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.
1,773
datasets available to search
ShareScore release 0.9.0
Dataset results
1,773 results for “Predictive model”
Dataser of Paper "A model to predict the kinetics of direct (endogenous) virus inactivation by sunlight at different latitudes and seasons, based on the equivalent monochromatic wavelength approach"
<p>- Data of the predicted maximum cumulated incident radiation for latitudes from 60°S to 60°N with steps of 5° as a function of the day of the year. These data have been predicted using the algorithm developed in this work which uses the value of the incident photon flux density of sunlight at solar noon.</p> <p>- Data of the maximum cumulated incident radiation for the 15<sup>th</sup> day of each month and for latitudes from 60°S to 60°N with steps of 5°. These data have been obtained by integrating the values of the spectral photon flux density of sunlight over the time.</p>
Some models for runoff prediction
<p>Some models for runoff prediction</p>
Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models
<p><strong>Abstract</strong></p> <p><strong>Background: </strong>Current ablation therapy for atrial fibrillation is sub-optimal and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more inter-individual variability.</p> <p><strong>Methods: </strong>Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent), undergoing first ablation. Patients were followed for 1-year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fibre orientation maps, electrical properties and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were post-processed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging and atrial fibrillation simulation metrics.</p> <p><strong>Results: </strong>We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging and simulation stress tests (average ten-fold cross-validation area under the curve 0.85 ± 0.09, recall 0.80 ± 0.13, precision 0.74 ± 0.13) outperformed those trained to history and imaging (area under the curve 0.66 ± 0.17), or history alone (area under the curve 0.61 ± 0.14). </p> <p><strong>Conclusion: </strong>A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalise selection for atrial fibrillation ablation.</p> <p><strong>Dataset Description: </strong>We include surface meshes in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the endocardial and epicardial fibre fields defined on the elements. </p> <p>We also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA. More details on the coordinate construction are given in our manuscript and <a href="https://www.ncbi.nlm.nih.gov/pubmed/31026761">https://www.ncbi.nlm.nih.gov/pubmed/31026761</a>. These coordinates can be used for registering datasets. </p> <p><strong>Publication</strong>: https://pubmed.ncbi.nlm.nih.gov/35089057/</p>
Supplementary Material: Predictive model using Cross Industry Standard Process for Data Mining
<p>The Supplementary Material of the paper "Supplementary Material: Predictive model using Cross Industry Standard Process for Data Mining" includes: <br> 1) APPENDIX 1: SQL Statements for data extraction. Appendix 2: Interview for operating Staff.<br> 2) The DataSet of the normalized data to define the predictive model.</p>
Supplementary material 2 from: Keck F, Hürlemann S, Locher N, Stamm C, Deiner K, Altermatt F (2022) A triad of kicknet sampling, eDNA metabarcoding, and predictive modeling to assess richness of mayflies, stoneflies and caddisflies in rivers. Metabarcoding and Metagenomics 6: e79351. https://doi.org/10.3897/mbmg.6.79351
Tables S1–S4
Biomassess of functional groups and ecosystem parameters predicted by ecosim models of Lake Dianchi
<p>Understanding the relative importance of multiple stressors is valuable to prioritize conservation and restoration measures. Yet, the effects of multiple stressors on ecosystem functioning remain largely unknown in many freshwaters. Here, we provided a methodology combining ecosystem modelling with linear regression to disentangle the effects of multiple stressors on matter flow, an important ecosystem function. Treating a shallow lake as the model ecosystem, we simulated matter flow dynamics during 1950s-2010s with different combinations of stressors by Ecopath with Ecosim (EwE) modelling, and determined the relative importance of each stressor by generalized linear mixed models. We found that matter flow of the lake food web was highly dynamic, attributing to effects of multiple anthropogenic stressors. Biological invasion played the strongest role in driving the matter flow dynamics, followed by eutrophication, while biomanipulation (i.e. phytoplankton control by planktivorous fish stocking) was of little importance. Eutrophication had a stronger role on primary producers, pelagic food chain and top predators, while biological invasion on consumers in the middle of food chains. The former was more important in driving the quantity of matter flow, while the latter on trophic transfer efficiencies. Scenario forecasting showed that reducing nutrients contents could largely shape the matter flow pattern, while biomanipulation had little effect. Our findings provided new insights into understanding the mechanistic links between anthropogenic stressors and ecosystem functioning by combining ecosystem modelling with linear regression.</p>
Data from: Incorporating single-step strategy into random regression model to enhance genomic prediction of longitudinal trait
In prediction of genomic values, single-step method has been demonstrated to outperform multi-step methods. In statistical analyses of longitudinal traits, random regression test-day model (RR-TDM) has clear advantages over other models. Our goal in this study was to evaluate the performance of the model integrating both single-step and RR-TDM prediction methods, called single-step random regression test-day model (SS RR-TDM), in comparison with the pedigree-based RR-TDM and genomic best linear unbiased prediction (GBLUP) model. We performed extensive simulations to exploit potential advantages of SS RR-TDM over the other two models under various scenarios with different level of heritability, the number of QTL as well as the selection scheme. SS RR-TDM was found to achieve the highest accuracy and unbiasedness under all scenarios, exhibiting robust prediction ability in longitudinal trait analyses. Moreover, SS RR-TDM showed better persistency of accuracy over generations than GBLUP model. In addition, we also found that the SS RR-TDM had advantages over RR-TDM and GBLUP in terms of a real dataset of human contributed by the GAW18 workshop. The findings in our study firstly proved the feasibility and advantages of the SS RR-TDM, and further enhanced strategies for the genomic prediction of longitudinal traits in the future.
Data from: A multifactor coupling prediction model for the failure depth of floor rocks in fully mechanized caving mining: a numerical and in situ study
To study the mining-induced failure depth of floor rocks in a fully-mechanized mining caving field affected by different coal seam pitches, mining face lengths, burial depths and aquifer water pressures, multifactor coupled orthogonal numerical tests on the failure depth of floor rocks were conducted. The numerical results show that the failure depth of floor rocks increases with increasing mining face length, coal seam pitch and burial depth. According to the relationship between failure depth and these impact factors, a multifactor coupled prediction model for the failure depth of floor rocks was established. In addition, the in-situ measurement of the failure depth of floor rocks in the Yitang Coal Mine in Huoxi coal field in Shanxi Province, China, was performed, and the in-situ failure depths of floor rocks in the 100502 (80 m) and 100502 (180 m) mining faces were approximately 12.50~14.65 m and 17.50~19.20 m, in good agreement with the results of the multifactor prediction model. Furthermore, the sensitivity of each impact factor in the prediction model of the floor failure depth was further analysed by F-test and range analysis, and the impact order of studied factors on the floor failure depth is coal seam pitch>mining face length>burial depth>aquifer water pressure.
Saved Deep Learning Models for Recurrence Score Prediction
<p>Saved Deep Learning Models for Recurrence Score Prediction</p>
UFO model for NLO predictions in supersymmetric QCD
<p>NLO Predictions in suspersymmetric QCD</p>
UFO model for NLO predictions for sgluon pair production
<p>Inclusive sgluon pair production</p>
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
<p>Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)</p>
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 15 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com)
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result
Universal Rapid Weather Prediction Model (Sonagi Model) Forecast Result / 10 Days / Created By Jihyeon Yoon(somehowme@gmail.com, flyingtext@nate.com) Latest prediction can be available on map at https://sonagi-weather.web.app Prediction is based on the observation data of - Korea Meteorological Administration API
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.