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.
37
datasets available to search
ShareScore release 0.9.0
Dataset results
37 results for “parametric model”
A zero to four parameter instationary thermal-block-type benchmark model for parametric model order reduction
<p>We specify a new benchmark for parametric model order reduction that is scalable both in degrees of<br> freedom as well as parameter dimension.</p>
Benchmarking parametric and machine learning models for genomic prediction of complex traits
<p>The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.</p>
Supplementary data: Optimising centralisation and decentralisation in distribution networks for perishable products through mathematical modelling, parametric analysis, and machine learning
<p><span>The success of distribution companies for perishable products is enabled by optimally configuring distribution networks, which allows for reducing total logistic costs while ensuring reduced product spoilage and high service levels. Since customer demand for perishable products varies over time, the network configuration should not be optimised once, but periodically reviewed. Among the decisions to be reviewed, determining whether to centralise or decentralise inventory (i.e., stock allocation in distribution centres) is crucial. However, the literature overlooks stock allocation decisions, and existing methodologies to compare the economic performance of centralised, decentralised, and hybrid policies neglect important cost items, also requiring advanced computational technologies and skills to be applied. This paper addresses these gaps by providing two contributions. In this dataset, a comparison has been made between the cost performance of centralized, decentralized, and hybrid stock allocation policies in distribution networks for perishable products. The dataset comprises 100,000 realistic case studies generated through a Sobol quasi-random low discrepancy series.</span></p>
Data for paper "Parametric Timed Model Checking for Guaranteeing Timed Opacity"
<p>Our zip contains all necessary scripts, models, binaries and instructions to reproduce the experiments of our paper<br> <em>Parametric Timed Model Checking for Guaranteeing Timed Opacity</em> published in the proceedings of ATVA 2019.</p> <p>It allows interested readers to reproduce exactly the content of <strong>Table 1</strong> and <strong>Table 2</strong> of our paper.<br> In addition, all result files are generated, containing the synthesized constraints, and the execution times of IMITATOR.</p>
Secondary organic aerosol formation from smoldering and flaming combustion of biomass: a box model parametrization based on volatility basis set
<p>Here, we studied the aging of emissions from flaming and smoldering-dominated wood fires in three different residential stoves, across a wide range of aging temperatures (-10°C, 2°C and 15°C) and emission loads. Organic gases (OGs) acting as SOA precursors were monitored by a proton transfer reaction time-of-flight mass spectrometer (PTR-TOF-MS), while the evolution 10 of the aerosol properties during aging in the smog chamber was monitored by a high resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS). We developed a novel box model based on the volatility basis set (VBS) to determine the volatility distributions of the oxidation products from different precursor classes found in the emissions, grouped according to their emission pathways and SOA production rates.</p>
Model output for analyses in Qian et al. (2023) "Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model".
<p>This archive contains post-processed data for analyses in Qian et al. (2023) "<strong>Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model</strong>". These data are model output from the Perturbed Parameters Ensemble (PPE) simulations conducted with DOE's E3SM Atmosphere Model Version 1 (EAMv1). There are 256x12 PPE simulations with each simulation run for 5 days following the approach of Cloud Associated Parameterization Testbed (CAPT) and transpose Atmosphere Model Intercomparison Project (AMIP) paradigm. The diagnostic variables associated with the parameterizations of turbulence and shallow convection, deep convection, cloud microphysics, and gravity wave drag at day 5 are saved and used for analyses in this paper. </p>
MRI-based Approaches for Multi-parametric Model to Early Predict Pathological Complete Response to Neoadjuvant Therapy in Breast Cancer
ClinicalTrials.gov study NCT04909554. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Benchmarking parametric and machine learning models for genomic prediction of complex traits
Open the record for dataset details and reuse information.
Validation results of Non-parametric models of pH Neutralization plant Dataset
<p>This repository contains a table with different validation results of a series of models tested in pH Neutralization plant and Clarke plant.</p> <p>In this case, the naming protocol was as follows: [plant name]_DataSet_[model variable].</p> <p>The content of every CSV file is presented as follows: </p> <p>Column 1: { name: Model_No, type: numeric integer, limit: None, description: Code to identifiy the model (Primary Key)}</p> <p>Column 2: { name: KS, type: numeric float, limits: [0 1], description: Kolgomorov-Smirnov test}</p> <p>Column 3: { name: AD, type: numeric float, limits: [0 1], description: Anderson-Darling test}</p> <p>Column 4: { name: SW, type: numeric float, limits: [0 1], description: Shapiro-Wilk test}</p> <p>Column 5: { name: WX, type: numeric float, limits: [0 1], description: Wilcoxon test}</p> <p>Column 6: { name: FIT, type: numeric float, limits: [-Inf 1], description: Goodness of Fit metric}</p> <p>Column 7: { name: TIC, type: numeric float, limits: [0 1], description: Theil Inequality Index}</p> <p>Column 8: { name: Willmott, type: numeric float, limits: [0 1], description: Willmott metric}</p> <p>Column 9: { name: Russell_Pr, type: numeric float, limits: [0 1], description: Phase of Russell }</p> <p>Column 10: { name: Russell_Mr, type: numeric float, limits: [0 Inf], description: Magnitude of Russell }</p> <p>Column 11: { name: SG_Mr, type: numeric float, limits: [-Inf 1], description: Sprague & Geers}</p> <p>Column 12: { name: Anova, type: numeric float, limits: [0 1], description: Anova test}</p> <p>Column 13: { name: Dvure, type: numeric float, limits: [0 100], description: Dvurecenska metric}</p> <p>Column 14: { name: DTW, type: numeric float, limits: [0 Inf], description: DTW metric}</p> <p>Column 15: { name: D_DTW, type: numeric float, limits: [0 Inf], description: derivative of DTW metric}</p>
Raw data and results for the paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>*Data* (comets_condBoot_data.zip)</p> <p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4). The archive contains 4 folders with the data simulated in the first 4 scenarios (N=200 simulated datasets in each folder):<br> - scenario 1 - pdemax.rich<br> - scenario 2 - pdemax.sparse<br> - scenario 3 - pdhillhigh.rich<br> - scenario 4 - pdhillhigh.sparse<br> The data used in scenarios 5 and 6 was a subset of the datasets simulated in scenarios 3 and 4 respectively. In scenario 5, 20 subjects were taken from each dataset (subjects 1-5, 26-30, 51-55, 76-80) from the datasets in folder pdhillhigh.rich. In scenario 6, the datasets were constituted by the first 20 subjects from each sampling group of the data simulated in pdhillhigh.sparse.</p> <p>*Results:* (comets_scenarioXXX_results.zip, XXX=1,.. 6)</p> <p>6 simulation scenarios were assessed in the paper. Each file corresponds to 1 of 6 folders, one for each scenario:<br> - scenario 1 - pdemax.rich/results<br> - scenario 2 - pdemax.sparse/results<br> - scenario 3 - pdhillhigh.rich/results<br> - scenario 4 - pdhillhigh.sparse/results<br> - scenario 5 - pdhillhigh.n20rich/results<br> - scenario 6 - pdhillhigh.n20sparse/results</p> <p>In each "results" subfolder, the results for each bootstrap method and each dataset are written to a separate file, eg for simulation 1 in the first scenario:<br> - case bootstrap: scenarioHill1_bootstrapCase_sim1.res <br> - non-parametric bootstrap: scenarioHill1_bootstrapNP_sim1.res<br> - conditional non-parametric bootstrap: scenarioHill1_bootstrapNPc_sim1.res<br> - parametric bootstrap: scenarioHill1_bootstrapPar_sim1.res<br> The folder also contains:<br> - the saemix estimates for the 200 simulations: scenarioHill1_fitOrig.res<br> - tables with the bias and SE for the different bootstraps over the set of simulations, used to evaluate the methods: rbiasSEboot200.res, rbiasSEboot.res, rbiasWRsampleEstimates.res</p> <p> </p>
Stimuli used in the experiments reported by Wallis et al., "A parametric texture model based on deep convolutional features closely matches texture appearance for humans"
<p># Stimuli for "A parametric texture model based on deep convolutional features closely matches texture appearance for humans" by Wallis et al.</p> <p>This repository contains the stimuli used in</p> <p>Wallis, Funke, Ecker, Gatys, Wichmann and Bethge (submitted). A parametric texture model based on deep convolutional features closely matches texture appearance for humans. </p> <p>Code can be found in a complementary archive (http://doi.org/10.5281/zenodo.438029; stored separately due to license restrictions on images).</p> <p>The first experiment reported in the paper uses the stimuli in the subdirectory `stimulus_set_3`; the second experiment uses `stimulus_set_4`.</p> <p>## License</p> <p><strong>The original texture images (stored in `stimulus_set_3/raw_textures`) and their derivatives (`stimulus_set_3/preprocessed_ims/` and `stimulus_set_4/preprocessed_ims/`) remain copyright of www.textures.com (shared here with permission for scientific, non-commercial purposes).</strong></p> <p>Other images are shared under a CC-BY-NC license.<br> </p>
The Prediction Model of NAC Response for Breast Cancer Based on The Parametric Dynamics Features.
ClinicalTrials.gov study NCT06370234. IPD Sharing: NO. Countries: 0. Publications: 16.
Parametrized Rain Impact Model for SMAP L2 V1.0
This is the PI-produced SMAP sea water salinity, level 2 v1.0 orbital/swath product from the NASA Soil Moisture Active Passive (SMAP) observatory. It is based on the Parameterized Rain Impact Model (PRIM) developed at the University of Central Florida (UCF) Central Florida Remote Sensing Lab (CFRSL), Orlando, FL; University of Washington (UW) Applied Physics Lab (APL), Seattle, WA. <br><br>The PRIM product range extended from March 31, 2015 to September 30, 2021. It includes data for a range of parameters: derived SMAP sea water salinity at surface, 1m depth and 5m depth, and probability of salinity stratification (PSS), rainfall rate and wind speed data. Each data file covers one 98-minute orbit (15 files per day), and corresponds to a <a href="https://doi.org/10.5067/SMP50-2TOCS">JPL SMAP Level 2B CAP Sea Surface Salinity V5.0</a> file which corresponds to a single orbit on a given day. <br><br>The SMAP satellite is in a near-polar orbit at an inclination of 98 degrees and an altitude of 685 km. It has an ascending node time of 6 pm and is sun-synchronous. With its 1000km swath, SMAP achieves global coverage in approximately 3 days, but has an exact orbit repeat cycle of 8 days. On board Instruments include a highly sensitive L-band radiometer operating at 1.41GHz and an L-band 1.26GHz radar sensor providing complementary active and passive sensing capabilities. Observations are global in extent and provided at 25km swath grid with an approximate spatial resolution of 60 km.
COORDE: A model comparison of resolved and parametrized orographic drag
<p>This data is used in the publication 'COnstraining ORographic Drag Effects (COORDE): a model comparison of resolved and parametrized orographic drag', under review in JAMES AGU.</p>
Radiative transfer modeling in structurally-complex stands: towards a better understanding of parametrization: Dataset
<p>This repository is linked to the paper "Radiative transfer modeling in structurally-complex stands: towards a better understanding of parametrization" submitted to Annals of Forest Science and written by Frédéric ANDRÉ (corresponding author), Louis DE WERGIFOSSE, François DE COLIGNY, Nicolas BEUDEZ, Gauthier LIGOT, Vincent GAUTHRAY-GUYÉNET, Benoit COURBAUD and Mathieu JONARD.</p> <p>The repository contains the two following archive files:</p> <ul> <li>RadiationDataset.zip: gather the HETEROFOR model input files containing the measured understorey radiation values, and the position and main characteristics (girth of the trunk at breast height, total height, height of largest crown extension, height of crown base and crown radii in four directions) of the trees in the surrounding of the radiation measurement locations</li> <li>GrowthDataset.zip: gather the following HETEROFOR model input files for each of the six plots considered for growth measurements: <ul> <li>Inventory files at the beginning and at the end of the growth period</li> <li>Thinning file, containing the list of the trees harvested during the study period</li> <li>Fruit litter fall file: fruit litter fall (kgC/ha) measured each year, determined separatly for each of the main species</li> <li>Soil horizon file: containing the main physico-chemical characteristics of the soil horizons</li> <li>Meteorology file: containing hourly records of the main meteorological variables (total radiation, air temperature, soil surface temperature, rainfall depth, air relative humidity, wind speed, wind direction, diffuse/global radiation ratio)</li> </ul> </li> </ul> <p>For more information concerning this repository or the study, please do not hesitate to contact Frédéric ANDRÉ (frederic.andre@uclouvain.be) or Mathieu JONARD (mathieu.jonard@uclouvain.be).</p>
Data from: Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates
Open the record for dataset details and reuse information.
Parametric CAD models reconstruction from B-Rep models
<p>This dataset is built to construct 3D parametric CAD models from B-Rep models (step files) and is based on the public datasets ABC and DeepCAD. The original B-Rep models are from ABC, and the corresponding CAD command sequences are from DeepCAD. In addition, the dataset contains two parts that are B-Rep models and CAD command sequences. Here, the B-Rep models are converted to UV-graphs (folder '/uv_graph'); the command sequences are in folder '/cad_vec'.</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.