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3,264 results for “FAST”
Some Similarities and Differences between the Observed Alfvénic Fluctuations in the Fast Solar Wind and Navier-Stokes Turbulence
<p>Three text data sets are uploaded: wind tunnel data (modane1.txt), solar-wind magnetic-field data (Flat1maginterp.txt) and solar-wind velocity data (Flat15interp3DP.txt)</p>
Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation
<p>Scanning transmission electron microscopy data related to paper "Scanning transmission electron microscopy data related to paper "Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part II: Post Acquisition Data Processing, Visualisation, and Structural Characterisation", <a href="https://doi.org/10.1017/S1431927620024307">https://doi.org/10.1017/S1431927620024307</a>.</p>
Dsuite - fast D-statistics and related admixture evidence from VCF files
<p>Patterson's D, also known as the ABBA-BABA statistic, and related statistics such as the f4-ratio, are commonly used to assess evidence of gene flow between populations or closely related species. Currently available implementations often require custom file formats, implement only small subsets of the available statistics, and are impractical to evaluate all gene flow hypotheses across datasets with many populations or species due to computational inefficiencies. Here we present a new software package Dsuite, an efficient implementation allowing genome scale calculations of the D and f4-ratio statistics across all combinations of tens or hundreds of populations or species directly from a variant call format (VCF) file. Our program also implements statistics suited for application to genomic windows, providing evidence of whether introgression is confined to specific loci and it can also aid in interpretation of a system of f4-ratio results with the use of the 'f-branch' method. Dsuite is available at https://github.com/millanek/Dsuite, is straightforward to use, substantially more computationally efficient than comparable programs, and provides a convenient suite of tools and statistics, including some not previously available in any software package. Thus, Dsuite facilitates the assessment of evidence for gene flow, especially across larger genomic datasets.</p>
Fast Remote Correlation Experiments for 1H Homonuclear Decoupling in Solids
<p>Experimental, pulse sequence and processing codes of the article ''Fast Remote Correlation Experiments for 1H Homonuclear Decoupling in Solids.''published in JMR.</p> <p>Read the readme.rtf file for more information.</p>
SpEDIT: A fast and efficient CRISPR/Cas9 method for fission yeast
<p>Underlying data and extended data for:</p> <p><em>SpEDIT</em>: A fast and efficient CRISPR/Cas9 method for fission yeast</p>
Data set on the main text of "A bright and fast source of coherent single photons"
<p>The data set that is presented in the main text is uploaded to the repository. Please note that all the data is scaled according to the axis on the paper, that means if the axis has a multiplication by 1e3 then the data is divided by 1e3.</p> <p>Each file is named after the corresponding subfigure.</p> <p>The preprint version of the article can be found in: https://arxiv.org/abs/2007.12654</p>
Marine fish traits follow fast-slow continuum across oceans
<p class="MsoNoSpacing">A fundamental challenge in ecology is to understand why species are found where they are and predict where they are likely to occur in the future. Trait-based approaches may provide such understanding, because it is the traits and adaptations of species that determine which environments they can inhabit. It is therefore important to identify key traits that determine species distributions and investigate how these traits relate to the environment. Based on scientific bottom-trawl surveys of marine fish abundances and traits of >1,200 species, we investigate trait-environment relationships and project the trait composition of marine fish communities across the continental shelf seas of the Northern hemisphere. We show that traits related to growth, maturation and lifespan respond most strongly to the environment. This is reflected by a pronounced "fast-slow continuum" of fish life-histories, revealing that traits vary with temperature at large spatial scales, but also with depth and seasonality at more local scales. Our findings provide insight into the structure of marine fish communities and suggest that global warming will favour an expansion of fast-living species. Knowledge of the global and local drivers of trait distributions can thus be used to predict future responses of fish communities to environmental change.</p>
Data used in "Fast metabolite identification with Input Output Kernel Regression"
<p>This repository contains the data used in [1] to evaluate the performance for metabolite identification from tandem mass spectra. These data have been extracted and processed in [2]. We used a subset of 4138 MS/MS spectra extracted from the GNPS public spectral library (https://gnps.ucsd.edu/ProteoSAFe/libraries.jsp) for training and evaluation. For searching, we used molecular structures from PubChem as candidate sets.</p> <p>Please mention and cite GNPS when using these data.</p> <p>The implementation of the method proposed in [1] is available on: https://version.aalto.fi/gitlab/kepaco/Fast-metabolite-identification-with-IOKR</p> <p><strong>Files description:</strong></p> <ul> <li><em>spectra.txt</em>: informations about the MS/MS spectra (GNPS identifier, compound name and INCHI identifier)</li> <li><em>data_GNPS.mat</em>: contains the molecular fingerprints, molecular formula and InCHI corresponding to the MS/MS spectra</li> <li><em>cv_ind.txt</em>: indices of the cross-validation folds</li> <li><em>ind_eval.txt</em>: indices of the examples used for evaluation</li> <li><em>candidates</em>: fingerprints and INCHI for the different candidate sets</li> <li><em>input_kernels</em>: contains 24 input kernel matrices</li> </ul> <p><strong>References:</strong></p> <p>[1] Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J.: Fast metabolite identification with Input Output Kernel Regression. In the proceedings of ISMB 2016, Bioinformatics 32(12): i28-i36, 2016. DOI: https://doi.org/10.1093/bioinformatics/btw246</p> <p>[2] Dührkop, K., Shen, H., Meusel, M., Rousu, J. and Böcker, S.: Searching molecular structure databases with tandem mass spectra using CSI:FingerID. PNAS, 112(41), 12580-12585, 2015. doi:10.1073/pnas.1509788112</p> <p> </p> <p> </p>
T-DNAreader: fast and precise identification of T-DNA insertion sites using RNA-seq data
<p><em><span>Agrobacterium</span></em><span>-mediated plant transformation, which enables the delivery of DNA using transfer DNA (T-DNA) binary vectors, is an essential technique in plant research. T-DNAs randomly integrate into the host genome and multiple T-DNAs can integrate in a single transformation event, necessitating the development of efficient, reliable tools to identify the T-DNA insertion sites (TISs). Here, we developed T-DNAreader to identify TISs from RNA-sequencing (RNA-seq) data with high precision, sensitivity, and speed, outperforming existing tools. T-DNAreader detected previously unknown TISs in characterized mutant plants. Overall, T-DNAreader enables the simple identification of TISs within transcribed regions and standardizes the characterization of T-DNA-containing transgenic plants.</span></p>
Ab Initio and Kinetic Modelling of β-D-xylopyranose under Fast Pyrolysis Conditions
<p>Zip file containing all the IRC connecting transition states to minima in our paper published in The Journal of Physical Chemistry A, <a title="DOI URL" href="https://doi.org/10.1021/acs.jpca.3c07063">https://doi.org/10.1021/acs.jpca.3c07063</a></p>
Raw data: Patterned apoptosis has an instructive role for local growth and tissue shape regulation in a fast-growing epithelium
<p>What regulates organ size and shape remains one fundamental mystery of modern biology. Research in this area has primarily focused on deciphering the regulation in time and space of growth and cell division, while the contribution of cell death has been overall neglected. This includes studies of the <i>Drosophila</i> wing, one of the best characterised systems for the study of growth and patterning, undergoing massive growth during larval stage and important morphogenetic remodeling during pupal stage. So far, it has been assumed that cell death was relatively neglectable in this tissue both during larval and pupal stage and as a result the pattern of growth was usually attributed to the distribution of cell division. Here, using systematic mapping and registration combined with quantitative assessment of clone size and disappearance as well as live imaging, we outline a persistent pattern of cell death and clone elimination emerging in the larval wing disc and persisting during pupal wing morphogenesis. Local variation of cell death is associated with local variation of clone size, pointing to an impact of cell death on local growth which is not fully compensated by proliferation. Using morphometric analyses of adult wing shape and genetic perturbations, we provide evidence that patterned death affects locally and globally adult wing shape and size. This study describes a roadmap for precise assessment of the contribution of cell death to tissue shape, and outlines an important instructive role of cell death in modulating quantitatively local growth and morphogenesis of a fast-growing tissue.</p><p>This repository contains the raw data (local projection images, ROI and quantificatiosn) of the Current Biology article "<strong>Patterned apoptosis has an instructive role for local growth and tissue shape regulation in a fast-growing epithelium".</strong></p>
Fast Radio Map Estimation and Automated Radio Network Design Using Deep Learning App and Dataset
<p>In this research, we present the Deep Learning architecture encoder/fully-connected for estimate optimal desings WLANs in indoor scenarios. This architecture was implemented for WLAN structures consisting of 1, 2, 3, 4, and 5 access points, with the capability to perform the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>, but in a fast manner.</p> <p><a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#general-dataset-structure">General Dataset Structure</a></p> <p>A major initial difficulty for starting the research was the lack of data, in this case, indoor scenario floor plans, users posisitons and optimal designs for training the architecture. Therefore, it was necessary to create an appropriate database that would facilitate the respective trainings. The dataset was created in base the <a href="https://link.springer.com/chapter/10.1007/978-3-662-44415-3_4">Balanced k-means algortihm</a>. This implementation was carried out in the MATLAB software <a href="https://github.com/johanflorez98/WLANs-optimal-designs-methodology">WLANs-optimal-designs-methodology</a>.</p> <p>Thus, this research provides a dataset that can be used for training multiple Deep Learning architectures and can facilitate future investigations into similar problems.</p> <p>We show a dataset composed by optimal designs for the 5GHz band WiFi in indoor scenarios: it has 102 indoor constructions plans and around 20 users distributions per plan floor as image and APs positions per case as coordinates. These distributions are random and several WLAN's structures: 1 to 5 access points.</p> <p>The above explain that we got a total of 61000 RME and CME, this presents that is a model without interference between channels.</p> <p>The pictures have a depth of 8 bits and size of <em>256pixels X 256pixels</em> equivalents to indoor constructions of <em>20 X 20</em> m<sup>2</sup>. These ones make reference to offices's spaces at general or classroom.</p> <p><a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset/blob/main/README.md#obtained-models">Obtained Models</a></p> <p>To evaluate the obtained models, the dataset consisting of floor plans 911 to 102 can be used, with available user distributions for each case of APs configurations as a test set, or other new images can be used.</p> <p>To manipulate the codes better, click here in the <a href="https://github.com/johanflorez98/Fast-Radio-Map-Estimation-and-Automated-Radio-Network-Design-Using-Deep-Learning-App-and-Dataset">repository</a>.</p>
Raw Data for: Fast rate dual-comb spectrometer in the water-transparent 7.5-11.5 μm region
<p>This repository shares the data acquired and used to produce the figures in the paper "Fast rate dual-comb spectrometer in the water-transparent 7.5-11.5 μm region" published in Optics Letters (DOI: <a href="https://doi.org/10.1364/OL.515199">https://doi.org/10.1364/OL.515199</a>)</p> <p><br>The "Figure_Data" zip file contains a ".txt" file for each Figure present in the Main Paper and Supplemental Document. The raw data acquired by the FT spectrometer and uploaded here for Figure 2 are afterward smoothed and normalized properly in the Main Paper to better show the spectra. Data shown in Figure 3 are calculated from the corresponding raw data. Data used for Figure 4 are calculated from the corresponding raw blank DCS data. Data for Figure 5, 6 and S1 are retrieved from the corresponding raw Gas DCS data upon Cepstral removal of the spectral baseline and spectral fit exploiting the Hitran Database.</p> <p><br>The "Raw_Data" zip file contains the postprocessed Data of the DCS measurements and the RIN data as acquired. The Raw data of the DCS measurements as acquired are too large to be uploaded here, but may be obtained from the authors upon request.<br>"NH3_1o6kHz_IGMmean", "N2O_5o1kHz_IGMmean", "N2O_26kHz_IGMmean" contain the DCS interferogram averaged over 15s of acquisition for Gas measurements, performed with different DeltaFrep as described in the main text of the paper.<br>"Blank_1o6kHz_IGMs", "Blank_5o1kHz_IGMs", "Blank_26kHz_IGMs" contain all the phase corrected DCS interferograms acquired over 100ms, used to calculate the average SNR shown in Figure 4.<br>"RIN_DFG" contain the raw data acquired from the detector for RIN calculations, with an average voltage values of 25mV.</p> <p><br>The scripts for processing the Raw Data and retrieve the data as shown in the paper are home made.</p> <p> </p> <p> </p>
Fast-slow traits predict competition network structure and its response to resources and enemies
<p>Plants interact in complex networks but how network structure depends on resources, natural enemies, and species resource-use strategy remains poorly understood. Here, we quantified competition networks among 18 plants varying in fast-slow strategy, by testing how increased nutrient availability and reduced foliar pathogens affected intra- and inter-specific interactions. Our results show that nitrogen and pathogens altered several aspects of network structure, often in unexpected ways due to fast and slow-growing species responding differently. Nitrogen addition increased competition asymmetry in slow-growing networks, as expected, but decreased it in fast-growing networks. Pathogen reduction made networks more even and less skewed because pathogens targeted weaker competitors. Surprisingly, pathogens and nitrogen dampened each other's effect. Our results show that plant growth strategy is key to understanding how competition responds to resources and enemies, a prediction from classic theories that has rarely been tested by linking functional traits to competition networks.</p>
Auxiliary data release for "Fast marginalization algorithm for optimizing gravitational wave detection, parameter estimation and sky localization"
<p>This release contains parameter estimation runs on synthetic injections, described in https://arxiv.org/abs/2404.02435 .</p> <p>Important note: The posterior samples provided are weighted, the weights are stored in a column named 'weights' . </p>
F I G U R E 3 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus
F I G U R E 3 Relationships between fast-start performance, body size (standard length), acclimation temperature, and test temperature in adult mummichogs. Graphs are arranged by performance variable in rows and by test temperature (24, 30, and 36 C) in columns. Colors indicate acclimation temperatures (blue is 24 C, purple is 30 C, and red is 36 C). Lines are the fitted performance–size relationships based on parameter estimates from best-fit mixed-effects models or model-averaged parameter estimates from supported models (see Table 1 and Table S3.). Effects of size on performance are apparent as nonzero slopes within individual graphs; effects of test temperature are evident as variation in average intercepts across test temperature graphs (i.e., comparing average intercepts for acclimation temperature–specific lines across columns within each row of graphs); effects of acclimation temperature are seen as differences in intercepts between acclimation temperature–specific lines within graphs; and interactions between size and temperature are apparent as variations in average slopes (i.e., averaged over acclimation-specific slopes) across test temperature graphs and as differences in acclimation temperature–specific slopes within graphs.
F I G U R E 1 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus
F I G U R E 1 Morphological scaling in mummichog ranging from YOY to larger adults. Morphological variables are body dimensions (top row) and median fin areas (bottom row), and standard length is a measure of the overall body size. All data were log transformed. Sample sizes for fin areas were reduced because individuals with damaged fins were not measured. Estimates for scaling coefficients (i.e., the slope of the scaling relationship, b1) ± 1 S.E. are given for each morphological variable. Isometric expectations for scaling coefficients are 1.0 for body dimensions and 2.0 for fin areas; only the caudal fin area deviates significantly from isometry.
F I G U R E 2 in Scaling of fast-start performance and its thermal dependence in mummichog Fundulus heteroclitus
F I G U R E 2 Representative kinematics for fast-starts in small (41 mm SL) and large (66 mm SL) adult mummichogs controlling for temperature. The top panel shows frames from high-speed videos at 10 ms intervals. To facilitate interpretation, original videos have been cropped to the same spatial dimensions and rotated to standardize starting orientation and escape direction (but note that digitization used unmodified videos). The graph in the bottom panel shows the angular displacement of the head (triangles) and the linear displacement of the fish center of mass (circles) for the trials depicted in the video frames, which were maximum performance trials for the small (filled shapes) and large (open shapes) fish tested at their acclimation temperature (24 C). The curve for head rotation was truncated at its maximum value to highlight the difference in time to form the initial C-bend. The curve for linear displacement continued until the end of stage 2 of the fast-start. Whereas small fish had higher rotation rates and executed the fast-start in less time, small and large fish moved their bodies over similar distances given equal amounts of time.
Multiwavelength Constraints on the Origin of a Nearby Repeating Fast Radio Burst Source in a Globular Cluster (Public Data Release)
<p>This Zenodo dataset contains the data for radio bursts B1-B9 from FRB 20200120E, as described in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) (see: https://doi.org/10.1038/s41550-024-02386-6).</p> <p>The following data products are included:</p> <ul> <li>Channelized total intensity (Stokes I) data containing radio bursts B1-B5 from FRB 20200120E, recorded using the Effelsberg radio telescope during Pinpointing Repeating CHIME Sources with the EVN (PRECISE) VLBI observations. These data have a time resolution of 8 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b1_8us_burst_data.npy</li> <li>frb20200120e_b2_8us_burst_data.npy</li> <li>frb20200120e_b3_8us_burst_data.npy</li> <li>frb20200120e_b4_8us_burst_data.npy</li> <li>frb20200120e_b5_8us_burst_data.npy</li> </ul> </li> <li>Channelized total intensity (Stokes I) data containing radio bursts B6-B9 from FRB 20200120E, recorded using the Effelsberg radio telescope. These data have a time resolution of 64 μs and were used in Figure 1 in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024). <ul> <li>frb20200120e_b6_64us_burst_data.npz</li> <li>frb20200120e_b7_64us_burst_data.npz</li> <li>frb20200120e_b8_64us_burst_data.npz</li> <li>frb20200120e_b9_64us_burst_data.npz</li> </ul> </li> <li>Frequency-summed total intensity (Stokes I) burst profiles of radio burst B4. The frequency range and time resolution of the data are listed below. These data were used in Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).<br> <ul> <li>frb20200120e_b4_8us_1254-1510mhz_burst_profile.npz; (frequency range, time resolution) = (1254-1510 MHz, 8 μs)</li> <li>frb20200120e_b4_1us_1302-1478mhz_burst_profile.npy; (frequency range, time resolution) = (1302-1478 MHz, 1 μs)</li> <li>frb20200120e_b4_31.25ns_1398-1414mhz_burst_profile.npy; (frequency range, time resolution) = (1398-1414 MHz, 31.25 ns)</li> </ul> </li> </ul> <p>We also provide the following Python code containing functions that can be used to load and plot the radio data. The plots generated by this code are similar to those shown in Figure 1 and Extended Data Figure 2 (panels b, c, and d) in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024).</p> <ul> <li>plot_frb20200120e_radio_data_pearlman+2024_nature_astronomy.py</li> </ul> <p>The X-ray data (from <em>NICER</em>, <em>XMM-Newton</em>, <em>Chandra</em>, and <em>NuSTAR</em>) used in A. B. Pearlman et al., <em>Nature Astronomy</em> (2024) are publicly available and can be accessed through NASA's High Energy Astrophysics Science Archive Research Center (HEASARC) archive.</p> <p>If the data or Python code included in this Zenodo repository are used, please include the following two citations in your work:</p> <ol> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster. <em>Nature Astronomy</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.1038/s41550-024-02386-6</a></li> <li>Pearlman, A. B., Scholz, P., Bethapudi, S. <em>et al.</em> Multiwavelength constraints on the origin of a nearby repeating fast radio burst source in a globular cluster (public data release). <em>Zenodo</em> (2024). <a href="https://doi.org/10.5281/zenodo.13359005">https://doi.org/10.5281/zenodo.13359005</a></li> </ol> <p>If you have questions about the contents of this Zenodo repository, please contact the lead author: Dr. Aaron B. Pearlman (<a href="mailto:aaron.b.pearlman@physics.mcgill.ca">aaron.b.pearlman@physics.mcgill.ca</a>)</p>
Evolution and the quasistationary state of collective fast neutrino flavor conversion in three dimensions without axisymmetry
<p>This repository contains the simulation data used in the study titled `Evolution and the quasistationary state of collective fast neutrino flavor conversion in three dimensions without axisymmetry`. All the necessary data are provided in the hdf5 file. Please check the article and the README enclosed herewith for more details.</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.