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213 results for “multiple models”
Phylogeographic and demographic modelling analyses of the multiple origins of the rheophytic goldenrod Solidago yokusaiana
<p>Understanding adaptation mechanisms is important in evolutionary biology. Parallel adaptation provides good opportunities to investigate adaptive evolution. To confirm parallel adaptation, it is effective to examine whether the phenotypic similarity has one or multiple origins and to use demographic modelling to consider the gene flow between ecotypes. <i>Solidago yokusaiana</i> is a rheophyte endemic to the Japanese Archipelago that diverged from <i>Solidago virgaurea</i>. This study examined the parallel origins of <i>S. yokusaiana</i> by distinguishing between multiple and single origins and subsequent gene flow. The haplotypes of non-coding chloroplast DNA and genotypes at 14 nuclear simple sequence repeat (nSSR) loci and single nucleotide polymorphisms (SNPs) revealed by double-digest restriction-associated DNA sequencing (ddRADseq) were used for phylogeographic analysis; the SNPs were also used to model population demographics. Some chloroplast haplotypes were common to <i>S. yokusaiana</i> and its ancestor <i>S. virgaurea</i>. Also, the population genetic structures revealed by nSSR and SNPs did not correspond to the taxonomic species. The demographic modelling supported the multiple origins of <i>S. yokusaiana</i> in at least four districts and rejected a single origin with ongoing gene flow between the two species, implying that <i>S. yokusaiana</i> independently and repeatedly adapted to frequently flooding riversides.</p>
Supplemental 3D Model Data - New insights into the evolutionary history of Fungi from a 407 million year old blastocladiomycota-like fossil showing multiple sporangia and an extensive hyphal network (SPIERSView and VAXML format)
<p>Three-dimensional reconstruction models of Fungi from a 407 million year old blastocladiomycota-like fossil showing multiple sporangia and an extensive hyphal network in SPIERSView and VAXML format. 2D and 3D (Red/Cyan) images also provided as a PDF.</p> <p>Notes:</p> <ol> <li>SPIERSView file (.SPV) models can conveniently be viewed using the SPIERSView software, freely available in both Windows and Mac versions from http://www.spiers‐software.org. However, note that low-performance computers may not possess a sufficiently powerful graphics card to render and rotate the model.</li> <li>VAXML file format models are saved as a ZIP-compressed VAXML datasets. VAXML uses one or more .STL files to define the geometry of objects that comprise the dataset, together with one .VAXML file that provides metadata on the dataset as a whole, and specifies how the .STL files should be put together. We recommend using the free SPIERS software to view this model format (http://spiers-software.org/). However, .STL files can be opened independently in several freely available software programs (e.g. MeshLab, Blender). Additional information on the VAXML format can be found here: http://spiers-software.org/VAXML.htm.</li> </ol>
Weather forecasts from multiple models and observations at Norwegian synop stations
<p>The data consists of temperature (2m) and wind speed (10m) observations at 183 Norwegian synop stations and corresponding weather forecasts generated by the following models</p><ul><li>Pangu-Weather</li><li>ECMWF HRES</li><li>ECMWF ENS reforecast</li><li>MEPS</li><li>ECMWF ENS reforecast control member</li><li>MEPS control member</li></ul><p>The data was created for the article <a href="https://arxiv.org/abs/2309.01247"><i>Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations</i> </a>with <a href="https://github.com/jbbremnes/pangu-asr">source codes</a> available in GitHub. The data is stored as a single JLD2 (HDF5) file containing a vector of six data frames.</p>
Forecasts, score summary files, target observational data and meteorological driver files to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen"
<p>This data publication includes raw ensemble forecast output (forecasts.zip), as well as summary score files (scores.zip) for process-based forecasts produced with the Forecasting Lake and Reservoir Ecosystems (FLARE) framework. In addition, it includes scores for climatology (climatology_scores.csv) and random walk (RW_scores.csv) null forecasts, formatted observational data of target variables (sunp-targets-insitu.csv), and meteorological driver files required for analysis to accompany the manuscript "Skill of process-based forecasts relative to multiple null models varies across time and depth for water temperature and dissolved oxygen". Forecasts were made of water temperature and dissolved oxygen at Lake Sunapee, NH in 2021 and 2022.</p>
Lightning Prediction in the Tehran Region Using the WRF Model with Multiple Physical Parameterizations and an Ensemble Approach
<p><span>The Grid Analysis and Display System (</span>GrADS)<span> </span><span>and</span><span> </span><span>Python</span><span> </span><span>scripts and the output data from simulations that we used in this study.</span></p>
Data assimilation products by using multiple climate model simulations and different combinations of proxies
<p>This dataset of the climate reconstruction by data assimilation using isotope ratios provides annual surface air temperature, precipitation amount, and other climate variables during 850–2000.</p> <p>Two isotopes-incorporated atmospheric general circulation models and 129 isotopic proxy data (65 corals, 43 ice cores, and 21 tree-ring cellulose) were used in this study. There are nine experiments using three type of simulations and three combinations of proxies.</p> <p>The associated publication: Shoji, S., Okazaki, A., & Yoshimura, K. (2020). Impact of proxies and prior estimates on data assimilation using isotope ratios for the climate reconstruction of the last millennium. (submitted to Earth and Space Science)</p> <p>[Data structure]<br> X(lon) x Y(lat) x Z(2) x Variables(8) x Year(1151)<br> Z(1): analyses<br> Z(2): priors</p>
Observational Dataset for "Constraining Global Coronal Models with Multiple Independent Observables", Badman et al. (2022). Arxiv : https://arxiv.org/abs/2201.11818
<p>Observational Dataset for "Constraining Global Coronal Models with Multiple Independent Observables", Badman et al. (2022). Arxiv : https://arxiv.org/abs/2201.11818</p> <p>-----------------------------<br> -----------------------------</p> <p>Contact : Samuel T. Badman (he/him) samuel_badman@berkeley.edu, Space Sciences Lab, UC Berkeley.</p> <p>-----------------------------<br> -----------------------------</p> <p>License : Creative Commons Attribution 4.0 International</p> <p>-----------------------------<br> -----------------------------</p> <p>Research Goal of Dataset : Data supports the above titled work in defining a framework for evaluating the magnetic structure of global coronal models via the evaluation of three single valued metrics. This repository contains observational data products used as input for the studies described in this work with the aim to allow external coronal modelers to reproduce and evaluate their own work against the same dataset we used.</p> <p>-----------------------------<br> -----------------------------</p> <p>Structure of files : This repository contains three subfolders each containing observational data relating to the three metrics defined in Badman et. al. (2022). These are :</p> <p>-----------------------------</p> <p>1) ``Metric1_EUVCarringtonMaps''</p> <p>Content :</p> <p><carr_maps.####.final.h5> : Carrington maps of extreme ultraviolet (EUV) emission as observed by the SDO/AIA. These files contain slices of different wavelengths together, saved in hdf5 format. Maps for Carrington rotations (#### = 2210,2215,2216,2221) span the time intervals of interest in the associated work. The 193 angstrom wavelength slice from these maps were used as input into the EZSEG algorithm (see manuscript text) to generate ``observations'' of coronal hole boundaries which can then be compared via binary classification to modeled open field boundaries.</p> <p><read_plot_example_metric1.py> : A python script which demonstrates reading in the hdf5 files and viewing the names of the different slices, then plots the 193 slice. The slice name of primary interest is 193A ('map_0193'), but slices at 171,211 angstrom, and a magnetogram are included.</p> <p>-----------------------------</p> <p>2) ``Metric2_StreamerBelt''</p> <p><read_plot_example_metric2.py> : A python script which demonstrates reading and plotting an example white light carrington map from this data set, as well as overplotting the downstream data extraction of the streamer maximum brightness (SMB) line.</p> <p><br> 2a) ``Metric2_StreamerBelt/WL_CarringtonMaps''</p> <p>Content :</p> <p><WL_CRMAP_YYYYMMDDTHHmmSS_LC2_5p0Rs.fits> : Carrington maps of white light intensity extracted at 5.0Rs altitude using coronagraph images taken by SOHO/LASCO, using the method described in the manuscript and Poirier et al. (2021). Maps at a daily cadence over each 60 day time interval studied in the manuscript are included here, incorporating the new data available as the sun rotated. Here saved as fits files.</p> <p><WL_CRMAP_YYYYMMDDTHHMMSS_LC2_5p0Rs.mat> : Carrington maps as above but saved in .mat format (MATLAB).</p> <p>2b) ``Metric2_StreamerBelt/SMB_Line_Extractions''</p> <p><C2_YYYMMDDHHmmSS_5.0Rs_SMB.ascii> : Downstream processed versions of the relevant White light carrington map from which the line of maximum brightness (SMB line) has been extracted, as well as the streamer belt "thickness" at each longitude. This is tabulated as a 3d coordinate gridded evenly in longitude, and each SMB grid point as a northwards and southwards thickness, tabulated in degrees. These data are described in the header of each file and the extraction process is described in detail in the manuscript.</p> <p>-----------------------------</p> <p>3)Metric3_InSituTimeSeries</p> <p>Content :</p> <p><E##_XYZ_polarity.txt> : In situ polarity timeseries for 60 day intervals at 1 hour cadences during PSP encounters ## = [01,02,03], measured by spacecraft XYZ = [PSP,STA,OMN], Parker Solar Probe, STEREO A and OMNI (Earth-L1 dataset). Data values are +/- 1 indicating if magnetic vector is directed sunward or antisunward for each hour. This value is determined as described in the main text by finding the peak of a histogram of 1D B_R values over that hour interval and taking its sign.</p> <p><read_plot_example_metric3.py> : A python script which demonstrates reading in the in situ timeseries for encounter 1 and plotting them.</p> <p><gen_ss_footpoints_psp.py> : A python script which demonstrates an open source method to produce source surface footpoints for a given spacecraft (here PSP) which can be used to sub-sample a HCS map provided by a modeler to generate a modeled time series which can be used to produce scores for metric 3 described in the associated manuscript.</p> <p>-----------------------------<br> -----------------------------</p> <p>Python scripts included in this dataset use python packages</p> <p>astropy - https://github.com/astropy/astropy<br> h5py - https://github.com/h5py/h5py<br> astrospice - https://github.com/dstansby/astrospice<br> matplotlib - https://github.com/matplotlib/matplotlib<br> sunpy - https://github.com/sunpy/sunpy</p> <p> </p> <p> </p>
Data availability: Random encounter model is a reliable method for estimating population density of multiple species using camera traps
<p>Data of the paper entitled "Random encounter model is a reliable method for estimating population density of multiple species using camera traps" published on Remote Sensing in Ecology and Conservation</p>
Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.
<p>Cloud computing is one of the most popular and sophisticated technologies adopted by organizations worldwide. Some world-leading organizations enhance their efficiency and effectiveness by using cloud computing technology. Working from home (WFH) has been a popular trend among organizations during the coronavirus (COVID-19) pandemic. The COVID-19 saw a breakthrough in work cultures and environments where working from home was a remarkable success in remote working environments, despite being a rare phenomenon in Sri Lanka. Yet, it is argued that the deployment of work from home has not been effective among Sri Lankan business organizations due to a lack of IT infrastructure, facilities, and knowledge. The purpose of the study is to investigate the impact of cloud computing, embracing the service models (Infrastructure as a Service, Platform as a Service, and Software as a Service) as theoretical lenses and testing the COVID-19 as the moderator. The study has been conducted based on a deductive approach and adopted a stratified random sampling method. The sample consisted of 384 IT employees among those who had experienced working from home. The study utilized multiple regression and found that cloud computing service models significantly impact work from home with the moderating effect of COVID-19.</p>
Datasets related to the paper " Inception Models for Fashion Image Captioning: An Extensive Study on Multiple Datasets"
<p>This collection contains three datasets in HDF5 format: FashionCap, ReducedInFashAI, ReducedFACAD.</p>
Development of an imaging toolbox to assess the therapeutic potential and biodistribution of macrophages in a mouse model of multiple organ dysfunction
<p>Data set to accompany manuscript entitled "Development of an imaging toolbox to assess the therapeutic potential and biodistribution of regenerative therapies in a mouse model of multiple organ dysfunction " which can be found on BioRxiv.</p>
Simulated tracer-gas distribution in a multiscale model of the human lung during multiple-breath nitrogene washout
<p><em><strong>Content</strong></em></p> <p><strong>baseline:</strong></p> <ul> <li>inletFlow (ASCII format data for flow rate at the mouth in m^3/s, sampling frequency 1kHz)</li> <li>primary_results (ASCII format data table with four colums: time in seconds, N2 concentration (normalized), <em>empty </em>-1, pleural pressure in Pascal)</li> </ul> <p><strong>compliance modification (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> <li>duct: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) witin the airway network. Sampling frequency 50Hz (separat vtk-file for each timestep). <em>Inspect for instance with the VisIt (Lawrence Livermore National Laboratory) free visualization software.</em></li> <li>lobule: unstructured VTK mesh data of several scalar quantities (airway dimensino, pressure, N2 concentration, flow velocity) within the trumpet lobules.</li> </ul> <p><strong>compliance modification (regional):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> <li>duct: (same format as described above)</li> <li>lobule: (same format as described above)</li> </ul> <p><strong>size modification (regional):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul> <p><strong>resistance modification (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul> <p><strong>healthy controls (local):</strong></p> <ul> <li>inletFlow (format as in baseline)</li> <li>primary_results (format as in basline)</li> </ul>
Consistent Modeling of GS 1826-24 X-Ray Bursts for Multiple Accretion Rates Demonstrates the Possibility of Constraining rp-process Reaction Rates
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018arXiv180505552M/abstract">Meisel (2018)</a>. MESA version 9793.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aac3d3">10.3847/1538-4357/aac3d3</a></p>
Model simulated potential natural vegetation state in the western US under preindustrial, historic, and future (RCP8.5) atmospheric conditions using multiple parameterizations of the dynamic vegetation model TRIFFID.
<p>DATA DESCRIPTION<br> Author contact information:<br> Linnia R. Hawkins<br> Oregon State University<br> lhawkins@oregonstate.edu; linnia.hawkins@gmail.com<br> Data supporting 2019 Journal of Advances in Modeling Earth Systems publication</p> <p>Simulations of the equilibrium vegetation distribution in the western US performed with the climate model HadAM3p-HadRM3p-MOSES2-TRIFFID</p> <p>step1: Identify Influential Parameters<br> All files labeled step1.<br> EXPERIMENT DESCRIPTION: Data used in step 1: identify influential parameters <br> sensitivity experiment adjusting one parameter at a time 38 individual parameters were adjusted to 7 values, equally spaced<br> over a defined plausible range. For reference nine simulations with the default model parameterization are included, initiated with unique initial potential temperature perturbations. </p> <p>The data contains the vegetation state variables at the end of four-year simulations (January 2004 to December 2007) during which two equilibrium time steps with the dynamic vegetation model TRIFFID (Cox et al., 2001). The results are averaged over three simulations initiated with unique atmospheric potential temperature perturbations.</p> <p>FILE DESCRIPTION:<br> NETCDF: Each netcdf file contains the fractional coverage (field1391), leaf area index (field1392), and the canopy height (field1393) for 5 plant functional types (PFTs: broadleaf, needleleaf, c3 grass, c4 grass, shrub) simulated for November 29, 2007. </p> <p> File labeling scheme: <br> step1_parameter_settingindex_3ICave.nc</p> <p> parameter: the name of the only model parameter adjusted<br> setting index: the index of the parameter setting (1-7)<br> index of 1 references the lowest plausible parameter setting<br> index of 7 references the highest plausible parameter setting<br> index of 4 references the parameter setting half way between the lowest and highest plausible parameter settings. <br> 3ICave: references that the results have been averaged over 3 initial conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> </p> <p>step2: ParameterSensitivity<br> all files labeled step2<br> EXPERIMENT DESCRIPTION:<br> Data used in step 2: parameter sensitivity <br> Perturbed Parameter Experiment (PPE) simultaneously adjusting 18 parameters.<br> Latin hypercube sampling was employed to generate 250 unique parameterizations references with a SETID (ranging from 1-359)<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p>FILE DESCRIPTION:<br> NETCDF: files contain either the fractional coverage (field1391) or the above ground biomass (field1512) for 5 plant functional types (PFTs) simulated for November 1907.</p> <p> File labeling scheme: <br> step2_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]</p> <p>TXT: files contain a list of the model parameterizations (for each PFT and variable) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p>RESTART: restart_region_TPPE_c374_1903-12-01.nc<br> The restart file contains the model state variables after spinup. This file was used to initiate all model simulations in step2.</p> <p>step3: ParameterSetSelection<br> All files labeled step3<br> EXPERIMENT DESCRIPTION:<br> Data used in step 3: parameter set selection<br> PPE simultaneously adjusting 10 parameters. <br> Latin hypercube sampling was employed to generate 140 unique model parameterizations, referenced with a SETID (ranging from 3-276).<br> The data provided contains the simulated vegetation state after 4 year simulations (December1903-November1907) with two TRIFFID equilibrium rounds. <br> Data is averaged over 5 initial atmospheric conditions.</p> <p><br> FILE DESCRIPTION:<br> NETCDF: files contain either the biomass (field1512) fractional coverage (field1391) canopy height (field1393) for 5 plant functional types (PFTs) simulated for November 1907 or the net primary productivity (NPP; item3262_monthly_mean) for December 1903 through November 1907. </p> <p> File labeling scheme: <br> step3_variable_parametersetindex_5ICave.nc</p> <p> variable: the name of the variable(s) contained in the file<br> setting index: the index of the parameter setting corresponding to the parameter set text files<br> 5ICave: references that the results have been averaged over 5 initial atmospheric conditions.</p> <p> Variables:<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> item3262_monthly_mean – Net primary productivity – units: (kgC/m2/sec) – all PFTs</p> <p>TXT: files contain a list of the model parameterizations (for each PFT) and the corresponding to the parameter set index. <br> Parameters are labeled in row 1<br> Parameter set indices are shown in column 1</p> <p><br> production_runs<br> files labeled PI, historical, and future<br> EXPERIMENT DESCRIPTION:<br> Data simulated in the production runs. Model spinup was performed under preindustrial conditions with 10 unique model parameterizations (pset0-pset9). The resulting vegetation distribution for each parameterization after spinup are included and labeled PIrestarts. These restarts were used to initiate (or restart) the simulations under historic and future (RCP8.5) climate conditions. Files labeled historic contains the simulated vegetation state after 5 year simulations (2004-09-01 to 2009-08-30) with one TRIFFID equilibrium round occurring at the end. Files labeled future contain the simulated vegetation state after 5 year simulations (2054-09-01 to 2059-08-30) with one TRIFFID equilibrium round occurring at the end. </p> <p>FILE DESCRIPTION:<br> NETCDF: files contain the fractional coverage (field1391), leaf area index (field1392), canopy height (field1393), and biomass(field1512) for 5 plant functional types (in the order broadleaf, needleleaf, C3 grass, C4 grass, shrub).</p> <p><br> File labeling scheme:<br> pset* where * refers to the model parameterization 0-9<br> files were simulated with the model parameterization *, initiated with a unique initial condition (perturbation to the potential temperature field). </p> <p> Variables (PIrestart)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf] <br> field1391_1 – fractional coverage of PFT – units: fraction – [needleleaf]<br> field1391_2 – fractional coverage of PFT – units: fraction – [c3grass]<br> field1391_3 – fractional coverage of PFT – units: fraction – [c4grass]<br> field1391_4 – fractional coverage of PFT – units: fraction – [shrub]<br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf]<br> field1392_1 – leaf area index of PFT – units: m2/m2 – [needleleaf]<br> field1392_2 – leaf area index of PFT – units: m2/m2 – [c3grass]<br> field1392_3 – leaf area index of PFT – units: m2/m2 – [c4grass]<br> field1392_4 – leaf area index of PFT – units: m2/m2 – [shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf]<br> field1393_1 – canopy height of PFT – units: meters – [needleleaf]<br> field1393_2 – canopy height of PFT – units: meters – [c3grass]<br> field1393_3 – canopy height of PFT – units: meters – [c4grass]<br> field1393_4 – canopy height of PFT – units: meters – [shrub]<br> </p> <p> Variables (historic/future)<br> field1391 – fractional coverage of PFT – units: fraction – [broadleaf; needleleaf; c3grass; c4grass; shrub] <br> field1392 – leaf area index of PFT – units: m2/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1393 – canopy height of PFT – units: meters – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> field1512 – above ground biomass of PFT – units: kgC/m2 – [broadleaf; needleleaf; c3grass; c4grass; shrub]<br> <br> </p>
Multiple-model stock assessment frameworks for precautionary management and conservation on fishery-targeted coastal dolphin populations off Japan
<p>1. Stock assessment approaches are often oversimplified due to lack of biological knowledge and insufficient data. In spite of worldwide attention, fishery-targeted coastal dolphin species in Japan have lacked in-depth quantitative stock assessments because of limited time-series of population size and an absence of associated biological information. We consequently developed integrated population models that analyzed multiple sources of data simultaneously with published biological information within a Bayesian framework.<br> 2. We estimate population status and trends for three main species targeted by fisheries, bottlenose dolphins <em>Tursiops truncatus</em>, Risso's dolphins <em>Grampus griseus</em>, and short-finned pilot whales <em>Globicephala macrorhynchus</em>, using single-species age-aggregated and age-structured population dynamics models. Models were fit to absolute abundance estimates from systematic line-transect surveys, four series of abundance indices calculated from fisher's logbooks, and historical catch records. Published biological information was used to develop prior distributions for the biological parameters. We assessed the possible effects of ecological disturbance and competition using state-space and multispecies models.<br> 3. The multispecies model was selected by the model selection both for the age-aggregated and the age-structured approaches.<br> 4. Single-species assessments found that population size declined 4.2% (Risso's dolphin) to 8.0% (bottlenose dolphin) for three species since the late 1800s based on median posterior values, while the state-space and multispecies models found that environmental disturbance and an interaction among species could have reduced population size more substantially.<br> 5. '<em>Policy implications</em>' Simple single-species models are often used to provide conservation and management advice for wild animals but, in this case, results from such models are overly optimistic because they overlook important ecological process. In contrast, current population status could be less favorable if environmental disturbance and interspecific competition actually control population dynamics. Even if that is not the case, considering ecological process in the model will provide more precautionary ways. Our approach of combining multiple modelling frameworks is applicable to many other management systems, and offers increased confidence in estimated status and trends over assessments that consider only a single model.</p>
Datasets and R script to replicate the theoretical modeling from the article entitled Multiple hosts, multiple impacts: the role of vertebrate host diversity in shaping mosquito life history and pathogen transmission
<p>Datasets and R script to replicate the theoretical modeling from the article entitled "<strong> </strong>Multiple hosts, multiple impacts: the role of vertebrate host diversity in shaping mosquito life history and pathogen transmission" by Vantaux A., Moiroux N., Dabire K. R., Cohuet A., Lefevre T. 2023</p>
Data used to create figures and tables in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains all simulation output and observational data of ground-based/satellite-retrieved meteorological and air quality for computing statistical metrics in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. Simulation and observational results of meteorological and air quality including four folders:</p> <p> Day_PBLH: Daily PBLH data</p> <p> Hour_air: Hourly air quality data regarding PM2.5, O3, SO2, NO2 and CO</p> <p> Hour_met: Hourly meteorological data regarding T2, Q2, RH2, WS10 and precipitation</p> <p> Hour_radiation: Hourly surface radiation data</p> <p>2. Simulation and satellite-retrieved results of meteorological and air quality including nine folders:</p> <p> AOD: Yearly and seasonal AOD data</p> <p> CF: Yearly and seasonal CF data</p> <p> CO: Yearly and seasonal CO data</p> <p> LWP: Yearly and seasonal LWP data</p> <p> NO2: Yearly and seasonal NO2 data</p> <p> O3: Yearly and seasonal O3 data</p> <p> Precipitation: Yearly and seasonal precipitation data</p> <p> Radiation: Yearly and seasonal radiation data</p> <p> SO2: Yearly and seasonal SO2 data</p>
Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
Mobile App Education Based on Orem's Self-Care Model in Multiple Myeloma Patients
ClinicalTrials.gov study NCT07088783. IPD Sharing: NO. Countries: 1. Publications: 2.
First-in-Human Trial of Single Ascending Dose, Multiple Ascending Dose and Malaria Challenge Model in Healthy Participants
ClinicalTrials.gov study NCT03261401. IPD Sharing: NO. Countries: 1. Publications: 1.
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.