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5,805 results for “Data model”
MASNUM model data and results
<p>This repository contains all publicly available data required in the Indian Ocean modeling using the MASNUM ocean circulation model, as well as the simulated temperature, diffusive term and current results. </p> <p>Gebco_08: the topography based on the global General Bathymetric Chart of the Oceans 2008 (GEBCO_08) data.</p> <p>Levitus: the annually mean Levitus data are interpolated as the initial temperature and salinity.</p> <p>NCEP_NCAR: The surface forcing including the momentum, heat and wind stress fluxes are calculated from the monthly mean surface fields of the National Centers for Environmental Prediction / National Center for Atmospheric Research (NCEP/NCAR) reanalysis data set from 1948 to 2021.</p> <p>OSCAR: the multi-year (1993-2021) monthly mean Ocean Surface Current Analyses Real-time (OSCAR) data are regarded as a reference in the comparison of the simulated current.</p> <p>results: the simulated temperature, diffusive term and current results using the MASNUM ocean model.</p> <p>result_glob: the global simulated results that can provide the data for the open boundary conditions in the Indian Ocean modeling.</p> <p>WOA13: The simulated temperature in the last 1 year are compared with the monthly World Ocean Atlas 2013 (WOA13) climatologic data which is regarded as a reference.</p> <p>BOA-Argo: The grided products from the Argo data, and used to validate the modeling further.</p>
Selected data analysed in the JGR Atmosphere manuscript " An application of the maximum entropy production method in the WRF Noah land surface model"
<p>The control experiment (hereafter WRF-CTL) and the MEP experiment (hereafter WRF-MEP) simulations results interpolated to the observation stations. The simulation period was 1 June to 31 August 2015 with 30 hours from 12:00 UTC (20:00 Beijing time (BJT)) each day, and the latest 24-hour outputs are provided.</p>
Processed receiver function data, dispersion measurement, and shear velocity model (Dharwar)
<p>Processed receiver function data at 1 sample per second, dispersion measurement, and shear velocity model (Dharwar).</p>
Data for figures in Model predictions of wave overwash extent into the marginal ice zone
<p>The data required to reproduce the figures in 'Model predictions of wave overwash extent into the marginal ice zone'.</p> <p>The data includes:</p> <ul> <li>Coefficient values produced by coupled floe-wave motions, required to determine overwash of a single floe and thus the overwash extent model.</li> <li>The data for the transects used to predict overwash in Figure 11 (Agulhas II), includes wave conditions and the ice concentration along the transect (other floe field properties remains constant).</li> </ul> <p>The code is uploaded at - </p> <pre>https://doi.org/10.5281/zenodo.7059554</pre>
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
<p>Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"</p> <p>Code available at https://github.com/MTreppner/multiomics_dgms</p>
Data from: Resolving the mesoscopic missing link: biophysical modeling of EEG from cortical columns in primates
<p class="MsoNormal"><span>Event-related potentials (ERP) are among the most widely measured indices for studying human <span>cognition. While their timing and magnitude provide valuable insights, their usefulness is limited by our understanding of their neural generators at the circuit level. Inverse source localization offers insights into such generators, but their solutions are not unique. To address this problem, scientists have assumed the source space generating such signals comprises a set of discrete equivalent current dipoles, representing the activity of small cortical regions. Based on this notion, theoretical studies have employed forward modeling of scalp potentials to understand how changes in circuit-level dynamics translate into macroscopic ERPs. However, experimental validation is lacking because it requires <em>in vivo</em> measurements of intracranial brain sources. Laminar local field potentials (LFP) offer a mechanism for estimating intracranial current sources. Yet, a theoretical link between LFPs and intracranial brain sources is missing. Here, we present a forward modeling approach for estimating mesoscopic intracranial brain sources from LFPs and predict their contribution to macroscopic ERPs. We evaluate the accuracy of this LFP-based representation of brain sources utilizing synthetic laminar neurophysiological measurements and then demonstrate the power of the approach <em>in vivo</em> to clarify the source of a representative cognitive ERP component. To that end, </span>LFP was measured across the cortical layers of visual area V4 in macaque monkeys performing an attention demanding task. <span>We show that area V4 generates dipoles through layer-specific transsynaptic currents that biophysically recapitulate the ERP component through the detailed forward modeling. The constraints imposed on EEG production by this method also revealed an important dissociation between computational and biophysical contributors. As such, this approach </span>represents an important bridge between laminar microcircuitry, through the mesoscopic activity of cortical columns to the patterns of EEG we measure at the scalp. </span></p>
QENS model of Battery data
<p>QENS modeling of diffusion coefficients of Li-Ion batteries during the PaNOSC Summer School 2022 to get to know some things about QENS and FAIR sharing of data.</p> <p>Modeled QUENS for diffusion coefficients of the pristine anodes of different battery cells, given in <a href="https://doi.org/10.1149/2.0301805jes">https://doi.org/10.1149/2.0301805jes</a></p>
Climate data for Machine Learning based 100-year flood flow prediction model
<p>This study evaluates the application of ML technique over northeast United States regions and compares its performance to the U.S. Geological Survey (USGS) Streamflow Statistics (StreamStats)</p>
Input data for Bayesian and information theoretic model selection and similarity analysis
<p>This data serves as input to the codes found in the following repository https://github.com/MariaFMoralesOreamuno/Bayesian_Information_theoretic_model_selection.git</p> <p> </p>
Thermochronology data in Ebro basin and model input parameters for computing cooling histories
<p>Two files (word and excel) containing Table DR1 that refer to the model input parameters and Table DR2 with details of the (U-Th-Sm)/He analyses. </p>
Dataset for Assessing the mycotoxin-related health impact of shifting from meat-based diets to soy-based meat analogues in a model scenario based on Italian consumption data
<p>Dataset used for <strong>Assessing the mycotoxin-related health impact of shifting from meat-based diets to soy-based meat analogues in a model scenario based on Italian consumption data.</strong></p>
Model data - Impact of Ural blocking on early-winter climate variability under different Barents-Kara sea ice conditions
<p>Model data for JGR paper : Impact of Ural blocking on early-winter climate variability under different Barents-Kara sea ice conditions</p>
Data from "Description and evaluation of the new UM-UKCA (vn11.0) Double Extended Stratospheric-Tropospheric (DEST vn1.0) scheme for comprehensive modelling of halogen chemistry in the stratosphere" by Bednarz et al., 2022
<p>Model output from the UM-UKCA simulations used in "Description and evaluation of the new UM-UKCA (vn11.0) Double Extended Stratospheric-Tropospheric (DEST vn1.0) scheme for comprehensive modelling of halogen chemistry in the stratosphere" by Bednarz et al. (2022), as well as plotting scripts used to make all figures.</p>
MIROC4-ACTM: Model setup, input and output data for CH4 LETKF (Bisht et al., GMD-D, 2022)
<p>Details in :</p> <p>Bisht, J. S. H., Patra, P. K., Takigawa, M., Sekiya, T., Kanaya, Y., Saitoh, N., and Miyazaki, K.: Estimation of CH<sub>4</sub> emission based on advanced 4D-LETKF assimilation system, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-719, 2022.</p>
Data from: Artificial intelligence model for analyzing colonic endoscopy images to detect changes associated with irritable bowel syndrome
<p><strong><span>Background/Aims</span></strong></p> <p><span>IBS is not considered to be an organic disease and usually shows no abnormality on lower gastrointestinal endoscopy, although biofilm formation, dysbiosis, and histological microinflammation have recently been reported in patients with IBS. In this study, we investigated whether an artificial intelligence (AI) colorectal image model can identify minute endoscopic changes, which cannot typically be detected by human investigators, that are associated with IBS.</span></p> <p><span>Study subjects were identified based on electronic medical records and categorized as IBS (Group I; n=11), IBS with predominant constipation (IBS-C; Group C; n=12), and IBS with predominant diarrhea (IBS-D; Group D; n=12). The study subjects had no other diseases. Colonoscopy images from IBS patients and from asymptomatic healthy subjects (Group N; n=88) were obtained. Google Cloud Platform AutoML Vision (single-label classification) was used to construct AI image models to calculate sensitivity, specificity, predictive value, and AUC. A total of 2479, 382, 538, and 484 images were randomly selected for Groups N, I, C and D, respectively. </span></p> <p><strong><span>Results</span></strong></p> <p><span>The AUC of the model discriminating between Group N and I was 0.95. Sensitivity, specificity, positive predictive value, and negative predictive value of Group I detection were 30.8%, 97.6%, 66.7%, and 90.2%, respectively. The overall AUC of the model discriminating between Groups N, C, and D was 0.83; sensitivity, specificity, and positive predictive value of Group N were 87.5%, 46.2%, and 79.9%, respectively.</span></p> <p><strong><span>Conclusions</span></strong></p> <p><span>Using the image AI model, colonoscopy images of IBS could be discriminated from healthy subjects at AUC 0.95. Prospective studies are needed to further validate whether this externally validated model has similar diagnostic capabilities at other facilities and whether it can be used to determine treatment efficacy.</span></p>
Data set containing the energy landscapes for GPO and GPP tropocollagen models under pulling forces
<p>Energy landscapes (databases of minima and transition states) for GPO and GPP repeat collagen models under constant pulling forces as explored with OPTIM and PATHSAMPLE with an AMBER force field.</p> <p>The systems are seven GPO or GPP per chain capped with ACE and NME.</p> <p> The forces applied are 0 pN (F0), 10 pN (F1), 50 pN (F2), 100 pN (F3), 250 pN (F4), 500 pN (F5) and 750 pN (F6).</p> <p>The folders contains numerous analysis scripts and graphs. Most of these assume python with numpy and pandas, as well as cpptraj from AMBERTools.</p>
Sige_model_subduction_data_impermeable
<p>This dataset includes the raw data of hydraulic modeling in subduction zones, which are used in the manuscript submitted to JGR by Kaneki & Noda. Details of the dataset can be found in Readme_data_impermeable.txt.</p>
Sige_model_subduction_data_basic
<p>This dataset includes the raw data of hydraulic modeling in subduction zones, which are used in the manuscript submitted to JGR by Kaneki & Noda. Details of the dataset can be found in Readme_data_basic.txt.</p>
Sige_model_subduction_data_basic_35km
<p>This dataset includes the raw data of hydraulic modeling in subduction zones, which are used in the manuscript submitted to JGR by Kaneki & Noda. Details of the dataset can be found in Readme_data_35km.txt.</p>
Sige_model_subduction_data_splay
<p>This dataset includes the raw data of hydraulic modeling in subduction zones, which are used in the manuscript submitted to JGR by Kaneki & Noda. Details of the dataset can be found in Readme_data_splay.txt.</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.