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447
datasets available to search
ShareScore release 0.9.0
Dataset results
447 results for “Model validation”
Development and Validation of the Client Centered Occupational Therapy Service Model
ClinicalTrials.gov study NCT04465422. IPD Sharing: NO. Countries: 1. Publications: 4.
Development and Validation of a Deep Learning-Based Survival Prediction Model for Pediatric Glioma Patients: A Retrospective Study Using the SEER Database and Chinese Data
ClinicalTrials.gov study NCT06199388. IPD Sharing: NO. Countries: 1. Publications: 2.
Validation of Novel BTE and SP Hearing Aid Models
ClinicalTrials.gov study NCT04882787. IPD Sharing: NO. Countries: 1. Publications: 2.
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
ClinicalTrials.gov study NCT06831357. IPD Sharing: NO. Countries: 1. Publications: 8.
Data from: Hindcast-validated species distribution models reveal future vulnerabilities of mangroves and salt marsh species
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Data from: Validating dispersal distances inferred from autoregressive occupancy models with genetic parentage assignments
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Data from: External validation of an electronic health record-based diagnostic model for histological acute tubulointerstitial nephritis
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Development and validation of the Health Belief Model questionnaire to promote smoking cessation for nasopharyngeal cancer prevention: a cross-sectional study
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Is my model fit for purpose? Validating a population model for predicting freshwater fish responses to flow management
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Pedology and plant provenance can improve predictions of species distributions of the Australian native flora: a calibrated and validated modelling exercise on 5,033 species
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Identifying the best approximating model in Bayesian phylogenetics: Bayes factors, cross-validation or wAIC?
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Data from: cross-validation matters in species distribution models: a case study with goatfish species
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The validation of new phase-dependent gait stability measures: a modelling approach
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Predicting <em>Agriotes</em> larval activity: Validation of the prognosis model SIMAGRIO-W to predict larval activity in top soil layers for <em>Agriotes</em> larvae in Eastern Austria
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Data from: How to validate a Bayesian evolutionary model
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Dataset from UNIBO for the deliverables 7.1 and 7.2, and SydILUC model setup, calibration and validation
<p>STAR-ProBio_ILUCLiteratureReview_v1.0 _Spreadsheet: Inventory of existing key drivers and parameters for ILUC quantification and future strategies to reduce ILUC risks, and collection of standardisation work related to the sustainability of biofuels and biomaterials.</p> <p>STAR-ProBio_ModelParameters_v13.0 _Spreadsheet: Inventory of parameters and relationships used in the SydILUC model, with related metadata, i.e. source (literature), units, suggested update period, etc... </p> <p>STAR-ProBio_CalibrationGlobalFAOSTAT_v3.0_Spreadsheet: SydILUC model historical dataset of Maize market and production data to feed, calibrate, validate and run the model</p> <p>STAR-ProBio_MaizeNationalFAOSTAT_v1.0 _Spreadsheet: SydILUC model historical dataset of Maize market and production data to feed, calibrate, validate and run the model – for the regional version of the model</p> <p>STAR-ProBio_BioplasticProductionSydILUC_v1.0 _Spreadsheet: Collection of all the yields for different bioplastics from different sources (sugar, starch, oil) – from the documents by “Biopolymers facts and statistics” by IfBB</p>
A validated physical model of the thermoelectric drift of Pt-Rh thermocouples above 1200 °C
<p>Data associated with a validated physical model of the thermoelectric drift of Pt-Rh thermocouples above 1200 °C (Metrologia 57 (2020) 025009) https://doi.org/10.1088/1681-7575/ab71b3</p>
Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation
<p>This excel file (DOI: <a href="https://doi.org/10.5281/zenodo.3755675">https://doi.org/10.5281/zenodo.3755675</a>) provides the collection of raw data used for developing the first integrative Quantitative Structure-Activity Relationship (QSAR) model using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD<sub>50</sub>) and ii) to profile the Mode of Action (MoA) of pesticides active substances in honey bees (<em>Apis mellifera</em>)<em>. </em>Chemical identifiers (e.g. SMILES, CAS n., InChI) and acute contact toxicity data (LD<sub>50</sub>) on honey bees were used to develop and validate i) a two-category QSAR model (toxic/non-toxic; n=411) (sensitivity =0.93), specificity =0.85), balanced accuracy =0.90), Matthews correlation coefficient MCC=0.78), and ii) a regression-based model (n=113) (R2=0.74; MAE=0.52). Similarly, current study proposes the first MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD<sub>50s</sub> data points from three different databases such as EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase. Such classification allows to further define MoAs and the target site of Plant Protection Products (PPPs) active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals.</p> <p>The full data collection and analysis of QSAR models, toxicity data (LD<sub>50</sub>) and Mode of Action (Moa) data are described in Carnesecchi et al., 2020 (DOI: doi.org/10.1016/j.scitotenv.2020.139243).</p> <p>This work was supported by the European Food Safety Authority (EFSA) [contract number: OC/EFSA/SCER/2018/01 and NP/EFSA/AFSCO/2016/02 (Edoardo Carnesecchi)].</p>
Model Validation Framework Evaluation Result
<p>CSV Files with the results of the Model Validation Framework Evaluation</p>
Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study
<p>Objectives: We aimed to develop <span class="il">and</span> validate <span class="il">a</span> <span class="il">postoperative</span> <span class="il">delirium</span> (POD) <span class="il">prediction</span> model for patients admitted to the intensive care unit (ICU).</p> <p>Design: <span class="il">A</span> prospective study was conducted.</p> <p>Setting: The study was conducted in the surgical, cardiovascular surgical, <span class="il">and</span> trauma surgical ICUs <span class="il">of</span> an affiliated hospital <span class="il">of</span> <span class="il">a</span> medical university in Heilongjiang Province, China.</p> <p>Participants: This study included 400 patients (≥18 years old) admitted to the ICU after surgery.</p> <p>Primary <span class="il">and</span> secondary outcome measures: The primary outcome measure was <span class="il">postoperative</span> <span class="il">delirium</span> assessment during ICU stay.</p> <p>Results: The model was developed using 300 consecutive ICU patients <span class="il">and</span> was validated using 100 patients from the same ICUs. The model was based on five risk factors: Physiological <span class="il">and</span> Operative Severity Score for the Enumeration <span class="il">of</span> Mortality <span class="il">and</span> Morbidity; acid-base disturbance; <span class="il">and</span> history <span class="il">of</span> coma, diabetes, or hypertension. The model had an area under the receiver operating characteristics curve <span class="il">of</span> 0.852 (95% confidence interval: 0.802–0.902), Youden index <span class="il">of</span> 0.5789, sensitivity <span class="il">of</span> 70.73%, <span class="il">and</span> specificity <span class="il">of</span> 87.16%. The Hosmer-Lemeshow goodness <span class="il">of</span> fit was 5.203 (P = 0.736). At <span class="il">a</span> cut-off <span class="il">of</span> 24.5%, the sensitivity <span class="il">and</span> specificity were 71% <span class="il">and</span> 69%, respectively.</p> <p>Conclusions: The model, which used readily available data, exhibited high predictive value regarding risk <span class="il">of</span> intensive care unit <span class="il">postoperative</span> <span class="il">delirium</span> (ICU-POD) at admission. Use <span class="il">of</span> this model may facilitate better implementation <span class="il">of</span> preventive treatments <span class="il">and</span> nursing measures.</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.