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1,045 results for “Generated Data”
Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"
<p>This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>.</p> <p>The file "goes-samples-2019-128x128.nc" contains the training dataset called "GOES-COT" in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files "gen_weights*.nc" contain the generator weights saved at different time steps during training for the two different datasets described in the paper.<br> </p>
Data from: "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters"
<p>This dataset was used for the training of a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars. The light curves correspond to data obtained from The Optical Gravitational Lensing Experiment (<a href="https://ui.adsabs.harvard.edu/abs/1992AcA....42..253U/abstract">OGLE</a>), while ancillary information was obtained from the Gaia Data Release 2 (<a href="https://ui.adsabs.harvard.edu/link_gateway/2016A&A...595A...1G/doi:10.1051/0004-6361/201629272">GAIA DR2</a>). This repository contains the preprocessed OGLE light curves and the GAIA measurements corresponding to each cross-matched source. We also provided a subsample of cross-matched sources that were carefully validated following several steps described in the companion article (paper reference).</p> <p>This dataset is realized in tandem with the corresponding <a href="https://github.com/jorgemarpa/PELS-VAE">GitHub</a> and <a href="https://arxiv.org/abs/2005.07773">article</a>.</p> <p> </p> <p> </p>
Data release for paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models"
<p>This data release for the paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models" [<a href="https://arxiv.org/abs/2010.05830">arXiv:2010.2010.05830</a>] contains posterior samples for the GW190412 binary black hole merger event obtained from public GWOSC data with the parallel bilby Bayesian inference package, dynesty nested sampler and a set of waveforms from the "generation X" of phenomenological waveform models: IMRPhenomXAS, IMRPhenomXHM, IMRPhenomXP, IMRPhenomXPHM, IMRPhenomT and IMRPhenomTHM. The provided file is a "meta file" that can be read with the <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a> python package. The posterior samples included correspond to runs [2,6,10,12,14,26] in Table III of the paper (standard settings for each waveform, standar priors and sampler settings of Nlive=2048 and Nact=10 or 50). If you make use of these samples, please cite both this data release and the paper.</p>
Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation"
<p>Data associated with the following publication: "Giant thermoelectric response of confined electrolytes with thermally activated charge carrier generation" (DOI: <a title="" href="https://doi.org/10.48328/tudatalib-1376">https://doi.org/10.48328/tudatalib-1376</a>)</p>
Synthetic time series data generation for edge analytics
<p>In this research, we create synthetic data with features that are like data from IoT devices. We use an existing air quality dataset that includes temperature and gas sensor measurements. This real-time dataset includes component values for the Air Quality Index (AQI) and ppm concentrations for various polluting gas concentrations. We build a JavaScript Object Notation (JSON) model to capture the distribution of variables and structure of this real dataset to generate the synthetic data. Based on the synthetic dataset and original dataset, we create a comparative predictive model. Analysis of synthetic dataset predictive model shows that it can be successfully used for edge analytics purposes, replacing real-world datasets. There is no significant difference between the real-world dataset compared the synthetic dataset. The generated synthetic data requires no modification to suit the edge computing requirements. The framework can generate correct synthetic datasets based on JSON schema attributes. The accuracy, precision, and recall values for the real and synthetic datasets indicate that the logistic regression model is capable of successfully classifying data</p>
What does it take to generate new growth - Survey data on company perceptions on innovative behavior
<p>This data includes raw survey data, a codebook and the survey form for the survey <em>what does it take to generate new growth? </em>The survey focused on comprehensively mapping the Finnish companies growth outlooks and their underlying management practices and principles. The study creates an overview of top managers’ views on Finnish companies’ growth, innovativeness, and the ability for renewal. It allows us to identify what sets high-growing companies apart from others. The Codebook is associated with an SPSS and CSV file including the data.</p>
Data and code used in manuscript: Basal freeze-on generates complex ice-sheet stratigraphy
<p>Mapped plumes location obtained from ice-sheet radio echo sounding data of North Greenland (https://data.cresis.ku.edu/data/rds/ for 2010-2014_Greenland files) and map of calculated freeze-on index are found in 'FreezeOnIndex_MappedPlume_Data.nc'. Model code of the three models used to obtain the findings shown in the manuscript 'Basal freeze-on generates complex ice-sheet stratigraphy'. As well as code to calculate the freeze-on index.</p>
Experimental data generated on the stability of hydrophobic porous materials
<div>/* **********</div> <div>/* This work is licensed under a Creative Commons Attribution 4.0 International License.</div> <div>/* **********</div> <div> </div> <div>Open access to experimental data generated by the project Electro-Intrusion (101017858, Horizon 2020, European Union, https://www.electro-intrusion.eu/en) along with the research to be used in intrusion-extrusion applications. Research pertaining to Task 2.1 (WP2). </div> <div>Underlying data for the publication Amayuelas, E. et al. Bimetallic Zeolitic Imidazole Frameworks for Improved Stability and Performance of Intrusion-Extrusion Energy Applications. The Journal of Physical Chemistry 2023, 127, 18310-18315. https://doi.org/10.1021/acs.jpcc.3c04368. Data related to Figures 2, 3 and 4 in the article.</div> <div> </div> <div>Dataset Identifier: 10.5281/zenodo.11273904</div> <div> </div> <div>Contact person: Eder Amayuelas (CIC energiGUNE). ORCID: </div> <div> </div> <div> </div> <div>The archive 'JPCC_3c04368.zip' contains 25 files:</div> <p> </p>
SignAture_Electricity_generation_data_compare_Latvia_2020_2022
<p>This dataset, related to the article 'Power System Modelling in the Baltic Countries: Data Accessibility and Consistency Aspects' (2023), compares electricity generation data for 2020 and 2022 from various sources in Latvia, providing both input and output values and associated metadata.</p>
Supplementary dataset to publication: Oxford nanopore technologies - a valuable tool to generate whole-genome sequencing data for in silico serotyping and the detection of genetic markers in Salmonella, Thomas et al 2023
<p>Bacteria of the genus <em>Salmonella</em> pose a major risk to livestock, the food economy, and public health. <em>Salmonella</em> infections are one of the leading causes of food poisoning. The identification of serovars of <em>Salmonella</em> achieved by their diverse surface antigens is essential to gain information on their epidemiological context. Traditionally, slide agglutination has been used for serotyping. In recent years, whole-genome sequencing (WGS) followed by <em>in silico</em> serotyping has been established as an alternative method for serotyping and the detection of genetic markers for <em>Salmonella</em>. Until now, WGS data generated with Illumina sequencing are used to validate <em>in silico</em> serotyping methods. Oxford Nanopore Technologies (ONT) opens the possibility to sequence ultra-long reads and has frequently been used for bacterial sequencing. In this study, ONT sequencing data of 28 <em>Salmonella</em> strains of different serovars with epidemiological relevance in humans, food, and animals were taken to investigate the performance of the <em>in silico</em> serotyping tools SISTR and SeqSero2 compared to traditional slide agglutination tests. Moreover, the detection of genetic markers for resistance against antimicrobial agents, virulence, and plasmids was studied by comparing WGS data based on ONT with WGS data based on Illumina. Based on the ONT data from flow cell version R9.4.1, <em>in silico</em> serotyping achieved an accuracy of 96.4 and 92% for the tools SISTR and SeqSero2, respectively. Highly similar sets of genetic markers comparing both sequencing technologies were identified. Taking the ongoing improvement of basecalling and flow cells into account, ONT data can be used for <em>Salmonella in silico</em> serotyping and genetic marker detection.</p>
Data for Figures 4, A-F and Table J of Publication "Blue skies over China: The effect of pollution-control on solar power generation and revenues"
<p>This repository contains the data to produce Figures 4, A-F and Table J and emission data in the paper:</p> <p>"Labordena M, Neubauer D, Folini D, Patt A, Lilliestam J (2018) Blue skies over China: The effect of pollution-control on solar power generation and revenues. PLoS ONE 13(11): e0207028. https://doi.org/10.1371/journal.pone.0207028"</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8130726)</p>
Data for: A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks
<p>This dataset shows the results obtained for a case study at TRL4 for the research paper title <em><strong>A tool based on the Industry Foundation Classes standard for dynamic data collection and automatic generation of Building Automation Control Networks</strong></em>, with DOI: https://doi.org/10.1016/j.jobe.2023.107625</p> <p>This dataset is an enhanced IFC (Industry Foundation Classes) file with the creation of the BACN (Building Automation Control Network). This IFC file includes the devices created automatically by the BACN2BIM tool (developed by CARTIF Technology Centre) for the case study validated at TRL4. The original IFC was obtained from the Institute for Automation and Applied Informatics (IAI) / Karlsruhe Institute of Technology (KIT) https://www.ifcwiki.org/images/e/e3/AC20-FZK-Haus.ifc, under an unrestricted license, as served as one of the case studies for this research.</p> <p>*Depending on the IFC viewer used, the included sensors may not be represented correctly. In this case, it is recommended to try with another IFC viewer, for example xBIM explorer https://docs.xbim.net/downloads/xbimxplorer.html or BimCollab Zoom Free https://www.bimcollab.com/en/support/downloads/</p>
Data release for "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors"
<p>We publish skymap files in fits format of the simulation in our work "Rapid pre-merger localization of binary neutron stars in third generation gravitational wave detectors". There are 68000 BNS events, and results of different negative latencies are zipped in different tar files. An example jupyter notebook for using the data is provided.</p> <p> </p> <p> </p>
Data - Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop
<p>This dataset contains measurement data and processing code for the results published in "Low-Noise Phase-Sensitive Optical Parametric Amplifier with Lossless Local Pump Generation using a Digital Dither Optical Phase-Locked Loop". The Pyrpl code change used in the work is also attached.</p> <p>This work was funded by the Swedish Research Council (grant VR-2015-00535).</p>
Supporting Data for: Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation Technology
<p>This is the full data set of all reviewed research items obtained from Google Scholar, Web of Science and Scopus for the Scoping Literature Review <em><a href="https://doi.org/10.1371/journal.pone.0246398">Information Retrieval Interfaces in Virtual Reality - A Scoping Review Focused on Current Generation VR technology</a>.</em></p>
Github data for static site generators (SSG) popularity
<p>Number of Github stars, forks, open issues, create and last modified dates for 30 open source static site generators (SSG), including Hugo, Jekyll and Gatsby.</p>
Rye microgrid load and generation data, and meteorological forecasts.
<p>This dataset contains timeseries for Rye Microgrid, Trondheim, Norway. The timeseries include solar and wind power generation, consumption and historical weather forecasts.</p> <p>From <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a>:</p> <p><em>"The Rye microgrid is a pilot within the EU research project REMOTE. It is a small microgrid placed at Langørgen, in the outskirts of Trondheim, and is a small energy system designed to supply electricity to a modern farm and three households. The REMOTE projects goal for Rye Microgrid is to run the system in islanded mode.</em></p> <p><em>The system has two sources of generation – a wind turbine and a rack of PV panels. In addition, the system has two storages – a battery with high charge and discharge response, but with limited storage and losses, and a hydrogen energy system, with lower charge and discharge rates, higher losses and storage capacity. When you want to charge the hydrogen system, electricity is used to run an electrolyser that makes hydrogen from water and stores the resulting hydrogen in a tank. The process can be reversed by producing electricity from hydrogen using a fuel cell. (...)</em></p> <p><em>Morover, when local production or discharges from storages are not sufficient to cover the demand, the microgrid can draw electricity from the grid at some costs."</em></p> <p> </p> <p>For further details, see: <a href="https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf">https://www.remote-euproject.eu/remote18/rem18-cont/uploads/2019/03/REMOTE-D2.2.pdf</a> and <a href="https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021">https://github.com/TronderEnergi/tronderenergi-ai-hackathon-2021</a></p> <p>rye_generation_and_load.csv is a comma-separated csv-file with the following columns (all values in <em>kW </em>and time as UTC):</p> <ul> <li>Consumption: Consumption of loads in system (residential and agriculture).</li> <li>Solar: Total production from all solar PV racks.</li> <li>Wind: Power production from wind turbine.</li> </ul> <p>met_data.h5: Contains historical weather forecasts data from The Norwegian Meteorological Institute (met.no) updated every 6 hours for the given location. The file is in hdf5 format. The forecasts include the following parameters: air_pressure_at_sea_level [Pa], air_temperature_2m [K], cloud_area_fraction [pu], integral_of_surface_downwelling_shortwave_flux_in_air_wrt_time [J/m<sup>2</sup>s], wind_direction_10m [deg], wind_speed_10m [m/s]</p> <p>The structure of the file is as follows:</p> <ul> <li>lat63_41_lon10_11 (coordinates) <ul> <li>[forecasted parameter] <ul> <li>forecast <ul> <li>2020-01-01T00Z (time forecast was issued) <ul> <li>axis0 (columns, index where each represent a point in a geographical grid. For example if axis=0,1,2,3, the tables contains the forecasts for the four closes points to the microgrid.)</li> <li>axis1 (rows, timestamps)</li> <li>block0_items (equal to axis0)</li> <li>block0_values (matrix, forecast values)</li> </ul> </li> </ul> </li> </ul> </li> </ul> </li> </ul>
SNP and indel discovery and genotyping in next-generation sequencing data
<p>Code, logs and data for discovery and genotyping of SNPs and indels, in the the D.melanogaster genome, using GATK HaplotypeCaller. Code is in the zipped folder named code.zip. Run logs for this code as in the zipped folder named logs.zip. The unfiltered vcf genotypes file is named lhm_rg_HC_2015-09-15.vcf.gz. The filtered vcf genotypes file is named f1.lhm_rg_HC_raw.vcf.gz. The vcf submitted to NCBI dbSNP (filtered, and with indels >50bp and variants with null alternate alleles both removed) is named dbSNP.lhm_rg_HC_raw.vcf.gz. The folder local_reference.zip contains the reference assembly files against which genotypes were called against, and includes the code used to format the data prior to use. Also included is genotypes data from the two in-house reference line samples sequenced (BDGP6+ISO1 mito/dm6, Bloomington <em>Drosophila</em> Stock Center no. 2057)</p> <p>Samples are 220 Sussex-LH<sub>M</sub> hemiclones, and 2 RG. The first run did not include chromosome 4 and the mitochondrial genome, so these were genotyped separately, and then added to the rest of the results.</p> <p>The link for the NCBI dbSNP record is currently https://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?sbid=1062461and the submitter handle is MORROW_EBE_SUSSEX.</p> <p>At the time of writting, the NCBI D.melanogaster build is still being updated, and therefore ss identifiers, but not rs identifers are available.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Structural variant discovery and genotyping in next-generation sequencing data
<p>Code, logs, data, and summaries for detection and genotyping of genomic structural variants in the D.melanogaster Sussex LHM hemiclones (and one in-house reference line individual), using Genomestrip/2.0</p> <p>The unfiltered CNV pipleline results are lhm_gs.cnvs.raw.vcf.gz</p> <p>Filtered CNV results (including removal of bad samples) are filtered.goodS.lhm_gs.cnvs.raw.vcf.gz</p> <p>The file uploaded to NCBI dbVAR (which comprises of the filtered CNVs and indels >50bp from the HaplotypeCaller method) is lhm_sx16.dbVAR.vcf.gz</p> <p>The NCBI dbVAR accession number is nstd134. Code, logs and summary data are in the zipped archives, named accordingly. The archive reference_data.zip contains additional input files required for Genomestrip, including a shell script for making some of them. The file gstrip_lhm_RG_bams.list is also an input for Genomestrip, indicating bam file names and paths.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p> <p> </p>
Auxiliary files and data to generate eddy flux and validate 2D model for MALTA
<p>This repository contains the following directories to accompany the manuscript 'A Zonally-Averaged Global Atmospheric Transport Model for Long-lived Trace Gases', submitted to JAMES:</p><p>1) <strong>GEOSChem </strong>This directory contains the run directory template and (slurm) runscript to generate the tracer fields used to generate the eddy fluxes. The GEOSChem model will have to be installed locally to run this, and the run directory built to your local area. It may be easiest to just copy the relevant bits in /Tracer_2D_template/ (i.e., the .rc files, /RestartFiles/, input.geos, reset_restart.py and species_database.yml) into a GEOSChem Transport run directory and change the directories in the copied files. If using slurm on an HPC, just change the directories in the runtracers_inputs.sh script to match that of your own HPC. Else, a different script will have to be written copying the slurm functionality.</p><p>2) <strong>GEOSChem_SF6 </strong>This directory contains the monthly mean SF6 mole fractions generated using GEOSChem used to validate the 2D model MALTA. Emissions come from the EDGAR v4.2 emissions inventory. Emissions after 2008 continue to use 2008 as the emissions value.</p><p>3) <strong>CFC11_inversion</strong> This directory contains the relevant script and files to quantify emissions of CFC-11 using an output mole fraction from the TOMCAT 3D model using MALTA, and compare these to the TOMCAT emissions used to generate the mole fractions. The directory paths at the beginning of the main script in CFC11_inversion.py must be changed to point to the remaining files in the /CFC11_inversion/ directory, and a save directory must be specified, before running locally. MALTA must be installed to run this.</p><p>4) <strong>singapore.dat </strong>This file contains the QBO winds above Singapore, taken from https://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html</p><p> </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.