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5,805 results for “Data model”
Data from: The multilocus multispecies coalescent: a flexible new model of gene family evolution
<p>Incomplete lineage sorting (ILS), the interaction between coalescence and speciation, can generate incongruence between gene trees and species trees, as can gene duplication (D), transfer (T) and loss (L). These processes are usually modelled independently, but in reality, ILS can affect gene copy number polymorphism, i.e., interfere with DTL. This has been previously recognised, but not treated in a satisfactory way, mainly because DTL events are naturally modelled forward-in-time, while ILS is naturally modelled backwards-in-time with the coalescent. Here we consider the joint action of ILS and DTL on the gene tree/species tree problem in all its complexity. In particular, we show that the interaction between ILS and duplications/transfers (without losses) can result in patterns usually interpreted as resulting from gene loss, and that the realised rate of D, T and L becomes non-homogeneous in time when ILS is taken into account. We introduce algorithmic solutions to these problems. Our new model, the <em>multilocus multispecies coalescent</em> (MLMSC), which also accounts for any level of linkage between loci, generalises the multispecies coalescent model and offers a versatile, powerful framework for proper simulation and inference of gene family evolution.</p>
Dataset for Gelatinous zooplankton-mediated carbon flows in the global oceans: A data-driven modeling study
<p>Gridded dataset of gelatinous zooplankton (GZ) biomass (mg C m<sup>-3</sup>) and numeric density (individuals m<sup>-3</sup>), time-averaged, in a 1-degree grid. Data are separated by phyla: Cnidaria, Ctenophora, and Chordata (pelagic tunicates). Original data compiled as part of the Jellyfish Database Initiative Project (JeDI; Condon et al. 2015, doi:10.1575/1912/7191) and converted to carbon biomass units for Lucas et al. 2014.</p> <p>Cnidarian additions to this dataset include records from the northern California Current (Brodeur et al., 2014) and Gulf of Mexico (Robinson et al., 2015). Chordata additions include salps from the Bermuda Atlantic Time Series (BATS; Stone & Steinberg, 2014), Western Antarctic Peninsula (WAP; Steinberg et al., 2015), and Southern Ocean, from KRILLBASE (Atkinson et al., 2017). Note that we excluded the KRILLBASE records from the WAP region that to prevent double-counting. See Methods in Luo et al. (2020) for details on biometric conversions to carbon biomass.</p> <p>Data were averaged by time (season, then year), and then within each 1-degree grid cell.</p> <p> </p> <p> </p> <p>Code for the model using this dataset is available at: <a href="https://github.com/jessluo/gz_biogeochem_pub">https://github.com/jessluo/gz_biogeochem_pub</a></p> <p> </p> <p><strong>Luo, Jessica Y.</strong>, Condon, R. H., Stock, C. A., Duarte, C. M., Lucas, C. H., Pitt, K. A., & Cowen, R. K. (2020). Gelatinous zooplankton‐mediated carbon flows in the global oceans: A data‐driven modeling study. <em>Global Biogeochemical Cycles</em>, 34, e2020GB006704. <a href="https://doi.org/10.1029/2020GB006704">https://doi.org/10.1029/2020GB006704</a></p>
Data from: Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning
<p>How the brain representation of conceptual knowledge vary as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. Here we asked how the brain representation of semantic categories is shaped by the depth of processing during mental simulation. Participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models - associated with the image referents of the words - and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.</p>
Supporting data for "A convolution method to assess subgrid-scale interactions between flow and patchy vegetation in biogeomorphic models"
<p>Dataset necessary to reproduce the results and analyses presented in the paper:</p> <p>Gourgue, O., van Belzen, J., Schwarz, C., Bouma, T.J., van de Koppel, J. & Temmerman, S. (2020) A convolution method to assess subgrid-scale interactions between flow and patchy vegetation in biogeomorphic models, Journal of Advances in Modeling Earth Systems, submitted.</p> <p>The dataset contains:</p> <ul> <li>Process-based model simulations, including their input files and the Python scripts to generate them (pre-processing), as well as the output files and Python scripts to post-process them.</li> <li>Flume experiment data processed for the model calibration.</li> <li>Python scripts to generate the figures and tables of the manuscript.</li> </ul>
Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations - data
<p>Data from the paper:<em> Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.</em></p> <p>Preprint: https://www.biorxiv.org/content/10.1101/840256v1</p> <p>Marek A. Pedziwiatr<br> marek.pedziwi@gmail.com<br> September 2020</p> <p> </p>
Processed data of grasshoppers, butterflies and moths for analyses of species trend models for the regional WWF Living Planet Index for Belgium
<p>This archive contains pre-processed datasets used for the analysis of species occupancy models, the results of which were used in the calculation of multi-species indices as part of the regional WWF Living Planet Index for Belgium.</p> <p>The datasets are csv files (comma separated and . as decimal mark). </p> <p>For each species group (moths, butterflies and grasshoppers), the following files are available:</p> <ul> <li>a species list (files with 'species' in the name)</li> <li>an observations list (files with 'observations' in the name - for butterfly or moth species with two distinct flight periods, also a file with the data for the second generation is available)</li> </ul> <p>For one species, <em>Fabriciana adippe</em>, separate files are available with corrected data.</p> <p>The species list files contain the following variables:</p> <ul> <li>species_id (unique species id)</li> <li>scientific_name (accepted scientific name according to the GBIF taxonomic backbone)</li> <li>species_name_NL (Dutch species name)</li> <li>species_name_FR (French species name)</li> <li>season_start (n-th day of the year that marks the beginning of the first -and possibly only- generation)</li> <li>season_end (n-th day of the year that marks the end of the first -and possibly only- generation)</li> </ul> <p>The observations files contain the following variables:</p> <ul> <li>species_id (a unique identifier)</li> <li>year (year of observation)</li> <li>month (month of observation)</li> <li>day (day of observation)</li> <li>julian_day (n-th day of the year)</li> <li>site_id (unique identifier for the 1 km x 1km EEA 1 km x 1 km reference grid square <a href="https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2">https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2</a>)</li> <li>source (name of data provider)</li> <li>count (max number of sightings for the species for that day and site</li> </ul>
Sample data for analysis of period/frequency gradient and phase gradient in spreadouts, ex vivo models of somitogenesis
<p>Here are timelapse imaging (as .tif) of a dynamic Notch signaling reporter (i.e. LuVeLu) in spreadouts, ex vivo models of somitogenesis. Also here are the corresponding period and phase wavelet movies, generated using a wavelet analysis workflow developed by Gregor Mönke. These sample data are used to run an accompanying Python script, available at: <a href="https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/FrequencyPhase_GradientSlopeAnalysis">https://github.com/PGLSanchez/EMBL_OscillationsAnalysis/tree/master/FrequencyPhase_GradientSlopeAnalysis</a></p>
VTFT_Demography: global ageclass simulation data from the LPJ-wsl v2.0 Dynamic Global Vegetation Model
<p>Forest ecosystem processes follow classic responses with age, peaking production around canopy closure and declining thereafter. Although age dynamics might be more dominant in certain regions over others, demographic effects on net primary production (NPP) and heterotrophic respiration (Rh) are bound to exist. Yet, explicit representation of ecosystem demography is notably absent in most global ecosystem models. This is concerning because the global community relies on these models to regularly update our collective understanding of the global carbon cycle. This paper aims to fill this gap in understanding by presenting the technical developments of a computationally-efficient approach for representing age-class dynamics within a global ecosystem model, the LPJ-wsl v2.0 Dynamic Global Vegetation Model. The modeled age-classes are initially created by fire feedbacks, wood harvesting, and abandonment of managed land, otherwise aging naturally until a stand-clearing disturbance is simulated or prescribed. In this paper, we show that the age-module can capture classic demographic patterns in stem density and tree height compared to inventory data, and that patterns of ecosystem function follow classic responses with age. We also present a few scientific applications of the model to assess the modeled age-class distribution over time and to determine the demographic effect on ecosystem fluxes relative to climate. Simulations show that, between 1860 and 2016, zonal age distribution on Earth was driven predominately by fire, causing a ~45-year difference in ages between boreal (50N-90N) and tropical (23S-23N) latitudes. Land use change and land management was responsible for an additional decrease in zonal age by -6 years in boreal and by -21 years in temperate (23N-50N) and tropical latitudes, with the anthropogenic effect on zonal age distribution increasing over time. A statistical model helped reduced LPJ-wsl complexity by predicting per-grid-cell annual NPP and Rh fluxes by three terms: precipitation, temperature and age-class; at global scales, R<sup>2</sup> was between 0.95 and 0.98. As determined by the statistical model, the demographic effect on ecosystem function was often less than 0.10 kg C m<sup>-2</sup> yr<sup>-1</sup> but as high as 0.60 kg C m<sup>-2</sup> yr<sup>-1</sup> where the effect was greatest. In eastern forests of North America, the demographic effect was of similar magnitude, or greater than, the effects of climate; demographic effects were similarly important in large regions of every vegetated continent. Spatial datasets are provided for global ecosystem ages and the estimated coefficients for effects of precipitation, temperature and demography on ecosystem function. The discussion focuses on our finding of an increasing role of demography in the global carbon cycle, the effect of demography on relaxation times (resilience) following a disturbance event and its implications at global scales, and a finding of a 40-Pg C increase in turnover from age dynamics at global scales. Whereas time is the only mechanism that increases ecosystem age, any additional disturbance not explicitly modeled will decrease age. This LPJ-based age-module therefore simulates the upper limit of age-class distributions on Earth and represents another step forward towards understanding the role of demography in global ecosystems.</p>
Simulation/Plot Data of collisional growth column model
<p>This dataset contains</p> <ul> <li>Simulation data of collisional column model as used in the GMD-study by Unterstrasser et al., 2020: Collisional growth in a particle-based cloud microphysical model: Insights from column model simulations using LCM1D (v1.0)</li> </ul> <ol> <li>Bott_1D.zip and Wang_1D.zip contain simulation data of reference bin models</li> <li>AON_1D.zip contains simulation data of the new column model with Lagrangian microphysics treating collisional grwoth with the All-or-Nothing algorithm</li> </ol> <ul> <li>Scripts to reproduce all figures (that show simulation data) of the above mentioned study.<br> Unpack Plot_scripts.zip;<br> In order to run the plot scripts it is also necessary to install the ColumnModel code which is hosted on GitHub. The (frozen) model version used here is released in a separate Zenodo upload (DOI: 10.5281/zenodo.4031214).</li> <li>Figure files and their according source files<br> Unpack Plots.zip</li> </ul> <p> </p> <p> </p>
Data and code for training and evaluating machine learning models for thunderstorm prediction from reanalysis data
<p>FIXED Data and Python code for training and evaluating machine learning models for predicting thunderstorms, associated with the paper:</p> <p>"Evaluation of machine learning classifiers for predicting deep convection"</p> <p>by Peter Ukkonen and Antti Mäkelä (to appear in JAMES)</p> <p>The data (preprocessed inputs and outputs) is stored as netCDF files and .mat files which can be loaded with Python. </p>
Experimental data for "Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models"
<p>This dataset is for the validation data in Chen et al. (2020). It contains data for three different LG M50 cells undergoing an experiment in which the cells are charged in a constant-current/constant-voltage fashion and discharge at a constant current for different C-rates (C/10, C/2, 1C and 1.5C). Apart from the current and voltage, the temperatures of the cell surface and the thermal chamber in which they are cycled is recorded too.</p> <p><strong>References:</strong></p> <p>Chang-Hui Chen <em>et al</em> 2020 <em>J. Electrochem. Soc.</em> <strong>167</strong> 080534 (<a href="https://doi.org/10.1149/1945-7111/ab9050">https://doi.org/10.1149/1945-7111/ab9050</a>)</p>
Data for GRL manuscript "Vertical Coordinate and Resolution Dependence of the Second Moment Turbulent Closure Models"
<p>The zipped directory contains data used for analysis and figures in the GRL manuscript "Vertical Coordinate and Resolution Dependence of the Second Moment Turbulent Closure Models"</p> <p>### OSP observations:</p> <p>Air Temperature: airt50n145w_hr.ascii </p> <p>Ocean Temperature: t50n145w_hr.ascii </p> <p>Ocean Salinity: s50n145w_hr.ascii</p> <p>Total Heat Flux: heatflux_papa.dat</p> <p>10m Wind Speed: wspd_papa.dat</p> <p>u and v components of Stokes drift velocity hourly time series given on vertical grid vertical_grid_128: uvstk_papa.dat</p> <p>### NCOM simulation results</p> <p>out_Model_LayerGrid.dat</p> <p>Model:</p> <p> h15 - use harcourt (2015) </p> <p> kc04 - use Kantha & Clayson (2004)</p> <p> myl2p5 - use Mellor & Yamda (1982) level 2.5</p> <p>Layer:</p> <p> 030, 040, 050, 080, 100</p> <p>Grid:</p> <p> s - uniform stretched grid</p> <p> u - uniform grid</p> <p> y - mixed layer enhanced grid</p> <p>The following variables are provied in all out_*.dat files as given in their headers</p> <p> depth (m) tke (cm2/s2) dis (cm2/s3) tl (cm) zkm (cm2/s) zkh (cm2/s) T (C) S (psu) pd (kg/m3) z-mid (m)</p> <p>### LES output</p> <p>Eddy viscosity computed using LES model</p> <p> LES simulation with all foring - Km_les_AllForcing.dat</p> <p> LES simulation without Stokes drift - Km_les_NoStk.dat</p> <p> LES simulation without Heat Flux - Km_les_NoHF.dat</p>
Simulated data for paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4) with a rich (scenarios 1 and 3) and a sparse design (scenarios 2 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</p>
Key input and output data for the multi-model analysis "Open Source Energiewende"
<p>This repository contains key input and output data of the multi-model analysis carried out in the project "Open Source Energiewende", financed by the German Federal Ministry for Economic Affairs and Energy.</p> <p>The results are presented and discussed in the paper "Power sector effects of cheaper stationary batteries: insights from an open multi-model analysis".</p> <p>The model codes are availabe in individual repositories, which are provided in the paper.</p>
Source code for models of floral initiation in pea and gene expression data extracted from published sources
<p>The dataset contains the source code for computational models of a gene network controlling transition to flowering in pea (<em>Pisum sativum</em>). The models were based on ordinary differential equations (ODE) or neural networks. It also includes data on the expression dynamics of genes involved in the network, which was used for model fitting. The expression data was extracted from the following papers: </p> <p>Hecht, V., Laurie, R. E., Schoor, K. Vander, Ridge, S., Knowles, C. L., Liew, L. C., Sussmilch, F. C., et al. (2011). The Pea GIGAS Gene Is a FLOWERING LOCUS T Homolog Necessary for Graft-Transmissible Specification of Flowering but Not for Responsiveness to Photoperiod. 23, 147–161. doi:10.1105/tpc.110.081042</p> <p>Sussmilch, F. C., Berbel, A., Hecht, V., Schoor, K. Vander, Ferrándiz, C., Madueño, F., et al. (2015). Pea VEGETATIVE2 Is an FD Homolog That Is Essential for Flowering and Compound In fl orescence Development. 27, 1046–1060. doi:10.1105/tpc.115.136150</p> <p>The source code of the DEEP software used for parameter optimization in the model fitting can be found in the Gitlab repository (https://gitlab.com/mackoel/deepmethod/-/tree/master).</p> <p>The files are the supplement to the following manuscript, submitted to Frontiers in Genetics:</p> <p>"Dynamical Modeling of the Core Gene Network Controlling Transition to Flowering in <em>Pisum sativum</em>" by Polina Pavlinova, Maria G. Samsonova, and Vitaly V. Gursky.</p> <p>All possible questions can be sent to: Polina Pavlinova (polina.pavlina1004@gmail.com), Vitaly Gursky (gursky@math.ioffe.ru).</p>
Data and code for paper "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning"
<p>Simulation results from the NOAA/SWPC Geospace model used in the paper Camporeale et al. (2020) "A gray-box model for a probabilistic estimate of regional ground magnetic perturbations: Enhancing the NOAA operational Geospace model with machine learning" published in J. Geophys. Res. (2020)</p> <p>MATLAB code is provided to train process the data and train the machine learning model and plot results.</p> <p>Manuscript available on <a href="https://arxiv.org/abs/1912.01038">https://arxiv.org/abs/1912.01038</a></p>
Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model - Simuation results and observed data
<p>This data set contains the simulation results and observed data at NDBC buoy locations.</p> <ul> <li>wave_data.pickle <ul> <li>File containing python data objects which store: station ID data, observed data, model data, and model output dates. Requires python 3.8.</li> </ul> </li> <li>data_access.py <ul> <li>Example python script which reads in a prints the data from wave_data.pickle. It also demonstrates how to access data from the objects stored in the pickle file.</li> </ul> </li> </ul>
Model output data for Marine Wild-Capture Fisheries after Nuclear War
<p>This is the model data material for Scherrer at al., PNAS. [Scherrer K. J. N., et al. Marine wild-capture fisheries after nuclear war, PNAS in press]. Input files include time series of gridded global oceanic Sea Surface Temperature and Net Primary Productivity (output from the CESM model) and socioeconomic input for the global fisheries model (BOATS). Output files include globally integrated time series (used in Figs. 1-3 and 6) and global gridded model output (used in Figs. 4-5) for each of the five ensemble member runs.</p>
Data from: Modelling the potential impacts of climate change on the distribution of ichthyoplankton in the Yangtze Estuary, China
<p><span><a name="_Hlk10553342"><b>Aim: </b></a>Species distribution models (SDMs) are an effective tool to explore the potential distribution of terrestrial, freshwater, and marine organisms; however, SDMs have been seldom used to model ichthyoplankton distributions and thus our understanding of how larval stages of fishes will respond to climate change is still limited. Here, we developed SDMs to explore potential impacts of climate change on habitat suitability of ichthyoplankton.</span></p> <p><span><b>Location: </b>Yangtze Estuary, China</span></p> <p><span><b>Methods: </b>Using long-term ichthyoplankton survey data and a large set of marine predictor variables, we developed ensemble SDMs for five abundant ichthyoplankton species in the Yangtze Estuary (<i>Coilia mystus</i>, <i>Hypoatherina valenciennei</i>, <i>Larimichthys polyactis</i>, <i>Salanx ariakensis</i>, and <i>Chelidonichthys spinosus</i>). Then, we projected their habitat suitability under present and future climate conditions.</span></p> <p><span><b>Results: </b>The ensemble SDMs had good predictive performance and were successful in estimating the known distributions of the five species. Model projections highlighted two contrasting patterns of response to future climates: while <i>C. mystus</i> will likely expand its range, the ranges of the other four species will likely contract and shift northward.</span></p> <p><span><b>Main conclusions: </b><a name="_Hlk15635552">According to our SDM projections, the five ichthyoplankton species that we tested in the Yangtze Estuary are likely to respond differently to future climate changes. These projected different responses seemingly reflect the differential functional attributes and life history strategies of these species. </a>To the extent that climate change emerges as a critical driver of the future distribution of these species, our findings provide an important roadmap for designing future conservation strategies for ichthyoplankton in this region.</span></p>
Input data for performing chemistry coupled PALM model system 6.0 simulations with different chemical mechanisms
<p>The data presented here comprised of input files that have been used to run chemistry coupled PALM model system 6.0 simulations for the article entitled "Development of an atmospheric chemistry model coupled to the PALM model system 6.0: Implementation and first applications". In this article we describe the implementation of an online-coupled gas-phase chemistry model in the turbulence resolving PALM model system 6.0.</p> <p>List of the input data required for performing chemistry model simulations with different chemical mechanisms is given below. A text file comprised of measured concentrations of NO, NO<sub>2</sub> and O<sub>3</sub> is also added.</p> <ol> <li>Fortran parameter (PARIN) files for four mechanisms and one meteorology-only simulation.</li> <li>Static file</li> <li>Dynamic file</li> <li>Two files (shortwave and longwave input data) for rrtmg radiation model</li> <li>Observation from two air quality stations in Berlin, Germany .</li> <li>PALM model source code revision 4450 (palm_trunk_rev-4450.tar.gz)</li> <li>PALM model source code revision 4601 (palm_trunk_rev-4601.tar.gz)</li> </ol> <p>The PALM model system 6.0 revision 4451 and 4601 (for chemistry flux profiles only) have been used for these simulations. </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.