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3,481 results for “data set”
Data set for Earthquakes Trigger Rapid Flash Boiling Front at Optimal Geologic Conditions
<p>Data set for manuscript "Earthquakes Trigger Rapid Flash Boiling Front at Optimal Geologic Conditions" submitted to Geophysical Research Letters.</p>
Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features - Validation Data Sets
<p>Two public validation cohorts were utilized in the MT-Pilot study, the Cancer Genome Atlas (TCGA) and cohort-1 Johns Hopkins University (JHU). These datasets included DNA, RNA, clinical data, and tissue protein analytes analyzed for survival outcome prediction using AI/Machine Learning modeling. </p>
Data sets for phylogenomic analyses in: Ant backbone phylogeny resolved by modelling compositional heterogeneity among sites in genomic data
<p>Ants are the most ubiquitous and ecologically dominant arthropods on Earth, and understanding their phylogeny is crucial for deciphering their character evolution, species diversification, and biogeography. Although recent genomic data have shown promise in clarifying intrafamilial relationships across the tree of ants, inconsistencies between molecular datasets have also emerged. Here I re-examine the most comprehensive published Sanger-sequencing and genome-scale datasets of ants using model comparison methods that model among-site compositional heterogeneity to understand the sources of conflict in phylogenetic studies. My results under the best-fitting model, selected on the basis of Bayesian cross-validation and posterior predictive model checking, identify contentious nodes in ant phylogeny whose resolution is <a>modelling-dependent. </a>I show that the Bayesian infinite mixture CAT model outperforms empirical finite mixture models (C20, C40 and C60) and that, under the best-fitting CAT-GTR+G4 model, the enigmatic <a><em>Martialis</em> </a><em>heureka</em> is sister to all ants except Leptanillinae, rejecting the more popular hypothesis supported under worse-fitting models, that place it as sister to Leptanillinae. These analyses resolve a lasting controversy in ant phylogeny and highlight the significance of model comparison and adequate modelling of among-site compositional heterogeneity in reconstructing the deep phylogeny of insects.</p>
ReaxFF Alumina Parametrization Data Set
<p>See README.md for an overview of the dataset and history of changes.</p>
Data sets for Polyplax serrata article "Highly-resolved genomes of two closely related lineages of the rodent louse Polyplax serrata with different host specificities"
<p><strong>Supplementary data for Polyplax serrata article 2023</strong></p> <p>Data included in this repository were generated and used in various genomic and phylogenetic analysis presented by the publication "<strong>Highly-resolved genomes of two closely related lineages of the louse </strong><em><strong>Polyplax serrata</strong></em><strong> with different host specificities</strong>"</p> <p><strong>Description of the data and file structure</strong></p> <p>Data provided for each analyzed taxa include:</p> <p>- Annotation table.</p> <p>- fasta format files for transcripts (CDS and mRNA).</p> <p>- fasta format file for genome.</p> <p>- protein fasta file.</p> <p>- gbk format file that includes the genome with its corresponding annotations.</p> <p>Additionally,</p> <p>- repeat families in fasta format were included for <em>Polyplax serrata</em> S and N lineages.</p> <p>- rRNA in fasta format were included for <em>Polyplax serrata</em> S and N lineages, <em>Pediculus humanus, Columbicola columbae </em>and <em>Brueelia nebulsa</em>. </p> <p><strong>Sharing/Access information</strong></p> <p>GenBank accession number of analyzed taxa:</p> <p>· <em>Aedes Aegypti</em> (GenBank accession no. GCF_002204515.2).</p> <p>· <em>Brueelia nebulsa</em> ( GenBank accession no. GCA_028293925.1).</p> <p>· <em>Columbicola columbae</em> (GenBank accession no. GCA_016920875.1).</p> <p>· <em>Cimex lectularis</em> (GenBank accession no. GCF_000648675.2).</p> <p>· <em>Glossina morsitans</em> (GenBank accession no. GCA_001077435.1).</p> <p>· <em>Pediculus humanus</em> (GenBank accession no. GCA_000006295.1).</p> <p>· <em>Rhodnius prolixus</em> (GenBank accession no. GCA_000181055.3).</p> <p>· <em>Polyplax serrata S lineage</em> (GenBank accession no. JAWJWF000000000).</p> <p>· <em>Polyplax serrata N lineage</em> (GenBank accession no. JAWJWE000000000).</p> <p> </p> <p>Note: All the latter genomes except for the two genomes of <em>Polyplax serrata</em> S and N lineages, were acquired from GenBank database and were subjected to the same gene prediction and annotation workflow as <em>P. serrata</em> genomes to maintain methodological consistence in downstream analysis of the annotation results.</p> <p><strong>Software</strong></p> <p>- Gene prediction and annotation was performed using Funannotate v1.18.14 (<a href="https://github.com/nextgenusfs/funannotate">https://github.com/nextgenusfs/funannotate)</a>).</p> <p>- Repeat were identified in the genomes of P. serrata S and N lineages using RepeatModeler v2.0.3.</p>
A data set on_Electrical and colloidal properties of hydrogenated nanodiamonds: effects of structure, composition and size
<p>The data set to paper: </p> <p>Electrical and colloidal properties of hydrogenated nanodiamonds: effects of structure, composition and size</p> <p>Stepan Stehlik1,2*, Ondrej Szabo2, Ekaterina Shagieva2, Daria Miliaieva2, Alexander Kromka2, Zuzana Nemeckova3, Jiri Henych3, 4, Jan Kozempel5, Evgeny Ekimov6, Bohuslav Rezek7</p> <p>1 New Technologies – Research Centre, University of West Bohemia, Univerzitní 8, 306 14, Pilsen, Czechia<br>2 Institute of Physics of the Czech Academy of Sciences, Cukrovarnická 10, 162 00 Prague 6, Czechia<br>3 Institute of Inorganic Chemistry of the Czech Academy of Sciences, 250 68 Husinec-Řež, Czechia<br>4 Faculty of Environment, Jan Evangelista Purkyně University in Ústí nad Labem, Pasteurova 3632/15, 400 96 Ústí nad Labem, Czechia<br>5 Department of Nuclear Chemistry, Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Břehová 7, Prague 1, 115 19, Czechia<br>6 Vereshchagin Institute for High Pressure Physics, Russian Academy of Sciences, Moscow 108840, Russia<br>7 Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czechia</p> <p>*principal investigator: stehlik@fzu.cz</p> <p>Data manager: Jan Jaroš: Jan.Jaros2@vut.cz</p> <p>Date of data collection: 1. 6. 2020 - 18. 1. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective Figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Figure 1: tiff, csv<br>Figure 2: tiff, csv<br>Figure 3: tiff, csv<br>Figure 4: tiff, csv<br>Figure 5: tiff, csv<br>Figure 6: tiff</p> <p>Data acquistion methods and conditions and data processing is provided in the Experimental part in the publication: DOI:10.1016/j.cartre.2024.100327</p> <p> </p> <p><span> </span></p>
Data set related to the manuscript "Dynamics and energetics of ion adsorption at the interface between a pure ionic liquid and carbon electrodes"
<p>Text files with data for plotting figures in the quoted manuscript. Example input files for simulations.</p>
A Stimuli Set of Forty Popular Music Drum Patterns with Perceived Complexity Estimates (Data set)
<p>Stimuli and datasets for the study "A Stimuli Set of Forty Popular Music Drum Patterns with Perceived Complexity Estimates"</p>
Example data set for Replay-triggered Brain-wide Activation in Humans
<p>Example data set for <em>Replay-triggered Brain-wide Activation in Humans </em></p> <p><em>Unzip the file and place it in the directory named "example_data_code".</em></p>
Eye tracking data set of academics making an omelette: An egg-breaking work
<p>Just as there are numerous ways to cook an egg, there are numerous ways to recreate a YouTube video of cooking an omelette. We created a dataset of 10 academics replicating a viral video of making an omelette. We evaluated the saccade behavior during the varying subtasks and found differences related to the actions (whisking, sprinkling, etc.) and the objects (eggs, butter, plate, etc.). This dataset can further offer insight into eye movements in complex tasks and is potentially even applicable for task planning and </p>
COVID-19 vaccine uptake data set
<p>The novel Coronavirus Disease 19 (COVID-19) caused devastating effects globally, and healthcare workers were among the most affected by the pandemic. Despite healthcare workers being prioritized in COVID-19 vaccination globally and in Ghana, hesitancy to receive the vaccines resulted in delayed control of the pandemic. In Ghana, healthcare workers had a vaccine acceptance of 39.3% before the vaccine rollout. Consequently, this study assessed the uptake of COVID-19 vaccination and associated factors among healthcare workers in Ghana in the post-vaccine roll-out period. This was an analytical cross-sectional study that used a semi-structured questionnaire to collect data on COVID-19 vaccination uptake and influencing factors. 256 healthcare workers were selected in Ayawaso West Municipality of Ghana using a stratified random sampling approach. Descriptive statistics were used to examine socio-demographic factors and Likert scale responses. Bivariable and Multivariable logistic regression were performed using IBM SPSS version 22 to identify predictors of vaccine uptake and a statistical significance was declared at p<0.05. More than three-fourths of participants 220 (85.9%) had received at least one dose of the COVID-19 vaccination, while 36 (14.9%) were hesitant. More than half 139 (54.3%) had adequate knowledge about COVID-19 vaccination and the majority 188 (73.4%) had positive perceptions about its effectiveness. Moreover, 218 (85.2%) of HCWs had a positive attitude towards COVID-19 vaccination. Positive attitude towards COVID-19 vaccination (AOR = 4.3; 95% CI: 1.4, 13.0) and high cues to action (AOR = 5.7; 95% CI: 2.2, 14.8) were the factors that significantly predicted uptake of COVID-19 vaccination among healthcare workers. COVID-19 vaccination among HCWs in Ghana is promising. However, hesitancy to receive the vaccination among a significant proportion of HCWs raises concerns. To ensure vaccination of all healthcare workers, interventions to promote vaccination should target key determinants of vaccination uptake, such as attitude towards the vaccination and cues to action.</p>
California MPA network ROV data set and code
<p>Dataset of remotely operated vehicle (ROV) surveys conducted across California's MPA network between 2005 and 2021 and code to conduct analyses and produce plots in the manuscript "Diving deep into the network: quantifying protection effects across California's marine protected area network using a remotely operated vehicle".</p>
Data Sets for Evaluation of the Psychometric Properties and Validity of the German Version of the Process Model of Emotion Regulation Scale (PMERQ)
<p>Data files relate to an investigation of the psychometric properties of the German Version of the Process Model of Emotion Regulation Scale (PMERQ). Data set 1 (pmerq_1) contains information regarding the age, gender, ethnicity, and educational status of participants. In addition, responses to the 45 items of the initial translation of the 10-scale PMERQ are included. Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the revised translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the 16-item German Interpersonal Emotion Regulation Questionnaire (IERQ), the 10-item German Emotion Regulation Questionnaire (ERQ), , the German version of the 10-item Big Five Inventory-10 (BFI-10), the 4-item German version of the Patient Health Questionnaire-4 (PHQ-4), the German version of the Satisfaction with Life Scale (SWLS), and the 17-item German Social Desirability Scale-17 (SES-17). Data set 2 (pmerq_2) contains identical sociodemographic variables and responses to the 45 items of the readability-improved translation of the 10-scale PMERQ. In addition, data set 2 contains responses to the German version of the Satisfaction with Life Scale (SWLS) and the German version of the 9-items UCLA Loneliness Scale (UCLA).</p>
Data set for "Omphacite breakdown: nucleation and deformation of clinopyroxene-plagioclase symplectites"
<p>EBSD raw data used in the publication:</p> <p>Zertani, S., Morales, L.F.G., Menegon, L. (2024). Omphacite breakdown: nucleation and deformation of clinopyroxene-plagioclase symplectites. Contributions to Mineralogy and Petrology, 179:xx. https://doi.org/10.1007/s00410-024-02125-0</p>
Data sets to the paper "Weakening surface hydrogen to enhance permeation in hydrogen selective membranes", E. Billeter and A. Borgschulte, Appl. Surf. Sci. (2024)
<p>Datasets to empirical data displayed in Figs. 1(c), 2(b), 3(a), 4(b), 4(c)</p>
BraggSpotFinder Data Set (BSD)
<p>This is the data set that was used to train and test a new BraggSpotFinder application based on the U-Net convolutional neural network.</p> <p>Data set consists on 304 individual CBF diffraction frames from 49 crystals. </p> <p>All diffraction frames were acquired using the rastering method at the AMX beamline, NSLS-II. </p> <p>See description in article: Dong et al., J. Appl. Cryst. (2024) https://doi.org/10.1107/S1600576724002450</p> <p> </p> <p> </p>
SHOWCASE Task 2.7 Biodiversity Indicators and Predictors data set
<p>The dataset contains five biodiversity (0-1 interval normalised) indicators from five case study areas (HU, ES, PT, NL and CH): wild bees abundance (field "Ind_WBA"), wild bees species richness (field "Ind_WBR"), spiders abundance (field "Ind_SpA"), spider species richness (field "Ind_SpR"), and vascular plants species richnes (field "Ind_PlaR"). Data are georeffered (EPSG: 3035) and coordinates are provided (field:" xcoord_EPSG3035" and "ycoord_EPSG3035"). Data were collected in two sampling rounds (field "Round"). Additionally the data set contains a set<span> of predictors for biodiversity indicators encompassing 27 variables, belonging to four different groups: 1. </span><span><span><span> </span></span></span><span>Landscape elements: proximity to roads and proximity to Small Woody Features (SWF, Copernicus land Monitoring Services, CLMS 2018); 2.<span> </span></span><span>Terrain descriptors: elevation, aspect, slope, and their derivatives (8 variables); 3. </span><span>Spectral signatures and Remote Sensing Indicatora (RSI) from Copernicus Sentinel 2 (14 variables); and </span><span><span>4.<span> </span></span></span><span>Biodiversity management (3 variables, dummy coded 0,1). </span></p> <table> <tbody> <tr> <td> <p><strong><span>Group </span></strong></p> </td> <td> <p><strong><span>Predictor </span></strong></p> </td> <td> <p><strong><span>Unit</span></strong></p> </td> <td> <p><strong><span> </span></strong></p> <p><strong><span>Source</span></strong></p> </td> <td> <p><strong><span>Resolution</span></strong></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>Road proximity<sup>a</sup></span></p> </td> <td> <p><span>m </span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>1</span></p> </td> <td> <p><span>SWF proximity<sup>b</sup></span></p> </td> <td> <p><span>m</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Elevation</span></p> </td> <td> <p><span>m a.s.l.</span></p> </td> <td> <p><span>Copernicus DEM</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Aspect</span></p> </td> <td> <p><span>degree from North</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Slope </span></p> </td> <td> <p><span>%</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Catchment slope </span></p> </td> <td> <p><span>%</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Catchment area </span></p> </td> <td> <p><span>m<sup>2</sup></span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Mod. Catchment area </span></p> </td> <td> <p><span>m<sup>2</sup></span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Topographic. wetness Index </span></p> </td> <td> <p><span>m/rad</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>2</span></p> </td> <td> <p><span>Valley depth</span></p> </td> <td> <p><span>m</span></p> </td> <td> <p><span>GIS calculation</span></p> </td> <td> <p><span>30 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>BI, bare Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Blue (B2, 490 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Green (B3, 560 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>IR, infra-red (B8, 842 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDBSI, Norm. Diff. Bare Soil Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDSI, Normalized Diff. Soil Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NDVI, Norm. Diff. Vegetation Index</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>NIR, Near Infra-Red (B8A, 865 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>Red (B4, 665 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSa, Soil Salinity </span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI1, Soil Salinity Index1</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI2, Soil Salinity Index2</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SoSI3, Soil Salinity Index3</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>10 m</span></p> </td> </tr> <tr> <td> <p><span>3</span></p> </td> <td> <p><span>SWIR Short Wave IR (B11,1610 nm)</span></p> </td> <td> <p><span>-</span></p> </td> <td> <p><span>Sentinel 2, GEE</span></p> </td> <td> <p><span>20 m</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Biodiversity Intervention</span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Year of intervention</span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> <tr> <td> <p><span>4</span></p> </td> <td> <p><span>Round </span></p> </td> <td> <p><span>Dummy 0,1</span></p> </td> <td> <p><span>EBA partners</span></p> </td> <td> <p><span>-</span></p> </td> </tr> </tbody> </table> <p><sup><span>a</span></sup><span> Source of vector data: Open Street Map. © OpenStreetMap contributors. Available under the Open Database License from: openstreetmap.org. </span></p> <p><sup><span>b</span></sup><span> Source of raster data (res. 5 m): </span><span><a href="https://land.copernicus.eu/en/products/high-resolution-layer-small-woody-features/small-woody-features-2018"><span>https://land.copernicus.eu/en/products/high-resolution-layer-small-woody-features/small-woody-features-2018</span></a></span><span>. </span><span><a href="https://doi.org/10.2909/a8e683b1-2f96-45c8-827f-580a79413018"><span>https://doi.org/10.2909/a8e683b1-2f96-45c8-827f-580a79413018</span></a></span><span> </span></p>
finnpiatscheck/Environmental-Effects-on-a-Fig-Wasp-Community: Data sets and R scripts
<p>These are the data and R scripts used in the paper "Landscape-Level Analysis of a Fig-Pollinator-Antagonist Community: Spatial and Temporal Variation in a Fig Wasp Community and its Response to Biotic and Abiotic Factors".</p>
Mugger data set of Bardia NP
Open the record for dataset details and reuse information.
Example data sets and input parameters for running various features in the Python code Dynpy
<p>This is a set of examples intended to be used with the Python code called Dynpy at <a href="https://zenodo.org/records/13241475">https://zenodo.org/records/13241475</a></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.