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447 results for “Model Validation”
KPIs for the validation of the GreenSoul behavioural models
<p>This document presents both the methodology followed to select the Key Performance Indicators (KPI) and the final KPIs selected to measure the impact achieved by the actions carried on during the pilots. To select the KPIs we first have prepared a comprehensive list of KPIs to consider, then we follow a Delphi Method to reach a consensus among a panel of experts about the how to score every KPI in several aspects and finally we perform a descriptive statistical analysis to select the best set of KPIs.</p>
Linked collectors and determiners for: Taxonomic utility of niche models in validating species concepts: A case study in Anthophora (Heliophila) (Hymenoptera: Apidae).
Natural history specimen data linked to collectors and determiners held within, "Taxonomic utility of niche models in validating species concepts: A case study in Anthophora (Heliophila) (Hymenoptera: Apidae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1363c785-b3e0-4322-9283-a6d272748735">https://bionomia.net/dataset/1363c785-b3e0-4322-9283-a6d272748735</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1363c785-b3e0-4322-9283-a6d272748735">https://gbif.org/dataset/1363c785-b3e0-4322-9283-a6d272748735</a>. Formatted as a Frictionless Data package.
Machine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"
<p>This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript:</p> <p><strong>Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).</strong></p> <p><br> This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.</p>
Proccessed data for Trend Validation of Metabolic Models Against Measurements Using Indirect Calorimetry
<p>A cleaned data set used to validate metabolism models in a muscuskeletal modeling software.<br> The dataset contains 240 rows and 18 columns. </p> <p>Labels:</p> <ul> <li>AnyMet = Metabolic output by the modelling software. Calculated as the mean energy cost per repetition [J] .</li> <li>VynMet = Metabolic output by the indirect calorimetry system (Vyntus CPX). Calculated as the mean energy cost per repetition [J].</li> <li>rest_energy = total energy cost during rest [J]. Measured with Indirect caliometry</li> <li>rest_time = total time of rest [min]</li> <li>Work = Energy cost times the displacement per rep [J].</li> <li>watt = Work divided by total duration of a repetition [J/s]</li> <li>extension time = duration of the extension part of the movement [s]</li> <li>flexion time = duration of the flexion part of the movement [s]</li> <li>bw = bodyweight [kg]</li> <li>height [m]</li> <li>CV = coefficient of variation for the measured rest_energy. </li> <li>model = model type used for AnyMet. </li> <li>Subject </li> <li>Contraction = Contraction type performed</li> <li>intensity = Intensity to overcome created by the dynamometer. </li> <li>mech_watt_kg = mechcanical watt, watt divided by bodyweight</li> <li>any_met_watt_kg = watt pr kg: (AnyMet / bw) / (extension time + flexion time)</li> <li>vyn_met_watt_kg = watt pr kg: (VynMet / bw) / (extension time + flexion time)<br> <br> There is also a zip file containing the raw data from the dynanometer and the Vyntus PGE system.</li> </ul>
CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"
<p>Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:</p> <p>Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"</p>
Dataset for "Radiation environment at the surface and subsurface of the Moon: Model development and validation" publication in Journal of Geophysical Research: Planets
<p>data set used for plots in manuscript "Radiation environment at the surface and subsurface of the Moon: Model development and validation" submitted to GRL</p>
Validation of STS Conceptual Model Designing Approach. DMI374 and DMI747 Data Sets.
<p>Surveys of DMI374 and DMI747 student groups were conducted for the purpose of validating the STS conceptual model designing method. A 5-point and dichotomous Likert questionnaire was used for the survey. The files contain response datasets used in statistical processing.</p>
Modelling the response of mangroves and saltmarshes to sea-level rise: model development and validation
<p>Data used to parameterise and calibrate a model (IWEM0D) of the response of coastal wetlands to sea-level rise. Model parameterisation with core data from Westernport Bay, Victoria, Australia.</p> <p>IWEM0D (Intertidal Wetland Evolution Model - 0D) simulates how mangrove forests and saltmarsh wetlands respond to sea-level rise. The model framework, as detailed in Rogers et al. (in review), treats surface elevation change over time as a function of:</p> <ul> <li>Present elevation <code>E</code></li> <li>Inorganic/mineral matter accumulation rate <code>MAR</code></li> <li>Organic matter addition rate <code>OAR</code> for mangroves and saltmarsh</li> <li>Autocompaction <code>AC</code></li> </ul> <p>Specifically, incremental change in surface elevation <code>E</code> over time <code>t</code> is modelled as:</p> <p><code>E[t+1] = E[t] + MAR[t] + OAR[t] - AC[t]</code></p>
Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment
<p>Selection of the data in the following paper:<br> Sengers, B. A. M., Steinfeld, G., Hulsman, P., & Kuehn, M. (2023). Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment. Wind Energy Science Discussions, 1-32.</p> <p>This data subset provides input parameters commonly used in wake models, as well as ten-minuted averaged cross sections of the flow field at 4 rotor diameters downstream, as measured by a nacelle-mounted lidar. </p> <p>Cite this as:<br> B.A.M. Sengers (2023). Dataset: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment. https://doi.org/10.5281/zenodo.7741395</p>
Describing ion transport and water splitting in an electrodialysis stack with bipolar membranes by a 2-D model: Experimental validation
<p>Electrodialysis with bipolar membranes (EDBM) has drawn attention motivated by their application in gener- ating reagents from salts. Due to the water splitting (WS) occurring at the junction of the bipolar membranes (BPMs), where the anion and cation layers are in strict contact, H+ and OH- are released from the BPM producing acid and alkali on the respective compartment. Considering this application, the interest of this work is to provide further understanding of the mechanisms of WS and transport of species in EDBM. This work develops and utilizes, for the first time, an experimentally validated two-dimensional (2-D) computational model, in which the Navier-Stokes and Nernst-Planck equations are coupled with the description of WS given by the Second Wien effect. In addition, a 1-D geometry is also proposed to perform a comparison between electroneutrality and Poisson charge conservation. The model is computationally solved using COMSOL Multiphysics. According to simulations, electroneutrality is valid for 2-D geometries. Moreover, the semipermeable characteristics of the membranes are assessed by means of evidencing a polarization effect resulting in a double-electric layer. The model proposed predicts a significant proton leakage, and facilitates the study of WS within the BPMs.</p>
Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
<div> <div> <div> <div> <div>This dataset includes experimental measurements taken on a laboratory testbed at Colorado State University that was used for model validation of a software toolkit, the Building Electrical Efficiency Analysis Model (BEEAM). This toolkit was developed for comparing electrical efficiency of AC versus DC distribution systems in buildings. The testbed emulated loads found in a small office building and included laptop computer chargers, LED lighting systems, and miscellaneous DC and AC loads. Measurements were taken under AC and DC configurations in electrically balanced and unbalanced loading conditions. Also included in the dataset is an uncertainty analysis. A complete description of the testbed, hardware, measurements and uncertainty analysis is contained in the paper cited below.</div> </div> </div> </div> </div> <div> </div> <div>Avpreet Othee, James Cale, Arthur Santos, Stephen Frank, Daniel Zimmerle, Omkar Ghatpande, Gerald Duggan and Daniel Gerber, <em>"A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings," Energies, 2023 (accepted, publication in progress).</em> </div>
Soil organic carbon models need independent time-series validation for reliable prediction
<p>Supplementary Data 1 to the paper: Soil organic carbon models need independent time-series validation for reliable prediction</p> <p>By: Le Noë, J., Manzoni, S., Abramoff, R.Z., Bölscher, T., Bruni, E., Cardinael, R., Ciais, P., Chenu, C., Clivot, H., Derrien, D., Ferchaud, F., Garnier, P., Goll, D., Lashermes, G., Martin, M.P., Rasse, D., Rees, F., Sainte-Marie, J., Salmon, E., Schiedung, M., Schimel, J., Wieder, W.R., Abiven, S., Barré, P., Cécillon, L., Guenet, B.</p>
Validation of an Idealized Aorta Model Analysed through Fluid-Structure Interaction Simulation with Robin-Neumann Partitioned Approach
<p>The aorta is multiphysics system where hemodynamics and wall structural mechanic are mutually influenced. A fluid-structure interaction approach is appropriate to describe the mechanical alterations suffered by the aortic wall in response to altered hemodynamic patterns. This work demonstrates the validation of the simulated idealized aorta model with a fluid-structure interaction (FSI) model through modified PIMPLE solver to use Robin-Neumann partitioned approach for the strongly-coupled algorithm using solids4foam v2. The validation involves the comparison of streamlines, pressure, and displacements with in vivo measurements. The geometry is reconstructed from the healthy aorta presented in 10.5281/zenodo.5801938. Our analysis shows that the streamlines and pressure pattern are comparable with the literature data acquired using rich medical imaging data. The maximum diameter deformation at the level of abdominal aorta is comparable with measured data and the diameter deformation profile along the cardiac cycle correctly follow the velocity profile. According to this results, our work shows a high-performance simulation suitable for several future works.</p>
The Awareness Assessment Model (Case Study Validation)
<p>Dataset of the case study validation of the paper "The Awareness Assessment Model: Measuring Awareness and Collaboration Support Over Participant's Perspective"</p>
Observational datasets for validation of Mediterranean Biogeochemical Copernicus Modelling System, period 2018-2020
<p>Datasets used for the validation of the biogeochemical component of the Mediterranean Analysis and Forecast center of the EU Copernicus Marine Service for the period 2018-2020.</p> <p>The list of datasets includes:</p> <p>1) the Delay Mode Satellite chlorophyll from https://data.marine.copernicus.eu/product/OCEANCOLOUR_MED_BGC_L3_NRT_009_141/description after interpolation to the 1/24° horizontal resolution, weekly averages and quality check with internal climatology</p> <p>2) the BGC-Argo float profiles of nitrate, chlorophyll and oxygen from Coriolis DAC (ftp://ftp.ifremer.fr/ifremer/argo; https://doi.org/10.17882/42182#76230) after an internal quality check procedure which is described in Salon et al., 2019. </p> <p>3) the climatological profiles for 16 subbasins of nitrate, phosphate, silicate, oxygen, DIC, alkalinity, pCO2 and pH computed from the Emodnet 2018 data collection and additional scientific datasets as described in Salon et al., 2019.</p> <p> </p> <p>Ref.: Salon, S., Cossarini, G., Bolzon, G., Feudale, L., Lazzari, P., Teruzzi, A., Solidoro, C. and Crise, A., 2019. Novel metrics based on Biogeochemical Argo data to improve the model uncertainty evaluation of the CMEMS Mediterranean marine ecosystem forecasts. <em>Ocean Science</em>, <em>15</em>(4), pp.997-1022.</p> <p> </p>
Post-fire flood hazard model (PF2HazMo) version 1.0.0: Model scripts and parameterization and validation data
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Data from: Experimental validation of a linear momentum and bluff-body model for high-blockage cross-flow turbine arrays
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Experimental measurements and uncertainty analysis for validation of the Building Electrical Efficiency Analysis Model (BEEAM)
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Converting between the International Prostate Symptom Score (IPSS) and the Expanded Prostate Cancer Index Composite (EPIC) urinary subscales: modeling and external validation
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Ecological niche modeling and first records from Namibia and Zimbabwe validate the amphi-equatorial distribution of Byrsinus pseudosyriacus (Hemiptera: Cydnidae) - Supplementary data
<p><em>Byrsinus pseudosyriacus (</em>Linnavuori, 1977), the most widely distributed Afrotropical species of the genus <em>Byrsinus</em> Fieber, 1860 known hitherto only from the Sudano-Eremian area is for the first time reported in two countries south of the Equator. The species potential distribution map was generated using the ecological niche modelling (ENM) methods that allowed this species to be regarded as amphi-equatorial in distribution<strong>.</strong></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
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.