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5,805 results for “Data model”
DeepStruc: Towards structure solution from pair distribution function data using deep generative models
<p>XYZ files, PDF dataset and XGB model to use MetalFinder which is one of the baseline models in the paper.</p>
Selected data analysed in the JGR Atmosphere manuscript "Noah-MP with the generic crop growth model Gecros in the WRF model: Effects of dynamic crop growth on land-atmosphere interaction"
<p>This depository contains the simulated 3-hr data of 2m-temperature (tas), latent heat flux (hfls), sensible heat flux (hfss), soil moisture (mros) of the top 1 m, convective available potential energy (cape), convection inhibition (cin), leaf area index (lai) for the model domain, which centers Germany. Further it contains the namelist.input of the WRF simulations of the CTRL and EXP_CROP run. The simulations are described in the manuscript "Noah-MP with the generic crop growth model Gecros in the WRF model: Effects of dynamic crop growth on land-atmosphere interaction" by Warrach-Sagi et al., 2022</p>
JAMES technical report, scripts and data for "'Comparison of C3 photosynthetic responses to light and CO2 predicted by the leaf photosynthesis models of Farquhar et al. (1980) and Goudriaan et al. (1985)"
<p>Dear reader,</p> <p>In this repository you will find 7 MATLAB scripts and 3 Excel datasets. The script "A_curves_Figure2.m" calls the function scripts "FvCB_model_Figure2.m", "FvCB_model_Figure2_noTPU" and "G85_model_Figure2" to create Figure 2 of the JAMES publication. The script "G85_Rd_FigureS1" calls the function script "G85_model_FigureS1" to create Figure S1 of the Supporting Information. The script "r2_RMSE_Table2" calculates statistics displayed in Table 2 of the JAMES publication. The Excel worksheet "Table2.xlsx" contains the values in Table 2 of the JAMES publication for quick data copying. The Excel worksheets "FvCBparameters_fitted_withTPU.xlsx" and "FvCBparameters_fitted_noTPU.xlsx" contains fitted FvCB model parameter values that were obtained using the "fitaci" function from the plantecophys R package (Duursma, 2015). <br> <br> Kind regards,</p> <p>Kevin van Diepen </p>
Data from: Hierarchical multi-taxa models inform riparian vs. hydrologic restoration of urban streams in a permeable landscape
The degradation of streams caused by urbanization tends to follow predictable patterns; however, there is a growing appreciation for heterogeneity in stream response to urbanization due to the local geoclimatic context. Furthermore, there is building evidence that streams in mildly sloped, permeable landscapes respond uncharacteristically to urban stress calling for a more nuanced approach to restoration. We evaluated the relative influence of local-scale riparian characteristics and catchment-scale imperviousness on the macroinvertebrate assemblages of streams in the flat, permeable urban landscape of Perth, Western Australia. Using a hierarchical multi-taxa model, we predicted the outcomes of stylized stream restoration strategies to increase the riparian integrity at the local scale or decrease the influences of imperviousness at the catchment scale. In the urban streams of Perth, we show that local-scale riparian restoration can influence the structure of macroinvertebrate assemblages to a greater degree than managing the influences of catchment-scale imperviousness. We also observed an interaction between the effect of riparian integrity and imperviousness such that the effect of increased riparian integrity was enhanced at lower levels of catchment imperviousness. This study represents one of few conducted in flat, permeable landscapes and the first aimed at informing urban stream restoration in Perth, adding to the growing appreciation for heterogeneity of the Urban Stream Syndrome and its importance for urban stream restoration.
Taylor Valley meteorological input data for ICEMELT model
<p>These input files were too large to be included in ICEMELT-Cross GitHub repository (https://doi.org/10.5281/zenodo.6808770). To run the ICEMELT model, copy and paste the contents of this repository into the 'input' directory.</p>
Grey-brick buildings, an open data set of calibrated RC models of Dutch residential building heat dynamics
<p>Building thermal modeling is the founding stone upon which numerous carbon reduction strategies in the building sector are built. Yet, as of today, little to no interpretable and calibrated models founded on real-world measurements have been open-sourced. This work attempts to remedy this deficiency and renders public improved results of a recently published stochastic model identification of building heat dynamics study evaluated over 225 Dutch residential buildings. Calibrated lumped resistance-capacity models are made available, along with thermal characterizations of the buildings and reported meta-data. The paper discusses how open-access building thermal models support a collection of building service applications such as building performance benchmarks, model-based control, demand-side management, policy impact assessment, and data augmentation. Insights provided present a starting point for open access benchmarks of building thermal dynamics, paving the way toward new scientific discoveries from common standards.</p>
Modelling data for article "Intrinsically disordered ectodomain modulates ion permeation through a metal transporter"
<p>Full-length hCtr1 models used to perform classical molecular dynamics (MD) simulations described in the article "Intrinsically disordered ectodomain modulates ion permeation through a metal transporter" by Aupič et al.</p> <p>The zipped file contains three folders named alpha, beta and unfolded. Each contains the full-length all-atom model of the human copper transporter 1 (hCtr1) with the N-terminal domains in the alpha, beta or the unfolded conformational state. We provide both the initial model (initial-model.pdb) created with MODELLER (version 10.0) homology modelling software (B. Webb, A. Sali, Comparative Protein Structure Modeling Using MODELLER. Curr. Protoc. Bioinforma. 54, 5–6 (2016)) and the model structure embedded in the lipid bilayer as obtained after a 1 μs MD simulation (after-md.pdb).</p>
Input data for deep learning model-analog
<p>This repository contains input data required to run the Deep Learning Model-Analog (<a href="https://github.com/kinyatoride/DLMA">GitHub</a>), as presented in the paper titled "Using Deep Learning to Identify Initial Error Sensitivity for Interpretable ENSO Forecasts" by Toride et al. A preprint is available at <a href="https://arxiv.org/abs/2404.15419">https://arxiv.org/abs/2404.15419</a>.</p> <p>The <code>cesm2</code> directory contains the Community Earth System Model Version 2 Large Ensemble (<a href="https://doi.org/10.26024/kgmp-c556" rel="nofollow">CESM2-LE</a>), while the <code>real</code> directory contains the Ocean Reanalysis System 5 (<a href="https://doi.org/10.24381/cds.67e8eeb7" rel="nofollow">ORAS5</a>) datasets. These datasets have been processed to provide detrended monthly anomalies and have been interpolated to two different resolutions: 2° × 2° and 5° × 5°. The 5°×5° files are used as input, while the 2°×2° files are used for analog forecasting.</p>
Daily climate and rainfall data for Niger 1983-2021, for use in SARRA-O crop simulation model
<p>This dataset contains daily rainfall and climate data for Niger, that can be used as input of the <a href="https://github.com/SARRA-cropmodels/SARRA-O">SARRA-O spatialized crop simulation model</a>. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for Niger, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²) at 0.1° spatial resolution from 01/01/1981 to 31/12/2021</li> <li>TAMSAT v3.0 (doi:<a href="http://doi.org/10.1038/sdata.2017.63">10.1038/sdata.2017.63</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.0375° spatial resolution from 01/01/1983 to 31/12/2021</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for Niger (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/1981 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>, on June 14th and 15th, 2023.</p> <p>The applicable licences are the licences of the respective datasets.</p>
Daily climate and rainfall data for northern Cameroon 2020-2022, for use in SARRA-Py crop simulation model
<p>This dataset contains daily rainfall and climate data for north Cameroon, that can be used as input of the SARRA-Py spatialized crop simulation model. This data can be directly put as input of SARRA-O model to perform computations and obtain simulation results.</p> <p>The archive contains :</p> <ul> <li>AgERA5 (doi:<a href="https://doi.org/10.24381/cds.6c68c9bb">10.24381/cds.6c68c9bb</a>) climatic data for north Cameroon, with daily geotiff files for minimum, maximum, mean temperature (°C), reference evapotranspiration calculated with Hargraeves formula (mm), and solar radiation flux (kJ/m²/d) at 0.1° spatial resolution from 01/01/2020 to 31/12/2022</li> <li>CHIRPS v2.0 (doi:<a href="https://doi.org/10.1038/sdata.2015.66">10.1038/sdata.2015.66</a>) satellite rainfall estimation data for north Cameroon (mm), with daily geotiff files at 0.05° spatial resolution from 01/01/2020 to 31/12/2022</li> </ul> <p>This data has been extracted from their original sources using the <a href="https://github.com/SARRA-cropmodels/SARRA-data-download">SARRA-data-downloader tool</a>.</p> <p>The applicable licences are the licences of the respective datasets.</p>
Validation of the Bond et. al. (2010) SDSS-derived kinematic models for the Milky Way's disk and halo stars with Gaia Data Release 3 proper motion and radial velocity data
<p>We validate the Bond et. al. (2010) kinematic models for the Milky Way's disk and halo stars with Gaia Data Release 3 data. Bond et al. constructed models for stellar velocity distributions using stellar radial velocities measured by the Sloan Digital Sky Survey (SDSS) and stellar proper motions derived from SDSS and the Palomar Observatory Sky Survey astrometric measurements. These models describe velocity distributions as functions of position in the Galaxy, with separate models for disk and halo stars that were labeled using SDSS photometric and spectroscopic metallicity measurements. We find that the Bond et al. model predictions are in good agreement with recent measurements of stellar radial velocities and proper motions by the Gaia survey. In particular, the model accurately predicts the skewed non-Gaussian distribution of rotational velocity for disk stars and its vertical gradient, as well as the dispersions for all three velocity components. Additionally, the spatial invariance of velocity ellipsoid for halo stars when expressed in spherical coordinates is also confirmed by Gaia data at galacto-centric radial distances of up to 15 kpc.</p>
Supplementary Data for Paper "Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo"
<p>This dataset contains data needed to reproduce all calculations and plots published in paper "Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo", B. Mazur, L. Firlej, and B. Kuchta, 2024, ACS Appl. Mater. Interfaces, DOI: 10.1021/acsami.4c02616. </p>
Merged HLS2 and GEDI data for estimating above ground biomass with IBM's granite-geospatial-biomass model
<p>This dataset contains merged Harmonized Landsat-Sentinel 2 (HLS2) (L30 only) and Global Ecosystem Dynamics Investigation (GEDI) L4A data following CRS:4326. It has been assembled for estimating above ground biomass with a fine-tuned granite geospatial foundation model developed by IBM Research. Please see https://huggingface.co/ibm-granite/granite-geospatial-biomass for more information on data preparation and model use.</p> <p><strong>HLS2—</strong>Masek, J., J. Ju, J. Roger, S. Skakun, E. Vermote, M. Claverie, J. Dungan, Z. Yin, B. Freitag, C. Justice. HLS Sentinel-2 MSI Surface Reflectance Daily Global 30m v2.0. 2021, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/HLS/HLSS30.002 </p> <p><strong>GEDI L4A—</strong>Dubayah, R.O., J. Armston, J.R. Kellner, L. Duncanson, S.P. Healey, P.L. Patterson, S. Hancock, H. Tang, M.A. Hofton, J.B. Blair, and S.B. Luthcke. 2021. GEDI L4A Footprint Level Aboveground Biomass Density, Version 1. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1907</p>
Supplemental Data Files for Advances in Organ-Specific Dosimetry Models for Radionuclides and Radiopharmaceuticals at Their Macro And Micro Scales
<p>Data sets for Aims 1 and 2 of my dissertation. Both aims contain three excel spreadsheets for a total of 6 files with the computed specific absorbed fractions and S-values.</p>
MetUM deterministic model data
<p>Met Office Unified Model deterministic forecast data presented in Senior et al. (2024, submitted). Precipitation (ppt) and zonal wind 850 hPa (uwnd-850) data are provided for global models configurations: CoMorph A, GAL9 and, regional model configurations RAL2T and RAL3 for 13 July 2021 case study. Models are initialised at 00:00 UTC on 09/07/21, 11/07/21 and 13/07/21.</p> <p><em><span>Senior et al. (submitted) Abrupt ending of MJO by CCKW precipitation </span><span>leaves swath of flooding across Indonesia. Submitted to Quarterly Journal of the Royal Meteorological Society.</span></em></p>
Mechanistic Exploration and Kinetic Modeling through In-Silico Data Generation and Probabilistic Machine Learning Analysis
<p>This zip file includes the dataset 'two_reactions_022624.csv,' which is used for training and testing ML/DL models in the paper 'Mechanistic Exploration and Kinetic Modeling through In-Silico Data Generation and Probabilistic Machine Learning Analysis,' as well as trained models and some files used for training the model. When running the model downloaded from GitHub, copy and paste the files downloaded from here into the subfolder with the same name and path as the one downloaded from GitHub.</p>
Data from "Cross-Domain Foundation Model Adaptation: Pioneering Computer Vision Models for Geophysical Data Analysis"
<p>This dataset includes the collected geoscientific data of<strong> lunar images, seismic data, and DAS arrays</strong>.</p> <p>It can be used to test the foundation model adaptation in the geophysical domain.</p> <p>Below are some brief desription of the datasets:</p> <p>1) Lunar images for<strong> crater detection<a href="https://moon.bao.ac.cn/">[CAS]</a></strong> : 1000 are used for training and 199 for testing, each with a size of 1022x1022.</p> <p>2) Seismic data for <strong>geobody identification<a href="https://kaggle.com/competitions/tgs-salt-identification-challenge">[TGS]</a></strong> : 3000 are used for training and 1000 for testing, each with a size of 224x224.</p> <p>3) Seismic data for <strong>facies classification<a href="https://www.aicrowd.com/challenges/seismic-facies-identification-challenge">[SEAM]</a></strong> : 250 are used for training and 45 for testing, each with a size of 1006x782.</p> <p>4) Seismic data for <strong>deep fault detection </strong>: 1081 are used for training and 269 for testing, each with a size of 896x896.</p> <p>5) DAS arrays for <strong>seismic event detection<a href="../records/8270895">[Biondi]</a></strong> : 115 are used for training and 28 for testing, each with a size of 512x512.</p> <p><strong>Tips: All the images are “float32” and the labels are "int8".</strong></p> <p>The test code has been published on GitHub: <strong><a href="https://github.com/ProgrammerZXG/Cross-Domain-Foundation-Model-Adaptation/">Cross-Domain-Foundation-Model-Adaptation</a></strong>.</p>
Data set for training a ML model to predict duration of MPI application phases (HPC system) - with previous phase info
<p>This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system.</p> <p>There are 10 different data sets corresponding to different HPC applications.</p> <p>These data sets contain information regarding the previous MPI call with same ID and type.</p>
Data set for training a ML model to predict duration of MPI application phases (HPC system) - without previous phases info
<p>This is the data used to train a ML model predicting the duration of MPI application phases, in a HPC system.</p> <p>There are 11 different data sets corresponding to different HPC applications.</p> <p>These data sets do not contain information regarding previous MPI calls</p>
SCB model data for article 'The effects of localized thermal pressure on equilibrium magnetic fields and particle drifts in the inner magnetosphere'
<p>The SCB model simulation data for the article 'The effects of localized thermal pressure on equilibrium magnetic fields and particle drifts in the inner magnetosphere'.</p> <p>The 'Rxx_A xx_Sxx_Lxxparameters' files are the data files, where Rxx is the beta_0 value, Axx is the A_e value, Sxx is the sigma_0 value and Lxx is the L_0 value (see the article for the definition of the values)</p> <p>The 'SCB_variables.xlsx' is a table to describe the variables in the data file.</p> <p>The 'read_scb.pro' is an IDL program to read the data files.</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.