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.
5,805
datasets available to search
ShareScore release 0.9.0
Dataset results
5,805 results for “Data model”
dhaw/fluCodeImperial: Using real-time data to guide decision-making during an influenza pandemic: a modelling analysis
<p><strong>All codes and data used for "Using real-time data to guide decision-making during an influenza pandemic: a modelling analysis" are included in this folder. The file "runExamples.m" contains a step-by-step method for reproducing figures and running model fits. Ensure that all data and code files are in the same directory, then a single execution of “runExamples” on the command line will generate all main and supplementary figures in the manuscript. There is one line of code per figure, clearly marked, that can be commented out as desired. In order to run the MCMC adaptive algorithm, a single line of code, also clearly marked, must be commented back in. Instructions to change the single-state example are given at the top of the file “runExamples.m”. The saved state selection of California (“state=1”) is consistent with all results presented in the manuscript. </strong></p> <p><strong> </strong></p> <p><strong>Plots make use of files from the following sources, with some modifications:</strong></p> <p><strong>Holger Hoffmann (2022). Violin Plot (https://www.mathworks.com/matlabcentral/fileexchange/45134-violin-plot);</strong></p> <p><strong>Evan (2022). Plot Groups of Stacked Bars (https://www.mathworks.com/matlabcentral/fileexchange/32884-plot-groups-of-stacked-bars);</strong></p> <p><strong>John Onofrey (2022). Shaded Plots and Statistical Distribution Visualizations (https://www.mathworks.com/matlabcentral/fileexchange/69203-shaded-plots-and-statistical-distribution-visualizations)</strong></p>
Model run scripts and data used for analyses in Zhang et. al. (2023, JAMES)
<p>This archive contains run scripts for CLUBB single-column model (SCM) simulations and post-processed data and analysis scripts used in Zhang et al. (2023, JAMES) entitled "removing numerical pathologies in a turbulence parameterization through convergence testing". The versions of the CLUBB-SCM code used for the simulations can be found on Zenodo under <a href="https://doi.org/10.5281/zenodo.7439423">10.5281/zenodo.7439423</a>, which includes two branches: </p> <p>1. clubb_release-clubb_paper_base.zip: contains the version of the CLUBB code directly from the master branch, which is the standpoint of the code for our paper. </p> <p>2. clubb_release-clubb_paper_code.zip: contains the version of the CLUBB code with all revisions discussed in our paper. These code changes have not been merged to CLUBB master branch during the publication period </p> <p>The code modifications in the above branches were based on the CLUBB’s source code available at <a href="https://github.com/larson-group/clubb_release">https://github.com/larson-group/clubb_release</a>.</p>
Can short-term data accurately model long-term environmental exposures? Investigating the multigenerational adaptation potential of Daphnia magna to environmental concentrations of organic ultraviolet filters
<p>Organic ultraviolet filters (UVFs) are contaminants of concern, ubiquitously found in many aquatic environments due to their use in personal care products to protect against ultraviolet radiation. Research regarding the toxicity of UVFs such as avobenzone, octocrylene and oxybenzone indicates that these chemicals may pose a threat to invertebrate species; however, minimal long-term studies have been conducted to determine how these UVFs may affect continuously exposed populations. The present study modeled the effects of a 5-generation exposure of <em>Daphnia</em> <em>magna</em> to these UVFs at environmental concentrations. Avobenzone and octocrylene resulted in minor, transient decreases in reproduction and wet mass. Oxybenzone exposure resulted in > 40% mortality, 46% decreased reproduction and 4-fold greater reproductive failure over the F0 and F1 generations; however, normal function was largely regained by the F2 generation. These results indicate that <em>Daphnia</em> are able to acclimate over long-term exposures to concentrations of 6.59 μg/L avobenzone, ~0.6 μg/L octocrylene or 16.5 μg/L oxybenzone. This suggests that short-term studies indicating high toxicity may not accurately represent long-term outcomes in wild populations, adding additional complexity to risk assessment practices at a time when many regions are considering or implementing UVF bans in order to protect these most sensitive invertebrate species.</p>
Data from simulations of the thalamocortical loop model
<p>This zip file contains, separated into different folders, the data and metadata resulting from simulating different stimuli protocols with a thalamocortical spiking network model (https://github.com/dguarino/T2). The model was developed using the Mozaik framework (https://github.com/antolikjan/mozaik), which itself relies on PyNN (http://neuralensemble.org/PyNN/), NEST (https://www.nest-simulator.org/), and other libraries (see the Mozaik specs).</p> <p>Inside the zipped folder, there will be the following sub-folders, each containing the python pickled output recording of the recorded spikes (with Vm, and conductances for a subset of simulated neurons) in the Neo format (https://neo.readthedocs.io/en/stable/ ):</p> <p>ThalamoCorticalModel_data_contrast_closed_____<br> ThalamoCorticalModel_data_contrast_open_____<br> ThalamoCorticalModel_data_luminance_closed_____<br> ThalamoCorticalModel_data_luminance_open_____<br> ThalamoCorticalModel_data_orientation_closed_____<br> ThalamoCorticalModel_data_orientation_feedforward_____<br> ThalamoCorticalModel_data_orientation_open_____<br> ThalamoCorticalModel_data_size_closed_____<br> ThalamoCorticalModel_data_size_closed_____large<br> ThalamoCorticalModel_data_size_closed_cross-oriented_____<br> ThalamoCorticalModel_data_size_feedforward_____<br> ThalamoCorticalModel_data_size_feedforward_____large<br> ThalamoCorticalModel_data_size_feedforward_____old<br> ThalamoCorticalModel_data_size_LGNonly_____<br> ThalamoCorticalModel_data_size_nonoverlapping_____<br> ThalamoCorticalModel_data_size_open_____<br> ThalamoCorticalModel_data_size_overlapping_____<br> ThalamoCorticalModel_data_size_overlapping_____old<br> ThalamoCorticalModel_data_spatial_closed_____<br> ThalamoCorticalModel_data_spatial_Kimura_____<br> ThalamoCorticalModel_data_spatial_LGNonly_____<br> ThalamoCorticalModel_data_spatial_open_____</p>
Data for m-NLP inference models using simulation and regression techniques
<p>This file contains data for the manuscript ``m-NLP inference models using simulation and regression techniques''.</p> <p>The "needle probe data.xlsx" file contains the simulation results and the fits to the simulation data. It also contains the coefficients a, b, and c for which a synthetic solution library can be constructed. Examples of the synthetic solution library are also included, named "nndlt.dat" and "nndlv.dat". The "center.out" file contains the Radial Basis Function density inference model created from the synthetic solution library.</p> <p>The NorSat-1 data can be obtained from: http://tid.uio.no/plasma/norsat/norsat1.html</p>
Supplementary data to: Soil Moisture to Runoff (SM2R) A data-driven model for runoff estimation across poorly gauged Asian water towers based on soil moisture dynamics
<p>This data archive includes simulated monthly runoff anomaly using the Soil Moisture to Runoff model during 1981‒2020 over the representative drainage basin in each water tower. Please see Readme for more data information. For calculation details please see the publication.</p>
Model data archive for a model-data intercomparison of the Eocene-Oligocene transition
<p>This data package contains data used for an model-data intercomparison originally<br> published in:</p> <p>D. K. Hutchinson, H. K. Coxall, D. J. Lunt, M. Steinthorsdottir, A. M. de Boer, M. Baatsen, A. von der Heydt, M. Huber, A. T. Kennedy-Asser, L. Kunzmann, J.-B. Ladant, C. H. Lear, K. Moraweck, P. N. Pearson, E. Piga, M. J. Pound, U. Salzmann, H. D. Scher, W. P. Sijp, K. K. Śliwińska, P. A. Wilson, and Z. Zhang, 2021: <strong>The Eocene-Oligocene transition: a review of marine and terrestrial proxy data, models and model-data comparisons</strong>, Climate of the Past, 17, 269-315.<br> <a href="https://doi.org/10.5194/cp-17-269-2021">https://doi.org/10.5194/cp-17-269-2021</a></p> <p>These data are also used in a further model-data intercomparison of Antarctic temperatures:</p> <p>Emily Tibbett, Natalie J Burls, David K. Hutchinson, Sarah J Feakins, (2023), <strong>Proxy-Model Comparison for the Eocene-Oligocene Transition in Southern High Latitudes, Paleoceanography and Paleocliamtology</strong>, In Review. Pre-print avaiable from:<br> <a href="https://www.authorea.com/doi/full/10.1002/essoar.10511735.2">https://www.authorea.com/doi/full/10.1002/essoar.10511735.2</a></p> <p>The package contains surface air temperature and sea surface temperature from an ensemble of model simulations of the Eocene-Oligocene transition. These data are provided at annual and monthly frequency. They are also provided on the original model grid, and an interpolated common grid used for the intercomparison. (The common grid is based on the HadCM3BL model grid.) All data are provided in NETCDF format with self-describing variable names.</p> <p>The name and explanation of the interpolated data files are contained in:<br> <strong>table_of_experiments.xlsx</strong></p> <p>Please read that spreadsheet to interpret the filenames, and see <strong>Table 2 (p291)</strong> of Hutchinson et al (2021) for experiment descriptions.</p> <p>Please also be mindful to cite the original authors of the simulations when using these data, whose work made this dataset possible. The appropriate citations are listed below:</p> <p>Reference DOI link <br> <strong>Baatsen et al (2020)</strong> <a href="https://doi.org/10.5194/cp-16-2573-2020">https://doi.org/10.5194/cp-16-2573-2020 </a> <br> <strong>Goldner et al (2014)</strong> <a href="https://doi.org/10.1038/nature13597">https://doi.org/10.1038/nature13597</a> <br> <strong>Ladant et al (2014a,b)</strong> <a href="https://doi.org/10.5194/cp-10-1957-2014">https://doi.org/10.5194/cp-10-1957-2014</a> <a href="https://doi.org/10.1002/2013PA002593 ">https://doi.org/10.1002/2013PA002593 </a><br> <strong>Hutchinson et al (2018, 2019) </strong><a href="https://doi.org/10.5194/cp-14-789-2018">https://doi.org/10.5194/cp-14-789-2018</a> <a href="https://doi.org/10.1038/s41467-019-11828-z">https://doi.org/10.1038/s41467-019-11828-z</a> <br> <strong>Kennedy et al (2015)</strong> <a href="https://doi.org/10.1098/rsta.2014.0419">https://doi.org/10.1098/rsta.2014.0419</a> <br> <strong>Zhang et al (2012, 2014)</strong> <a href="https://doi.org/10.5194/gmd-5-523-2012">https://doi.org/10.5194/gmd-5-523-2012</a> <a href="https://doi.org/10.1038/nature13705">https://doi.org/10.1038/nature13705</a> <br> <strong>Sijp et al (2009)</strong> <a href="https://doi.org/10.1175/2009JCLI3003.1">https://doi.org/10.1175/2009JCLI3003.1</a></p>
Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study
<p>Data and R codes necessary to replicate the analyses presented in the paper entitled "Data stochasticity and model parametrisation impact the performance of species distribution models: insights from a simulation study", published in Peer Community in Ecology (<a href="https://doi.org/10.24072/pcjournal.263">10.24072/pcjournal.263</a>).</p>
Data for publication "Surface tension models for binary aqueous solutions: A review and intercomparison"
<p>This repository contains the data of the publication:</p> <p>Title: "Surface tension models for binary aqueous solutions: A review and intercomparison"<br> Authors: Judith Kleinheins, Nadia Shardt, Manuella el Haber, Corinne Ferronato, Barbara Nozière, Thomas Peter, and Claudia Marcolli<br> Date: 2023</p> <p>Abstract: The liquid–air surface tension of aqueous solutions is a fundamental quantity in multi-phase thermodynamics and fluid dynamics and thus relevant in many scientific and engineering fields. Various models have been proposed for its quantitative description. This Perspective gives an overview of the most popular models and their ability to reproduce experimental data. In addition, we propose a new model based on the logistic function which reproduces sigmoidal curve shapes (Sigmoid model) to<br> empirically fit experimental surface tension data. All models were tested on ten binary aqueous solutions of electrolytes and organic molecules. The surface tension of weakly surface active substances is well reproduced by all models. In contrast, only a few models successfully modelled the surface tension of aqueous solutions with strongly surface-active substances. For substances with a solubility limit, usually no experimental data is available for the surface tension of supersaturated solutions and the pure liquid solute. We discuss ways in which these can be estimated and emphasize the need for further research. The newly developed Sigmoid model best reproduces the surface tension of all tested solutions and can be recommended as a model for a broad range of substances and over the entire concentration range.</p>
CAP22 Tomographic model: Data, Software, Plotting
<p>CAP22 (Boyce et al., 2023), is an absolute P-wavespeed tomographic model with focus on North American, specifically Canadian and Alaskan, mantle structure, provided with and without crustal correction.</p> <p>Please cite the following work when using this data set:</p> <p>Boyce, A., Liddell, M. V., Pugh, S., Brown, J., McMurchie, E., Parsons, A., Estève, C., Burdick, S., Darbyshire, F. A., Cottaar, S. Bastow, I. D., Schaeffer, A. J., Audet, P., Schutt, D. L., Aster, R. C. (2023). A new P-wave tomographic model (CAP22) for North America: Implications for the subduction and cratonic metasomatic modification history of western Canada and Alaska. <em>Journal of Geophysical Research: Solid Earth</em>, 128, e2022JB025745. <a href="https://doi.org/10.1029/2022JB025745">https://doi.org/10.1029/2022JB025745</a></p> <p>This repository accompanies the publication of the tomographic model on the IRIS-EMC that can be found here: https://ds.iris.edu/ds/products/emc-cap22/ or with the following doi: 10.17611/dp/emc.2023.cap22.1. Please find CAP22 and its related files for a detailed description of the distributed model.</p> <p>The CAP22_TOMO_SHARE.tar repository contains:</p> <ul> <li>Raw_data: Hand-picked and processed seismic data (.SAC) from western Canada and Alaskan seismic networks.</li> <li>Original_documentation: Documentation for original distribution of inversion code from MIT global seismology group.</li> <li>Tomography_CAP22: Implementation of MIT inversion algorithm used to produce CAP22 including formatted data.</li> <li>Plotting: Codes and necessary files to reproduce figures in main manuscript.</li> </ul> <p>The following authors contributed to this work:</p> <p>A. Boyce: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> M. V. Liddell: Centre de recherche Geotop, Université du Québec à Montréal, QC, Canada.<br> S. Pugh: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> J. Brown: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> E. McMurchie: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> A. Parsons: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> C. Estève: Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria.<br> S. Burdick: Wayne State University, Geology Department, Detroit, MI, USA.<br> F. A. Darbyshire: Centre de recherche Geotop, Université du Québec à Montréal, QC, Canada.<br> S. Cottaar: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> I. D. Bastow: Department of Earth Science and Engineering, Royal School of Mines, Prince Consort Road, Imperial College London, London, UK.<br> A. J. Schaeffer: Geological Survey of Canada Pacific Division, Sidney, BC, Canada.<br> P. Audet: University of Ottawa, Department of Earth and Environmental Sciences, Ottawa, ON, Canada.<br> D. L. Schutt: Colorado State University, Department of Geosciences and Warner College of Natural Resources, Fort Collins, CO, USA.<br> R. C. Aster: Colorado State University, Department of Geosciences and Warner College of Natural Resources, Fort Collins, CO, USA.</p> <p> </p> <p> </p>
Pretrained models and results for "Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model"
<p>This dataset contains the pretrained model weights and precomputed results for the paper "Thunderstorm nowcasting with deep learning: a multi-hazard data fusion model" submitted to <em>Geophysical Research Letters</em>. A preprint of the paper can be found at <a href="https://arxiv.org/abs/2211.01001">https://arxiv.org/abs/2211.01001</a>.</p> <p>The ML code can be found at <a href="https://github.com/MeteoSwiss/c4dl-multi">https://github.com/MeteoSwiss/c4dl-multi</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory:</p> <ul> <li>Results (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-results-lightningdl.zip?versionId=54046830-4c7e-48c6-af42-d6d5606af86b">c4dl-results-lightningdl.zip</a>) -> results/</li> <li>Pretrained models (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-models-lightningdl.zip?versionId=364bca7c-e6ad-4ed9-9264-57c759ea0ac6">c4dl-models-lightningdl.zip</a>) -> models/</li> <li>If you want to train models, data (<a href="https://zenodo.org/api/files/0cfc0cf4-755a-4618-8341-39b107a3901c/c4dl-patches-2020-additional.zip">c4dl-patches-2020-additional.zip</a>) -> data/2020/</li> </ul> <p>Additionally, you will need the datasets from <a href="https://zenodo.org/deposit/6802292">this Zenodo archive</a>. Follow the instructions there for downloading.</p>
Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package
<p>Here we present field observations of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the PLM1, PLM6, and PLM7 wells in the East River Colorado (USA) sampled in May, 2021. This observation dataset, along with the presented python modeling scripts to interpret the data, can aide in quantifying groundwater residence times and recharge conditions. The README files describes the directories and scripts.</p>
A big data–model integration approach for predicting epizootics and population recovery in a keystone species
<p>Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex non-linear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a non-parametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, <em>Cynomys ludovicianus</em>) and sylvatic plague as an example. We synthesized colony data between 2001–2020 from eight USDA Forest Service National Grasslands across the range of BTPD in central North America. We then modeled extinctions due to plague and colony recovery of BTPD in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summer/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally > 0.80). Thus, these spatially-explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially-explicit forecasting of disease-induced population fluctuations, for use in natural resource management decision-making.</p>
Biasadjusted Regional Climate Model Data for Europe - Temperature
<p>This repository contains the bias-adjusted temperature data used in the production of numbers and figures contained in our research article entitled "Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change".</p> <p>Ferdini S., von Cossel M., Wulfmeyer V., Warrach-Sagi K. (2023) Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change. <em>Global Change Biology - Bioenergy.</em></p>
Biasadjusted Regional Climate Model Data for Europe - Precipitation
<p>This repository contains the bias-adjusted precipitation data used in the production of numbers and figures contained in our research article entitled "Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change".</p> <p>Ferdini S., von Cossel M., Wulfmeyer V., Warrach-Sagi K. (2023) Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change. <em>Global Change Biology - Bioenergy.</em></p>
Data Set for Predicting the Performance of ATL Model Transformations
<p>Model transformation languages are special-purpose languages, which are designed to define transformations as comfortably as possible, i.e., often in a declarative way. With the increasing use of transformations in various domains, the complexity and size of input models are also increasing. However, developers often lack suitable models for performance testing. We have therefore conducted experiments in which we predict the performance of model transformations based on characteristics of input models using machine learning approaches. This dataset contains our raw and processed input data, the scripts necessary to repeat our experiments, and the results we obtained.</p> <p>Our input data consists of the time measurements for six different transformations defined in the Atlas Transformation Language (ATL), as well as the collected characteristics of the real-world input models that were transformed. We provide the script that implements our experiments. We predict the execution time of ATL transformations using the machine learning approaches linear regression, random forests and support vector regression using a radial basis function kernel. We also investigate different sets of characteristics of input models as input for the machine learning approaches. These are described in detail in the provided documentation.pdf. The results of the experiments are provided as raw data in individual cvs files. Additionally, we calculated the mean absolute percentage error in % and the 95th percentile of the absolute percentage error in % for each experiment and provide these results. Furthermore, we provide our Eclipse plugin, which collects the characteristics for a set of given models, the Java projects used to measure the execution time of the transformations, and other supporting scripts, e.g. for the analysis of the results.</p> <p>A short introduction with a quick start guide can be found in README.md and a detailed documentation in documentaion.pdf.</p>
Data for "Including ash in UKESM1 model simulations of the Raikoke volcanic eruption reveal improved agreement with observations" by Wells et al., 2023
<p>Data used for figures in "Including ash in UKESM1 model simulations of the Raikoke volcanic eruption reveal improved agreement with observations" by Wells et al., 2023</p> <p>See https://github.com/awells96/Raikoke for code to reproduce the figures.</p>
Data from: Whole-body variational modularity in the zebrafish: An inside-out story of a model species
<p><span>Actinopterygians are the most diversified clade of extant vertebrates. Their impressive morphological disparity bears witness to tremendous ecological diversity. Modularity, the organization of biological systems into quasi-independent anatomical/morphological units, is thought to increase evolvability of organisms and facilitate morphological diversification. Our study aims to quantify patterns of variational modularity in a model actinopterygian, the zebrafish (</span><em>Danio rerio</em>), using 3D-geometric morphometrics on osteological structures isolated from micro-CT scans. 72 landmarks were digitised along cranial and postcranial ossified regions of 30 adult zebrafishes. Two methods were used to test modularity hypotheses, the covariance ratio and the distance matrix approach. We find strong support for two modules, one comprised of paired fins and the other comprised of median fins, that are best explained by functional properties of subcarangiform swimming. While the skull is tightly integrated with the rest of the body, its intrinsic integration is relatively weak supporting previous findings that the fish skull is a modular structure. Our results provide additional support for the recognition of similar hypotheses of modularity identified based on external morphology in various teleosts, and at least two variational modules are proposed. Thus, our results hint at the possibility that internal and external modularity patterns may be congruent.</p>
Data for regional coupled model paper
<p>This data is for the JGR manuscript (A Regional Air-Sea Coupled Model Developed for the East Asia and Western North Pacific Monsoon Region).</p>
Late summer transition from a free-tropospheric to boundary layer source of Aitken mode aerosol in the high Arctic, model data
<p>This dataset contains model output that was used in the publication "Late summer transition from a free-tropospheric to boundary layer source of Aitken mode aerosol in the high Arctic" (Price et al., 2022, in prep).</p> <p> </p> <p>The output is from the UK Earth System Model run in atmosphere-only configuration at N96 resolution. Output is between September 2016 - December 2018 and is given variously as 3D monthly means, surface 3-hourly means, and 3D 2-hourly instantaneous values, depending on the variable in question.</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.