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102 results for “Data Model comparison”
Paleoclimate Data-Model Comparison and the Role of Climate Forcings over the Past 1500 Years
<p>The past 1500 years provide a valuable opportunity to study the response of the climate system to external forcings. However, the integration of paleoclimate proxies with climate modeling is critical to improving the understanding of climate dynamics. In this paper, a climate system model and proxy records are therefore used to study the role of natural and anthropogenic forcings in driving the global climate. The inverse and forward approaches to paleoclimate data-model comparison are applied, and sources of uncertainty are identified and discussed. In the first of two case studies, the climate model simulations are compared with multiproxy temperature reconstructions. Robust solar and volcanic signals are detected in Southern Hemisphere temperatures, with a possible volcanic signal detected in the Northern Hemisphere. The anthropogenic signal dominates during the industrial period. It is also found that seasonal and geographical biases may cause multiproxy reconstructions to overestimate the magnitude of the long-term preindustrial cooling trend. In the second case study, the model simulations are compared with a coral d18O record from the central Pacific Ocean. It is found that greenhouse gases, solar irradiance, and volcanic eruptions all influence the mean state of the central Pacific, but there is no evidence that natural or anthropogenic forcings have any systematic impact on El Nino-Southern Oscillation. The proxy climate relationship is found to change over time, challenging the assumption of stationarity that underlies the interpretation of paleoclimate proxies. These case studies demonstrate the value of paleoclimate data-model comparison but also highlight the limitations of current techniques and demonstrate the need to develop alternative approaches.</p>
Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al.
<p>Radiance data for "Systematic Comparison of Vectorial Spherical Radiative Transfer Models in Limb Scattering Geometry" by Zawada et al. which is to be submitted to Atmospheric Measurement Techniques. </p> <p>A comprehensive inter-comparison of seven radiative transfer models in the limb scattering geometry has been<br> performed. Every model is capable of accounting for polarisation within a fully spherical atmosphere. Three models (GSLS, SASKTRAN-HR, and SCIATRAN) are deterministic, and four models (MYSTIC, SASKTRAN-MC, Siro, and SMART-G)<br> are statistical using the Monte Carlo technique. This dataset consists of the raw radiance data used to perform the intercomparisons, atmospheric input data for the optical properties of the atmosphere, and data specifying the geometry of the test cases.</p> <p>Data is provided in NetCDF4 format with documentation present inside the variable attributes.</p> <p>More detail on the comparison scenarios can be found within the published article. (Link to be added when available).</p>
Output data of the models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022)
<p>Output data of the different models used in "Comparison of six approaches to predicting droplet activation of surface active aerosol. Part 1: moderately surface active organics" by Vepsäläinen et al. (2022).</p> <p>Output data is included for 50 nm particles containing malonic acid (mna), succinic acid (sca) and glutaric acid (glutarica), mixed with ammonium sulphate (AS) in different organic mass fractions. </p> <p>A plotter that allows the user to plot the Köhler curves, surface tensions and organic<br> partitioning factors during droplet growth from the model output data provided is included. </p>
Simulation Data for "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip"
<p>Simulation data from Jiang et al. (2022), "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip," <em>Journal of Geophysical Research: Solid Earth</em><em>.</em></p> <p>The archive includes simulation data for 3D SEAS benchmarks BP4-QD and BP5-QD that are analyzed in our paper (descriptions in NOTES.txt) </p> <p><strong>BP4-QD Benchmark Simulations:</strong><br>1000 m: jiang.5, lambert.8, barbot.3, barbot.2, dliu.2, li.4<br>500 m: jiang.3, lambert.3, barbot.5, barbot.7, ozawa</p> <p><strong>BP5-QD Benchmark Simulations:</strong><br>2000 m: jiang.6, lambert.8, liu.4, cattania.5, dli.7, barbot.3, dliu.10, li.3<br>1000 m: jiang.2, lambert.7, liu.5, cattania.3, ozawa, dli.5, barbot, dliu.6, li.2<br>500 m: jiang.4, lambert.9, liu.6, cattania.4, ozawa.2, dli.6, barbot.2, dliu.8<br>250 m: lambert.10, liu.7</p> <p><strong>BP5-QD with Off-Fault Data:</strong><br>1000 m: lambert.7, dli.5, barbot, dliu.6, li.2<br>500 m: lambert.9, dli.6, barbot.2, dliu.8</p> <p>Tables 2–4 in our paper summarizes details of numerical codes and selected simulations.</p> <p>The benchmark descriptions and the full suite of simulation data are available at SEAS online platform https://strike.scec.org/cvws/seas/.</p>
Data archive and code for "Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison"
<p>This upload contains data and code related to the paper "Predicting September Arctic Sea Ice: A Multi-Model Seasonal Skill Comparison" by M. Bushuk, S. Ali, D. Bailey, Q. Bao, L. Batte, U. S. Bhatt, E. Blanchard-Wrigglesworth, E. Blockley, G. Cawley, J. Chi, F. Counillon, P. Goulet Coulombe, R. Cullather, F. X. Diebold, A. Dirkson, E. Exarchou, M. Gobel, W. Gregory, V. Guemas, L. Hamilton, B. He, S. Horvath, M. Ionita, J. E. Kay, E. Kim, N. Kimura, D. Kondrashov, Z. M. Labe, W. Lee, Y. J. Lee, C. Li, X. Li, Y. Lin, Y. Liu, W. Maslowski, F. Massonnet, W. N. Meier, W. J. Merryfield, H. Myint, J. C. Acosta Navarro, A. Petty, F. Qiao, D. Schroder, A. Schweiger, Q. Shu, M. Sigmond, M. Steele, J. Stroeve, N. Sun, S. Tietsche, M. Tsamados, K. Wang, J. Wang, W. Wang, Y. Wang, Y. Wang, J. Williams, Q. Yang, X. Yuan, J. Zhang, and Y. Zhang, published in the Bulletin of the American Meteorological Society, DOI: https://doi.org/10.1175/BAMS-D-23-0163.1.</p> <p>See README.txt for a description of the datasets and code.</p>
Measurement and model data comparisons for the HALO-FAAM formation flight during EMeRGe on 17 July 2017
<p>Within the project “Effect of Megacities on the transport and transformation of pollutants on the Regional and Global scales” (EMeRGe), the measurement flight of 13 July 2017 was performed for comparison of the instrumentation onboard of the research aircraft HALO and FAAM. The aircraft flew for 1.6 h in close formation along a racetrack pattern at three flight levels in Southern Germany. The flight started in a rather dry and clean troposphere and ended in a more polluted convective boundary layer. 28 measurement pairs sampled on both aircraft were found suitable for comparison. 17 further pairs of data are available from sampling on either HALO or FAAM. In addition, observations obtained at the DWD Hohenpeissenberg and results from 6 models are included in the comparisons. Overall, about 30% of the measured data pairs show deviations within the combined error estimates. Some measurements deviate considerably from model results.</p> <p>This dataset contains a pdf of the report and a zip file of the comparison data as described in that report.</p>
Electronic Supplement / Data Archive for "Comparison of a Neutral Density Model With the SET HASDM Density Database"
<p>These files provide supplemental data to accompany the paper "Comparison of a Neutral Density Model With the SET HASDM Density Database,” submitted to <em>Space Weather, </em>with manuscript number 2021SW002888. Details are provided in the file DataArchiveDocumentation.pdf.</p>
Model input and output data of the FlexMex model comparison
<p>This data collection includes the input and output data of the FlexMex model experiment (grant number: 03ET4077A-H) funded by the German Federal Ministry for Economic Affairs and Energy (BMWi). The aim of the FlexMex project is to better understand the interrelationships of modelling approaches and model results in the mapping and analysis of technical-structural flexibilities in future electricity systems.</p> <p>The data are separated in the two subfolders InputData and OutputData. For the input data, a distinction is made between scalar and time series data.</p> <p>Please find additional information on the models, test cases and analysis in the ReadMe and the publications cited there.</p>
Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures
<p>This dataset comes from the following paper:</p> <p>Chiara Pasini, Oscar Ramponi, Stefano Pandini, Luciana Sartore, Giulia Scalet, Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures, J. of Materi Eng and Perform, 2024. <a href="https://doi.org/10.1007/s11665-024-10199-x">https://doi.org/10.1007/s11665-024-10199-x</a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. m of the neural network</li> <li>. inp of the Abaqus finite element simulations</li> </ul>
Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle"
<p>Supplementary material for "Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle". </p> <p>Clerc, C., Bopp, L., Benedetti, F., Vogt, M., and Aumont, O.: Including filter-feeding gelatinous macrozooplankton in a global marine biogeochemical model: model-data comparison and impact on the ocean carbon cycle, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-1282, 2022.</p> <p>Three directories can be downloaded:</p> <p><strong>DataOBS</strong> : AtlantECO [WP2] – Traditional microscopy dataset – Thaliacea (Salpida+Doliolida+Pyromosomatida) abundance and biomass concentration data, presented in Clerc et al. (2022). </p> <p><strong>FigPaper </strong>: Source code and .nc files for the figures presented in Clerc et al. (2022) (https://doi.org/10.5194/egusphere-2022-1282). </p> <p><strong>MY_SRC_PISCES_NEMO_3.6 :</strong> Additional fortran routines for the compilation of PISCES-FFGM, the model developed for Clerc et al. (2022), from NEMO-3.6 (https://www.nemo-ocean.eu)</p>
Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models
1. Of the several approaches that are used to analyze functional trait-environment relationships, the most popular is community-weighted mean regressions (CWMr) in which species trait values are averaged at the site level and then regressed against environmental variables. Other approaches include model-based methods and weighted correlations of different metrics of trait-environment associations, the best known of which is the fourth-corner correlation method. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using four different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should always be avoided. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using five different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but suffered from low power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should be avoided.
Data for "Measurement report: Comparison of airborne in-situ measured, lidar-based, and modeled aerosol optical properties in the Central European background – identifying sources of deviations"
<p>A unique set of data is presented, derived from measurements conducted at the rural central European observatory at Melpitz, Germany. Data derived from remote sensing (lidar), airborne platforms (helicopter, balloon), and ground-based in-situ methods is included. Measured and Mie-modeled optical aerosol parameters are presented in the dry- and ambient state. Modeled optical parameters are based on Mie-theory. For ambient state hygroscopic growth simulations are utilized.</p>
Data for "Wave dispersion and dissipation in landfast ice: comparison of observations against models"
<p>Data to replicate Figures 2, 3 and 7 in the manuscript: Wave dispersion and dissipation in landfast ice: comparison of observations against models. Article submitted for review to The Cryosphere (https://doi.org/10.5194/tc-2021-210)</p>
Raw data to "Opioid sequestration by intravenous lipid emulsion – comparison of lipophilicity in a cell-free system and cellular model"
<p>Data that resulted from the conduction of the in vitro part of the project: Intravenous lipid emulsions as a treatment in acute opioid poisoning - pharmacokinetic and pharmacodynamic evaluation in the rabbit model. It served as raw data for the publication Opioid sequestration by intravenous lipid emulsion – comparison of lipophilicity in a cell-free system and cellular model (draft title). </p>
New Findings on Existing Resilient Modulus Constitutive Models through Performance Comparison on LTPP Data
<p>This dataset is the result of the study entitled, "New Findings on Existing Resilient Modulus Constitutive Models through Performance Comparison on LTPP Data".</p>
Supporting data for article comparison and Uncertainty Analysis of Species Distribution Models
<p>Downloaded from Web of Science for the supporting data of article comparison and Uncertainty Analysis of Species Distribution Models.</p>
Data for "Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model"
<p>The data files arranged here correspond to the data used in the paper: “Multifractal comparison of reflectivity and polarimetric rainfall data from C- and X-band radars and respective hydrological responses of a complex catchment model”, submitted to <em>Water</em>.</p> <p>The data organized as follows:</p> <ul> <li>Data_type_20150912_time_steps.mat: the rainfall data for 3 different products of the X-band radar (FIR filter, a=200, b=1.6; FIR filter, a=150, b=1.3; simple filter, a=150, b=1.3) for the event of 12-13 September 2015, over an area of 64 km x 64 km.</li> <li>Data_type_Event_time_steps.mat: X-band radar data (FIR filter, a=150, b=1.3) for the events of 16 September 2015 and 5-6 October 2015, over an area of 64 km x 64 km.</li> <li>Sub-catchment_name_Data_type_Event.txt: the rainfall series for each of 26 sub-catchments of the model, for 3 different types of rainfall data (C-band, X-band and rain gauges) for the events of 12-13 September 2015, 16 September 2015 and 5-6 October 2015.</li> <li>X-band_Pixels_Event.txt: the rainfall series for all 6 X-band radar pixels corresponding to the 6 rain gauges for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015).</li> <li>X-Band_Optim 20150916_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with X-band data for the 16 September 2015 event, with the implementation of the tool mimicking the regulation optimization.</li> <li>Data_type_Event_Measurement_point_name.txt: flow simulated at each of the 4 measurement points with 3 different types of rainfall data (C-band, X-band and rain gauges) for the 3 studied events (12-13 September 2015, 16 September 2015, and 5-6 October 2015), without the implementation of the tool mimicking the regulation optimization.</li> </ul> <p>The original C-band radar data remains property of Météo-France and was provided to the authors for this research study, without any possibility of data disclosure.</p> <p>The details on how the rainfall series were generated over each sub-catchment could be found in the paper.</p> <p>The authors greatly acknowledge partial financial supports of the Chair “Hydrology for resilient cities” endowed by Veolia, and of the Department of Science and Technology of the Brazilian Army. The authors are thankful to M Bernard Urban (Météo-France) for providing access to the C-band radar data and documentation in the framework of the INTERREG NWE RainGain project.</p>
Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome
<p>Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS metaproteomic measurements using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data highlighting specific needs to further improve DIA tools to enable analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising data collection strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.</p>
Experimental data for Dynamic cover effects in lateral bedrock channel bank abrasion: Experiment and model comparison
<p>Experimental data for bank erosion.xlsx contains the data used for the figures in the paper, and the distribution of bedrock bank erosion in the longitudinal direction in Run 1 - Run 18.</p>
Euclid: Modelling massive neutrinos in cosmology — a code comparison (data record: data-snapshot-0.0eV-1024Mpc)
<p>This record is part of a distributed data set associated with the paper ‘Euclid: Modelling massive neutrinos in cosmology — a code comparison’. This record holds the <strong>simulation snapshot data for the 0.0eV 1024Mpc simulation</strong>.</p> <p>See the <a href="https://doi.org/10.5281/zenodo.7297976">main repository</a> for details.</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
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DANDI Archive for NWB datasets
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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.