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5,805 results for “Data model”
Data from: Nonlinear decoding models enable music reconstruction from human auditory cortex activity
<p>This dataset is associated with the manuscript "Nonlinear decoding models enable music reconstruction from human auditory cortex activity", and provides all preprocessed files necessary to replicate the results.</p> <p>In this study, we recorded intracranial EEG data (specifically, ECoG) in 29 patients with pharmacoresistant epilepsy while they were passively listening to a Pink Floyd song.</p> <p>The present dataset consists of preprocessed neural activity (High-Frequency Activity, 70-150 Hz), electrode coordinates (in MNI template space) and auditory stimulus (raw wave file, and 32- and 128-frequency-bin auditory spectrogram). HFA and both auditory spectrograms have a sampling rate of 100 Hz, and are temporally aligned (duration of 190.72 s).</p> <p>The code we used to preprocess and analyze the data is hosted on GitHub, <a href="https://github.com/ludovicbellier/PF_HFAdecoding">here</a> for the manuscript and <a href="https://github.com/ludovicbellier/PF_HFAdecoding">there</a> for the predictive modeling functions.</p>
benchmarking and modeling data used in Chang et al. (2023)
<p>The benchmarking and modeling data analyzed in Chang et al. (2023): Observational constraints reduce model spread but not uncertainty in global wetland methane emission estimates</p>
Primary collected data for modelling the additive MAR/R process by means of the PBF-LB process based on the example of tool steel 1.2709 powder
<p>For the evaluation of a MAR/R process, not only process-, material- and demonstrator-specific correlations and data must be combined. In addition to secondary data (e.g. databases, publications, etc.), primary data (e.g. process times, volume flows, etc.) must also be collected for the specific application.</p> <p>The attached table shows the primary data to be collected for the cradle-to-gate process depending on the process phases and steps. This data is used as support for ecological as well as economic process and component evaluations.</p>
Supplementary Data: Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider
<p><strong>Supplementary Data</strong></p> <p><em>Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider</em></p> <p>This record contains the full dataset generated for the study "<em>Simple, but not simplified: A new approach for optimising beyond-Standard Model physics searches at the Large Hadron Collider</em>". It contains the following files:</p> <ul> <li>data_cross_sections_full_exact.csv - a CSV file with the soft breaking parameters M1, M2, mu and tanb, the neutralino/chargino masses, the cross sections for neutralino-neutralino, chargino-neutralino and chargino-chargino production at the LHC operating at a CM energy of 13 TeV, and the branching ratios of the unstable charginos/neutralinos. </li> <li>benchmark_points.zip - this zip file contains the SLHA files for the four benchmark points as shown in the paper, together with the prospino output for the cross section. </li> </ul> <p> </p>
Data underpinning "Disorder-induced spin-charge separation in the 1-D Hubbard model"
<p>Many-body localisation is believed to be generically unstable in quantum systems with continuous non-Abelian symmetries, even in the presence of strong disorder. Breaking these symmetries can stabilise the localised phase, leading to the emergence of an extensive number of quasi-locally conserved quantities known as local integrals of motion, or l-bits. Using a sophisticated non-perturbative technique based on continuous unitary transforms, we investigate the one-dimensional Hubbard model subject to both spin and charge disorder, compute the associated l-bits and demonstrate that the disorder gives rise to a novel form of spin-charge separation. We examine the role of symmetries in delocalising the spin and charge degrees of freedom, and show that while symmetries generally lead to delocalisation through multi-particle resonant processes, certain subsets of states appear stable.</p>
Data set related to the manuscript "Investigating particle size effects on NMR spectra of ions diffusing in porous carbons through a mesoscopic model"
<p>Graphical files in the agr format for all the figures in the manuscript entitled "Investigating particle size effects on NMR spectra of ions diffusing in porous carbons through a mesoscopic model". Examples of input files for the lattice simulations are also provided.</p>
Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data
<p>Model, data, and a script to a paper of respective name</p>
Data used to simulations in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China"
<p>This dataset contains input data of simulations by WRF-CMAQ, WRF-Chem and WRF-CHIMERE in the GMD manuscript "Inter-comparison of multiple two-way coupled meteorology and air quality models (WRF v4.1.1-CMAQ v5.3.1, WRF-Chem v4.1.1 and WRF v3.7.1-CHIMERE v2020r1) in eastern China", as follows:</p> <p>1. WRF-CMAQ input data including emission, ICs and lateral BCs of meteorology and air quality:</p> <p>YYYYMM.zip represents the input data for each month for simulations. Due to the large size of the compressed file containing input data each month, there may be interruptions when uploading it to Zenodo. Therefore, we will split each compressed file into 50MB. If users want to browse the file, they can download the segmented files, and then merge them into the YYYYMM.zip file using the Linux command line "unzip 'YYYYMM.zip.*' -d combined"</p>
Data and Code for "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application"
<p>This data and code archive provides all the data and code for replicating the empirical analysis that is presented in the journal article "A Ray-Based Input Distance Function to Model Zero-Valued Output Quantities: Derivation and an Empirical Application" authored by Juan José Price and Arne Henningsen and published in the Journal of Productivity Analysis (DOI: <a href="https://doi.org/10.1007/s11123-023-00684-1">10.1007/s11123-023-00684-1</a>).</p> <p>We conducted the empirical analysis with the "R" statistical software (version 4.3.0) using the add-on packages "combinat" (version 0.0.8), "miscTools" (version 0.6.28), "quadprog" (version 1.5.8), sfaR (version 1.0.0), stargazer (version 5.2.3), and "xtable" (version 1.8.4) that are available at CRAN. We created the R package "micEconDistRay" that provides the functions for empirical analyses with ray-based input distance functions that we developed for the above-mentioned paper. Also this R package is available at CRAN (https://cran.r-project.org/package=micEconDistRay).</p> <p>This replication package contains the following files and folders:</p> <ul> <li><strong>README</strong><br> This file</li> <li><strong>MuseumsDk.csv</strong><br> The original data obtained from the Danish Ministry of Culture and from Statistics Denmark. It includes the following variables: <ul> <li><em>museum</em>: Name of the museum. </li> <li><em>type</em>: Type of museum (Kulturhistorisk museum = cultural history museum; Kunstmuseer = arts museum; Naturhistorisk museum = natural history museum; Blandet museum = mixed museum).</li> <li><em>munic</em>: Municipality, in which the museum is located.</li> <li><em>yr</em>: Year of the observation.</li> <li><em>units</em>: Number of visit sites.</li> <li><em>resp</em>: Whether or not the museum has special responsibilities (0 = no special responsibilities; 1 = at least one special responsibility).</li> <li><em>vis</em>: Number of (physical) visitors.</li> <li><em>aarc</em>: Number of articles published (archeology).</li> <li><em>ach</em>: Number of articles published (cultural history).</li> <li><em>aah</em>: Number of articles published (art history).</li> <li><em>anh</em>: Number of articles published (natural history).</li> <li><em>exh</em>: Number of temporary exhibitions.</li> <li><em>edu</em>: Number of primary school classes on educational visits to the museum.</li> <li><em>ev</em>: Number of events other than exhibitions.</li> <li><em>ftesc</em>: Scientific labor (full-time equivalents).</li> <li><em>ftensc</em>: Non-scientific labor (full-time equivalents).</li> <li><em>expProperty</em>: Running and maintenance costs [1,000 DKK].</li> <li><em>expCons</em>: Conservation expenditure [1,000 DKK]. </li> <li><em>ipc</em>: Consumer Price Index in Denmark (the value for year 2014 is set to 1).</li> </ul> </li> <li><strong>prepare_data.R</strong><br> This R script imports the data set MuseumsDk.csv, prepares it for the empirical analysis (e.g., removing unsuitable observations, preparing variables), and saves the resulting data set as DataPrepared.csv.</li> <li><strong>DataPrepared.csv</strong><br> This data set is prepared and saved by the R script prepare_data.R. It is used for the empirical analysis.</li> <li><strong>make_table_descriptive.R</strong><br> This R script imports the data set DataPrepared.csv and creates the LaTeX table /tables/table_descriptive.tex, which provides summary statistics of the variables that are used in the empirical analysis.</li> <li><strong>IO_Ray.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions with the 'optimal' ordering of outputs, imposes monotonicity on this distance function, creates the LaTeX table /tables/idfRes.tex that presents the estimated parameters of this function, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>IO_Ray_ordering_outputs.R</strong><br> This R script imports the data set DataPrepared.csv, estimates a ray-based Translog input distance functions, imposes monotonicity for each of the 720 possible orderings of the outputs, and saves all the estimation results as (a huge) R object allOrderings.rds.</li> <li><strong>allOrderings.rds</strong> (not included in the ZIP file, uploaded separately)<br> This is a saved R object created by the R script IO_Ray_ordering_outputs.R that contains the estimated ray-based Translog input distance functions (with and without monotonicity imposed) for each of the 720 possible orderings.</li> <li><strong>IO_Ray_model_averaging.R</strong><br> This R script loads the R object allOrderings.rds that contains the estimated ray-based Translog input distance functions for each of the 720 possible orderings, does model averaging, and creates several figures in the folder /figures/ that illustrate the results.</li> <li><strong>/tables/</strong><br> This folder contains the two LaTeX tables table_descriptive.tex and idfRes.tex (created by R scripts make_table_descriptive.R and IO_Ray.R, respectively) that provide summary statistics of the data set and the estimated parameters (without and with monotonicity imposed) for the 'optimal' ordering of outputs.</li> <li><strong>/figures/</strong><br> This folder contains 48 figures (created by the R scripts IO_Ray.R and IO_Ray_model_averaging.R) that illustrate the results obtained with the 'optimal' ordering of outputs and the model-averaged results and that compare these two sets of results.</li> </ul>
Code and Data to support "Atmospheric circulation-constrained model sensitivity recalibrates Arctic climate projections"
<p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/sic.sep.5member.dat">sic.sep.5member.dat</a> contains direct binary data of spatial monthly averaged sea ice concentrations for 1979 January to 2020 December from the CESM2 wind-nudging runs.</p> <p><a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.01.nc</a> to <a href="https://zenodo.org/api/files/b2d03cf8-c9e1-4ffb-8120-eb08237612e6/cism2.exp.smb.01.nc">cism2.exp.smb.05.nc</a> contain netcdf files of annual averaged surface mass balance output from the CESM2-CISM2 wind-nudging runs between 1979 and 2020.</p> <p>topal&ding_code1.py - data preparation Python code</p> <p>topal&ding_code2.py - creating the main text and supplementary figures.</p>
Raw Data to "Performance of the COSMO solvation model for photoacidity and basicity in water"
<p>This data is a supplement to the publication entitled "Performance of the COSMO solvation model for photoacidity and basicity in water" in the the Journal of Computational Chemistry A (DOI: 10.1002/jcc.27173).<br> It contains:</p> <p>Additional information on the file structure is given in the README file.</p>
Equilibrium climate sensitivity experiments using EC-Earth3-LR model — Surface Air Temperature data
<p>Three experiments was conducted using a EC-Earth model with the EC-Earth3-LR configuration (REF), which couples atmosphere, land, ocean and sea-ice components. First, we performed a pre-industrial (PI) control simulation (E280) using pre-industrial forcing, holding atmospheric constituents constant at 1850 levels (e.g., CO<sub>2</sub> concentration at 280 ppm). This simulation was initialized by a pre-run steady restart file (from a 500-year pre-industrial control simulation) and ran for 2000 years. We also conducted two sensitivity experiments (E400 and E560) by adjusting the CO<sub>2</sub> concentration to 400 ppm and 560 ppm, respectively, at the start year of the E280 experiment, and continued for over 3000 years (3069 years for E400, and 3013 years for E560). For our statistical analysis, we only considered the integration periods after the spin-up, using the last 2000-year outputs from the three simulations.</p> <p>The dataset contains Earth system model results from EC-Earth3 presented in the study by Cao et al. (2023).</p> <p>Cao, N., Zhang, Q., Wang, Z., Power, K.E., & Liu, C. (2023). The non-negligible impact of internal multi-centennial climate variability on estimating equilibrium climate change. Submitted to <em>Geophysical Research Letters</em>.</p> <p> </p> <p><strong>Model configuration</strong><br> Time periods: 2000-year time slice for all three experiments<br> ESM configuration: EC-Earth3-LR<br> Horizontal resolution: ~1.125° (~125 km)</p> <p><strong>Available data</strong><br> Annual mean data for Surface Air Temperature data.</p>
Data for results provided in IPACS_visits_based_model paper
<p>Data used for generating plots and results in IPACS paper: </p> <p><strong>Planning integrated care services using simulation modelling for visits-based home care with time varying resource</strong> requirementsAlison Harper<sup> </sup>, David Worthington<sup> </sup>, Zehra Önen-Dumlu, Paul Forte<sup> </sup>, Christos Vasilakis<sup> </sup>, Martin Pitt<sup> ,</sup> Richard Wood</p> <p><strong>Abstract:</strong> Delays to hospital discharge impact the wider healthcare system and are a contributory factor to emergency department overcrowding, ambulance offload and response delays experienced both in the UK and globally. Health and care services comprising of care provided in community facilities and at home support timely discharge from hospital for patients with complex care needs. It attempts to avoid costly discharge delays and consequent risks to patient physical and mental health. Home care is provided by visiting care staff in patients’ homes, with time-varying visit requirements, typically reducing over time. This paper reports the development of a novel discrete-time stochastic simulation model in a large health and social care system in England, which models patients using a time-varying resource draw, rather than a fixed capacitated resource. Our results are compared to steady-state and time-dependent analytical approximations to identify areas of the parameter space in our case study where the analytical models perform sufficiently well, and to enable an empirical case for the use of time-varying resources to model visits-based home care. The flexibility of our simulation model and our purposeful engagement with stakeholders have enabled the resulting tool to be in routine use for home care resource planning. It is available open source for take-up by researchers or analysts involved in home care demand and capacity planning.</p>
Machine-actionable data descriptions for Austria's digital landscape model
<p>The digital landscape model (DLM) is a compilation of several data sets that, among other things, form the data basis for official maps. It covers a variety of the 34 themes of the EU directive INSPIRE. The introduction of these themes enriched the metadata and made similar datasets easier to find. Unfortunately, the competent authority (BEV) only provides the associated data descriptions as a PDF export of an Excel spreadsheet without translations. This dataset contains the raw data and visualizations.</p>
Data for: Improvements in the Land and Crop Modeling over Flooded Rice Fields by Incorporating the Shallow Paddy Water (Submitting to the Journal of Advances in Modeling Earth Systems)
<p>We incorporated the shallow paddy surface water layer into the Noah-MP land surface model to improve its performance of surface heat fluxes over flooded rice paddies. Field measurements from two crop sites, i.e., SAITO (early rice) and SAGA (late rice), were used to initialize and evaluate the modified Noah-MP model (Maruyama, 2021). Additionally, we investigated the roles of some key parameters in the land and crop modeling. </p> <p>Note that, all numerical experiments in this study were conducted at the field scale using the offline version of Noah-MP (Niu et al., 2011) running within the High-Resolution Land Data Assimilation System (HRLDAS v3.9; Chen et al., 2007). Please refer to the official HRLDAS/Noah-MP unified Github repository (<a href="https://github.com/NCAR/hrldas">https://github.com/NCAR/hrldas-release</a>) for the original model codes.</p> <p>The related model code modifications and model outputs were included in this dataset. Surface observations for the nearest AMeDAS or meteorological observatory stations were obtained from the Japan Meteorological Agency website (<a href="https://www.jma.go.jp/jma/indexe.html">https://www.jma.go.jp/jma/indexe.html</a>), and were also provided in this dataset.</p> <p> </p> <p>References</p> <p>Chen, F., Manning, K. W., LeMone, M. A., Trier, S. B., Alfieri, J. G., Roberts, R. D., et al. (2007). Description and evaluation of the characteristics of the NCAR high‐resolution land data assimilation system. <em>Journal of Applied Meteorology and Climatology</em>, 46(6), 694-713. <a href="https://doi.org/10.1175/JAM2463.1">https://doi.org/10.1175/JAM2463.1</a></p> <p>Maruyama, A. (2021). Data for: Coupling land surface and crop models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies (version 2) [Data set]. Mendeley Data. <a href="https://doi.org/10.17632/tv23z95r5g.2">https://doi.org/10.17632/tv23z95r5g.2</a></p> <p>Niu, G., Yang, Z., Mitchell, K., Chen, F., Ek, M., Barlage, M., et al. (2011). The community Noah land surface model with multiparameterization options (Noah‐MP): 1. Model description and evaluation with local‐scale measurements. <em>Journal of Geophysical Research, </em>116, D12109. <a href="https://doi.org/10.1029/2010JD015139">https://doi.org/10.1029/2010JD015139</a></p>
Data obtained during classification of intertidal habitats using UAV imagery in the Galapagos Archipelago (Orthophotos, digital elevation models (DEM) and orthophoto-draped 3D models)
<p>In the repository 5 folders exist. 1) Digital elevation models (DEMs), 2) Intertidal habitat map, 3) Othophoto draped 3D models, 4) Orthophotos, and 5) Processing reports. The data has been collected in Puerto Ayora at Santa Cruz in August 2017, the most urbanized island of the Galapagos Archipelago. The purpose of this study was to investigate the image classification opportunities for these intertidal habitats using Uncrewed Aerial Vehicle (UAV) imagery. This dataset is cited in an open-access publication: https://doi.org/10.3390/drones7070416. </p>
FunMap feature data file used for model training
<p>The provided file is a TSV (Tab-Separated Values, gzipped) file that serves as input for training a machine learning model. The file structure consists of rows representing gene pairs and columns containing 16 Spearman correlation coefficients and 16 mutual ranks. The Spearman correlation coefficients capture the strength and direction of the relationship between gene pairs. Additionally, the mutual ranks are derived from the correlation coefficients and are utilized in the training process of the model.</p>
Data & scripts - The effect of temperature-dependent material properties on simple thermal models of subduction zones
<p>Data and scripts used in Van Zelst et al. (2023, Solid Earth): 'The effect of temperature-dependent material properties on simple thermal models of subduction zones'. Includes the data and figures for the benchmark of Van Keken et al. (2008) plus the results from our code xFieldstone; processing and visualisation scripts; raw and final figures; and all results for all model runs used in the publication (as listed in Table 1 in the paper). </p>
Data from: Integrating ecological niche and hydrological connectivity models to assess the impacts of hydropower plants on an endemic and imperiled freshwater turtle
<p>We built this dataset to assess the impacts of hydropower plants on the distribution of an endemic and imperiled freshwater turtle with very unique ecological requirements, the Williams' side-necked turtle (<em>Phrynops</em> <em>williamsi</em>). To prevent and mitigate impacts, we prioritized sites for species conservation by classifying planned HPP locations according to their predicted adverse effects on species distribution. The dataset has two files: i) species occurrence records and ii) hydropower plant data. The first dataset was fully built by the authors and the second was modified from the Brazilian Electricity Regulatory Agency (ANEEL) georeferenced data system.</p>
U-T training and test data for LayerFault model
<p>Here are the training and testing data sets involved in the numerical experiments in the article that has been submitted to the journal “Journal of Geophysical Research: Solid Earth”, named “Joint Model and Data-Driven Simultaneous Inversion of Velocity and Density”: LayerFault model. Each dataset consists of two parts: a training dataset and a testing dataset. Both training and testing data sets contain three parts: seismic data, velocity model and density model.</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.