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3,264 results for “FAST”
Advanced Hybrid Battery Thermal Management System for Fast Charging of Electric Vehicles
Open the record for dataset details and reuse information.
Data for: Living fast, dying young: anthropogenic habitat modification influences the fitness and life history traits of a cooperative breeder
<p>Datasets and scripts for the study: Living fast, dying young: anthropogenic habitat modification influences the fitness and life history traits of a cooperative breeder</p>
Fig. 4 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)
Fig. 4. Hierarchical Bayesian clustering analyses of three Pachyrhynchus weevils implemented in STRUCTURE under admixture model: (A) P. sarcitis, optimal K = 2 in subset i, ii, iv, v, vi and optimal K = 3 in subset iii; (B) P. nobilis, optimal K = 2 in each subset; (C) P. semperi, optimal K = 2 in subset i, and optimal K = 3 in subset ii. Barplots represent the probability that individual assigned to the genetic cluster.
Fig. 5 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)
Fig. 5. The best-fit demographic models of (A) Pachyrhynchus sarcitis, (B) P. nobilis and (C) P. semperi inferred from G-PhoCS.Topological assumption based on SNAPP population tree, and the following demographic parameters were estimated according to per-generation mutation rate μ = 2.9 × 10 − 9 and generation time g = 3. The width of the column represents the mean of effective population size (Ne), and the dashed line indicates divergence time (T: Thousand year). Migration rate (mtot) is not demonstrated because all value of mtot is not significant.
Fig. 6 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)
Fig. 6. Density plot of genealogical divergence index (gdi) of Pachyrhynchus sarcitis (A), P. nobilis (B), and P. semperi (C). The value of gdi> 0.7 supports as a distinct species, 0.2 <gdi <0.7 represents ambiguous species status and gdi <0.2 means populations belonged to the same species.The letters represent populations in the SNAPP trees (Fig. 3), and the detail information of gdi value of each island population is showed inTable 6.
Fig. 2 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)
Fig. 2. Concatenated SNPs trees of (A) Pachyrhynchus sarcitis, (B) P. nobilis, and (C) P. semperi constructed by GTR model in RAxML using concatenated SNPs data.The numbers on the branches indicate the bootstrap supports. Colorful boxes highlight populations from the same island group, and colorful bars represent each island population. Black dots denote the monophyletic lineage that are grouped by an island, and red dots denote the monophyletic lineage that are grouped by more than one island.
Fig. 3 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)
Fig. 3. Population trees of (A) Pachyrhynchus sarcitis, (B) P. nobilis, and (C) P. semperi inferred by Bayesian coalescent model in SNAPP, and probability distribution of all sampled tree was visualized by DensiTree. The number on the branch indicates the Bayesian posterior probability. Colorful boxes highlight the island populations from the same island group.The letters A to H were used in G-PhoCS and BPP analyses.
Fig. 1 in Comparative Phylogeography in the Taiwan-Luzon Volcanic Belt Indicates Fast Diversification History of Pachyrhynchus Weevils (Coleoptera: Curculionidae)
Fig. 1. Geographic distribution of Pachyrhynchus sarcitis, P. nobilis, and P. semperi in the Taiwan–Luzon volcanic belt.The three island groups are marked with different color series (green, blue, and brown), and each color on island corresponds to different populations in the analyses.
Reproducibility Package for SyRep: Efficient Synthesis and Repair of Fast Re-Route Forwarding Tables for Resilient Networks
<p>This is a reproducibility package for the paper SyRep: Efficient Synthesis and Repaird of Fast Re-Route Forwarding Tables for Resilient Networks that is going to appear at DSN'24 conference.</p>
Mutation Reporter: fast identification of mutations in NGS files
Open the record for dataset details and reuse information.
Data for Observation of 1H-1H J-couplings in fast magic-angle-spinning solid-state NMR spectroscopy
<p>Supporting data for Observation of 1H-1H J-couplings in fast magic-angle-spinning solid-state NMR spectroscopy.</p> <p>Raw and processed NMR data and fitting codes.</p> <p>See individual README.txt in each zip file for details.</p>
Fast Polymeric Functionalization Approach for the Covalent Coating of MoS2 Layers
<p>Includes data relevant for publication with DOI doi.org/10.1021/acsami.1c08294</p>
Fast response of Amazon rivers to Quaternary climate cycles
<p>Data table of Amazon rivers. Data sources are described in section 2.2 of the accompanying manuscript.</p>
Burst timescales and luminosities as links between young pulsars and fast radio bursts Dataset
<p>The dataset to reproduce the results and plots in Nimmo et al. 2021b (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv210511446N/abstract">https://ui.adsabs.harvard.edu/abs/2021arXiv210511446N/abstract</a>). The scripts to produce the plots can be found here: <a href="https://github.com/KenzieNimmo/FRB20200120E_timescales">https://github.com/KenzieNimmo/FRB20200120E_timescales</a></p> <p>The software used to make the data products are:</p> <ul> <li>SFXC (Keimpema et al. 2015;<a href="https://github.com/aardk/sfxc/"> https://github.com/aardk/sfxc/</a>) </li> <li>DSPSR (van Straten & Bailes 2011; <a href="http://dspsr.sourceforge.net/">http://dspsr.sourceforge.net/</a>)</li> <li>PSRCHIVE (Hotan et al. 2004; <a href="http://psrchive.sourceforge.net">http://psrchive.sourceforge.net</a>)</li> <li>numpy (Harris et al. 2020; <a href="https://numpy.org/install/">https://numpy.org/install/</a>)</li> </ul> <p>A complete list of the data products:</p> <ul> <li>8us/125kHz full polarisation archive files (dspsr) for all 5 bursts presented in the work (coherently and incoherently dedispersed to 87.75pc/cc). Created from filterbank data made using SFXC. Use load_file.load_archive from the above github link to load data into python as a numpy array. <ul> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib</li> <li>pr158a_corr_no0017_8us_125kHz_FullPol_FullDedisp_dm87.75.ar.calib</li> </ul> </li> <li>31.25ns/16MHz Stokes I filterbank data for B2, B3 and B4. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using SFXC. <ul> <li>pr141a_corr_no0069_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor2_Ef.fil</li> <li>pr143a_corr_no0015_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> <li>pr143a_corr_no0057_31.25ns_16MHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> </ul> </li> <li>1us/500kHz Stokes I filterbank data for B2, B3 and B4. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using SFXC. <ul> <li>pr141a_corr_no0069_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor2_Ef.fil</li> <li>pr143a_corr_no0015_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> <li>pr143a_corr_no0057_1us_500kHz_StokesI_FullDedisp_dm87.7527_SFXC.cor_Ef.fil</li> </ul> </li> <li>125ns/4MHz full pol archive file of burst B3. Coherently and incoherently dedispersed to 87.7527pc/cc. Created using dspsr from a filterbank file made by SFXC. <ul> <li>pr143a_corr_no0015_125ns_4000kHz_FullPol_FullDedisp_dm87.7527_SFXC.cor_Ef.calib</li> </ul> </li> </ul> <p>We also provide a number of numpy files containing analysis products for ease of reproducing the figures. </p> <ul> <li>2D autocorrelation functions of the dynamic spectra of all 5 bursts. Additionally we give the 2D Gaussian fits to the ACFs and the Lorentzian fits to the frequency ACFs (for measuring the scintillation bandwidth) <ul> <li>ACF_b*_8us_f8.npy</li> <li>fitACF_b*_8us_f8.npy</li> </ul> </li> <li>Peak signal-to-noise ratio (S/N) of burst B3 profile at 1us resolution as a function of dispersion measure (DM) with a Gaussian fit to the result <ul> <li>pr143a_corr_no0015_500ns_1000kHz_StokesI_FullDedisp_dm87.7527_DS.npy (500ns dynamic spectrum)</li> <li>DM_vs_peakSN_sfxc_IF1-11.npy</li> <li>DM_vs_peakSN_sfxc_IF1-11_fit.npy</li> </ul> </li> <li>Power spectrum (PS) of 31.25ns profile of B2, B3 and B4 with the power law (PL) fits and power law+lorentzian (PL_lor) fit for B3 <ul> <li>PS_B2_IF4678_31.25ns.npy</li> <li>PS_B3_IF3568_31.25ns.f8.npy (downsampled by a factor of 8)</li> <li>PS_B3_IF3568_31.25ns.npy</li> <li>PS_B4_IF67910_31.25ns.npy</li> <li>PL_fit_B2.npy</li> <li>PL_fit_B3.npy</li> <li>PL_fit_B4.npy</li> <li>PL_lor_fit_B3.npy</li> </ul> </li> <li>Faraday spectra of B1, B2, B3, B4 <ul> <li>no0015_8us_125kHz_faradayspec.npy</li> <li>no0057_8us_125kHz_faradayspec.npy</li> <li>no0069_8us_125kHz_faradayspec.npy</li> <li>no0069_8us_125kHz_faradayspec_2.npy</li> </ul> </li> <li>Stokes Q and U spectra for B1, B2, B3, B4 and the corresponding joint QU fits <ul> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib_QUdata.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor2_Ef.ar.calib_QUfit.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr141a_corr_no0069_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr143a_corr_no0015_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUdata.npy</li> <li>pr143a_corr_no0057_8us_125kHz_FullPol_FullDedisp_dm87.75.cor_Ef.ar.calib_QUfit.npy</li> </ul> </li> </ul> <p> </p> <p> </p>
Material for "The INNpinJeR: A new wall-free reactor for studying fast gas-phase reactions"
<p>This database includes all material for "The INNpinJeR: A new wall-free reactor for studying fast gas-phase reactions":</p> <p>- OpenFOAM Simulation input and result files for the INNpinJeR and 2 python scripts that use the OpenFOAM results: 1st: centerline approach only, 2nd: 2D chemistry + transport simulation on flow field (new).</p> <p>- experimental data for tetramethyl ethylene ozonolysis</p> <p>- experimental data for cyclohexene ozonolysis + NO ramp</p> <p>- plotting scripts, running on julia v1.0 for visualizing the experimental data</p> <p>- a README for installation requirements to run the scripts</p> <p> </p> <p> </p> <p> </p>
The Prototype of a Fast Vertical Ionosonde Based on Modern SDR Devices - paper dataset
<p>Dataset for paper "The Prototype of a Fast Vertical Ionosonde Based on Modern SDR Devices"</p> <p>Content:</p> <p>1. SDRLab122-16_firmwares.tbz - RED PITAYA SDRLAB 122-16 firmwares for AM & PSK ionosonde's TX part with 5 ms pulse period.</p> <p>2. ionosonde_gr_oot.tbz - archive with git-repo of GNU Radio OOT Trigger Module (the part of the ionosonde's receiving part).</p> <p>3. ion-fast_receiver.tbz - archive with programs (gnu radio companion flow graphs, python & bash scripts) for plotting ionograms (the part of the ionosonde's receiving part)</p> <p>4. npy_data.tar - archive with examples of received ionograms in numpy format.</p> <p>5. png_ionogram_file_examples.tbz - archive with examples of received ionograms in png-image format.</p> <p>6. raw_iq_data_complex64_am.iq - raw iq data example (sample_rate = 10e6, central freq = 5500000 Hz, data_type= complex64) AM modulation with 5 ms pulse period.</p> <p>7. raw_iq_data_complex64_psk.iq - raw iq data example (sample_rate = 10e6, central freq = 5500000 Hz, data_type= complex64) PSK modulation with 5 ms pulse period.</p> <p>8. AM-Large_antenna.mp4 - video demonstration of the ionosonde operation using a large diagnostic antenna in the amplitude manipulation mode</p> <p>9. AM-CADI_antenna.mp4 - video demonstration of the ionosonde operation using the CADI's antenna in the amplitude manipulation mode.</p> <p>10. PSK_CADI_antenna.mp4 - video demonstration of the ionosonde operation using the CADI's antenna in the phase manipulation mode.</p> <p>11. AM-Chirp_ionosonde_antenna.mp4 - video demonstration of the ionosonde operation using the CHIRP ionospnde's inclined antenna in the amplitude manipulation mode.</p>
Fresnel imaging of ferromagnetic domain wall motion using fast pixelated electron detector
<p>This deposit contains supplementary datasets and data processing scripts used in the Master Thesis by Rajith Aravinth at the Norwegian University of Science and Technology (NTNU).</p>
Supplementary files for "Fast and sensitive quantification of AccQ-Tag derivatized amino acids and biogenic amines by UHPLC-UV from complex biological samples"
<p>Supplementary files for "Fast and sensitive quantification of AccQ-Tag derivatized amino acids and biogenic amines by UHPLC-MS from complex biological samples"</p>
Acoustically propelled nano- and microcones: fast forward and backward motion
<p>Supplementary data for the following manuscript: Johannes Voß, Raphael Wittkowski, "Acoustically propelled nano- and microcones: fast forward and backward motion", <em><strong>Nanoscale Adv.</strong></em>, 2022,<strong>4</strong>, 281-293, doi=10.1039/D1NA00655J</p>
Data set "Narrow, fast, and "cool" mantle plumes caused by strain-weakening rheology in Earth's lower mantle" (G-cubed)
<p>This archive contains data related to the manuscript “Narrow, fast, and “cool” mantle plumes caused by strain-weakening rheology in Earth’s lower mantle” by Gülcher et al., (G-cubed). It contains output quantities and selected fields for the numerical models shown and discussed in the paper (M###). Moreover, it contains the input parametric files implemented in StagYY. Please read the README.dat file for more information. </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.