cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.18126/m26d20
title: The Third Sandia Fracture Challenge

description: The Third Sandia Fracture Challenge (SFC3) is a collaborative effort for assessment of the state-of-the-art predictive capability in ductile failure.  The Challenge is designed such that computational modelers are asked to predict ductile failure in an unfamiliar geometry using provided standard materials data.  The SFC3, issued in December 2016, is centered on an additively manufactured 316L stainless steel geometry with through holes and internal cavities that could not be produced by conventional machining.  The provided information to enable the predictions includes: base material tensile behavior for different material configurations and loading rates; notched base material tensile behavior; fabrication drawings of the base material specimens and the Challenge (unfamiliar) geometry; electron backscatter diffraction of the Challenge geometry mid-plane; micro-computed tomography data of all Challenge geometry specimens; scanning electron microscope images of the fracture surface of tensile and notched tensile base material specimens; macroscopic images of fractured base material specimens; descriptions of the boundary conditions for the base-material and Challenge-geometry tests; questions to be answered for the prediction (quantities of interest); and an overview presentation of the Challenge.
DOI: 10.24432/c5461k
title: extention of Z-Alizadeh sani dataset
DOI: 10.26180/5c6f2cd343977
title: Simulation results comparing topconfects to other methods

description: Simulation results comparing the topconfects R package to other methods, to accompany a paper describing to topconfects method. The topconfects method finds confidently large differential gene expression effect sizes while correcting for multiple testing.<br>
DOI: 10.34740/kaggle/ds/4891165
title: SunoCaps
DOI: 10.34808/8xkq-7714
title: Permanent traffic counting stations - Expressway S6 in Gdansk (dataset containing 5-min aggregated traffic data and weather information)

description: The data includes traffic data from permanent traffic count station located on the expressway S6 in the Tri-City Agglomeration area in Poland. The data covers the three year period between 2014 and 2017 and one direction of traffic (southbound). 
Tricity Bypass Road (expressway S6) is the eastern end segment of the Polish National Road No. 6 which runs along the Baltic coast between the cities of Szczecin and the Tricity Metropolitan Area, comprising the cities of Gdansk, Sopot and Gdynia. The expressway runs North-South along the edge of the metropolitan area and constitutes a key element of the transportation system in the area.
A permanent traffic count station is located in the middle of the expressway, between Gdansk Osowa and Gdansk Owczarnia interchanges. The station has 4 inductance loops per lane installed according to TSL and HA specification. Data are collected continuously and comprise for each vehicle: time of passage, spot speed, class and length, gap between cars.
The data was aggregated to 5-min intervals. Weather and daytime information was added to the database. For each interval the following information is available:
time-related data: year, month, day, hour, day of the week, season of the year, time of day (dawn, day, night, dusk), type of day (normal day =0, public holiday =1, summer/winter holiday =2),
weather conditions: air temperature, rane state (0 - no rain, 0.5 - drizzle, 1 - light rain, 2 - moderate rain, 3 - continues rain, 4 - light snow, 5 - intensive snow), rain intensity (0-100), surface condition (0 - dry, 1 - moist, 2 - wet, 3 - slippery), horizontal visibility class (according to maritime visibility scale),
vehicle traffic data: 
average speed (harmonic mean), including the division into lanes (l1 - right lane, l2 - left lane) and by vehicle class (pc - passenger car, hv - heavy vehicle), 
standard deviation of the mean speed, including lanes and vehicle classes, 
average speed in free traffic (as the average speed of passenger cars keeping a minimum distance of 7 seconds in front of and 5 seconds behind the vehicle), 
traffic volume, including lanes and vehicle classes, 
average vehicle headways in front and behind vehicle, including lane and vehicle classes.

This study is part of MOP-DZ research project: RID2B - Modern methods of calculating the road capacity and assessment of traffic conditions of roads outside municipal agglomerations, including expressway roads, financed by the National Centre for Research and Development (Poland) and the General Directorate for National Roads and Motorways. Fig. 1. Location of the traffic counting station on S6 expressway in Tricity Fig 2. Cross-section at the location of traffic counting station (source: Google Maps) Fig. 3. Speed-volume relationship (5-min intervals) for the data
DOI: 10.5066/f7np22km
title: Estimated Annual Agricultural Pesticide Use

description: Annual county level pesticide use is estimated for selected herbicides, insecticides, and fungicides applied to agricultural crops grown in the conterminous United States from 1992 through present. Downloadable tabular data files are provided for each year in which estimates were made. In addition maps and charts of pesticide use are provided to show the geographic distribution of estimated use. It is anticipated that as data become available for each consecutive year new estimates, in the form of tabular data as well as maps and charts, will be added to the site. Methods on how estimates are made is included on the site. All tabular data available on the site have been approved for release
DOI: 10.5066/p1ekdxw3
title: Daily and annual abundances of natural- and hatchery-origin age-0 fall Chinook salmon (Oncorhynchus tshawytscha) passing Lower Granite Dam, Washington 1992 - 2021

description: The main contents of this data release are estimates of daily and annual abundances of federally-threatened natural-origin age-0 fall Chinook salmon (Oncorhynchus tshawytscha) passing Lower Granite Dam, Washington. Fish from this population are sampled as they pass Lower Granite Dam, but these sample data cannot be used directly to infer abundance because capture probability at Lower Granite Dam varies throughout the migration and because of the contemporaneous presence of unmarked hatchery-origin fish which are visually indistinguishable from natural-origin fish. Estimates of abundance were obtained by fitting a statistical mark-recapture model that integrated four related datasets: 1) the release/recapture data of passive integrated transponder juvenile salmon tagged as either part of a natural-origin fish survey or as part of hatchery monitoring efforts, 2) daily counts of fish sampled by the smolt monitoring program at Lower Granite Dam, 3) hatchery release data and 4) environmental and operational covariates of Lower Granite Dam. An integrated Bayesian mark-recapture model fit joint parameters for daily fish arrival and capture probabilities to these data to estimate the daily abundance of hatchery- and natural-origin fish. In addition to the main results, this data release contains the raw data used to the fit the models and related parameters such as daily capture probability estimates and daily abundances of hatchery-origin fish.
DOI: 10.5281/zenodo.10278960
title: Questionnaire and Data of Neck Pain and Distance learning among university Students During COVID-19

description:  The shift to online learning during COVID-19 led to increased musculoskeletal discomforts and impacted students' quality of life. Neck pain, once a minor issue, has become more prevalent due to prolonged electronic device use in new learning methods. This study aims to measure the prevalence of neck pain among University of Sharjah (UOS) students during the COVID-19 online learning period and to investigate the factors that provoked it.
 This cross-sectional study used an online survey distributed to UOS students via social media from February 16 to March 12, 2021. Demographic data, Neck Disability Index (NDI) assessments, and pain management information were gathered and analyzed using SPSS 24 through univariate and bivariate methods.
The prevalence of neck pain during COVID-19, among 325 UOS students was found to be 62.7%, 64.41% of which had neck pain at the time of doing the survey. The mean NDI percentage point was 19.19%. The most common location of pain was the back of the neck 38.77%). Participants reported prolonged sitting (81.23%) while leaning forward as the most prevalent cause of their neck pain. Bivariate analysis showed that the most significant factors affecting the NDI were lifting, personal care, headaches, concentration, sleeping, recreational activities, reading, anxiousness, depression, and work. Moreover, subjects who did not seek medical attention treatment had significantly higher NDI scores as compared to those who did (83.89 vs 125.80, p=0.002).
By determining the prevalence of neck pain among UOS students, this research can attract attention to the importance of postural changes and time spent in e-learning on neck pain, and the gaps of previously published articles can be filled. Future studies about neck pain and additional preventative measures should be catalyzed in the UAE.
DOI: 10.5281/zenodo.10564938
title: Databases generated for Manuscript titled "Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures"

description: Databases generated for manuscript "Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures"

There are two zip files containing generated databases:

The zip file titled "database.zip" contains generated database for calculated fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtb.csv (e.g. 000N000Etb.csv for 0°N, 0°E). Each .csv file contains four separate columns and variable rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to Thermal Emission Spectrometer (TES) measured temperature. "Tau" corresponds to calculated Dust visible opacities using the methodology presented in this work. The rows in each file vary based on the temporal observations from TES at each location. 

The zip file titled "fitdatabase.zip" contains generated database for fitted fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtbf.csv (e.g. 000N000Etbf.csv for 0°N, 0°E). Each .csv file contains six separate columns and three hundred and sixty rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to measured temperature. The fitting algorithm interpolates points between values in the calculated flux database and applies a rolling mean fit with a window spanning ten degrees in solar longitude centered at each calculated flux point. "FLAG" indicates the amount of points of calculated flux points that exist within the ten degree window centered at each flux point to demonstrate to the user how much data had to be fitted. "From Ls" shows the leftmost edge of the rolling mean fit window. "To Ls" shows the rightmost edge of the rolling mean fit window. The rows in each file correspond to one degree of solar longitude the fitting algorithm was designed to cover each solar longitude bin.
DOI: 10.5281/zenodo.7064870
title: The coupling of the hydrated proton to its first solvation shell

description: This repository contains tabulated raw data for figures 1, 2, and 4 for the manuscript entitled "The coupling of the hydrated proton to its first solvation shell". Also, all necessary inputs and instructions to reproduce the data are provided.
DOI: 10.5281/zenodo.7267486
title: The OHEJP BeONE Project – Listeria monocytogenes genome assembly dataset

description: <strong>Dataset</strong> This dataset comprises the genome assemblies of 1,426 <em>Listeria monocytogenes</em> samples collected by the BeONE Consortium on behalf of the One Health European Joint Programme “BeONE: Building Integrative Tools for One Health Surveillance” (https://onehealthejp.eu/jrp-beone/). Additionally, a complementary dataset is also made available (https://zenodo.org/record/7116878), comprising genome assemblies of 1,874 <em>L. monocytogenes</em> samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). File “<strong>BeONE_Lm_metadata.xlsx</strong>” contains the genome assembly statistics for each isolate, including European Nucleotide Archive accession numbers and <em>in-silico</em> Multi Locus Sequence Type, and information regarding year of sampling, country and source. The archive “<strong>BeONE_Lm_assemblies.zip</strong>” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. <strong>Dataset selection and curation</strong> This anonymized dataset of <em>L. monocytogenes </em>genome assemblies was generated using Next Generation Sequencing data collected within the BeONE Consortium available at the European Nucleotide Archive under BioProject Accession Number PRJEB57166. Read quality control, trimming and assembly were performed with Aquamis v1.3.9 (Deneke et al. 2021) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was &gt;98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,426 isolates passed the dataset curation step and were included in the final dataset. <strong>Funding</strong> This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. <strong>Acknowledgements</strong> We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.
DOI: 10.5281/zenodo.7267785
title: The OHEJP BeONE Project – Salmonella enterica genome assembly dataset

description: <strong>Dataset</strong> This dataset comprises the genome assemblies of 1,540 <em>Salmonella enterica</em> samples collected by the BeONE Consortium on behalf of the One Health European Joint Programme “BeONE: Building Integrative Tools for One Health Surveillance” (https://onehealthejp.eu/jrp-beone/). Additionally, a complementary dataset is also made available (https://zenodo.org/record/7119735), comprising genome assemblies of 1,434 <em>S. enterica</em> samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). File “<strong>BeONE_Se_metadata.xls</strong>x” contains the genome assembly statistics for each isolate, including European Nucleotide Archive accession numbers, in-silico Multi Locus Sequence Type and Serotype, and information regarding year of sampling, country and source. The archive “<strong>BeONE_Se_assemblies.zi</strong>p” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. <strong>Dataset selection and curation</strong> This anonymized dataset of <em>S. enterica</em> genome assemblies was generated using Next Generation Sequencing data collected within the BeONE Consortium available at the European Nucleotide Archive under BioProject Accession Number PRJEB57179. Read quality control, trimming and assembly were performed with Aquamis v1.3.9 (Deneke et al. 2021) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was &gt;98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 1,540 isolates passed the dataset curation step and were included in the final dataset. In-silico serotyping was performed with SeqSero2 v1.2.1 (Zhang et al. 2019). <strong>Funding</strong> This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. <strong>Acknowledgements</strong> We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.
DOI: 10.5281/zenodo.7267844
title: The OHEJP BeONE Project – Escherichia coli genome assembly dataset

description: <strong>Dataset</strong> This dataset comprises the genome assemblies of 308 <em>Escherichia coli</em> samples collected by the BeONE Consortium on behalf of the One Health European Joint Programme “BeONE: Building Integrative Tools for One Health Surveillance” (https://onehealthejp.eu/jrp-beone/). Additionally, a complementary dataset is also made available (https://zenodo.org/record/7120057), comprising genome assemblies of 1,999 <em>E. coli</em> samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). File “<strong>BeONE_Ec_metadata.xlsx</strong>” contains the genome assembly statistics for each isolate, including European Nucleotide Archive accession numbers, in-silico Multi Locus Sequence Type and Serotype, and information regarding year of sampling, country and source. The archive “<strong>BeONE_Ec_assemblies.zip</strong>” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. <strong>Dataset selection and curation</strong> This anonymized dataset of <em>E. coli</em> genome assemblies was generated using Next Generation Sequencing data collected within the BeONE Consortium available at the European Nucleotide Archive under BioProject Accession Number PRJEB57098. Read quality control, trimming and assembly were performed with Aquamis v1.3.9 (Deneke et al. 2021) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was &gt;98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 308 isolates passed the dataset curation step and were included in the final dataset. In-silico serotyping was performed with seq_typing v2.2. <strong>Funding</strong> This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. <strong>Acknowledgements</strong> We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.
DOI: 10.5281/zenodo.7267879
title: The OHEJP BeONE Project – Campylobacter jejuni genome assembly dataset

description: <strong>Dataset</strong> This dataset comprises the genome assemblies of 610 <em>Campylobacter jejuni </em>samples collected by the BeONE Consortium on behalf of the One Health European Joint Programme “BeONE: Building Integrative Tools for One Health Surveillance” (https://onehealthejp.eu/jrp-beone/). Additionally, a complementary dataset is also made available (https://zenodo.org/record/7120166), comprising genome assemblies of 3,076 <em>C. jejuni</em> samples selected among the Whole-Genome Sequencing (WGS) data publicly available in the European Nucleotide Archive (ENA) or in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA). File “<strong>BeONE_Cj_metadata.xlsx</strong>” contains the genome assembly statistics for each isolate, including European Nucleotide Archive accession numbers and <em>in-silico</em> Multi Locus Sequence Type, and information regarding year of sampling, country and source. The archive “<strong>BeONE_Cj_assemblies.zip</strong>” contains all the genome assemblies (.fasta format) of each isolate presented in the metadata file. <strong>Dataset selection and curation</strong> This anonymized dataset of <em>C. jejuni</em> genome assemblies was generated using Next Generation Sequencing data collected within the BeONE Consortium available at the European Nucleotide Archive under BioProject Accession Number PRJEB57119. Read quality control, trimming and assembly were performed with Aquamis v1.3.9 (Deneke et al. 2021) using default parameters. Assembly quality control (QC), including contamination assessment, as well as MLST ST determination were performed with the same pipeline. All genome assemblies passing the QC were included in the final dataset. Among the others, we noticed that a considerable proportion of assemblies was flagged as “QC fail” exclusively due to the “NumContamSNVs” parameter, suggesting that this setting might have been too strict. After manual inspection of a random subset, assemblies for which the percentage of reads corresponding to the correct species was &gt;98% were recovered and integrated in the final dataset (those samples are labeled in the Metadata file). In total, 610 isolates passed the dataset curation step and were included in the final dataset. <strong>Funding</strong> This work was supported by funding from the European Union’s Horizon 2020 Research and Innovation programme under grant agreement No 773830: One Health European Joint Programme. <strong>Acknowledgements</strong> We thank the National Distributed Computing Infrastructure of Portugal (INCD) for providing the necessary resources to run the genome assemblies. INCD was funded by FCT and FEDER under the project 22153-01/SAICT/2016.