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15,733 results for “article”
Data supplementing article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry" submitted to Geophysical Research Letters
<p>These data supplement the article "Tidal response to sea-level rise in different types of estuaries: the importance of length, bathymetry, and geometry". </p> <p>contact: Jiabi Du, jiabi.du@gmail.com</p> <p>Below are descriptions of the data files included here:</p> <p>1. ReadMe.txt</p> <p>- the description of each model configuration is listed in this file, inlcuding the estuarine geometry type, length, and bathymetry type. </p> <p>2. model grid and surface elevation output</p> <p>- In each directory named after the grid_name, a grid file with .gr3 format is included. <br> The .gr3 file contains two parts: the location of each node (first part), and the node number for each triangle grid (second part).</p> <p>- Two sub_directory name 'base' and 'slr' contains the time series of water level. </p> <p>3. MatlaScript ExtractWL</p> <p>- a function named "f_extract_wl.m" are used to extract the station output</p> <p> </p>
Additional datasets and code accompanying the article "Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines"
<p> </p> <p> </p> <p>Datasets and code accompanying the manuscript titled “Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines” by Rajarshi Ghosh, Ninak Oak and Sharon E. Plon.</p>
Unwarranted Inferences from Statistical Mediation Tests – An Analysis of Articles Published in 2015
<p>Recent attempts to improve on the quality of psychological research focus on good practices required for statistical<br> significance testing. The scrutiny of theoretical reasoning, though superordinate, is largely neglected, as<br> exemplified here in a common misunderstanding of mediation analysis. Although a test of a mediation model<br> X -> Z -> Y is conditional on the premise that the model applies, alternative mediators Z′, Z″, Z‴ etc. remain<br> untested, and other causal models could underlie the correlation between X, Y, Z, researchers infer from a single<br> significant mediation test that they have identified the true mediator. A literature search of all mediation analyses<br> published in 2015 in Sciencedirect shows that the vast majority of studies neither consider alternative causal<br> models nor alternative mediator candidates. Ignoring that mediation analysis is conditional on the truth of the<br> focal mediation model, they pretend to have demonstrated that Z mediates the influence of X on Y. Recommendations<br> are provided for how to overcome this dissatisfying state of affairs.</p>
RDF Dataset for article: A confidence predictor for logD using conformal regression and a support-vector machine
<p>RDF dataset described in article: "A confidence predictor for logD using conformal regression and a support-vector machine" (Manuscript in preparation).</p> <p>The dataset contains conformal logD values at 90% confidence level, computed for 91M compounds from PubChem, in RDF format.</p> <p>The .hdt.gz version contains the dataset in RDF HDT format (http://www.rdfhdt.org/), compressed with tar and gzip. The archive contains both the .hdt file, and an index file, generated by the hdtSearch C++ tool.</p> <p>The .ttl.gz file is a gzipped file in RDF Turtle format (https://www.w3.org/TR/turtle/).</p>
Data supplementing the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal
<p>These data supplement the article "Avoiding quantification bias in metabarcoding: application of a cell biovolume correction factor in diatom molecular biomonitoring" V. Vasselon, A. Bouchez, F. Rimet, S. Jacquet, R. Trobajo, M. Corniquel, K. Tapolczai, I. Domaizon submitted to Methods in Ecology and Evolution journal</p> <p>The directory contains the following files:</p> <p>1<strong>5 fastq files raw reads (5 mock communities, 3 replicates)</strong><strong>.rar </strong>- contains the 15 fastq files provided by the sequencing platform with demultiplexed DNA reads (raw data prior any bioinformatics treatments).</p> <p><strong>15 fastq files information.xlsx</strong> :</p> <p>- contains the information relative to the 15 fastq files corresponding to the PGM raw data of the 5 mock communities (sequenced with 3 replicates), including: the ID of the fastq files, the mock community name, the replicate number, the final sample Id and the number of raw reads per fastq file.</p> <p>- contains the information of the proportion of the 8 diatoms species (%) used to create the 5 mock communities (estimated from microscopy).</p>
Supporting Information for the article "Synthesis and Antibacterial Activity of Polymerizable Acryloyloxyalkyltriethyl Ammonium Salts", published in ChemPlusChem 2017, 82(10), 1235-1244.
<p>Supporting Information for the article "Synthesis and Antibacterial Activity of Polymerizable Acryloyloxyalkyltriethyl Ammonium Salts", published in ChemPlusChem 2017, 82(10), 1235-1244 (DOI: 10.1002/cplu.201700194).</p> <p>The file is available free of charge at the publisher's website at the following URL: http://onlinelibrary.wiley.com/store/10.1002/cplu.201700194/asset/supinfo/cplu201700194-sup-0001-misc_information.pdf?v=1&s=3935a62996b49f0a39ddac08c0d37928db2dc817</p> <p>The file contains the characterization data and the copy of 1H and 13C NMR Spectra for all product synthesized in the paper published in ChemPlusChem 2017, 82(10), 1235-1244 (DOI: 10.1002/cplu.201700194).</p> <p> </p>
Raw data for the article "Combining STPA and BDD for Safety Analysis and Verification in Agile Development: A Controlled Experiment"
<p>These files include the raw data of pre-questionnaire, operation report, post-questionnaire and hypothesis testing in the experiment. </p>
Data Set for the Journal Article "SCINE - Software for Chemical Interaction Networks"
<p>This data archive contains all data and software described and used in the following publication:</p> <p>Thomas Weymuth, Jan P. Unsleber, Paul L. Türtscher, Miguel Steiner, Jan-Grimo Sobez, Charlotte H.<br>Müller, Maximilian Mörchen, Veronika Klasovita, Stephanie A. Grimmel, Marco Eckhoff, Katja-Sophia<br>Csizi, Francesco Bosia, Moritz Bensberg, Markus Reiher, "SCINE --- Software for Chemical Interaction<br>Networks", in preparation.</p> <p>The directory structure is as follows:</p> <ul> <li>software: contains all software used for the example exploration <ul> <li>requirements.txt: lists all Python packages needed to create the virtual environment with which the exploration has been carried out; the virtual environment was created with Python 3.6.8.</li> <li>start.py: script to initialize the database with the reactants</li> <li>step_1.py: script to create the first set of reaction trials; after having set up the trials, execute the script "start.py" with the option "continue"</li> <li>step_2.py: script to create the second set of reaction trials; after having set up the trials, execute the script "start.py" with the option "continue"</li> <li>puffin_1.3.0.sif: Singularity image containing a full Puffin instance (version 1.3.0) to execute all calculations of the exploration</li> <li>submit_container.sh: script to submit the Puffin image to the queueing system</li> </ul> </li> <li>data: contains a complete dump of the database created during the example exploration; additionally, this directory contains a script called "import.sh" which can be used to reimport the data into a MongoDB instance</li> </ul>
Data for article: Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018
<p>The data consists of the indicators for spatio-temporal analysis of wood Ecosystem Service (ES) potential, supply, and demand across Europe between 2008 and 2018. This dataset was used for the analysis of temporal trends of wood ES in the study "Mapping temporal variations in ecosystem services: a case study of European wood supply and demand between 2008 and 2018".</p> <p>The data are collected and compiled from open access statistical databases. They consist of three parts:</p> <p>1. The PDF file with a detailed description of the data and all the input sources from which it was derived.</p> <p>2. The Zip file with 3 separate Excel files containing the short description of the indicators for mapping spatio-temporal changes, namely wood ES potential, wood ES supply and wood ES demand and their values between 2008 and 2018 at three different levels: continental, national and regional. Note that for the regional level only supply and demand indicators are available.</p> <p>3. The tiff file representing the spatial resolution for visualisation and analysis of the data.</p> <p> </p> <p> </p> <p>Resolution:</p> <p>Data are available for 3 spatial levels, in the temporal dimension between 2008 and 2018 (annually). All 3 indicators are available at continental (European) and national scales. The study area covers 24 countries of the European Union (EU) and Switzerland. Supply and demand are also available at regional level. At the regional scale, we assessed supply and demand using the nomenclature of territorial units for statistics (NUTS 3; n = 1061) and local administrative units (LAU; n = 957) from the year 2016 (<a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units.">Eurostat, 2016</a>). The visualisation of the resolution is available in the tiff file attached to the data.</p> <p>The data are visualised and analysed in the ETRS 1989 LAEA projection.</p> <p> </p> <p>For more information on the indicators used, data collection and processing, see the supplementary files of the published article.</p> <p> </p> <p> </p> <p><strong>DEFINITIONS of ES mapping indicators used in the study: </strong></p> <p><em>ES potential</em> - the hypothetical maximum yield of services potentially available for supply.</p> <p><em>ES supply</em> – the amount of the mobilized service within the ecosystem capable to provide a service at a given location in a certain time (frequently referred in literature as ES flow).</p> <p><em>ES demand </em>- the need for the ecosystem-based service by the end users. In this study demand is analysed from the perspective of service end-user.</p>
Replication package for the article 'The residential patterns of Swiss urban elites. Continuity and change across elite categories (1890-2000),' to appear in the journal 'European Societies,' authored by Pierre Benz; Michael A. Strebel; Roberto Di Capua & André Mach.
<p>The documents in this replication package serve to reproduce the analysis for the article <br>'The residential patterns of Swiss urban elites. Continuity and change across elite categories (1890-2000),' to appear in<br>the journal 'European Societies,' authored by Pierre Benz; Michael A. Strebel; Roberto Di Capua & André Mach.<br>This replication package is created by Pierre Benz (pierre.benz@unil.ch).</p>
Data and scripts for the article entitled "High temperature sensitivity of Arctic isoprene emissions explained by sedges"
<p>The package includes the data and scripts for generating the figures for the paper entitled "High temperature sensitivity of Arctic isoprene emissions explained by sedges".</p>
Dataset for the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection"
<p>Datasets to accompany the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection". Written by J. Elgy and P. D. Ledger. </p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (v.1.5.0).</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.</p>
Supplemental Data for the Journal Article Entitled The mechanistic origins of heterogeneous void growth during ductile failure accepted for publication by Acta Materialia
<p>This repository contains the Supplemental Data for the Journal Article Entitled The mechanistic origins of heterogeneous void growth during ductile failure accepted for publication by Acta Materialia.</p> <p>Authors: M.W. Vaughan<sup>a</sup>, H. Lim<sup>a</sup>, B. Pham<sup>a</sup>, R. Seede<sup>a</sup>, A. T. Polonsky<sup> a</sup>, K. Johnson<sup> a</sup>, P. J. Noell<sup>a,*</sup></p> <p>a: Sandia National Laboratories, Albuquerque, NM 87123</p> <p>Each folder contains a ReadMe.txt describing the files and their organization within each folder. </p> <p><br>Acknowledgements:<br><span>This work was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. The views expressed in the article do not necessarily represent the views of the U.S. DOE or the United States Government. </span></p>
Data Repository for the article: "Control of multi-modal scattering in a microwave frequency comb"
<p><strong>Abstract:</strong></p> <p>Control over the coupling between multiple modes of a frequency comb is an important step toward measurement-based quantum computation with a continuous-variable system.<br>We demonstrate the creation of square-ladder correlation graphs in a microwave comb with 95 modes.<br>The graphs are engineered through precise control of the relative phase of three pumps applied to a Josephson parametric oscillator. <br>Experimental measurement of the mode scattering matrix is in good agreement with theoretical predictions based on a linearized equation of motion of the parametric oscillator. <br>The digital methods used to create and measure the correlations are easily scaled to more modes and more pumps, with the potential to tailor a specific correlation graph topology.</p> <p> </p> <p>Article DOI:</p> <p>ArXiV: <a href="https://doi.org/10.48550/arXiv.2402.09068">arXiv.2402.09068</a></p> <p> </p> <p><strong>Content:</strong></p> <p>This repository contains the datased used in the article "Control of multi-modal scattering in a microwave frequency comb". Scripts to generate the figures are also included.</p>
Raw Bibliobmetric Data for the article "The Scientific Landscape of Phytoremediation of Tailings: A Bibliometric and Scientometric Analysis"
<p>Bibliometric data for CiteSpace related to the scientific article "The Scientific Landscape of Phytoremediation of Tailings: A Bibliometric and Scientometric Analysis".</p>
Case study input data set for article "Stochastic planning of energy system transformation pathways under uncertain industry demands"
<p>The data set contains input data for the model EMPRISE of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p>
Data and code AEM article: Catching some air: A method to spatially quantify aerial triazole resistance in Aspergillus fumigatus
<h2>Name</h2> <p>Catching_some_air</p> <h2><a href="#description"></a>Description</h2> <p>This script project was written to visualise and analyse the data used in the manuscript: Catching some air: A method to spatially quantify aerial triazole resistance in <em>Aspergillus fumigatus</em>.</p> <p>In the R script we load and clean the data from the international air sampling pilot, analyse it, generate figures of the sampled regions, the CFU totals and resistance fractions. The genotyping and phenotyping data of isolated resistant strains.</p> <p>The following files are required to run this R script:</p> <ul> <li>RF_air_IP_cleaned.csv This fine contains total and resistance counts as well as metadata on samples from international air sampling pilot and includes the following variables:</li> </ul> <p>Sample ID: an arbitrary number given to the packages prior to them being handed out</p> <p> </p> <p>Country: Country in which sample was taken</p> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <p>City/Town: City/Town in which the sample was taken</p> <p>Start date: date on which the trap was deployed and the stickers exposed to the air</p> <p>End date: date on which the trap was taken down and the stickers were re-covered and no longer exposed to the air</p> <p>Total.ITR: A. fumigatus CFU count in the permissive layer of the itraconazole-treated plate</p> <p>Res.ITR: CFU count of colonies that had breached the surface of the itraconazole-treated layer after incubation and were visually (with the unaided eye) sporulating.</p> <p>RF.ITR: The itraconazole (~4 mg/L) resistance fraction = Res.ITR/Total.ITR</p> <p>Total.VOR: A. fumigatus CFU count in the permissive layer of the voriconazole-treated plate</p> <p>Res.VOR: CFU count of colonies that had breached the surface of the voriconazole-treated layer after incubation and were visually (with the unaided eye) sporulating.</p> <p>RF.VOR: The voriconazole (~2 mg/L) resistance fraction = Res.VOR/Total.VOR</p> <p>Total control: CFU count on the untreated growth control plate</p> <p>Date.Batch: The date on which proccessing of the sample was started. To be more specific, the date at which Flamingo medium was poured over the seals of the sample and incubation was started.</p> <p>Note: note on the sample based on either information given the participant or observations in the lab.</p> <p>Exclude: Binary to quickly filter out samples that were considered unsuitable for further analysis either because low or high CFU counts. See manuscript for rationale.</p> <p>Lat: Latitude at the centre of the sampled region, does not relate to sample-specific location.</p> <p>Long: Longitude at the centre of the sampled region, does not relate to sample specific location.</p> <ul> <li>Weather_data_IP_study_nov_dec_jan22_23.csv : contains raw weather data of the sampled regions during the sampling interval of the pilot downloaded from: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview" target="_blank" rel="nofollow noreferrer noopener">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview</a> (see link for full description of the data and the units). Contains the following variables:</li> </ul> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <p>Wind Nov : 10 m Wind speed (m/S) for the month november 2022 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>Wind Dec : 10 m Wind speed (m/S) for the month december 2022 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>Wind Jan : 10 m Wind speed (m/S) for the month januari 2023 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>UV Nov: UV radiation at the surface (J/m^2) for the month november 2022. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>UV Dec: UV radiation at the surface (J/m^2) for the month december 2022. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>UV Jan: UV radiation at the surface (J/m^2) for the month januari 2023. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>Temp Nov: Temperature (K) for the month november 2022. This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Temp Dec: Temperature (K) for the month december 2022. This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Temp Jan: Temperature (K) for the month januari 2023. This parameter is the temperature of air at 2m above the surface of land, sea or inland waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Precipitation Nov: Total precipitation (m) for the month november 2022. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>Precipitation Dec: Total precipitation (m) for the month december 2022. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>Precipitation Jan: Total precipitation (m) for the month januari 2023. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>lat_rep: Latitude at the centre of the sampled region, does not relate to sample specific location.</p> <p>lon_rep: Longitude at the centre of the sampled region, does not relate to sample specific location.</p> <ul> <li>Genotyping_IP_cleaned.csv : contains the TR-type genotypes of the isolated resistant strains Contains the following variable:</li> </ul> <p>Order: Ordering variable included in the file to readily be able to order the isolates by the order in which they were isolated. Contains the following variables:</p> <p>Strain: Strain code with "I" for strains isolated from itraconazole and V for strains isolated from voriconazole followed by a number indicating the order in which they were isolated from the air sample plate.</p> <p>Air sample: The plate/air sample from which the isolate originates</p> <p>Triazole: The triazole treatment the resistant strain grew on can be ITRA (itraconazole) or VORI (voriconazole)</p> <p>Country: Country in which sample was taken</p> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <h2><a href="#project-status"></a>Project status</h2> <p>The manuscript has been published in AEM under the DOI: https://doi.org/10.1128/aem.00271-24</p>
Data for article: 'Synthesis and Reactivity of an Anionic Diazoolefin'
<p>This upload contains raw data (NMR, Mass, IR, UV) files for the article: </p> <p>Bastiaan Kooij, Zhaowen Dong, Farzaneh Fadaei-Tirani, Rosario Scopelliti, and Kay Severin,</p> <p>'Synthesis and Reactivity of an Anionic Diazoolefin' </p> <p>Angew. Chem. Int. Ed. <strong>2023</strong>, DOI: 10.1002/anie.202308625</p>
données relative à l'article : "Étude de l'influence de différents paramètres sur la taille des nanoparticules de SiO2"
Open the record for dataset details and reuse information.
Dataset of "Life history traits and cancer prevalence in birds" article
<p>Data used in the "Life history traits and cancer prevalence in birds" article. This dataset provides taxonomic information about the bird species used in this article, as well as the species' neoplasia and malignancy prevalence, and life history trait data.</p>
ScienceDex guides
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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.