used on a dataset of 1365 samples provided by hospitals in Sulaymaniyah, Iraq to diagnose COVID-19. Using ML algorithm provided us to find mortality rate of this disease, and detect which factor has major effect to patient death. It is shown here that support vector machine (SVM), decision tree (DT), and naive Bayes algorithms can classify COVID-19 patients, and DT is best one among them at an accuracy (96.7 %).Index Terms: Coronavirus disease, Coronavirus, Forecasting, Machine learning, Kurdistan-IRAQCorresponding author’s e-mail: Brzu T. Muhammed, Department of Computer Science, Kurdistan Technical Institute, Sulaymaniyah, Iraq. E-mail: brzu.tahir@kti.edu.krdReceived: 22-11-2020 Accepted: 19-05-2021 Published: 23-05-2021ORIGINAL RESEARCH ARTICLEUHD JOURNAL OF SCIENCE AND TECHNOLOGYAccess this article onlineDOI: 10.21928/uhdjst.v5n1y2021.pp66-70E-ISSN: 2521-4217P-ISSN: 2521-4209Copyright © 2021 Muhammed, et al. This is an open access article distributed under the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (CC BY-NC-ND 4.0)
DOI: 10.17632/jwjxmf3z74.1
title: Distribution of Athyrium in Arunachal Pradesh, India

description: This data is related to distribution of species of Athyrium in Arunachal Pradesh state in north-east India.
DOI: 10.5281/zenodo.7314041
title: Biomedical Spanish CBOW Word Embeddings in Floret

description: <strong>Biomedical Spanish CBOW Word Embeddings in Floret</strong> The embeddings have been trained with a biomedical Spanish corpus using floret with the following hyperparameters: mode: str = "floret",<br> model: str = "cbow",<br> dim: int = 300,<br> mincount: int = 10,<br> minn: int = 5,<br> maxn: int = 6,<br> neg: int = 10,<br> hashcount: int = 2,<br> bucket: int = 50000,<br> thread: int = 128, The embeddings were trained on the concatenation of all corpora from the <strong>Spanish biomedical corpus</strong> that includes Spanish data from various sources for a total of 1.1B tokens across 2,5M documents. Source No. tokens Medical crawler 903,558,136 Clinical cases misc. 102,855,267 EHRs documents<strong>*</strong> 95,267,204 Scielo 60,007,289 BARR2 Background 24,516,442 Wikipedia (Life Sciences) 13,890,501 Patents 13,463,387 EMEA 5,377,448 Mespen (MedlinePlus) 4,166,077 PubMed 1,858,966 More information about the corpus can be found here https://aclanthology.org/2022.bionlp-1.19/ and here https://arxiv.org/abs/2109.07765 The processing took place on an HPC node equipped with an AMD EPYC 7742 (@ 2.250GHz) processor with 128 threads. <strong>How to use</strong> First initialize the spacy vectors from the floret table (.floret file): <pre><code class="language-bash">spacy init vectors es floret_embeddings_bio_es.floret floret_embeddings_bio_es --mode floret</code></pre> <pre><code>import spacy # Load the floret vectors floret_embeddings = spacy.load("floret_embeddings_bio_es") # Get the embeddings of some words diabetes = floret_embeddings.vocab["diabetes"] insulina = floret_embeddings.vocab["insulina"] radiografia = floret_embeddings.vocab["radiografia"] # Get some similarities print(diabetes.similarity(insulina)) print(diabetes.similarity(radiografia)) # diabetes should be more similar to insuline than radiografia </code></pre> <strong>Intended Uses and Limitations</strong> At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this card will be updated. <strong>Authors</strong> The Text Mining Unit from Barcelona Supercomputing Center. <strong>Contact Information</strong> For further information, send an email to plantl-gob-es@bsc.es <strong>Funding</strong> This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL. <strong>Copyright </strong> Copyright (c) 2022 Secretaría de Estado de Digitalización e Inteligencia Artificial
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan-TL).
DOI: 10.5281/zenodo.7314040
title: Biomedical Spanish CBOW Word Embeddings in Floret

description: <strong>Biomedical Spanish CBOW Word Embeddings in Floret</strong> The embeddings have been trained with a biomedical Spanish corpus using floret with the following hyperparameters: mode: str = "floret",<br> model: str = "cbow",<br> dim: int = 300,<br> mincount: int = 10,<br> minn: int = 5,<br> maxn: int = 6,<br> neg: int = 10,<br> hashcount: int = 2,<br> bucket: int = 50000,<br> thread: int = 128, The embeddings were trained on the concatenation of all corpora from the <strong>Spanish biomedical corpus</strong> that includes Spanish data from various sources for a total of 1.1B tokens across 2,5M documents. Source No. tokens Medical crawler 903,558,136 Clinical cases misc. 102,855,267 EHRs documents<strong>*</strong> 95,267,204 Scielo 60,007,289 BARR2 Background 24,516,442 Wikipedia (Life Sciences) 13,890,501 Patents 13,463,387 EMEA 5,377,448 Mespen (MedlinePlus) 4,166,077 PubMed 1,858,966 More information about the corpus can be found here https://aclanthology.org/2022.bionlp-1.19/ and here https://arxiv.org/abs/2109.07765 The processing took place on an HPC node equipped with an AMD EPYC 7742 (@ 2.250GHz) processor with 128 threads. <strong>How to use</strong> First initialize the spacy vectors from the floret table (.floret file): <pre><code class="language-bash">spacy init vectors es floret_embeddings_bio_es.floret floret_embeddings_bio_es --mode floret</code></pre> <pre><code>import spacy # Load the floret vectors floret_embeddings = spacy.load("floret_embeddings_bio_es") # Get the embeddings of some words diabetes = floret_embeddings.vocab["diabetes"] insulina = floret_embeddings.vocab["insulina"] radiografia = floret_embeddings.vocab["radiografia"] # Get some similarities print(diabetes.similarity(insulina)) print(diabetes.similarity(radiografia)) # diabetes should be more similar to insuline than radiografia </code></pre> <strong>Intended Uses and Limitations</strong> At the time of submission, no measures have been taken to estimate the bias and toxicity embedded in the model. However, we are well aware that our models may be biased since the corpora have been collected using crawling techniques on multiple web sources. We intend to conduct research in these areas in the future, and if completed, this card will be updated. <strong>Authors</strong> The Text Mining Unit from Barcelona Supercomputing Center. <strong>Contact Information</strong> For further information, send an email to plantl-gob-es@bsc.es <strong>Funding</strong> This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL. <strong>Copyright </strong> Copyright (c) 2022 Secretaría de Estado de Digitalización e Inteligencia Artificial
description: Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan-TL).
DOI: 10.17632/k6b67z7hj4.1
title: US Gambling Stagnation--Will New Gambling Forms Make a Difference DATA SET

description: Data used for paper by same name.
DOI: 10.17632/s3c72592bc.1
title: Processed model data for "Cross-shelf Penetrating Fronts in the East China Sea in Summer"

description: This dataset is the processed model data for "Cross-shelf Penetrating Fronts in the East China Sea in Summer". The article is intended to submit to JGR: Ocean.
The data is the output from a data assimilative model using ROMS 4D-Var. Details of the data assimilative model are shown in He et al. (2022).
As the original data accout for too much strorage space, this dataset stores the daily averaged subtidal data. The processed data are in matlab format. Each file contains all variable in a month.
The variable "DayNum" represents the time in the format of "days since January 0, 0000". The date can be obtained by the matlab "datestr" function.
The grid information is saved in a matlab structure. See the "get_roms_grid" function in the ROMS matlab tools for details.
Reference:
He, Z., Yang, D., Wang, Y., & Yin, B. (2022). Impact of 4D-Var data assimilation on modelling of the East China Sea dynamics. Ocean Modelling, 102044. doi: https://doi.org/10.1016/j.ocemod.2022.102044
DOI: 10.17632/s3c72592bc.2
title: Processed model data for "Two Key Mechanisms of cross-shelf penetrating fronts in the East China Sea：Flow Convergence and Thermocline Undulation"

description: This dataset is the processed model data for "Two Key Mechanisms of cross-shelf penetrating fronts in the East China Sea：Flow Convergence and Thermocline Undulation". The article is intended to submit to JGR: Ocean.
The data is the output from a data assimilative model using ROMS 4D-Var. Details of the data assimilative model are shown in He et al. (2022).
As the original data accout for too much strorage space, this dataset stores the daily averaged subtidal data. The processed data are in matlab format. Each file contains all variable in a month.
The variable "DayNum" represents the time in the format of "days since January 0, 0000". The date can be obtained by the matlab "datestr" function.
The grid information is saved in a matlab structure. See the "get_roms_grid" function in the ROMS matlab tools for details.
Reference:
He, Z., Yang, D., Wang, Y., & Yin, B. (2022). Impact of 4D-Var data assimilation on modelling of the East China Sea dynamics. Ocean Modelling, 102044. doi: https://doi.org/10.1016/j.ocemod.2022.102044
DOI: 10.17632/w4t73tvrf8.1
title: Coconut tree crown

description: Coconut tree crown images collected during a harvest season in a commercial coconut plantation. A total of 400 Red-Green-Blue (RGB) images (of size 512 × 512 pixels) acquired during daylight between 8:00 to 17:00 (morning to evening) under natural lighting conditions, using a handheld digital single-lens reflex (DSLR) camera. The experimenter stood on the ground near the tree trunk and used the camera to photograph the tree crown. Images were collected at different positions (elevation), angles and lighting conditions (frontal, back, and scattered lighting).
DOI: 10.5281/zenodo.7350376
title: SciExpeM - 10.5281/zenodo.7350376

description: Karimi, Miad; Ochs, Bradley, 0000-0003-1759-5701; Liu, Zefang; Ranjan, Devesh; Sun, Wenting - Combustion And Flame, 2019, (204), 304-319
DOI: 10.5281/zenodo.7350375
title: SciExpeM - 10.5281/zenodo.7350376

description: Karimi, Miad; Ochs, Bradley, 0000-0003-1759-5701; Liu, Zefang; Ranjan, Devesh; Sun, Wenting - Combustion And Flame, 2019, (204), 304-319
DOI: 10.6084/m9.figshare.21608184
title: Obesity and exercise on sperm

description: The impact of obesity and exercise on sperm
DOI: 10.6084/m9.figshare.21608184.v2
title: Obesity and exercise on sperm

description: The impact of obesity and exercise on sperm
DOI: 10.6084/m9.figshare.21608184.v1
title: Obesity and exercise on sperm

description: The impact of obesity and exercise on sperm
DOI: 10.6084/m9.figshare.21608013.v1
title: obesity and exercise

description: The impact of obesity on sperm
DOI: 10.34740/kaggle/dsv/4569606
title: Printed Urdu Character Nastalique Font (PUCNaF)
DOI: 10.57777/kgm2022000008
title: 고구마002
DOI: 10.57777/kgm2022000009
title: 땅콩002
DOI: 10.57777/kgm2022000010
title: 옥수수001
DOI: 10.57777/kgm2022000011
title: 호밀004
DOI: 10.57777/kgm2022000012
title: 감자001
DOI: 10.57777/kgm2022000013
title: 고구마003
DOI: 10.57777/kgm2022000014
title: 고추001
DOI: 10.57777/kgm2022000015
title: 땅콩003
DOI: 10.57777/kgm2022000016
title: 콩002
DOI: 10.57777/kgm2022000017
title: 옥수수002
DOI: 10.57777/kgm2022000018
title: 콩001
DOI: 10.57777/kgm2022000019
title: 땅콩004
DOI: 10.57777/kgm2022000020
title: 고구마006
DOI: 10.57777/kgm2022000021
title: 고추003
DOI: 10.57777/kgm2022000022
title: 고추002
DOI: 10.57777/kgm2022000023
title: 땅콩001
DOI: 10.57777/kgm2022000024
title: 고구마004
DOI: 10.57777/kgm2022000025
title: 고추004
DOI: 10.34740/kaggle/dsv/4569687
title: AutoXGB
DOI: 10.5517/ccdc.csd.cc2dkrvl
title: CCDC 2221610: Experimental Crystal Structure Determination

description: Related Article: shi zhenyu|2022|CSD Communication|||
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: 10.6084/m9.figshare.20538711.v1
title: Figure 2 Circadian gene expressions in BKO cells

description: qPCR CT values for circadian gene expressions in Bmal1 KO cells
DOI: 10.6084/m9.figshare.20539026.v1
title: Figure 4 A and C data set Nrf2 expression and GPx activity level in 661W and BKO cells

description: Data sets for Nrf2 circadian expression in 661W and BKO cells and circadia variation for GPx activity level
DOI: 10.6084/m9.figshare.20539485.v1
title: Figure 5 The survival rate after pretreatment of Lip1 followed by oxidative stress challenge.

description: The survival rate after Lip1 or vehicle treatment in 661W and BKO cells at two time ponts.
DOI: 10.6084/m9.figshare.20539737.v1
title: Figure 6 The cell viability in Bmal1 rescued cells following oxidative stress challenge

description: The data set for the survival rate in Bmal1 rescued cells following oxidative stress challenge.
DOI: 10.5281/zenodo.6647609
title: Streamflow drought hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale

description: <strong>1) Indicators of streamflow drought hazard (SDHI) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong> <strong>Indicators of drought magnitude: </strong>SPI12, SPEI12, SSI1, SSI12, EP1, RDQI1 <strong>Indicators of drought severity: </strong>CDQI1-Q50, CDQI1-Q80, CDQI1-Q80-HS, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-Q50_f, CDQI1-Q80_f, CDQI1-WUs_f, CDQI1-WUs-EFR_f, CEP1(20%)_f, CRDQI1(-50%)_f <strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude </strong>(WaterGAP_ArcID_lon_lat.txt) <strong>3) Streamflow observations and SDHI (based on observations) for two GRDC gauging stations: </strong>Input_Figure_2_Little_Colorado_River.txt (station near Cameron) and Input_Figure_2_Danube_River.txt (station at Hofkirchen) <strong>4) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt <strong>5) Input data for computing Pearson correlation between SSI1 based on observations and each of the five indicators SSI1 (simulated), SPI3, SPI6, SPI9, and SPI12.</strong> Indicators computed for 218 out of 220 GRDC gauging stations with continuous streamflow observations between 1986 and 2015. The folder also contains a list of the 220 GRDC station numbers and the related WaterGAP grid cell ID ("arcid").
DOI: 10.17632/bdxrcmst37.1
title: Matlab_script_MR_paper_Garel_Binet_Gourier

description: Matlab script for the calculations of orientation probability densities and for EPR spectrum simulations in the paper in Magnetic Resonance by E. Garel, L. Binet and D. Gourier
DOI: 10.17632/bwjy757vfs.1
title: Data-MR_paper-Garel_Binet_Gourier

description: Data from paper "Mechanical ordering of pigment crystallites in oil binder: Can EPR reveal the gesture of an artist" by E. Garel, L. Binet and D. Gourier in Magnetic Resonance
DOI: 10.6084/m9.figshare.21601311
title: Supplementary Material for: Sternocleidomastoid Muscle Thickness Correlates with Exercise Tolerance in Patients with COPD

description: <b><i>Background:</i></b> Patients with chronic obstructive pulmonary disease (COPD) have difficulties inhaling as the diaphragm becomes flattened and weakened due to lung hyperinflation. This weakened respiratory function is compensated for by the increased activity of the accessory respiratory muscles, such as the sternocleidomastoid muscle (SCM). <b><i>Objectives:</i></b> This study aimed to evaluate the difference in the SCM thickening fraction (SCM TF) of each respiratory phase (end-expiration, resting inspiration, and end-inspiration), as measured using ultrasonography (US), between patients with COPD and control subjects. We also evaluate the correlation between the SCM TF of each respiratory phase and exercise tolerance in patients with COPD. <b><i>Methods:</i></b> Patients with COPD (<i>n</i> = 44) and age-matched controls (<i>n</i> = 20) underwent US for determination of the SCM TF. Ventilation parameters, including the peak oxygen uptake (peak VO<sub>2</sub>) and the change in the inspiratory capacity, were measured during cardiopulmonary exercise testing. The SCM thickness and TF was measured during end-expiration, resting breathing, and end-inspiration. <b><i>Results:</i></b> The SCM was significantly thinner in patients with COPD than in controls at end-expiration. The increase in the SCM TF from end-expiration to end-inspiration in patients with COPD did not differ significantly from that in control subjects. In contrast, the SCM TF from end-expiration to resting inspiration was significantly greater in patients with COPD than in control subjects. The peak VO<sub>2</sub> was strongly positively correlated with the SCM TF from end-expiration to end-inspiration in patients with COPD (<i>r</i> = 0.71, <i>p</i> &lt; 0.01). <b><i>Conclusions:</i></b> The SCM may be thinner in patients with COPD than in controls. The SCM TF may also be associated with exercise tolerance.
DOI: 10.6084/m9.figshare.21601575
title: Supplementary Material for: Ultrathin Bronchoscopy for the Diagnosis of Peripheral Pulmonary Lesions: A Meta-Analysis

description: <b><i>Background:</i></b> Ultrathin bronchoscopy (external diameter, ≤3.5 mm) is useful for the diagnosis of peripheral pulmonary lesions because of its good accessibility. <b><i>Objectives:</i></b> We performed a meta-analysis to investigate the diagnostic yield of ultrathin bronchoscopy for peripheral pulmonary lesions. <b><i>Methods:</i></b> We performed a systematic search of MEDLINE and EMBASE (from inception to May 2021), and meta-analysis was performed using R software. The diagnostic yield was evaluated by dividing the number of successful diagnoses by the total number of lesions, and subgroup analysis was performed to identify related factors. <b><i>Results:</i></b> Nineteen studies with a total of 1,977 peripheral pulmonary lesions were included. The pooled diagnostic yield of ultrathin bronchoscopy was 0.65 (95% confidence interval, 0.60–0.70). Significant heterogeneity was observed among studies (χ<sup>2</sup>, 87.75; <i>p</i> &lt; 0.01; <i>I</i><sup>2</sup>, 79.5%). In a subgroup analysis, ultrathin bronchoscopy with 1.2 mm channel size showed a diagnostic yield of 0.61 (95% confidence interval, 0.53–0.68), whereas ultrathin bronchoscopy with 1.7 mm channel size showed 0.70 (95% confidence interval, 0.66–0.74) (χ<sup>2</sup>, 5.35; <i>p</i> = 0.02). In addition, there was a significant difference in diagnostic yield based on lesion size, histologic diagnosis (malignant vs. benign), bronchus sign, and lesion location from the hilum, whereas no significant difference was found based on lobar location. The overall complication rate of ultrathin bronchoscopy was 2.7% (pneumothorax, 1.1%). <b><i>Conclusions:</i></b> Ultrathin bronchoscopy is an excellent tool for peripheral pulmonary lesion diagnosis with a low complication rate. The diagnostic yield of ultrathin bronchoscopy was significantly higher with larger channel size, which might be attributed to the availability of radial endobronchial ultrasound.
DOI: 10.6084/m9.figshare.21601776
title: Supplementary Material for: Dipeptidyl-Peptidase 4 Inhibitor-Induced Variants of Bullous Pemphigoid: A Case Series of Four Patients

description: Bullous pemphigoid is the most common acquired bullous disease with an autoimmune basis and a tendency to involve mostly old people. By rising incidence of diabetes all over the world, consumption of antidiabetes medications has also increased. One of the most used antidiabetes drugs is gliptin family (dipeptidyl-peptidase 4 inhibitor). Recently, this class of oral antidiabetic agents showed a correlation with the occurrence of bullous pemphigoid and its subtypes, including mucous membrane pemphigoid and pemphigoid nodularis. We are reporting a case series of 4 diabetes patients that we diagnosed with bullous pemphigoid subtypes (mucous membrane pemphigoid, pemphigoid nodularis, and its rarest subtype, linear IgA bullous dermatosis) after taking different drugs of gliptin family.
DOI: 10.5281/zenodo.6590886
title: Site data archive for "Harmonized gap-filled dataset from 20 urban flux tower sites" for the Urban-PLUMBER project

description: ------------------------------------------------------------------------------------------------------------------------------------------- <strong>This version has been superseded. The latest version is at https://doi.org/10.5281/zenodo.5517550</strong> ------------------------------------------------------------------------------------------------------------------------------------------- Eddy covariance flux tower and site characteristics data for Urban-PLUMBER sites associated with the manuscript: "Harmonized, gap-filled dataset from 20 urban flux tower sites" https://doi.org/10.5194/essd-2022-65 Use of any data must give credit through citation of the above manuscript and other sources as appropriate.<br> We recommend data users consult with site contributing authors and/or the coordination team in the project planning stage. Relevant contacts are included in timeseries metadata. For site information and timeseries plots see https://urban-plumber.github.io/sites. For processing code see https://github.com/matlipson/urban-plumber_pipeline. Within each site folder: - index.html: A summary page with site characteristics and timeseries plots.<br> - SITENAME_sitedata_vX.csv: comma seperated file for numerical site characteristics e.g. location, surface cover fraction etc.<br> - timeseries/ (following files available as netCDF and txt)<br> - SITENAME_raw_observations_vX: site observed timeseries before project-wide quality control.<br> - SITENAME_clean_observations_vX: site observed timeseries after project-wide quality control. <br> - SITENAME_metforcing_v`: site observed timeseries after project-wide quality control and gap filling.<br> - SITENAME_era5_corrected_vX: site ERA5 surface data (1990-2020) with bias corrections as applied in the final dataset.<br> - log_processing_SITENAME_vX.txt: a log of the print statements through running the create_dataset_SITENAME scripts. Authors Mathew Lipson, Sue Grimmond, Martin Best, Andreas Christen, Andrew Coutts, Ben Crawford, Bert Heusinkveld, Erik Velasco, Helen Claire Ward, Hirofumi Sugawara, Je-Woo Hong, Jinkyu Hong, Jonathan Evans, Joseph McFadden, Keunmin Lee, Krzysztof Fortuniak, Leena Järvi, Matthias Roth, Nektarios Chrysoulakis, Nigel Tapper, Oliver Michels, Simone Kotthaus, Stevan Earl, Sungsoo Jo, Valéry Masson, Winston Chow, Wlodzimierz Pawlak, Yeon-Hee Kim. Corresponding author: Mathew Lipson &lt;m.lipson@unsw.edu.au&gt;
DOI: 10.5281/zenodo.7111987
title: Evaluation of a wind tunnel designed to investigate the response of evaporation to changes in the incoming longwave radiation at a water surface. Part I and II

description: Data in spreadsheet format for wind tunnel evaporation experiments 2019-2022.