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 Non-parametric Discrete Fracture Network Model
<p>Database used to build discrete fracture networks through a non-parametric approach from Gómez et al. 2023 (DOI: 10.1007/s00603-022-03194-y). The data is structured in twelve.csv files, each with an array of size n-by-3, containing the orientation of the discontinuity (dip direction and dip of the pole) and its pseudo-trace length in meters, with n being the number of fractures in each file.</p>
parametric CAD model of the lumbar spine
<p>7Tepe Parametric Lumbar Spine Model</p> <p>Tweaking parameters of associative CAD models offer interesting pathways for the application of artificial intelligence methods, such as optimization by genetic algorithms and neural networks.</p> <p>Construction of a robust lumbar verteral assembly requires a holistic design approach. The concept of using a layout-sketch as the core of a cad model is becoming more popular due to its top-down design philosophy. However, there remains local issues such as the contact properties at the transverse facet joints, which cannot be taken care of alone by the top-down approach. Inverting the perspective to a bottom-up approach and observing some of the intricate geometrical features of for example the laminae leads to a model that can simply be represented by a relatively scarce number of parameters, yet capturing the physiological aspect of the organ in quite an accurate way.</p> <p>For those who are interested in what our 7Tepe Parametric Lumbar Spine Model version 0.1 currently looks like, just download the source in Parasolid format (x_t extension) and import it to your favorite CAD system. Currently, we have not shared our source CAD file belonging to proprietary software products, but we intend to do so in the near future.</p> <p>Designed by Ogulcan in proprietary solid modeling software.</p>
Dataset for Bayesian parametric models for survival prediction in medical applications
<p><strong>Data Source</strong></p> <p>The data for these experiments were derived from these sources:</p> <p>* Hosmer Jr DW, Lemeshow S, May S. Applied Survival Analysis: Regression Modeling of Time-to-Event Data. 2nd ed: John Wiley & Sons; 2008.</p> <p>* Jd K, Prentice R. The statistical analysis of failure time data. New York: John Wiley and Sons; 1980.</p> <p>* Fleming T, Harrington D. Counting Processes and Survival Analysis: John Wiley & Sons; 1991.</p> <p> </p> <p>The raw data was downloaded from web archive.</p> <p>https://web.archive.org/web/20170114043458/http://www.umass.edu/statdata/statdata/data/</p> <p><strong>Contents</strong></p> <p>Each folder contains the original data, a textfile with a description of the data, and the pre-processed version with one-hot encoded variables. An additional YAML file is included with the list of included variables, name of the time and censor variable, name of continuous variables and splitting and partitioning information.</p>
COSMOS isolated galaxy images (parametric models generated with GalSim)
<p>Isolated galaxy images generated with GalSim from parametric models extracted from the Hubble COSMOS catalog.</p> <p>These files contain images and data for 10 000 images of isolated galaxy:</p> <ul> <li><strong>galaxies_isolated_10000_images.npy: </strong>numpy array of shape (10 000, 10, 64, 64), 10 000 images of size 64x64 pixels, in 10 filters (4 Euclid filters and 6 <em>ugrizy</em> LSST filters, in that order). Images contain Poissonian noise.</li> <li><strong>galaxies_isolated_10000_data.csv: </strong>corresponding parameters: <ul> <li>SNR: signal-to-noise ratio</li> <li>redshift: redshift of the galaxy</li> <li>e1: e1 parameter of ellipticity (e = e1 + i.e2)</li> <li>e2: e2 parameter of ellipticity (e = e1 + i.e2)</li> <li>mag: magnitude</li> </ul> </li> </ul>
Deflections of the s-FKLP model for parametric study purposes
<p>The test data included are for plate deflections obtained according to the s-FKLP model produced for parametric study purposes. The methodology and analysis of the obtained data is included in the Open Access article:</p> <p>Stempin, P.; Pawlak, T. P. & Sumelka, W.<br>Formulation of non-local space-fractional plate model and validation for composite micro-plates<br><em>International Journal of Engineering Science, </em><em>Elsevier BV, </em><strong>2023</strong><em>, 192</em>, 103932.</p> <p>DOI: https://doi.org/10.1016/j.ijengsci.2023.103932</p>
Automatic message sequence chart creation from simulation run of the parametric colored Petri net model of the Chandy-Lamport algorithm with four processes
<p><span>The video shows the creation of the message sequence chart from a simulation run of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm using the CPN tool with four constituting processes. The picture shows the resulting message sequence chart. </span></p> <p><strong><span>Message Sequence Chart of Parametric Model With 4 Processes via Automatic Simulation Run_SuppInfo.mp4</span></strong><span>: This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The model's number of constituting processes is parametric and was set to four. The video was generated using the authors' updated CPN tool extension server. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition at each step in an automatic simulation run.</span></p> <p><strong><span>Picture of Message Sequence Chart of Parametric Model With 4 Processes_SuppInfo.png:</span></strong><span> This picture shows the automatically generated message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm that is visible in the above video clip. The number of constituting processes was set to four. </span></p>
A non-parametric model-free analysis of actigraphic recordings of acute insomnia patients
Both parametric and non-parametric approaches to time series analysis have advantages and disadvantages. Parametric methods, although powerful and widely used, can yield inconsistent results due to the oversimplification of the observed phenomena, they require the setting of arbitrary constants for their creation and refinement, and, although these constants relate to assumptions about the observed systems, it can lead to erroneous results when treating a very complex problem with a sizable list of unknowns. Their non-parametric counterparts, instead, are more widely applicable but present a higher detrimental sensitivity to noise and low density in the data. For the case of approximately periodic phenomena, such as human actigraphic time series, parametric methods are widely used and concepts such as acrophase are staple in chronobiology; in this work we present a non-parametric method of analysis of actigraphic time series from insomniac patients and healthy age-matched controls, the method is fully data-driven, reproduces previous results in the context of sleep-onset delay and, crucially, extends the concept of acrophase not only to circadian but also for ultradian spectral components.
Codes and model output supporting Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards
<p>Codes and model output supporting Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards (Advances in Statistical Climatology, Meteorology and Oceanography, May 2022)</p>
Experimental data for Distributed parametric model checking timed automata under non-Zenoness assumption
<p>This is the experimental data mentioned in the paper "Distributed parametric model checking timed automata under non-Zenoness assumption", published in FMSD (Formal Methods in System Design), 2022.</p> <p>Experiments were conducted using the distributed version of IMITATOR 2.9.2 (Butter Incaberry) on two Intel Xeon Silver 4114 at 2.2GHz (20 cores in total), and 96GiB of memory, running under Ubuntu 16.04 LTS 64-bit.</p>
Рис. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., гоΛотип; 2 — Calyptra thalictri alexander ssp. n., паратип; 3 — кΛаΑограмма Calyptra thalictri. Построена метоΑом максимаΛьного схоΑства, параметрическая моΑеΛь Тамура-Неи, 10 000 бутстрапрепΛикаций; 4 — биотоп Calyptra thalictri alexander ssp. n. Fig. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., holotype; 2 — Calyptra thalictri alexander ssp. n., paratype; 3 — cladogram of Calyptra thalictri. Based on the maximum likelihood method, Tamura-Nei parametrical model, 10000 bootstrap replications; 4 — biotope of Calyptra thalictri alexander ssp. n. in A New Subspecies Of (Borkhausen, 1790) (Lepidoptera: Erebidae, Calpinae) From Kyrgyzstan
Рис. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., гоΛотип; 2 — Calyptra thalictri alexander ssp. n., паратип; 3 — кΛаΑограмма Calyptra thalictri. Построена метоΑом максимаΛьного схоΑства, параметрическая моΑеΛь Тамура-Неи, 10 000 бутстрапрепΛикаций; 4 — биотоп Calyptra thalictri alexander ssp. n. Fig. 1. Calyptra thalictri: 1 — Calyptra thalictri alexander ssp. n., holotype; 2 — Calyptra thalictri alexander ssp. n., paratype; 3 — cladogram of Calyptra thalictri. Based on the maximum likelihood method, Tamura-Nei parametrical model, 10000 bootstrap replications; 4 — biotope of Calyptra thalictri alexander ssp. n.
Model output used in the manuscript "Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective"
<p>This *.zip file contains the model output from ensemble simulations for the Lorenz 84-Stommel 61 model (hereafter L84-S61; <a href="https://doi.org/10.3402/tellusa.v53i5.12229" target="_blank" rel="noopener">Van Veen et al, 2001</a>; <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth, 2013</a>). To run these simulations, we used the Low-EFFourth ensemble generator (<a href="https://doi.org/10.48550/arXiv.2506.03313" target="_blank" rel="noopener">de Melo Viríssimo, 2025a</a>; <a href="https://doi.org/10.5281/zenodo.15566109" target="_blank" rel="noopener">de Melo Viríssimo, 2025b</a>), which is a MATLAB-based framework that allows for large ensembles of low-dimensional dynamical systems to be run and studied in a systematic way (<a href="https://doi.org/10.5194/egusphere-egu23-14755" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2023</a>).</p> <p>These model outputs are presented and discussed in the manuscript "<em>Micro and macro parametric uncertainty in climate change prediction: a large ensemble perspective</em>", published by the Bulletin of the American Meteorological Society (<a href="https://doi.org/10.1175/BAMS-D-24-0064.1" target="_blank" rel="noopener">de Melo Viríssimo and Stainforth, 2025</a>). The manuscript describes the experiments performed, the parameter values used and the modifications done to the original L84-S61 model. For this matter, we also refer you to <a href="https://doi.org/10.1088/1748-9326/8/3/034021" target="_blank" rel="noopener">Daron and Stainforth (2013)</a> and <a href="https://doi.org/10.1063/5.0180870" target="_blank" rel="noopener">de Melo Viríssimo et al. (2024)</a>.</p> <p>All files uploaded were generated from simulations run by the lead author.</p> <p>For specific information about each file uploaded, please refer to the README file. The details of each experiment are also presented in the supplementary materials of the manuscript. If you have any questions, please feel free to contact me.</p>
Parametric 3D CAD model of human foot
<p>The parametric 3D CAD model of human foot was developed from CT data. A CT (Toshiba® Aquilion 4 equipment) scan was performed on a 29 years old male (65 Kg). 345 slices were captured with a slice distance of 1.0 mm (see Figure 2.a). Scans were made for both feet in their neutral posture in which there is the least tension or pressure on tendons, muscles and bones. Medical images were, then, exported into standard .DICOM format (image resolution 512x512 pixels) and processed by using ScanIP® and SolidWorks.</p> <p>Bone structure was composed of 19 bones: tibia, fibula, talus, calcaneus, cuboid, navicular, 3 cuneiforms (bones of the metatarsus), 5 metatarsals (bones of the metatarsus) and 5 components of the phalanges (bones of the toes). Phalange bones (a proximal and a distal phalanx for the great toe; proximal, middle and distal phalanges for the second to fifth toes) were fused together since their relative motion do not affect plantar pressures.</p> <p>More details can be found in the publications below:</p> <ol> <li><strong>Franciosa, P.</strong>, Gerbino S., From CT Scan to Plantar Pressure Map Distribution of a 3D Anatomic Human Foot, in Proc. of COMSOL Conference’10, Paris (France), November 17-19, 2010.</li> <li><strong>Franciosa, P.</strong>, Gerbino, S., Lanzotti A., Silvestri L., Improving Comfort of Shoe Sole through Experiments based on CAD-FEM Modeling, Medical Engineering and Physics, doi:10.1016/j.medengphy.2012.03.007, 2013.</li> </ol>
LPJ-GUESS model results for parametrizing the CLASH model
<p>Results from the LPJ-GUESS model to parametize the CLASH model, as used in the manuscript 'CLASH - Climate-responsive Land Allocation model with carbon Storage and Harvests'</p> <p>The dataset contains netCDF files for four sets of runs (cropland, pastures, forests and LUH2 land-use). In each of these the LPJ-GUESS model is driven with four different climate scenarios modelled wiht three different Earth-system models (EC-Earth3, CanESM and MPI).</p> <p>This data was used to parametrize the CLASH model (https://doi.org/10.5281/zenodo.8271846).</p>
Dataset - Biomass Steam Gasification, High-Temperature Gas Cleaning, and SOFC Model: A Parametric Analysis
<p>Dateset of the Article "Biomass Steam Gasification, High-Temperature Gas Cleaning, and SOFC Model: A Parametric Analysis".</p> <p>Effect of S/B ratio on syngas; Effect of the S/B ratio on electrical efficiency; Effect of the S/B ratio on current density; Effect of the current density on cell voltage; Effect of the current density on power density; Effect of the current density on electrical efficiency; Influce of CO concentration on SOFC power production; Influce of the air flow rate to the cathode on SOFC power production.</p> <p>Gasification technology is actually one of the most effective ways to produce power and hydrogen from biomass. Solid oxide fuel cells (SOFCs) have proved to be an excellent energy conversion device. They can transform the chemical energy content in the syngas, produced by a gasifier, directly into electrical energy. A steady-state model of a biomass-SOFC was developed using process simulation software, ASPEN Plus (10, AspenTech, Bedford, MA, USA). The objective of this work was to implement a biomass-SOFC system capable of predicting performance under diverse operating conditions. The system is made of a gasification zone, gas cleaning steps, and SOFC. The SOFC modelling was done without external subroutines, unlike most models in the literature, using only the existing ASPEN Plus blocks, making the model simpler and more reliable. The analysis of the syngas composition out of each cleaning step is in accordance with literature data. Then, a sensitivity analysis was carried out on the main parameters. The results indicate that there must be a trade-off between voltage, electrical efficiency, and power with respect to current density and it is preferable to stay at a low steam-to-biomass ratio. The electrical efficiency achieved under the operating conditions is 57%, a high value, making these systems very attractive.</p> <p><a href="https://doi.org/10.3390/en13225936">https://doi.org/10.3390/en13225936</a></p>
A non-parametric model-free analysis of actigraphic recordings of acute insomnia patients
Open the record for dataset details and reuse information.
Simulated data for paper "Conditional non-parametric bootstrap for non-linear mixed effect models"
<p>Data was simulated according to an Emax model (scenarios 1 and 2) or a Hill model (scenarios 3 and 4) with a rich (scenarios 1 and 3) and a sparse design (scenarios 2 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>
Model checking of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm
<p>This video shows model checking of a proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The number of constituting processes is parametric in this model. It shows the functions written in ML language and the result of calling them for customized analysis of the proposed model's state space graph. The last ML code at the end of the page, "state space," summarizes the model checking and operates by calling previously displayed functions. This video aimed to demonstrate the proposed verification process of the parametric model.</p>
Automatic message sequence chart creation from simulation run of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm
<p><span>These videos show the creation of two message sequence charts from simulation runs of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm using the CPN tool with three constituting processes. </span></p> <p><strong><span>Message Sequence Chart of <span> </span>Parametric Model With 3 Processes via Automatic Simulation Run_SuppInfo.mp4</span></strong><span>: This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport distributed global snapshot algorithm using the CPN tool version 4.0.0. The number of constituting processes is parametric in the model and was set to three. The video was generated using the authors' updated CPN tool extension server. The automatic simulation run of the model has been used to create this video. The CPN tool randomly selects the enabled transition at each step in an automatic simulation run.</span></p> <p><strong><span>Message Sequence Chart of Parametric Model With 3 Processes via Step-By-Step Simulation Run_SuppInfo.mp4:</span></strong><span> This video shows the automatic generation of a message sequence chart of the proposed parametric colored Petri net model of the Chandy-Lamport algorithm in a step-by-step simulation run with our updated extension server of the CPN tools version 4.0.0. The number of constituting processes is parametric in the model and was set to three. We manually fired our selected enabled transition of the model to create this video. </span></p>
Supporting Information for the Journal Article "Self-Parametrizing System-Focused Atomistic Models"
<p>This dataset contains the supporting information published together with the article "Self-Parametrizing System-Focused Atomistic Models" (<a href="https://doi.org/10.1021/acs.jctc.9b00855"><em>J. Chem. Theory Comput.</em>, <strong>2020</strong>, <em>16</em>, 1646</a>).</p>
Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models [source code]
<p>J. Heiland & Y. Kim, 'Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models', GAMM 2024</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.