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5,805 results for “Data model”
data and model associated with "Generalization of learned responses in the mormyrid electrosensory lobe" published in eLife
<p>Data and code for model of negative image formation associated with "Generalization of learned responses in the mormyrid electrosensory lobe" published in eLife.</p>
Open access data for 2-year percutaneous osseointegrated implants - A sheep model
<p>Percutaneous osseointegrated <strong>(OI)</strong> devices for amputees are metallic endoprostheses, surgically implanted into the residual bone that protrude through the skin, allowing attachment of an exoprosthetic. In contrast to standard socket-type systems, these percutaneous OI devices can provide an improved prosthetics attachment platform. However, bone adaptations, which include atrophy and/or hypertrophy along the extent of the host bone-endoprosthetic interface, are known clinical outcomes and are dependent upon the load transfer region of the device to the host bone. The goal of this study was to determine if a percutaneous OI device, designed with a porous coated distal region and a collar, could promote and maintain stable bone attachment. A total of eight, 18 to 24-month old, mixed-breed sheep were surgically implanted with a percutaneous OI device. For 24-months, animals were allowed to bear weight as tolerated and monitored for signs of bone remodelling. At necropsy, the endoprosthesis and the surrounding tissues were harvested, radiographically imaged, and histomorphometrically analyzed to determine the periprosthetic bone adaptation in five animals. Bone growth into the porous coating was achieved in all five animals. Serial radiographic data showed stress-shielding related bone adaptation based on the placement of the endoprosthetic stem. When collar placement achieved end-bearing against the transected bone, distal bone conservation/hypertrophy was observed. The results supported the use of distally porous coated percutaneous OI devices for distal load-transfer and host bone maintenance.</p>
Hyperparameter tuning and performance assessment of statistical and machine-learning models using spatial data.
<p>This is a research compendium (RC) for the publication "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data".</p> <p>The code (including figures, appendices and the manuscript) is packed in <strong>pathogen-modeling-3.zip </strong>or can be found directly in the <a href="https://github.com/pat-s/pathogen-modeling">Github repository</a>.</p> <ul> <li><strong>Publication figures</strong>: analysis/paper/submission/3/latex-source-files/</li> <li><strong>Appendices</strong>: analysis/paper/submission/3/</li> </ul> <p>This RC represents a static snapshot at the time of submission. The Github repository will receive changes after the publication was published.</p> <p><strong>Data sources</strong></p> <ul> <li>Atlas Climatico: <a href="http://opengis.uab.es/wms/iberia/index.htm">http://opengis.uab.es/wms/iberia/index.htm</a></li> <li>DEM: ftp://ftp.geo.euskadi.eus/lidar/MDE_LIDAR_2016_ETRS89/</li> <li>Lithology: <a href="http://www.geo.euskadi.eus/geonetwork/srv/spa/main.home">http://www.geo.euskadi.eus/geonetwork/srv/spa/main.home</a></li> <li>pH: <a href="https://esdac.jrc.ec.europa.eu/content/soil-ph-europe#tabs-0-description=0">https://esdac.jrc.ec.europa.eu/content/soil-ph-europe#tabs-0-description=0</a></li> <li>soil: <a href="https://www.isric.org/explore/soilgrids">https://www.isric.org/explore/soilgrids</a></li> </ul> <p><strong>Licenses</strong></p> <p>All files are shared via the given license with the exception of "soil.tif" which is shared via the <strong>ODbL </strong>license<strong>.</strong></p>
Application Cases of Inverse Modelling with the PROPTI Framework - Data Set
<p><strong>Contents</strong></p> <p>Set of simulation data, supplementary for a paper submitted to (published: 15 June 2019) the Fire Safety Journal, with the title <a href="https://www.sciencedirect.com/science/article/pii/S0379711219300438">"Application Cases of Inverse Modelling with the PROPTI Framework"</a>. See also our project at <a href="https://www.researchgate.net/project/PROPTI-An-Generalised-Inverse-Modelling-Framework">ResearchGate</a>.</p> <p>This repository contains the complete input data for each IMP run of the mass loss calorimeter, shown in this paper. This comprises of the experimental data files, the templates for the simulation models and the input file for PROPTI.</p> <p>The data base files are provided. This includes the original ones created by PROPTI during the run, as well as the cleaned data base files, used to create the plots, and the extracted best parameter sets per generation. Plots, created during the IMP runs as means of monitoring the progress are also included.</p> <p>Furthermore, the repository contains a small collection of Jupyter notebooks which have been used to process the data base files and create the plots presented in this paper.</p> <p>The full factorial simulations were set up from within a Jupyter notebook. This notebook and the conducted simulations are also part of this repository.</p> <p>Data of the various TGA simulations are provided within a very <a href="https://zenodo.org/record/2538851#.XSXfAXtCSUk">similar repository</a>, linked to a <a href="https://www.researchgate.net/publication/328933654_PROPTI_-_A_Generalised_Inverse_Modelling_Framework">conference paper</a> (ESFSS 2018, Nancy, France).</p> <p>Finally, the simulation input files, PROPTI input, as well as the custom script for file handling in concert with OpenFOAM, are provided.</p> <p> </p> <p><strong>Technical Information</strong></p> <p>Each ZIP archive represents a sub-directory of the original directory. For the analysis scripts, the Jupyter notebooks, to work properly out of the box it is necessary to keep this structure. Thus, simply extract all archives into the same directory.</p> <p>Note: Size on disc, after extraction, is about 4.1 GB. Version 2 adds about 5.1 GB.</p> <p> </p> <p><strong>Version 2:</strong></p> <p>Version 2 contains new IMP runs that address an error in determining the normalised residual mass, see Jupyter Notebook "RevisedTargetAssessment.ipynb", as well as input from the reviewers. The IMP runs are denoted by "08" after the optimisation algorithm label, e.g. "MLC_FSCABC_08_new_75kw_Ins".</p>
A structural model of the human serotonin transporter in an outward-occluded state: MD simulation data
<p>The uploads contain relevant data to supplement the study https://www.biorxiv.org/content/10.1101/637009v1, where the details of the methods are described.</p> <p>charmm_energy_minimization.inp is the input file that was used to run an energy minimization on structural models</p> <p>The two archives contain relevant MD simulation data in coordinate, parameter and trajectory files:</p> <p>hSERT_Ce.tar.gz outward-open X-ray structure PDB 5I71</p> <p>hSERT_Ceo.tar.gz outward-occluded structural model</p>
Data for the Carbon Erosion Dynamics Model (CE-DYNAM)
<p>Data on soil erosion by rainfall and runoff and data on the turnover rates between carbon pools on land of the Rhine catchment for the period 1850-2005. This dataset belongs to the model code that will be published in the near future along with a paper in submission to the GMD journal.</p>
Supplementary data to Schmiester et al. *Efficient parameterization of large-scale dynamic models based on relative measurements*
<p>This archive contains Supplementary data to the manuscript <em>Efficient parameterization of large-scale dynamic models based on relative measurements</em> by Leonard Schmiester, Yannik Schälte, Fabian Fröhlich, Jan Hasenauer and Daniel Weindl.</p>
Webis Simulation Data Mining Bridge Models Corpus 2012 (Webis-SDMbridge-12)
<p>This corpus provides the simulation data mining community with a collection of 14641 bridge models and simulated behavior.</p> <p><strong>1. Folder "1-designs"</strong></p> <p>The text files in this directory should contain all information for the<br> independent variables any machine learning experiment. For reference, all 14641 IFC models are supplied in subfolders 001 to 147.</p> <p><strong>2. Folder "2-simulation"</strong></p> <p>This folder contains samples of the simulation output that may be viewed in Paraview (http://www.paraview.org). The original model contains the "Org" filename fragment, and the maximum and minimum behaviors are indicated with "Max" and "Min" filename fragments. Displacement, strain, and stress behaviors are all given. Only three of the 14641 models are given as the file sizes are<br> around 1.4 to 2.2 megabytes each. The complete data (approximately 81 gigabytes) can be regenerated and provided if necessary on request (email webis@medien.uni-weimar.de).</p> <p><strong>3. Folder "3-aggregation"</strong></p> <p>Maximum displacement, strain, and stress measurements are given in the text files individually, and together in the files with the "vtk" filename fragment. This data should be sufficient for the dependent variables of any machine learning experiment.</p>
Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites: modeling data
<p>These data are related to:</p> <p>Moreau, J., Kohout, T., Wünnemann K., Halodova, P., Haloda, J., 2019.<br> Shock physics mesoscale modeling of shock stage 5 and 6 in ordinary and enstatite chondrites.<br> Icarus, 332, 50-65. <a href="https://doi.org/10.1016/j.icarus.2019.06.004">https://doi.org/10.1016/j.icarus.2019.06.004</a></p> <p>Any use of these files, scripts (partial or complete) in research papers, please reference the paper above + Moreau et al. (2017, 2018) (references compiled in the above-mentioned paper).</p> <p>To use these files, you will need:<br> - authorized access to the iSALE shock physics code (iSALE-Dellen version) re-compiled with our modifications, with reference<br> to the manual in your work<br> - access to the pySALEPlot tool for iSALE users made by T. Davison acknowledged in your work<br> - running the iSALE models to generate the different jdata.dat files (average size of a jdata.file is 7 Go)<br> - python<br> - Ubuntu or macOS</p> <p> </p> <p>(more info in README.txt file)</p>
SUMO intersection model and vehicle trip data
<p>There are two parts of the data: 1) a SUMO model of a typical intersection that consists of 4 approaches, each of which consists of 3 movements (left turn, right turn, and straight); 2) the vehicle trip information data generated by SUMO under different volume files, which is used to train the intersection signal control algorithm.</p> <p>The SUMO model contains five ".xml" files (node, edge, connection, net, and additional files) which are used to construct and configure the model. One can refer to the official SUMO tutorial for the format and functions of these files: (<a href="https://sumo.dlr.de/wiki/Tutorials/Hello_Sumo">https://sumo.dlr.de/wiki/Tutorials/Hello_Sumo</a>) </p> <p>The vehicle trip data is generated by SUMO as an output (which is specified in ".sumocfg" file). One can refer to the official tutorial (<a href="https://sumo.dlr.de/wiki/Simulation/Output/TripInfo">https://sumo.dlr.de/wiki/Simulation/Output/TripInfo</a>) to understand the data format.</p> <p>Note that readers capable to read ".xml" files like Notepad++ are required to read the SUMO model and vehicle trip data.</p>
Data for Superconductivity in the Hubbard model and its interplay with next-nearest hopping t'
<p>The results for <a href="https://arxiv.org/abs/1806.01465">https://arxiv.org/abs/1806.01465</a></p> <p>"Superconductivity in the Hubbard model and its interplay with next-nearest hopping t'",</p> <p>including both manuscript and supplemental material.</p>
Measured BSDF and data-driven model of a laser cut panel (LCP001)
<p>Model generated from the measured Bidirectional Scattering Distribution Function (BSDF) of a LCP. The RADIANCE tool-chain pabopto2bsdf, bsdf2ttree was employed. Initial tensor resolution set to 128x128 incident, and equal number of outgoing, scattered directions (corresponding to approx. 1.4 degree). Subsequent adaptive data reduction by approx. 98%. Photometric BSDF.</p> <p>The measured data is included as Differential Scattering Function (DSF): DSF = BSDF x cos(theta_s), where theta_s is the off-normal angle of the outgoing, scattered direction of light (the first column in the measured data). The data was measured on a scanning gonio-photometer (pab advanced technologies pgII) at the optical laboratory of CC Building Envelopes. A halogen lamp was employed, focud on the detector plane for maximum resolution, with a hot-mirror installed in the illuminator to block near infrared emission. The Si photocell of the detector was equipped with a weighing filter to match photometric response v(lambda). The profiles through the unobstructed beam are given for the phi=0°,180°, and phi=90°,270° planes.</p> <p>The angular coordinates theta=90, phi=0 correspond to the intended up direction when installed vertically, e.g. in a window.</p> <p>When publishing any work making use of this data-set, please reference either this data-set, including its Digital Object Identifier (DOI:<a href="https://doi.org/10.5281/zenodo.3375294">10.5281/zenodo.3375294</a>), or (preferred) by this article which describes sample, model and its exemplary application:</p> <p>Lars Oliver Grobe. Photon mapping in image-based visual comfort assessments with BSDF models of high resolution. Journal of Building Performance Simulation. DOI:10.1080/19401493.2019.1653994</p>
Data for "COSMO-BEP-Tree v1.0: a coupled urban climate model with explicit representation of street trees"
<p>In order to represent the interactions between street trees, urban elements and the atmosphere in realistic regional weather and climate simulations, we coupled the vegetated urban canopy model BEPTree and the mesoscale weather and climate model COSMO.</p> <p>The performance and applicability of the coupled model, named COSMO-BEP-Tree, are demonstrated over the urban area of Basel, Switzerland, during the heatwave event of June-July 2015.</p> <p>The data includes:</p> <p>1. <em>datasets</em><br> Datasets of building geometries (Shapefile, WGS84), trees (GeoTiff, WGS84), Landsat 7 scene (GeoTIFF, WGS84) and imperviousness (GeoTIFF, WGS84).</p> <p>2. <em>model outputs</em><br> The processed model outputs (.npy files, generated with Python v3) are provided for all the simulations, in terms of time series at the observation sites and spatial distributions. The full 3D model outputs, 1 TB) can be provided by request by contacting the author (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>3. <em>model inputs</em><br> Input namelists for the COSMO-BEP-Tree model and initial/static conditions. The full 3D boundary conditions (60 GB) can be provided by request (<a href="mailto:mussetti.gianluca@gmail.com">mussetti.gianluca@gmail.com</a>).</p> <p>4. <em>observations</em><br> Measurement data (.txt).</p> <p>5. <em>post-processing scripts</em><br> Jupyter (Python 3) Notebook files used to generate the figures and to analyse model results. Tested in Python 3.6.5.</p>
Data: Oxfordshire traffic and models
<p>These are the datasets and code used in two papers:</p> <p><em>1 - Diagnosing the performance of human mobility models at small spatial scales using volunteered geographic information (</em>https://arxiv.org/abs/1905.07964)</p> <p><em>2 - Estimating Traffic Disruption Patterns with Volunteered Geographic Information (</em>https://arxiv.org/abs/1907.05162).</p> <p>The files contain demographic and geographic data for electoral wards in the county of Oxfordshire, UK, as well as code to run the models described in the first paper.</p> <p>Tij matrices will also be public subject to approval by the data controller.</p>
Wikidata's linked data for cultural heritage digital resources: an evaluation based on the Europeana Data Model
<p>Wikidata is an open data source with many potential applications. Our study aims to evaluate the usability of Wikidata as a linked data source for acquiring richer descriptions of digital objects within the context of Europeana, a data aggregator from the cultural heritage domain. Specifically, we aim to crawl and convert Wikidata using the standard approaches and operations developed for the (Semantic) Web of Data, i.e. using technologies like linked data consumption and RDF(S)/OWL ontology expression and reasoning. We also seek to re-use existing “semantic” specifications, such as conversions to and from generic data models like Schema.org and SKOS. We have developed an experimental set-up and accompanying software to test the feasibility of this approach. We conclude that Wikidata’s linked data is able to express an interesting level of semantics for cultural heritage, but quality can still be improved and a human operator still must assist linked data applications to interpret Wikidata’s RDF.</p>
Data and model scripts for "Temporal shifts in iso/anisohydry revealed from daily observations of plant water potential in a dominant desert shrub"
<p>Model code and data as used for the first revision submitted to New Phytologist, Sept. 2019. </p> <p>Models are coded in JAGS and run in R via the package "rjags." Two model versions are presented:</p> <p>1) "mod_SAM.R" and "script_SAM.R" run the full, time-varying SAM model, utilizing both the plant water potential ("res.Rdata") and the environmental covariate data ("cov2.Rdata") and associated initial values ("initsSAM.Rdata")</p> <p>2) "mod_SIMPLE.R" and "script_SIMPLE.R" run the simple, time-invariant model, utilizing only the plant water potential data ("res.Rdata") and associated initial values ("initsSIMPLE.Rdata")</p>
Bahamas National Hazard Analysis. Data Inputs and Outputs for the InVEST Coastal Vulnerability Model.
<p>The following folders contain the model inputs and outputs for the InVEST Coastal Vulnerability model that were used in the analysis discussed in:</p> <p>Silver JM, Arkema KK, Griffin RM, Lashley B, Lemay M, Maldonado S,<br> Moultrie SH, Ruckelshaus M, Schill S, Thomas A, Wyatt K and Verutes G<br> (2019) Advancing Coastal Risk Reduction Science and Implementation by<br> Accounting for Climate, Ecosystems, and People. Front. Mar. Sci. 6:556.<br> doi: 10.3389/fmars.2019.00556</p> <p>The readme.txt file contains information about data layers.</p>
TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"
<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> time = 3672 ;<br> stations = 6 ;<br> name_strlen = 6 ;<br> depth = 3 ;<br> height = 201 ;<br> variables:<br> double time(time) ;<br> time:standard_name = "time" ;<br> time:long_name = "time of measurement" ;<br> time:units = "hours since 2016-06-01 00:00:00" ;<br> time:timezone = "UTC" ;<br> time:calendar = "proleptic_gregorian" ;<br> double lat(stations) ;<br> lat:standard_name = "latitude" ;<br> lat:long_name = "station_latitude" ;<br> lat:units = "degrees_north" ;<br> double lon(stations) ;<br> lon:standard_name = "longitude" ;<br> lon:long_name = "station_longitude" ;<br> lon:units = "degrees_east" ;<br> double elev(stations) ;<br> elev:standard_name = "altitude" ;<br> elev:long_name = "station_altitude" ;<br> elev:units = "m ASL" ;<br> double height(height) ;<br> height:standard_name = "altitude" ;<br> height:long_name = "station_altitude" ;<br> height:units = "m ASL" ;<br> double depth(depth) ;<br> depth:standard_name = "soil_depth" ;<br> depth:long_name = "soil sensor depth" ;<br> depth:units = "cm" ;<br> char station_name(name_strlen, stations) ;<br> station_name:long_name = "station_name" ;<br> station_name:cf_role = "timeseries_id" ;<br> double T(time, stations) ;<br> T:_FillValue = -9999. ;<br> T:standard_name = "temperature" ;<br> T:long_name = "2m air temperature" ;<br> T:units = "degree_Celsius" ;<br> T:source = "TERENO-preAlpine" ;<br> double Q(time, stations) ;<br> Q:_FillValue = -9999. ;<br> Q:standard_name = "mixing_ratio" ;<br> Q:long_name = "2m mixing ratio" ;<br> Q:units = "g kg-1" ;<br> Q:source = "TERENO-preAlpine" ;<br> double ET_i(time, stations) ;<br> ET_i:_FillValue = -9999. ;<br> ET_i:standard_name = "evapotranspiration_intensive" ;<br> ET_i:long_name = "lysimeter evapotranspiration intensive management" ;<br> ET_i:units = "g kg-1 h-1" ;<br> ET_i:source = "TERENO-preAlpine" ;<br> double ET_e(time, stations) ;<br> ET_e:_FillValue = -9999. ;<br> ET_e:standard_name = "evapotranspiration_extensive" ;<br> ET_e:long_name = "lysimeter evapotranspiration extensive management" ;<br> ET_e:units = "g kg-1 h-1" ;<br> ET_e:source = "TERENO-preAlpine" ;<br> double LvE_cor(time, stations) ;<br> LvE_cor:_FillValue = -9999. ;<br> LvE_cor:standard_name = "latent_heat_flux" ;<br> LvE_cor:long_name = "energy balance corrected flux tower latent heat flux" ;<br> LvE_cor:units = "W m-2" ;<br> LvE_cor:source = "TERENO-preAlpine" ;<br> double HTs_cor(time, stations) ;<br> HTs_cor:_FillValue = -9999. ;<br> HTs_cor:standard_name = "sensible_heat_flux" ;<br> HTs_cor:long_name = "energy balance corrected flux tower sensible heat flux" ;<br> HTs_cor:units = "W m-2" ;<br> HTs_cor:source = "TERENO-preAlpine" ;<br> double GHF(time, stations) ;<br> GHF:_FillValue = -9999. ;<br> GHF:standard_name = "ground_heat_flux" ;<br> GHF:long_name = "flux tower ground heat flux" ;<br> GHF:units = "W m-2" ;<br> GHF:positive = "up" ;<br> GHF:source = "TERENO-preAlpine" ;<br> double SW(time, stations) ;<br> SW:_FillValue = -9999. ;<br> SW:standard_name = "short_wave_radiation" ;<br> SW:long_name = "downward short wave radiation" ;<br> SW:units = "W m-2" ;<br> SW:source = "TERENO-preAlpine" ;<br> double LW(time, stations) ;<br> LW:_FillValue = -9999. ;<br> LW:standard_name = "long_wave_radiation" ;<br> LW:long_name = "downward long wave radiation" ;<br> LW:units = "W m-2" ;<br> LW:source = "TERENO-preAlpine" ;<br> double VWC_25(time, depth) ;<br> VWC_25:_FillValue = -9999. ;<br> VWC_25:standard_name = "volumetric_water_content" ;<br> VWC_25:long_name = "DE-Fen SoilNet volumetric water content first quartile" ;<br> VWC_25:units = "vol. %" ;<br> VWC_25:source = "TERENO-preAlpine" ;<br> double VWC_50(time, depth) ;<br> VWC_50:_FillValue = -9999. ;<br> VWC_50:standard_name = "volumetric_water_content" ;<br> VWC_50:long_name = "DE-Fen SoilNet volumetric water content second quartile" ;<br> VWC_50:units = "vol. %" ;<br> VWC_50:source = "TERENO-preAlpine" ;<br> double VWC_75(time, depth) ;<br> VWC_75:_FillValue = -9999. ;<br> VWC_75:standard_name = "volumetric_water_content" ;<br> VWC_75:long_name = "DE-Fen SoilNet volumetric water content third quartile" ;<br> VWC_75:units = "vol. %" ;<br> VWC_75:source = "TERENO-preAlpine" ;<br> double T_prof(time, height) ;<br> T_prof:_FillValue = -9999. ;<br> T_prof:standard_name = "temperature_profile" ;<br> T_prof:long_name = "DE-Fen HATPRO spline interpolated temperature profile" ;<br> T_prof:units = "K" ;<br> T_prof:source = "scaleX campaign 2016" ;<br> double A_prof(time, height) ;<br> A_prof:_FillValue = -9999. ;<br> A_prof:standard_name = "humidity_profile" ;<br> A_prof:long_name = "DE-Fen HATPRO spline interpolated absolute humidity profile" ;<br> A_prof:units = "kg m-3" ;<br> A_prof:source = "scaleX campaign 2016" ;<br> double PRW(time) ;<br> PRW:_FillValue = -9999. ;<br> PRW:standard_name = "precipitable_water" ;<br> PRW:long_name = "DE-Fen HATPRO column precipitable water" ;<br> PRW:units = "kg m-2" ;<br> PRW:source = "scaleX campaign 2016" ;</p> <p>// global attributes:<br> :history = "2019-09-12: File created." ;<br> :institution = "Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research" ;<br> :Contact_person = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :Author = "Benjamin Fersch (benjamin.fersch@kit.edu)" ;<br> :source = "https://www.tereno.net, https://scalex.imk-ifu.kit.edu" ;<br> :Conventions = "CF-1.6" ;<br> :License = "Creative Commons Attribution Non Commercial Share Alike 4.0 International" ;</p> <p> </p>
Details of offspring and source data for analysis of metabolic health and dietary preference in a rat model of acute alcohol exposure.
<p>This Excel file contains information on the number of offspring used to examine each outcome and the raw data for each data Table and Figure within a manuscript submitted to Journal of Physiology. </p>
Monthly NAO data for GRL paper "Understanding the Signal-to-noise Paradox with a Simple Markov Model"
<p>The datasets include monthly NAO data for GRL paper "Understanding the Signal-to-noise Paradox with a Simple Markov Model". The monthly NAO index is estimated based on the leading empirical orthogonal function mode of the Mean Sea Level Pressure (SLP) over the North Atlantic. The monthly NAO index has been normalized for each dataset seperately. The description of each file is shown as follows: </p> <ul> <li>monthly_nao_cmip5_models_1871_2005.nc: monthly NAO index derived from 40 CMIP5 model outputs (historical run; first realization)</li> <li>cmip5_model_list: list of 40 CMIP5 models</li> <li> monthly_nao_era20c_1900_2005.nc: monthly NAO index derived from the ECMWF's twentieth-century reanalysis data (ERA20C)</li> <li>monthly_nao_noaa20c_1871_2005.nc: monthly NAO index derived from the NOAA's twentieth-century reanalysis version-2 data (NOAA20C) </li> </ul>
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.