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5,805 results for “Data model”
Mechanical data of rotary shear experiments, temperature measurements, and temperature numerical models for the manuscript: "Mechanical energy dissipation during seismic dynamic weakening in calcite-bearing faults"
<p>All data included in this data repository is ancillary to the manuscript "Energy dissipation during dynamic weakening in calcite-bearing fault rocks", submitted to Journal of Geophysical Research: Solid Earth. </p><p>The data consists in time series of high velocity friction experiments run with SHIVA (INGV, Rome), time series acquired from a two-color pyrometer (UC3M), the synchronization of the two, and numerical models. The data format is .mat, proprietary to Matlab, but they can be easily accessed with Python (see <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">link</a>). Each .mat contains vector of the measured variables when opened from Matlab, or dictionaries when opened using the scipy.loadmat() function. </p><p>SHIVA and PYRO red data (calibrated data), fin data (synchronized data), and shivaRED vect data (numerical model data) are included in this data repository in separate folders. Numerical models are grouped in subfolder by type of model (the relation fin data to model is 1:n). We included the scripts to convert SHIVA raw data into SHIVA red data (<a href="https://github.com/aretu/shivaUNIX">link to shivaUNIX</a>), SHIVA and PYRO red data into fin data (/scripts/syncing2021.m), to obtain numerical models from fin data (<a href="https://github.com/aretu/shivaRED">link to shivaRED</a>), and to plot data (/scripts/making plots.ipynb).</p>
Data for: Ecological associations distribution modelling of marine plankton at global scale (2024)
<p>Datasets used to generate and project ADMs.</p> <p><a href="https://gitlab.univ-nantes.fr/combi-ls2n/adm">Click here to access to the git repository</a></p>
Data from: Distribution models predict climate-related range alteration or extinction of eleven threatened tropical rainforest trees in the Western Ghats
<p>This dataset contains information related to species occurence data and species distribution modeling (SDM) analysisr of eleven threatened tree species. Occurrences are compiled from extensive field surveys in the Anamalai Hills along with data from the Global Biodiversity Information Facility (GBIF.org) and earlier work done within the southern Western Ghats, India.</p> <p>References:<br>Page, N. V., & Shanker, K. (2020). Climatic stability drives latitudinal trends in range size and richness of woody plants in the Western Ghats, India. PLOS ONE, 15(7), e0235733. https://doi.org/10.1371/journal.pone.0235733</p> <p>GBIF.org (2022) GBIF Occurrence Download, 2 August 2022. DOI:10.15468/dl.gnvuxj</p> <p><br>AUTHOR #1<br>1. Name: A.P. Madhavan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: madhavan@ncf-india.org<br>4. ORCID: https://orcid.org/0009-0009-2754-8256</p> <p>AUTHOR #2<br>1. Name: Kshama Bhat<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: kshama@ncf-india.org<br>4. ORCID: ORCID: https://orcid.org/0000-0002-6190-2687</p> <p>AUTHOR #3<br>1. Name: Srinivasan Kasinathan<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: srini@ncf-india.org<br>4. ORCID: https://orcid.org/0000-0001-7323-6653</p> <p>AUTHOR #4<br>1. Name: Divya Mudappa <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: divya@ncf-india.org <br>4. ORCID: https://orcid.org/0000-0001-9708-4826</p> <p>AUTHOR #5<br>1. Name: Navendu Page<br>2. Work Address: Wildlife Institute of India, Post Box No. 18, Chandrabani, Dehradun, Uttarakhand 248001, India<br>3. Email address: navendu.page@gmail.com<br>4. ORCID: ORCID: https://orcid.org/0000-0002-9413-7571</p> <p>AUTHOR #6<br>1. Name: T. R. Shankar Raman <br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Email address: trsr@ncf-india.org <br>4. ORCID: https://orcid.org/0000-0002-1347-3953</p> <p>Keywords: tropical rainforest, climate change, tree distributions, species distribution models, range shifts, Western Ghats</p> <p><br>Geographic Coverage:<br>1. Location/Study Area: Southern Western Ghats Montane Rain Forests, Southern Western Ghats Moist Deciduous Forests, India<br>2. GPS coordinates: SWG (73.95° – 80.33° E, 8.06° – 13.11°N) </p> <p>Temporal coverage<br>Starts: 2020-08-01<br>Ends: 2024-03-28</p> <p>Besides this README.txt file, the dataset includes three comma-delimited text files (csv); two R scripts, and 1 kml file of surveyed trails.</p> <p>CSV files with the data in columns as explained below:</p> <p>1) Focal_Tree_Dat.csv</p> <p>Comp: Number identifier<br>FT_ID: Unique tree no for each individual<br>Focal_tree: Scientific name of species<br>Date: Date of occurrence observation<br>Place: Area/locality description<br>Trail: Unique trail ID<br>Waypoint: Waypoint number <br>Time: Time in hh:mm format <br>Location: Specific description of occurrence locality <br>Latitude: Latitude in decimal degrees N <br>Longitude: Longitude in decimal degrees E <br>Elevation: Elevation in metres <br>Slope: Cateory of slope <br>ID_Notes: Notes on identification<br>Phenophase: Phenophase expression at the time of observation <br>GBH: Girth at breast height in centimetres (comma separated list of numbers in case of multi-stemmed trees) <br>Tree_ht: Tree height in metres<br>Canopy_ht: Maximimum height of the surrounding canopy in metres<br>Substrate: Soil substrate composition<br>Invasives: Name of invasive species (if present) <br>Stature: Vegetation strata position <br>Relatively: Stature of focal individual relative to other surrounding individuals <br>Deadwood: Description of deadwood on the tree <br>Damage: Description of damage on the bole <br>Shape: Description of tree canopy shape<br>Closure: Canopy closure at focal tree <br>Seedlings: Number of conspecific seedlings present in 5 m radius of focal tree <br>Saplings: Number of conspecific saplings present in 5 m radius of focal tree<br>Trees: Number of conspecific trees present in 5 m radius of focal tree<br>Remarks: Remarks </p> <p>2) Ffspecies.csv</p> <p>Source: Source of occurrence <br>ID: State/location of occurrence<br>Region: Biogeographic region of occurrence <br>decimalLatitude: Latitude in decimal degrees N<br>decimalLongitude: Longitude in decimal degrees E<br>species: Scientific name of species</p> <p>4) ft_surveys.csv</p> <p>Date: Date of survey of sample trail<br>Prot_type: Category indicating whether protected area or fragment <br>Place: Area/locality description<br>Route_description: Specific landmark description of trail<br>Trail: Unique trail ID <br>Trail_distance: Tracked distance of trail in km <br>Corrected_trail_distance: Corrected distance of trail in km<br>Track_filename_kml: File name of gps track<br>Sample_collected: Name of species if sample collected <br>Observers: Name of observers <br>Remarks: Remarks</p> <p>ANALYSES SCRIPTS<br>flexsdm_script.R<br>Script containing the analysis of all maxent distribution modeling and associated analysis</p> <p>Franklinia_density.Rmd<br>Script of density and abundance related analysis</p> <p> </p>
Glider Data South Atlantic Subtropical Model Water (SAMOWA) Project 2018
<p>Glider data collected at the South Atlantic ocean during the winter of 2018 (see Sato et al. (2024), JGR-Oceans, for details).</p> <p>The files are organized by tracks performed by the glider.</p> <p>The data are in Matlab format and the variables are:</p> <p>dd is the julian day of 2018, lat is latitude, lon is longitude, pre is pressure, rhoi, density, </p> <p>and ssi and tti are the absolute salinity and the conservative temperature.</p> <p>Uncompress the file with: tar xvf samowa_glider2018_mat.tgz</p> <p>If you want to read this file in python, use: loadmat from scipy.io.</p> <p>All variables are in SI units.</p> <p> </p>
Data used to study the short-term effects of hurricane Ida on nitrate-nitrogen runoff loading using E3SM land model
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Data for ICSE 2024 paper "Learning in the Wild: Towards Leveraging Unlabeled Data for Effectively Tuning Pre-trained Code Models".
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Data for "• Can we achieve atmospheric chemical environments in the laboratory? An integrated model-measurement approach to chamber SOA studies"
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Model Data for Doubly Periodic SCREAM Comparison with ARM Observations
<p>Includes the SAM LES, E3SM SCM, and DP-SCREAM simulations used in the study.</p>
The WRF-CMAQ-BCG model data
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raw data - Establishment and Evaluation of an Improved Ultrasound-Guided Model of Inflammatory Pseudotumor of the Liver in Rabbits
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Preprocessed Data for BIOTIC Model
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Global Aerosol Models (AEROEX) based on 3-Decade AERONET data.
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raw data - Comparison of High-Flow Nasal Cannula Oxygen and Conventional Oxygen Therapy in a Rat Model of Severe Carbon Monoxide Poisoning
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High-precision solid Earth tidal geometry model (Geo) data
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Data for "Development and Evaluation of a New Correlated K-distribution Scheme for BCC_RAD Radiative Transfer Model"
<p>Data and matlab scripts for plotting.</p>
Data for "Modelling the dense granular flow rheology of particles with different surface friction: implications for geophysical mass flows"
<p>Data and codes needed to replicate all figures in the paper.</p>
Data and results of opscr demographic and spatial projection models
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Data and results of opscr model for pyrenean bears
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Data presented in the paper "Impact of Giant Sea Salt Aerosol Particles on Precipitation in Marine Cumuli and Stratocumuli: Lagrangian Cloud Model Simulations"
<p>View Readme.</p>
Data for the article entitled "Strongly Coupled Data Assimilation of Ocean Observations into an Ocean-Atmosphere Model" by Tang et al. 2021, GRL
<p>We stored the output data for the free run and the data assimilation experiments. All the data is stored in netCDF format and named by XX1_ensmean_XX2_monmean.nc. The prefix XX1 indicates the simulation scenarios, where 'free_run' refers to the free run, 'wcda' the weakly coupled assimilation run, 'scda' the strongly coupled data assimilation run without vertical localization for atmosphere, and 'scda_vert' the strongly coupled assimilation run with vertical localization for atmosphere. The XX2 represents variables from the simulations, where 'temp2' refers to 2 meter temperature, 'u10' 10 metre U wind component, 'v10' 10 metre V wind component, 'st_p' temperature at pressure levels, 'uv_p' U and V component of wind at pressure levels, and 'q_p' specific humidity at pressure levels.</p>
ScienceDex guides
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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.