cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/fpdfrx2f3z.1
title: Constitutive expression of Progesterone Receptor B in the uterine epithelium prevents implantation by inhibiting estrogen regulation of LIF signaling. Li et al

description: The loss of uterine epithelial Progesterone receptor (PGR) is crucial for successful embryo implantation in both humans and mice. The two major isoforms PGRA and PGRB have divergent functions under both physiological and pathological conditions. The present study compares phenotypes and gene signatures of PGRA and PGRB in uterine epithelium using uterine epithelial-specific constitutively expressed PGRA or PGRB mouse models. The cistrome and transcriptome analysis reveals substantial overlap between epithelial PGRA and PGRB, and both disrupts embryo implantation through FOXO1 pathways. Constitutive epithelial PGRA and PGRB expression impairs ESR1 occupancy at the promoter of Lif leading to reduced Lif transcription, further exaggerates SGK1 expression leading to enhanced PI3K-SGK1 activities, and both contributes to the decline of nuclear FOXO1 expression. Our study demonstrates that PGRA and PGRB in the uterine epithelium act on a similar set of target genes and commonly regulate the LIF-SGK1-FOXO1 signaling pathway for embryo implantation.
DOI: 10.17632/n8fsktx99h.1
title: The test data for the effectiveness evaluation of SRMS

description: The test data for the effectiveness evaluation of SRMS.
DOI: 10.17632/5b8njbsb24.1
title: Stroboscopic training and perceptual-cognitive skills

description: Repeated effect of stroboscopic view training on perceptual-cognitive skills in soccer players
DOI: 10.6084/m9.figshare.14319733
title: Injury crashes and the relationship with disease causing excessive daytime sleepiness

description: The objective of this study was to understand the relationship between some of the most common diseases that are known to contribute to excessive daytime sleepiness (EDS) and traffic injury crashes. Specific focus was on the relationship between disease and crash type (single-vehicle or multiple-vehicle crash) and between disease and injury severity. This registry-based study considered all passenger car drivers involved in a crash in Sweden between 2011 and 2016 who were 40 years or older at the time of the crash (<i>n</i> = 54,090). For each crash-involved driver, selected medical diagnoses registered from 1997 until the day before the crash were extracted from the National Patient Register. The drivers were assigned to 1 of 4 groups, depending on prior diseases: sleep apnea (SA; group 1, <i>n</i> = 2,165), sleep disorders (group 2, <i>n</i> = 724), Parkinson’s or epilepsy (group 3, <i>n</i> = 645) and a reference group (group 4, <i>n</i> = 50,556). Logistic regression analysis compared single-vehicle crashes with multiple-vehicle crashes and moderately/severely injured drivers with slightly/uninjured drivers. Drivers with EDS-related diseases (groups 1–3) had higher probability of a single-vehicle crash than a multiple-vehicle crash compared to the reference group. The most sizeable effect was found for Parkinson’s/epilepsy with an odds ratio (OR) of 2.5 (confidence interval [CI], 2.1–3.0). For multiple-vehicle crashes, the probability of a moderate/severe injury was higher for drivers with other sleep disorders (OR = 1.5; CI, 1.0–2.2) and Parkinson’s/epilepsy (OR = 1.6; CI, 1.1–2.3) compared to the reference group. This study has made first steps toward understanding the relationship between some of the most common diseases that are known to contribute to EDS and crashes. Having Parkinson’s/epilepsy, in particular, elevated the probability of a single-vehicle crash compared to a multiple-vehicle crash. A single-vehicle crash was seen as indicative of causing a crash; thus, having Parkinson’s/epilepsy could be interpreted as a risk factor for crash involvement. Having Parkinson’s/epilepsy, as well as other sleep disorders, was also related to more severe outcomes in multiple-vehicle crashes, given that a crash occurred. This was not identified in single-vehicle crashes.
DOI: 10.6084/m9.figshare.14319733.v1
title: Injury crashes and the relationship with disease causing excessive daytime sleepiness

description: The objective of this study was to understand the relationship between some of the most common diseases that are known to contribute to excessive daytime sleepiness (EDS) and traffic injury crashes. Specific focus was on the relationship between disease and crash type (single-vehicle or multiple-vehicle crash) and between disease and injury severity. This registry-based study considered all passenger car drivers involved in a crash in Sweden between 2011 and 2016 who were 40 years or older at the time of the crash (<i>n</i> = 54,090). For each crash-involved driver, selected medical diagnoses registered from 1997 until the day before the crash were extracted from the National Patient Register. The drivers were assigned to 1 of 4 groups, depending on prior diseases: sleep apnea (SA; group 1, <i>n</i> = 2,165), sleep disorders (group 2, <i>n</i> = 724), Parkinson’s or epilepsy (group 3, <i>n</i> = 645) and a reference group (group 4, <i>n</i> = 50,556). Logistic regression analysis compared single-vehicle crashes with multiple-vehicle crashes and moderately/severely injured drivers with slightly/uninjured drivers. Drivers with EDS-related diseases (groups 1–3) had higher probability of a single-vehicle crash than a multiple-vehicle crash compared to the reference group. The most sizeable effect was found for Parkinson’s/epilepsy with an odds ratio (OR) of 2.5 (confidence interval [CI], 2.1–3.0). For multiple-vehicle crashes, the probability of a moderate/severe injury was higher for drivers with other sleep disorders (OR = 1.5; CI, 1.0–2.2) and Parkinson’s/epilepsy (OR = 1.6; CI, 1.1–2.3) compared to the reference group. This study has made first steps toward understanding the relationship between some of the most common diseases that are known to contribute to EDS and crashes. Having Parkinson’s/epilepsy, in particular, elevated the probability of a single-vehicle crash compared to a multiple-vehicle crash. A single-vehicle crash was seen as indicative of causing a crash; thus, having Parkinson’s/epilepsy could be interpreted as a risk factor for crash involvement. Having Parkinson’s/epilepsy, as well as other sleep disorders, was also related to more severe outcomes in multiple-vehicle crashes, given that a crash occurred. This was not identified in single-vehicle crashes.
DOI: 10.6084/m9.figshare.14413186.v1
title: Severity analysis of road transport accidents of hazardous materials with machine learning

description: The aim of this study was to explore a suitable method for analyzing road transport accidents that involve hazardous materials and to explore the main factors that influence the occurrence of accidents of varying severity. The 2015–2019 reported crash data from the Ministry of Transport of the People's Republic of China were obtained, and road transport crashes involving hazardous materials were extracted as the analysis data. The dataset was classified into three injury severity categories: property damage only (PDO), injured (INJ), and fatal (FAT). A statistical model and three machine learning-based models were developed: a random parameters logit model (RPLM), multilayer perceptron (MLP), decision tree C5.0 (C5.0) and support vector machine (SVM). The four models were trained/estimated using the training/estimation dataset, and the best model was used to model accidents of the three different severity levels. The main factors that influence the occurrence of accidents at each crash severity level were obtained. C5.0 had the best modeling performance. The direct accident form (DAF), indirect accident form (IAF) and road segment (RS) were determined to be the critical determinants of PDO accidents. The DAF, IAF, road type, RS and time had a substantial effect on INJ accidents. The DAF, IAF, hazardous material type (HMT) and road surface condition were important factors in the occurrence of FAT accidents. Different data have unique characteristics, and the best modeling and analysis method should be chosen accordingly. The safety of road transport of hazardous materials in China is poor, and the losses caused by accidents are substantial. Strengthening the monitoring of travel speed and travel time; improving driver safety awareness, driving skills and the ability to mitigate emergencies; improving the configuration of vehicle safety equipment and the linkage with the control center and rescue center; improving the environmental differences between inside a tunnel and outside a tunnel; reducing the design of long downhill and steep slope sections; reducing the transport plan in unsafe environments; and improving the ability of road management to mitigate bad environments can be effective measures to reduce the severity of road transport accidents involving hazardous materials.
DOI: 10.6084/m9.figshare.14413186
title: Severity analysis of road transport accidents of hazardous materials with machine learning

description: The aim of this study was to explore a suitable method for analyzing road transport accidents that involve hazardous materials and to explore the main factors that influence the occurrence of accidents of varying severity. The 2015–2019 reported crash data from the Ministry of Transport of the People's Republic of China were obtained, and road transport crashes involving hazardous materials were extracted as the analysis data. The dataset was classified into three injury severity categories: property damage only (PDO), injured (INJ), and fatal (FAT). A statistical model and three machine learning-based models were developed: a random parameters logit model (RPLM), multilayer perceptron (MLP), decision tree C5.0 (C5.0) and support vector machine (SVM). The four models were trained/estimated using the training/estimation dataset, and the best model was used to model accidents of the three different severity levels. The main factors that influence the occurrence of accidents at each crash severity level were obtained. C5.0 had the best modeling performance. The direct accident form (DAF), indirect accident form (IAF) and road segment (RS) were determined to be the critical determinants of PDO accidents. The DAF, IAF, road type, RS and time had a substantial effect on INJ accidents. The DAF, IAF, hazardous material type (HMT) and road surface condition were important factors in the occurrence of FAT accidents. Different data have unique characteristics, and the best modeling and analysis method should be chosen accordingly. The safety of road transport of hazardous materials in China is poor, and the losses caused by accidents are substantial. Strengthening the monitoring of travel speed and travel time; improving driver safety awareness, driving skills and the ability to mitigate emergencies; improving the configuration of vehicle safety equipment and the linkage with the control center and rescue center; improving the environmental differences between inside a tunnel and outside a tunnel; reducing the design of long downhill and steep slope sections; reducing the transport plan in unsafe environments; and improving the ability of road management to mitigate bad environments can be effective measures to reduce the severity of road transport accidents involving hazardous materials.
DOI: 10.6084/m9.figshare.14355917
title: The association between vehicle rim type and risk of occupant injury

description: Modern vehicles generally use steel fabricated or alloy blended rims. The manufacturing process and atomic structure of the rim both yield different responses under destructive loading. The aim of this research was to investigate to what extend the type of vehicle rim may influence occupant injury risk. A matched cohort study of frontal German In-Depth Accident Study collisions was devised. The risk of injury to various body regions was compared between vehicles with steel and alloy rims. Occupants in vehicles with alloy rims were at a greater risk of thoracic injury (relative risk [RR] = 1.57; 95% confidence interval [CI], 1.01–2.42) and thoracic abdomen injury (RR = 1.62; 95% CI, 1.10–2.39) at the Maximum Abbreviations Injury Scale (MAIS) 2+ severity. Risk of thoracic injury was greatest for the cluster of occupants seated on the nonimpacted side in frontal collisions (RR = 2.21; 95% CI, 1.01–4.86). MAIS 2+ injury to the head/face/neck yielded no association (RR = 0.98; 95% CI, 0.66–1.47). Alloy rims are more brittle and, as a result, destructive loading is realized with less severe impact. The critical failure increases the amount of loading that needs to be distributed by the restraint system and results in injury.
DOI: 10.6084/m9.figshare.14355917.v1
title: The association between vehicle rim type and risk of occupant injury

description: Modern vehicles generally use steel fabricated or alloy blended rims. The manufacturing process and atomic structure of the rim both yield different responses under destructive loading. The aim of this research was to investigate to what extend the type of vehicle rim may influence occupant injury risk. A matched cohort study of frontal German In-Depth Accident Study collisions was devised. The risk of injury to various body regions was compared between vehicles with steel and alloy rims. Occupants in vehicles with alloy rims were at a greater risk of thoracic injury (relative risk [RR] = 1.57; 95% confidence interval [CI], 1.01–2.42) and thoracic abdomen injury (RR = 1.62; 95% CI, 1.10–2.39) at the Maximum Abbreviations Injury Scale (MAIS) 2+ severity. Risk of thoracic injury was greatest for the cluster of occupants seated on the nonimpacted side in frontal collisions (RR = 2.21; 95% CI, 1.01–4.86). MAIS 2+ injury to the head/face/neck yielded no association (RR = 0.98; 95% CI, 0.66–1.47). Alloy rims are more brittle and, as a result, destructive loading is realized with less severe impact. The critical failure increases the amount of loading that needs to be distributed by the restraint system and results in injury.
DOI: 10.17632/x5jrx4smdz.1
title: Sustainability in fashion retail

description: This dataset contains the selected articles that were analyzed in the systematic literature review.
DOI: 10.17632/5p66jmn38p.1
title: The aroma of eco-enzymes from nutmeg, clove, and eucalyptus plant waste

description: The aroma of eco-enzymes from nutmeg, clove, and eucalyptus plant waste
DOI: 10.17632/bygfmk4rx9.1
title: Clinical Laboratory Test Reports

description: This dataset includes 260 clinical laboratory test reports issued by 24 laboratories in Egypt. It is used as a part of a laboratory test data extraction research. The research is concerned with tagging unstructured and semi-structured heterogeneous lab test data for future unification. The body of all reports is in English; however, headers and footers are in both English and Arabic languages.

The dataset includes pdf-to-image, scanner-scanned, and mobile-scanned lab test reports. Some of the issuers are Alfa Laboratories, Alborg Labs, Almokhtabar Laboratories, Royal Lab, Alshams Labs, Labmed Egypt, Mabaret Elasafra Laboratories, Grand Lab, Green Lab, Omega Labs, and Nasr City Hospital. Many of the reports are issued by different branches. In addition, some of the laboratories exist in governorates such as Cairo, Giza, Alexandria, and Red Sea.

This data was collected from volunteers with full authorization for the researcher to allow conducting data extraction research. This dataset is split into two parts. Firstly, the first part is training lab test reports that are used to extract test patterns to generate training data. Secondly, the other part is used for testing and evaluation of named-entity recognition models.
DOI: 10.17632/w69674f3hy.1
title: Literature sample_Information Security & Privacy in hospitals

description: Literature sample collected for a Sytematic Literature Review in Information Security and Privacy in hospitals.
DOI: 10.21227/jff2-x257
title: Adaptive Conformation Sampling Dataset Zaman_TCBB21

description: We have long known that the characterization of protein three-dimensional structure is key to obtaining a detailed understanding of protein function. Computational approaches to protein structure characterization have largely addressed a narrow formulation of the problem, where the goal is the determination of one structure, also known as the native structure, from a given protein amino-acid sequence. However, many researchers over the years have argued for broadening our view of proteins to account for the multiplicity of native structures. Our understanding of proteins has become more nuanced, and we now know of many protein molecules that make use of large motions, often of several angstroms, to switch between different structures that allow them to tune/regulate interactions with diverse molecular partners (and so engage in complex cellular reactions). Elucidating such structures de novo is considered to be an exceptionally difficulty problem, as it requires exploration of possibly a very large structure space in search of competing, near-optimal energy minima. This dataset is associated with our paper titled, "Adaptive Stochastic Optimization to Improve Protein Conformation Sampling", where we report on a novel stochastic optimization method capable of revealing very distinct structures for a given protein from knowledge of its amino-acid sequence. The method leverages evolutionary search techniques and adapts its exploration of the vast structure space to balance between exploration and exploitation in the presence of a computational budget. This dataset provides the biologically-active conformations of the protein targets used for evaluation and necessary data (sequence, fragment files) for conformation sampling. The dataset includes a benchmark metamorphic test dataset for researchers to continue advancing work on this problem. The paper is under review and we will update the link to the paper once it is published. The codes associated with this dataset can be found in, https://github.com/psp-codes/adaptive-conformation-sampling
DOI: 10.17632/hg7g2c9927.1
title: UGCs on Tourist Perceptions of Culture Shock in China UAE and India and Post Visit Behavioral Intentions 

description: Data has comments by tourists post their visit (around 450)-2017-2020- regarding culture shock experiences from which ABSA and EA was conducted to predict behavioral intentions of tourists
DOI: 10.17632/6v53kfxf46.1
title: A video dataset on agricultural words in Indian sign language

description: The dataset includes the videos of the dynamic hand gestures for the agricultural words from Indian sign language (ISL).  This dataset is used in a work on the recognition of hand gestures for the Indian sign language (ISL) words commonly used by deaf farmers. A hybrid deep learning model with convolutional long short term memory (LSTM) network has been exploited for gesture classification. The model has attained an average classification accuracy of 76.21% on the proposed dataset of ISL words from the agricultural domain. The work is published in the journal Expert Systems with Applications https://www.sciencedirect.com/science/article/abs/pii/S0957417421010009.
DOI: 10.25422/azu.data.16822399
title: Data for "Upper Gila, Salt, and Verde Rivers: Arid Land Rivers in a Changing Climate"

description: The datasets include monthly and annual streamflow, precipitation, and temperature data, for the years 1921-2019, for the Upper Gila, Salt, and Verde River basins in southwestern New Mexico and Arizona. The data were plotted and analyzed to determine the main climatic controls on streamflow.<br><br><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i>
DOI: 10.6084/m9.figshare.17054123
title: Bat 3D positions for description of flight patters

description: The files provide x, y, z consecutive positions of n bats that belong to the same group which are flying for approx. 5 sec in a flight cage. <br><br>File names include group number and recording event.<br>
DOI: 10.6084/m9.figshare.17054123.v2
title: Bat 3D positions for description of flight patters

description: The files provide x, y, z consecutive positions of n bats that belong to the same group which are flying for approx. 5 sec in a flight cage. <br><br>File names include group number and recording event.<br>
DOI: 10.6084/m9.figshare.17054123.v1
title: Bat 3D position for description of flight patters

description: The files provide x, y, z consecutive positions of n bats that belong to the same group which are flying for approx. 5 sec in a flight cage. <br><br>File names include group number and recording event.<br>
DOI: 10.6084/m9.figshare.17054138.v1
title: Bat 3D positions for measuring group cohesion

description: The data sheet present x, y, z non-consecutive positions of n bats flying for a period of 2-5 minutes in a flight cage. All bats belong to the same social group.<br>File names include recording event, group number, and year.
DOI: 10.6084/m9.figshare.17054138
title: Bat 3D positions for measuring group cohesion

description: The data sheet present x, y, z non-consecutive positions of n bats flying for a period of 2-5 minutes in a flight cage. All bats belong to the same social group.<br>File names include recording event, group number, and year.
DOI: 10.6084/m9.figshare.3761562.v2048
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.17005273.v6
title: BED file for Drosophila telomeric TEs and gene capture

description: *_lessfilt_genBrowser.bed: no filter on divergence; no filter on contig length; filter on telomeric hit length (&gt;=longest*0.5)<br>*_filt_genBrowser.bed: filter on divergence (&lt;= 0.3); filter with contig length (&gt;=50kb); filter with the longest hit