Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
96
datasets available to search
ShareScore release 0.9.0
Dataset results
96 results for “dispersion modelling”
Dataset and R-script for simple mechanistic model of Heracleum sosnowskyi seed dispersal by wind
<p>The dataset contains:</p> <p>- primary data about Heracleum sosnowskyi seeds traits (terminal velocity, mass, area, wing loading) and release heights for <em>H. sosnowskyi</em> populations from two geographically distant Russia regions;</p> <p>- results of experiments of model seeds launches under different wind speeds;</p> <p>- R script for exploratory statistical analysis, linear regressions and mechanistc models testing.</p> <p>The anemochorous seed dispersal was generalized with a number of empirical and mechanistic models of varying complexity. The aim of this work was to develop the simplest possible mechanistic model of <em>Heracleum sosnowskyi</em> that allows to determine the distance of seed dispersal by wind with an accuracy comparable to that of empirical measurements. We measured and compared the characteristics of the seeds (terminal velocity, mass, area, wing loading) as well as the release height for <em>H. sosnowskyi</em> populations from two geographically distant Russia regions. We tested two simplest mechanistic models: a ballistic model and a wind gradient model using identical artificial seeds with characteristics similar to those of real <em>H. sosnowskyi</em> seeds. The wind gradient model gave the best results, despite the fact that uniform in shape, weight and size artificial <em>H. sosnowskyi</em> seeds, when dropped simultaneously from the same height, fly off at different distances. This model provides an estimate of dispersal distances with an accuracy comparable to that of empirical measurements. We plan to use the presented model to develop an individual-based model that will allow us to calculate the flight distances of <em>H. sosnowskyi</em> propagules, taking into account real weather conditions in different years and in different parts of its invasion range. All primary data and R-scripts used are freely available at the Zenodo repository (https://doi.org/10.5281/zenodo.3766035).</p> <p> </p>
Ultrasonic guided-wave experiment data for manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textilecomposites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Experimental data in support of manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'
<p>This data set contains the ultrasonic guided wave signals (signal amplitudes as a function of time for different sensors) that were generated and recorded using the transducers and controlling instrument in support of the manuscript entitled 'A homogenisation scheme for Lamb ultrasound wave dispersion in textile composites through multiscale wave and finite element modelling'. The controlling software was programmed in MATLAB and that the attached files are in accordance to the .mat file format.</p> <p>The file names follow the notation described below with an example:</p> <p>S1_10kHz_2cyc (illustrated with an example): S1 represents the number of sensors; 10kHz represents the exciting frequency; 2cyc represents the cycle number of input waveform.</p> <p>Details on the experiment setup are provided within an extra file ('Readme' file).</p>
Particle Dispersion Model Dataset
<p>Data repository for the paper:</p> <p>Dispersion Model for Level Control of Bubbling Fluidized Beds with Particle Cross-Flow</p> <p>Chemical Engineering Research and Design 2025</p>
Data for: "Dynamic species distribution modeling reveals the pivotal role of human-mediated long-distance dispersal in plant invasion"
<p>All the data needed to reproduce the results and Figures of our article:</p> <p>Botella, C., Bonnet, P., Hui, C., Joly, A., & Richardson, D. M. (2022). Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion. <em>Biology</em>, <em>11</em>(9), 1293. <a href="https://doi.org/10.3390/biology11091293">https://doi.org/10.3390/biology11091293</a></p> <p>Please, find the R scripts and guidelines to reproduce our results on the article's Github repository :</p> <p><a href="https://github.com/ChrisBotella/plectranthus_barbatus/tree/main">https://github.com/ChrisBotella/plectranthus_barbatus/tree/main</a></p>
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Simulating the dispersal of Monochamus galloprovinciallis : R script of the dispersal model and video of the simulation
<p>This folder contains the R script to simulate the dispersal of Monochamus galloprovincialis from an individual-based model and the resulting video. This study was conducted in the frame of the FP7 project called "REPHRAME" and a working group of ANSES (French Agency for Food, Environmental and Occupational Health & Safety).</p> <p>This material complements the following publication:</p> <p>Robinet C, David G, Jactel H (2019) Modeling the distances traveled by flying insects based on the combination of flight mill and mark-release-recapture experiments. Ecological Modelling, 402: 85-92.<br> https://doi.org/10.1016/j.ecolmodel.2019.04.006</p>
Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers
<p>Hydraulic and tracer data used in the Chillan case study presented in "Data used in Quantifying the impact of uncertainty in the dispersion coefficient on water quality modelling in rivers"</p>
Fig. 3 in A New Model Of Stink Bug Traps: Heated Trap For Capturing Halyomorpha Halys During The Autumn Dispersal Period
Fig. 3. Mean number of stink bugs observed in the trapping site (statistics: repeated measure ANOVA, Durbin-Conover pairwise comparison test, P ≤ 0.05)
Fig. 2 in A New Model Of Stink Bug Traps: Heated Trap For Capturing Halyomorpha Halys During The Autumn Dispersal Period
Fig. 2. Arrangement of the traps. The edges of slots were marked with white lines on the photo, because of the better visualization
Machine Learning Models for Surface Wave Dispersion Curve Inversion using Mixture Density Networks
<p>Machine learning (ML) approach for dispersion curve inversion using mixture density networks (MDN) based on Keil and Wassermann (2023).</p> <p>The ML approach presented here allows the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S-wave velocity structure in the upper 100 m. This is achieved by a two-step ML approach, where 1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and 2) individual trained mixture density networks output the depth estimates together with a fully probabilistic solution of the S-wave velocity structure. We trained the model to distinguish structures with 2 - 7 subsurface layers.</p> <p>The trained classification NN and the individual MDNs are located in the folder ./trained_models.<br> With the jupyter notebook Prediction.ipynb the dispersion curve inversion can be performed using the already trained ML models.<br> With the jupyter notebooks Training-MDN.ipynb and Training-classification.ipynb the models can be trained on new data.<br> The code for the set-up of the MDN is based on Earp et al. (2020).</p> <p> </p> <p>More details and updates on the code can be found on: <a href="https://github.com/SabrinaKeil/MDN_Inversion">https://github.com/SabrinaKeil/MDN_Inversion</a> </p>
Genetic architecture of dispersal behaviour in the post-harvest pest and model organism Tribolium castaneum
<p>Dispersal behaviour is an important aspect of the life-history of animals. However, the genetic architecture of dispersal related traits is often obscure or unknown, even in well studied species.<em> Tribolium castaneum</em> is a globally significant post-harvest pest and established model organism, yet studies of its dispersal have shown ambiguous results and the genetic basis of this behaviour remains unresolved. We combine experimental evolution and agent-based modelling to investigate the number of loci underlying dispersal in <em>T.castaneum</em>, and whether the trait is sex-linked. Our findings demonstrate rapid evolution of dispersal behaviour under selection. We find no evidence of sex-biases in the dispersal behaviour of the offspring of crosses, supporting an autosomal genetic basis of the trait. Moreover, simulated data approximates experimental data under simulated scenarios where the dispersal trait is controlled by one or few loci, but not many loci. Levels of dispersal in experimentally inbred lines, compared with simulations, indicate that a single locus model is not well supported. Taken together, these lines of evidence support an oligogenic architecture underlying dispersal in <em>Tribolium castaneum</em>. These results have implications for applied pest management and for our understanding of the evolution of dispersal in the coleoptera, the world's most species-rich order.</p>
Genetic architecture of dispersal behaviour in the post-harvest pest and model organism Tribolium castaneum
Open the record for dataset details and reuse information.
Model simulation data used in "Modelling mineral dust emissions and atmospheric dispersion with MADE3 in EMAC v2.54" (Beer et al., Geosci. Model Dev., 2020)
<p>This dataset contains the output and the namelist setups of the EMAC-MADE3 global model simulations analysed and discussed in Beer et al. (<em>Geosci. Model Dev.</em>, 2020).</p>
Data from: Validating dispersal distances inferred from autoregressive occupancy models with genetic parentage assignments
1.Dispersal distances are commonly inferred from occupancy data but have rarely been validated. Estimating dispersal from occupancy data is further complicated by imperfect detection and the presence of unsurveyed patches. 2.We compared dispersal distances inferred from seven years of occupancy data for 212 wetlands in a metapopulation of the secretive and threatened California black rail (Laterallus jamaicensis coturniculus) to distances between parent-offspring dyads identified with 16 microsatellites. 3.We used a novel autoregressive multi-season occupancy model that accounted for both unsurveyed patches and imperfect detection to quantify patch isolation using buffer radius (BRM) and incidence function (IFM) connectivity measures at 15 scales (1–10, 15, 20, 25, and 30 km). Connectivity measures were then fit as colonization covariates in occupancy models to estimate a model-averaged dispersal distance. 4.As predicted, colonization was more strongly related to connectivity at small spatial scales (< 10 km). AIC weights were greatest at 7 km for BRM and at 4 km for IFM. 5.Model-averaged dispersal distances (BRM = 7.46 km; IFM = 5.48 km) showed good agreement with the mean (± SE) dispersal distance from 23 parent-offspring dyads (5.58 ± 1.92 km), indicating reasonably accurate mean dispersal distances can be inferred from occupancy data when isolation strongly affects colonization.
Data for "Wave dispersion and dissipation in landfast ice: comparison of observations against models"
<p>Data to replicate Figures 2, 3 and 7 in the manuscript: Wave dispersion and dissipation in landfast ice: comparison of observations against models. Article submitted for review to The Cryosphere (https://doi.org/10.5194/tc-2021-210)</p>
Data to support publication figures and animation scripts at GitHub: Modeling weather-driven long-distance dispersal of spruce budworm moths (Choristoneura fumiferana)
<p>Long-term studies of insect populations in the North American boreal forest have shown the vital importance of long-distance dispersal to the maintenance and expansion of insect outbreaks. In this work, we extend several concepts established previously in an empirically-based dispersal flight model with recent work on the physiology and behavior of the adult eastern spruce budworm (SBW) moth, Choristoneura fumiferana (Clem.). An outbreak of defoliating SBW in Quebec, ongoing since the mid-2000s, already covers millions of hectares of forests in eastern Canada and threatens to spread into neighboring areas through annual summertime episodes of long-distance dispersal. Such flight events in favorable conditions frequently include billions of SBW moths dispersing in the warm atmospheric boundary layer, typically starting around sunset and often lasting through several hours of wind-driven transport over hundreds of kilometers. Successful SBW dispersal to possibly distant host forest areas depends acutely on the weather. Here we describe the components and results of SBW–pyATM, an open-source individual-based modeling framework developed in Python for the simulation of these weather-driven SBW dispersal events. Using seasonal SBW phenology results from BioSIM at known outbreak locations and high-resolution Weather Research and Forecasting (WRF) model output, we focus on modeling dispersal flights over two successive nights in July 2013 in southern Quebec. Our flight model closely reproduces the SBW spatial patterns and motions observed by weather surveillance radar over the St. Lawrence estuary. With SBW–pyATM we can estimate landing locations for both male and female SBW and the resulting spatial patterns of egg distribution, allowing us eventually to forecast future larval defoliation activity in new locations where immigration could help overcome local limitations on SBW populations. This information could then support forest management decisions where SBW outbreaks threaten valuable resources.</p>
Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite"
<p>Model sets and data used in the preprint "Evaluating functional dispersal and its eco-epidemiological implications in a nest ectoparasite", reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100013). See preprint and supplementary materials.</p>
Modeling the dispersion of dissolved natural gas condensates from the Sanchi incident
<p>This is the data for submission '<strong>Modeling the dispersion of dissolved natural gas condensates from the Sanchi incident</strong>'. This zip file contains 6 subfolders. The folder named "initialdataofmodel" contains the daily oceanic velocity field for the model derived from Global Ocean Sea Physical Analysis and Forecasting Products distributed by CMEMS, which can be available from <a href="http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_ANALYSIS_FORECAST_PHY_001_024">http://marine.copernicus.eu/services-portfolio/access-to-products/?option=com_csw&view=details&product_id=GLOBAL_ANALYSIS_FORECAST_PHY_001_024</a> . Other folders contain the data for different figures (experiments) based on the model we used in the paper. The model output data (lon,lat,depth) is written by Fortran as a format of 'format(8e14.6)' .</p>
Hypothetical ensemble dispersion model runs with statistical verification
<p>This dataset contains output from the dispersion model NAME (Numerical Atmospheric-dispersion Modelling Environment) generated by modelling the hypothetical eruption of two volcanoes (Hekla and Oraefajokull both in Iceland) and a radiological release from 12 different locations across Europe. The hypothetical eruption of Hekla is a 12km eruption lasting 24 hours and the hypothetical eruption of Oraefajokull is a 25km eruption lasting 24 hours. Each of the radiological releases is a 1PBq Cs-137 release over 6 hours. The scenarios were modelling using three different sets of met data from the Met Office unified model; data from the global deterministic forecast, data from the global ensemble forecast and data constructed to form a global analysis. </p> <p>Output from the runs is stored in gzipped tar files labelled <name>_scenario_<year><month>.tar.gz where <name> is hekla, orae (short for oraefajokull) or radiological. Each of these tarballs contains the output from all the runs with a release start date in the year and month given in the file name. The data is stored in NetCDF format with each NetCDF file containing the run output from a single run with one type of met data. For example the NetCDF file: 20181109T1200Z_engl_members.nc contains all the output from the run started at 12 UTC on 9 November 2018 using the ensemble global forecast met data. </p> <p>For the radiological scenario output is the total integrated air concentration and the total deposition after 48 hours. For the volcanic eruption scenarios output is the hourly air concentration of volcanic ash on 22 vertical levels, hourly ash column load and hourly ash deposits.</p> <p>Two additional files are included. These contain the Brier skill score computed by comparing the ensemble and deterministic output to the analysis output and the maximum distance at which threshold concentrations are exceeded. Full details of the computation can be found in a paper submitted to Atmospheric Chemistry and Physics.</p> <p>@Crown Copyright, Met Office</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.