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27,923 results for “model”
Point cloud data from terrestrial laser scanning for stem volume modelling of Scots pine trees
<p>Stem volume is a key forest inventory attribute characterizing growth and yield of individual trees and forest stands. Three-dimensional information from terrestrial laser scanning (TLS) can be used to reconstruct tree stems and provide information on stem volume as well as stem shape. We collected diameter at breast height and height information with traditional field measurements as well as preprocessed TLS point cloud data on 230 Scots pine trees (<em>Pinus sylvestris L.</em>) from southern Finland. The data set here includes three-dimensional information on Scots pine tree stems derived from TLS point clouds. The usage of this data set can include, but is not limited to, development of point cloud processing algorithms for single tree stem reconstruction and investigations of of stem volume modelling for Scot pine. </p> <p>This data set includes two files: Scots_pines.txt includes DBH and height information based on field measurements from the 230 Scots pine trees. File includes the following columns: treeID, DBH, and h, where DBH is presented in cm and h (i.e. tree height) in m. Stem_points.zip, on the other hand, includes 230 laz-files where figure in the name of the laz-file refers to the tree ID in Scots_pines.txt-file. Laz-files include three columns that describe x, y, and z, coordinates (in meters) of stem points in a local coordinate system extracted from the normalized TLS point clouds (i.e. z coordinate describes height above ground).</p>
3D density models of the Los Humeros and Acoculco geothermal fields, Mexico.
<p>The GEMex project addresses different challenges in the development of Enhanced Geothermal Systems (EGS) and Superhot Geothermal Systems (SHGS) in the Trans-Mexican Volcanic Belt. Although they are located in similar tectonic settings, the geothermal conditions in Acoculco and Los Humeros differ and they can be categorized as an EGS and a SHGS system, respectively. The Los Humeros field is currently under conventional exploitation. North of the current production area, temperatures higher than 380°C are expected. The Acoculco site presents temperatures >300°C at a depth of 2 km, but a reservoir has not been identified. The main goal of this work is to visualize and characterize the reservoir conditions using gravity data. To accomplish this, we processed data from a total of 344 gravity stations at Los Humeros and 84 stations at Acoculco. The datasets contain the 3D density model of the Los Humeros and Acoculco geothermal fields as density contrasts values in g/cm³. The background density is 2.67 g/cm³.</p>
Alaska 2020 update for USGS G19AP00019: Initial Development of Alaska Community Seismic Velocity Models
<p>Seismic velocity model AKEP2020 uses earthquake travel-time and ambient noise group velocity data to update Eberhart-Phillips et al. (2006: AKEP2006)</p> <p> </p> <p>The model is provided in a table: vlAKEP2020xyzltlnSFDRE.tbl.txt</p> <p>and in the simul output from velocity inversion: vlAKEP2020.out.txt</p> <p>Map plots of Vp and Vp/Vs are also provided, with lines denoting limits of adequate data.</p> <p> </p> <p>Velocity Inversion Procedure Notes for AKEP2020 model</p> <p> </p> <p>The 2006 AK model and the 2020 updated model both use Transverse Mercator coordinate transformation with central meridian= -150 and counterclockwise rotation of 162.1. Earth-flattening transformation is used for velocity during ray-tracing. The depths are relative to sea-level and station elevations are used. For the group-velocity data, the surface is taken as the 30-km median filtered topography.</p> <p>Velocity with the 3D gridded model is defined by linearly interpolating between nodes.</p> <p>A gradational inversion approach was used with earthquake and shot travel-time data, and group velocity observations for periods 6-15 s. The table provides, from the computed resolution matrix, the diagonal resolution element (DRE), and the spread function (SF), for each Vp and Vp/Vs node.</p> <p> </p> <p>This material is based upon work supported by the U.S. Geological Survey under Grant No. G19AP00019. Note that an earlier model, AKEP2018, from the first year of this funded project was reported in Eberhart-Phillips et al. (2019).<br> <br> The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey</p>
Data from: "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters"
<p>This dataset was used for the training of a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars. The light curves correspond to data obtained from The Optical Gravitational Lensing Experiment (<a href="https://ui.adsabs.harvard.edu/abs/1992AcA....42..253U/abstract">OGLE</a>), while ancillary information was obtained from the Gaia Data Release 2 (<a href="https://ui.adsabs.harvard.edu/link_gateway/2016A&A...595A...1G/doi:10.1051/0004-6361/201629272">GAIA DR2</a>). This repository contains the preprocessed OGLE light curves and the GAIA measurements corresponding to each cross-matched source. We also provided a subsample of cross-matched sources that were carefully validated following several steps described in the companion article (paper reference).</p> <p>This dataset is realized in tandem with the corresponding <a href="https://github.com/jorgemarpa/PELS-VAE">GitHub</a> and <a href="https://arxiv.org/abs/2005.07773">article</a>.</p> <p> </p> <p> </p>
Models and Infrastructure used in "Deep Statistical Model Checking"
<p>This repository contains the models and all other infrastructure (learning procedure, NNs, Jani generator, maps, modes & mcsta binaries) used in the FORTE 2020 paper "Deep Statistical Model Checking".</p>
Paleoclimate Data-Model Comparison and the Role of Climate Forcings over the Past 1500 Years
<p>The past 1500 years provide a valuable opportunity to study the response of the climate system to external forcings. However, the integration of paleoclimate proxies with climate modeling is critical to improving the understanding of climate dynamics. In this paper, a climate system model and proxy records are therefore used to study the role of natural and anthropogenic forcings in driving the global climate. The inverse and forward approaches to paleoclimate data-model comparison are applied, and sources of uncertainty are identified and discussed. In the first of two case studies, the climate model simulations are compared with multiproxy temperature reconstructions. Robust solar and volcanic signals are detected in Southern Hemisphere temperatures, with a possible volcanic signal detected in the Northern Hemisphere. The anthropogenic signal dominates during the industrial period. It is also found that seasonal and geographical biases may cause multiproxy reconstructions to overestimate the magnitude of the long-term preindustrial cooling trend. In the second case study, the model simulations are compared with a coral d18O record from the central Pacific Ocean. It is found that greenhouse gases, solar irradiance, and volcanic eruptions all influence the mean state of the central Pacific, but there is no evidence that natural or anthropogenic forcings have any systematic impact on El Nino-Southern Oscillation. The proxy climate relationship is found to change over time, challenging the assumption of stationarity that underlies the interpretation of paleoclimate proxies. These case studies demonstrate the value of paleoclimate data-model comparison but also highlight the limitations of current techniques and demonstrate the need to develop alternative approaches.</p>
RIBuild: Analysis of models for failure
<p>The dataset consists of data used for analysing a number of models, each related to a specific failure mode or failure mechanism that affects the material properties of building materials. Three RIBuild partners (KUL, UNIVPM, RISE) were responsible of performing laboratory tests to evaluate the models chosen to characterize a specific failure mode (frost, algae, mould). One RIBuild partner (DTU/AAU) used measurement data from a WP3 test setup to validate simulations of wood rot in wooden beam ends.</p> <p>Further details to be found in RIBuild deliverable D2.2.</p> <p>Overview of data files to be found in 'RIBuild data WP2 Model analysis' as part of this dataset.</p>
A Finite State Tranducer that models Chinese Historical Phonology
<p>This is a finite state transducer that tries to model Chinese historical phonology from Old Chinese as reconstructed by Baxter and Sagart in <em>Old Chinese: a New Reconstruction</em> (Oxford, 2014) to Middle Chinese as presented in the system of Baxter in <em>A Handbook of Old Chinese Phonology </em>(Mouton, 1992).</p>
Antarctic time series of temperature, precipitation, and stable isotopes in precipitation from the ECHAM5/MPI-OM-wiso past1000 climate model simulation
<p>This data set contains time series of two-metre air temperature (tas), surface temperature (ts), total precipitation (pr), oxygen-18 isotopic composition in precipitation (oxy), and deuterium isotopic composition in precipitation (dtr) from the past-millennium (800-1999 CE) simulation of the fully coupled ECHAM5/MPI-OM-wiso atmosphere-ocean general circulation model equipped with stable isotope diagnostics (Sjolte et al., 2018, Werner et al., 2016) used in the publication of Münch et al. (2021).</p> <p>The data here are provided for the Antarctic region, i.e., all model grid cells south of 60° S. The model's atmospheric component was run with a T31 spectral resolution (3.75° x 3.75°) and with 19 vertical levels, resulting in a total of N = 768 model grid cells covered by this data set. Note, however, that all time series off the continent of Antarctica have been set to NA values, so that the effectively available number of model grid cells is N<sub>eff</sub> = 442.</p> <p>Time series are provided at the original monthly resolution of the model output and on annual resolution obtained from the monthly resolution data. At annual resolution, the temperature and isotopic composition data are available as normal time averages and as precipitation-weighted time averages. In addition to the time series, the spatial field of time-invariant means is supplied, also as normal and precipitation-weighted time averages.</p> <p>Data are available as netcdf files and as R data files. In addition, processing code (bash and R scripts) are provided to reproduce the processing from monthly to annnual and time-invariant resolution and to read the data into the R data format. To process the R data, you will need the CRAN packages "ncdf4" and "lubridate", and the package "pfields" available on GitHub (see References).</p>
Finite element method (FEM) models for translational research in non-invasive brain stimulation
<p>Finite element method (FEM) models for non-invasive brain stimulation modeling using SimNIBS or other compatible software.<br> The mouse and monkey models are described in detail in Alekseichuk et al., Comparative modeling of transcranial magnetic and electric stimulation in mouse, monkey, and human, NeuroImage 2019.<br> The Petri dish model follows a typical experimental setup for in-vitro TMS, similar to what is described in Lenz et al. Repetitive magnetic stimulation induces plasticity of inhibitory synapses, Nature Communications 2016.<br> <br> The following files are included:<br> 1. Brain tissue slice in a Petri dish.<br> 2. Normal adult male nude mouse "Digimouse" (brain volume of 0.38 cm3).<br> 3. Normal adult male capuchin monkey "S" (brain volume of 68.31 cm3).<br> <br> The models include the following tissues (coded with numbers):<br> 1. White matter volume<br> 2. Grey matter volume<br> 3. CSF volume<br> 4. Skull volume<br> 5. Soft tissues volume<br> 8. Eyeballs volume<br> 1001. White matter outer surfaces<br> 1002. Grey matter outer surfaces<br> 1003. CSF outer surfaces<br> 1004. Skull outer surfaces<br> 1005. Soft tissues outer surfaces<br> 1008. Eyeballs outer surfaces<br> <br> With any questions, please, contact the corresponding authors of the relevant papers or <a href="mailto:aopitz@umn.edu">aopitz@umn.edu</a> (Alexander Opitz).</p>
Data release for paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models"
<p>This data release for the paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models" [<a href="https://arxiv.org/abs/2010.05830">arXiv:2010.2010.05830</a>] contains posterior samples for the GW190412 binary black hole merger event obtained from public GWOSC data with the parallel bilby Bayesian inference package, dynesty nested sampler and a set of waveforms from the "generation X" of phenomenological waveform models: IMRPhenomXAS, IMRPhenomXHM, IMRPhenomXP, IMRPhenomXPHM, IMRPhenomT and IMRPhenomTHM. The provided file is a "meta file" that can be read with the <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a> python package. The posterior samples included correspond to runs [2,6,10,12,14,26] in Table III of the paper (standard settings for each waveform, standar priors and sampler settings of Nlive=2048 and Nact=10 or 50). If you make use of these samples, please cite both this data release and the paper.</p>
WATET Model
<p>WATET is a fully distributed coupled hydrological and solute transport model. In this setup it simulates the water and conservative solute flow in the Plynlimon catchment (Wales).</p>
Large-eddy simulation investigating the role of double-diffusive convection in basal melting of Antarctic ice shelves: model output
<p>Model output used in the publication:</p> <p>M. G. Rosevear, B. Gayen, B. K. Galton-Fenzi, The role of double-diffusive convection in the basal melting of Antarctic ice shelves. <em>Proc. Natl. Acad. Sci. </em>(2021) https://doi.org/10.1073/pnas.207541118</p> <p>See README.md for a description of the data.</p>
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Unravelling winter diatom blooms in temperate lakes using high frequency data and ecological modeling
<p>This repository contains the dataset and the R script of the lake ecological model linked to the following publication:</p> <p>Article title: Unravelling winter diatom blooms in temperate lakes using high frequency data and ecological modeling</p> <p>Journal title: Water Research</p> <p>Article Number: 116681</p> <p>Abstract: In temperate lakes, it is generally assumed that light rather than temperature constrains phytoplankton growth in winter. Rapid winter warming and increasing observations of winter blooms warrant more investigation of these controls. We investigated the mechanisms regulating a massive winter diatom bloom in a temperate lake. High frequency data and process-based lake modeling demonstrated that phytoplankton growth in winter was dually controlled by light and temperature, rather than by light alone. Water temperature played a further indirect role in initiating the bloom through ice-thaw, which increased light exposure. The bloom was ultimately terminated by silicon limitation and sedimentation. These mechanisms differ from those typically responsible for spring diatom blooms and contributed to the high peak biomass. Our findings show that phytoplankton growth in winter is more sensitive to temperature, and consequently to climate change, than previously assumed. This has implications for nutrient cycling and seasonal succession of lake phytoplankton communities. The present study exemplifies the strength in integrating data analysis with different temporal resolutions and lake modeling. The new lake ecological model serves as an effective tool in analyzing and predicting winter phytoplankton dynamics for temperate lakes.</p>
ConFire Model input/output for South America
<p>ConFIRE input and output used in the submission of Kelley et al. "Low Climatic Influence found in 2019 Amazonia Fires".</p> <p>"input" dir including "amazon_inference_data-2002-MCDBA_obs-TERRA_M__T.csv", just contains a csv file of all common grid data in other "input" subdirectories, which can be used in "optimise_run_model/bayesian_inference.ipynb" notebook. The files in the subdirectories are then used to make gridded model output using "optimise_run_model/make_model_output.ipynb".</p> <p>Output, also provided, is summerized in the files in "outputs/sampled_posterior_ConFire_solutions/constant_post_2018_full_2002_BG2020/"</p> <p>The three files contain:</p> <ul> <li>fire_summary_frequancy_of_counts.nc - Contains one spatial variable (long name "firecount frequency of occurrence") is the probability of a fire count (along the model_level_number dimension) at a particular month (time dimension) according to the models full posterior, <span class="math-tex">\(P(y_j)\)</span>(see papers supplementary). All months start on Jan 2001. </li> <li>fire_summary_precentile.nc - Contains one spatial variable (long name "firecount at percentile"), the fire count at each percentile of the full posterior in 1% increments from 1-99% (along the model_level_number dimension). Note, by definition, the 0a nd 100% percentile is 0 and <span class="math-tex">\(\infty \)</span> .</li> <li>fire_summary_observed_liklihood.nc - the position ( "variable_0") and p-value ("variable") of MCD64A1 in the model posterior.</li> <li>model_summary.nc - Summary of each variable of ConFire: <ul> <li>"burnt_area": burnt area or fire count summary, described as percentiles as per "fire_summary_precentile", but this time just assessing parameter uncertainty, i.e <span class="math-tex">\(P(\beta | Y_s)\)</span> in supplementary of paper.</li> <li>All other variables describe different model controls in the same "burnt_area". Full information on how controls are constructed can be found in Kelley et al. (2019) with updates listed in supplementary of this paper. <ul> <li>"fuel_continuity", "moisture_content", "ignitions", "suppression" are the actual values of the control</li> <li>"standard_<<control>>" is the standard limitation imposed by the control.</li> <li>"potential_<<control>> is the potential limitation</li> <li>"sensitivity_<<control>> is the senstivity of fire to a particular control.<br> <br> See Kelley et al. 2019 for the definition of limitation types and sensitivity</li> </ul> </li> </ul> </li> </ul> <p>Kelley, D.I., Bistinas, I., Whitley, R. <em>et al.</em> How contemporary bioclimatic and human controls change global fire regimes. <em>Nat. Clim. Chang.</em> <strong>9, </strong>690–696 (2019) doi:10.1038/s41558-019-0540-7</p> <p> </p>
Data for "On the Practice of Semantic Versioning for Ansible Galaxy Roles: An Empirical Study and a Change Classification Model"
<p>This dataset accompanies a replication package provided for a study on Semantic Versioning for Ansible Galaxy roles.</p> <p>The replication package is available at https://github.com/ROpdebee/ansible_semver_ext_replication</p>
Values of the reference prior for the Poi(s+b) model from JINST 7 (2012) P01012
<p>Plain text table with the values of the reference prior π(s) for the Poi(s+b) model used in the statistical inference about counting experiments, as explained in JINST 7 (2012) P01012, doi:10.1088/1748-0221/7/01/P01012, http://arxiv.org/abs/1108.4270. The values are useful to find approximate expressions which are quicker to compute than the original prior, as explained in http://arxiv.org/abs/1407.5893 (where this dataset is referred to).</p> <p>Each line is a sequence of spaces-separated values, and the file can be considered a table. The first line starts with two strings "shape" and "rate" which represent the titles of the corresponding columns in the data table. They refer to the shape and rate parameters defining the background prior. Next, N signal values starting from s=0 to s=70 are reported. They are the values at which π(s) is computed for any subsequent line.</p> <p>Starting from the second line, the format is always the same. The first two values are the shape and rate parameters defining the background prior used to compute π(s) in this line. Next, the N values π(s=0), ..., π(s=70) are reported. As π(0) = 1, the third column is constant (it might be useful to debug the data reading).</p> <p>As explained in http://arxiv.org/abs/1108.4270, simple functional forms may be used to fit the N points (s, π(s)). As the shape and rate parameters from the user's application may be different from those reported in this table, the following procedure shall give a very good approximation to π(s). In the (log(shape), log(rate)) parameters space, locate the neighboring points to the user's background parameter values (in log-log scale). Then interpolate each of the π(s) values to obtain a set of N values (a linear interpolation in log-log scale shall be sufficient). Finally, fit these interpolated values to find the reference prior for the user's application.</p>
Pseudo-nitzschia multistriata gene models
<p>The resource contains fasta files with the <em>Pseudo-nitzschia multistriata</em> gene models and proteins, Pm-1.4_mRNA_v3.fa and Pm-1.4_peptide_v3.fa, a file with the annotation, psmu_mRNA_uniref_2015_06_filt_ann_out.txt, and a file with mapping information, genes_v3_WA.gff3.</p>
Modelled distributions of fish and epibenthic invertebrates in the southern North Sea
<p>These data include distribution maps of fish and invertabrate species in the southern North Sea from 2014 until 2023. The maps are modelled using point data of presence/absence and biomass (per trawled km²) from scientific fisheries surveys to estimate the distribution of the probability of occurrence (POC) or biomass (kg per km²), respectively. Also included are forecasts of species' distributions assuming increasing water temperatures in the southern North Sea according to the ICCP scenario RCP8.5.</p> <p>Each files contains a raster stack with layers for each species. The data can be read into the R using the 'stack'-command from the 'raster'-package. The raster stacks contain layers with headers, which code the species and size group. For some species of relevance to fisheries managment, Numbers behind the latin names of the species give information on the included size classes in cm with 'no' indicating no size class information was available.</p> <p>The file names are composed of the follwing elements:</p> <p>'bio' = biomass</p> <p>'poc' = probability of occurrence</p> <p>'emp' = observed occurrence/abundance data from fisheries surveys with employed spatial smoother</p> <p>'sdm' = modelled distributin data from random forests</p> <p>'fc' = forecast distributions based on temperature predictors according to RCP8.5</p> <p>'rel.ca2' = core areas (CA) of distribution representing values > then the mid-point of modelled POC value range</p> <p>Year numbers give the time frame of empirical data or model predictions. </p> <p> </p> <p><strong>You can access the .tiff-files with the following R-commands using the directory path where you have stored the files:</strong></p> <p><em><strong>library(raster)</strong></em></p> <p><em><strong>poc<-stack("your_path/poc.sdm.2014_2023.tiff")</strong></em></p> <p><em><strong>poc$gadus.morhua_5_113 </strong># Plots distribution of Atlantic cod as probability of occurrence observed at a size range from 5 - 113 cm tail length</em></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.