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5,805 results for “Data model”
MME-only models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME-only ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME/CRPS models trained with clean data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 1 (i.e., those trained with clean data, not with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select 50. To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
MME/CRPS models trained with lightly perturbed data for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>This tar file contains all 100 trained models in the MME/CRPS ensemble from Experiment 2 (i.e., those trained with lightly perturbed data). To pare the ensemble down to 50 models, we randomly select 50. To read one of the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
Input data and some models (all except multi-model ensembles) for JAMES paper "Machine-learned uncertainty quantification is not magic"
<p>The tar file contains two directories: data and models. Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data). The data in these directories are unnormalized. The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file. These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively. To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths. The other arguments should be as follows:</p><p>--uniformize=1</p><p>--predictor_norm_type_string="z_score"</p><p>--vector_target_norm_type_string=""</p><p>--scalar_target_norm_type_string=""</p><p> </p><p>Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data. To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.</p>
Cytokine Data MIA Model
<p>The data set includes maternal and fetal cytokines, as measured by Meso Scale Discovery Electrochemiluminescence (<i>MSD</i>) in a poly(I:C)-based mouse model of maternal immune activation.</p>
Figure 2 in Integrating phylogenomic and morphological data to assess candidate species-delimitation models in brown and red-bellied snakes (Storeria)
Figure 2. Map of Storeria ranges and sampling locations, showing geographical extent of populations, and range of former species with respect to re-delimited taxa. A, previous geographical extent of Storeria occipitomaculata is shown in red; circles indicate sampling localities. The asterisk indicates the sample of Storeria 'hidalgoensis' examined for morphology, with the range of this subpopulation, now considered part of S. occipitomaculata, indicated in pink. The range of Storeria storerioides is indicated in blue, with the sampling locality indicated by a square. B, previous geographical extent of Storeria dekayi is shown in yellow, with pentagons indicating sampling localities of S. dekayi, triangles indicating Storeria victa, and a line drawn to approximate the range boundary. The asterisk in the Central American population indicates the collection location of the specimen of Storeria 'tropica' examined for morphology, which is now considered part of S. dekayi.
Data associated with the study, "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model".
<p>Raw data associated with the study entitled "Non-Invasive Biomarkers for Detecting Progression Toward Hypovolemic Cardiovascular Instability In A Lower Body Negative Pressure Model". There were 16 subjects with 1. electrocardiogram (ECG), 2. mean arterial pressure (MAP), 3) photoplethysmography (pleth), 4) Bioimpedance Cardiography measured via a Cheetah (Startling/Medtronic) system referred to as cheetah, 5) electrical impedance from a Sentec electrical impedance tomography system, referred to as ST, and electrical impedance from a sciospec impedance analyzer, referred to as SS. Each subject underwent a lower body negative pressure (LBNP) procedure, where the LBNP was increased modeling a small hemorrhage by drawing blood to their lower extremeties. The dataset contains 5 mat (MATLAB data files), with time reported in minutes on the day of the study, i.e.10 am = 600 minutes. The file details are as follows:</p><ul><li><strong>LBNP data</strong>: LBNP_level_times.mat. The data contains 1 structure (LBNPinf) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>lbnp: LBNP value at each noted time</li></ul></li><li><strong>Vital Sign data</strong>: raw_labchart_data.mat. The data contains 1 structure array (Labchart) of length 16 (corresponding to each subject) with the following fields<ul><li>ts: time vector in minutes</li><li>ecgs: ECG data</li><li>MAP: MAP data</li><li>pleth: pleth data recorded from a single channel (V)</li></ul></li><li><strong>Sentec EIT data</strong>: raw_av_ST_impedance_data.mat. The data contains 1 structure array (STout) of length 16 (corresponding to each subject) with the following fields<ul><li>t_thx: time vector in minutes corresponding to thorax data</li><li>Z_thx: average impedance data at each time over the thorax</li><li>t_spl: time vector in minutes corresponding to abdomen data</li><li>Z_spl: average impedance data at each time over the abdomen</li></ul></li><li><strong>Sciospec EIS data</strong>: raw_sciospec_dat.mat. The data contains 1 cell array & 1 structure array (sciodat) of length 16 (corresponding to each subject).<ul><li>Cell array: Locations of the Sciospec measurements: 'Thoracic', 'Abdominal', 'Arm'</li><li>Sciodat fields:<ul><li>sciodat structure array of length 3 corresponding to the 'Thoracic', 'Abdominal', 'Arm' locations, respectively. Each component has the following fields<ul><li>tvec: time vector in minutes</li><li>fs: frequencies that the impedance is recorded over (Hz)</li><li>Zmat: matrix of impedance data size time versus frequency</li><li>erflg: not used</li></ul></li></ul></li></ul></li><li><strong>Bioimpedance cardiography data</strong>: raw_cheetah_bioimpedance.mat. The data contains a cell array of column labels (col_labs, 1x11) and a matrix (cheetah_db) of the BC data.</li></ul><p> </p><p> </p><p> </p>
ACCESS-AM2 daily and hourly cloud and radiation data for model evaluation at Macquarie Island
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) daily and hourly data used for the study described in Pei et al. (2023), accepted in <i>Atmospheric Chemistry and Physics (ACP),</i> 'Assessing the cloud radiative bias at Macquarie Island in the ACCESS-AM2 model<i>' .</i> </p><p>Included files: </p><ul><li>ACCESS_AM2_daily.zip - daily model outputs from 2016-04 to 2018-03</li><li>ACCESS_AM2_hourly.zip - hourly model outputs from 2017-09 to 2018-02</li></ul><p>The code that performs the analysis/generates this data can be found here: https://github.com/ZhangchengPei/codes-for-MI-paper</p>
Data for : Ecological associations distribution modelling of marine plankton at global scale
<p>Git repository : https://gitlab.univ-nantes.fr/combi-ls2n/adm</p>
Model data and input files
<p>The zip file contains the matlab code of the model and the input data that was used to run the model. The zip file contains a README.docx that explains the contents of the zip file</p>
Data for Scallop (Pecten maximus) Identification in Natural Marine Habitats: A NetHarn Model Approach.
<p>The data have been divided into distinct training and testing subsets, each encompassing still images corresponding to individual stations along with their respective annotations or predictions as CSV files.</p><p>This research explores the potential of Artificial Intelligence (AI), specifically the NetHarn model provided by the VIAME toolkit, to identify and count king and queen scallops from towed underwater video transects. The study utilizes video footage from NatureScot, captured using custom camera systems (DDV and miniDDV), providing a diverse dataset with variations in habitat, image quality, and camera specifications. Necessary details from the original report by Pascoe et al. (2021) are provided in the manuscript.</p><p>Pasco, G., James, B., Burke, L., Johnston, C., Orr, K., Clarke, J., Thorburn, J., Boulcott, P., Kent, F., Kamphausen, L. and Sinclair, R. (2021) 'Engaging the Fishing Industry in Marine Environmental Survey and Monitoring Scottish Marine and Freshwater Science Vol 12 No 3'. doi:10.7489/12365-1</p>
Model data for "The GERB Obs4MIPs Radiative Flux Dataset: A new tool for climate model evaluation", submitted to Earth System Science Data
<p>© Crown Copyright, Met Office</p><p>The E1hrClimMon files contain the monthly mean diurnal cycles of TOA radiative fluxes (all-sky and clear-sky) for amip experiment of two configurations of HadGEM3: GC3.1 and GC5.0. The monthly mean diurnal cycle is constructed by averaging each UTC hourly mean over the entire month. The HadGEM3 OLR diagnostics used in this study differ from those submitted to CFMIP3. The OLR diagnostics submitted to CFMIP3 contain a correction that accounts for the surface temperature adjustment by the boundary layer scheme in model time steps between radiation time steps. This OLR diagnostic adjustment is introduced to conserve energy, but it significantly distorts the diurnal cycle of OLR. For comparison with the GERB obs4MIPs products, the OLR without this correction is recommended.</p><p>The COSP file contains the average monthly climatologies for the variables cfadLidarsr532 and clisccp for the amip simulations of GC3.1 and GC5.0.</p>
The Dutch National Flexible Groundwater Model prototype input data
<p>This dataset includes the necessary model input data to run the Netherlands' National Flexible Groundwater Model prototype. For this, pre-processing is required using the quad2d tools, provide by https://github.com/verkaik/quad2d/releases/tag/quad2d_v0.1.</p>
ROM data and code for Dakar Niño variability under global warming investigated by a high-resolution regionally coupled model
Open the record for dataset details and reuse information.
Data Set of Paper on An Architecture Model-Based Framework for Continuous Data Protection Legal Assessments
<p>Data Set of Paper "An Architecture Model-Based Framework for Continuous Data Protection Legal Assessments".<br>The data set contains illustrations of the example used for evaluating accuracy, including all variations, and model instances of the case studies used for evaluating applicability.</p>
Data accompanying "Fractional quantum Hall states with variational Projected Entangled-Pair States: a study of the bosonic Harper-Hofstadter model"
Open the record for dataset details and reuse information.
Model codes, data, and plot scripts for the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning"
<p>The model codes, data, and plot scripts used in the paper "A numerical modeling study on the Earth's surface brightening effect of cirrus thinning".</p><ul><li>Mods: the modified CAM model code used in these three experiments.</li><li>Results: post-processing NCL scripts and the results used for making plots.</li><li>figs: the NCL scripts and figures used in the paper.</li><li>Parcel: parcel model that represents the ice nucleation process.</li></ul><p> </p>
Data for "Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry"
<p>Neural and FEM E-field simulation data generated for Aberra AS, Wang R, Grill WM, Peterchev AV. (2023). "Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry". <i>Brain Stimulation</i>. Dataset includes:</p><ul><li><i>cell_data/ </i>- Coordinates and morphology information for all model neurons</li><li><i>nrn_sim_data/</i> - Polarization data from NEURON simulations for all 25 model neurons included in the study, either in response to uniform E-field or tDCS.</li><li><i>layer_data/ - </i>surface meshes used for placing and orienting neuron models and corresponding sampling grids for CNNs</li><li><i>simnibs/</i> - E-field simulation and head mesh data generated within SimNIBS simulation environment</li></ul><p>To generate figures using these data, use the matlab code stored in the tDCSsim_Aberra2023 repository (https://github.com/Aman-A/tDCSsim_Aberra2023) </p>
Data files associated with the tomographic velocity model in the Alaska subduction zone
<p>For P- and S-wave arrival time data, it includes three files:</p><p>1) AK-stations: all of the stations used in the tomography</p><p>latitude, longitude, elevation(km), network name</p><p>2)Alaska-Data-P and Alaska-Data-S: seismic P and S wave arrival times</p><p>Event time, latitude, longitude, depth, total arrival times for per event</p><p> station, arrival time, phase</p><p>For P- and S- wave models, it includes two files:</p><p>1)Alaska-velocitymodel_Vp and Alaska-velocitymodel_Vs<br> depth, latitude, longitude, velocity perturbation(%) </p>
Data and code for manuscript ``Insights on the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty''
<p>Supporting data and code for manuscript:</p><p>Seroussi, H., Verjans, V., Nowicki, S., Payne, A. J., Goelzer, H., Lipscomb, W. H., Abe-Ouchi, A., Agosta, C., Albrecht, T., Asay-Davis, X., Barthel, A., Calov, R., Cullather, R., Dumas, C., Galton-Fenzi, B. K., Gladstone, R., Golledge, N. R., Gregory, J. M., Greve, R., Hattermann, T., Hoffman, M. J., Humbert, A., Huybrechts, P., Jourdain, N. C., Kleiner, T., Larour, E., Leguy, G. R., Lowry, D. P., Little, C. M., Morlighem, M., Pattyn, F., Pelle, T., Price, S. F., Quiquet, A., Reese, R., Schlegel, N.-J., Shepherd, A., Simon, E., Smith, R. S., Straneo, F., Sun, S., Trusel, L. D., Van Breedam, J., Van Katwyk, P., van de Wal, R. S. W., Winkelmann, R., Zhao, C., Zhang, T., and Zwinger, T.: Insights into the vulnerability of Antarctic glaciers from the ISMIP6 ice sheet model ensemble and associated uncertainty, The Cryosphere, 17, 5197–5217, https://doi.org/10.5194/tc-17-5197-2023, 2023.</p><p> </p><p>It contains the code to prepare the datasets, to create the figures and the data for the analysis, and the scalar values computed for the 198 Antarctic glaciers stored by ice flow models.</p><p>The files Glacier_XX contain the data to emulate the results for individual glaciers.</p><p>The files Antarctica and AntarcticaWithCtrl contain the data to emulate the results for the Antarctic runs without and with the ctrl_proj experiment.</p><p>The files GROUP_ICEFLOW contain the ice flow model data for all the experiments recomputed for the 198 glaciers in Antarctica.</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.