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1,773 results for “Predictive model”
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
<p>We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations.</p> <p>Due to storage limitations, the full dataset is much too large to be published (~50TB). Instead, a subset consisting of 6-hourly warm season (May-September) 2-meter temperature, precipitation, and 500hPa height is presented. If you wish to access the full dataset (as presented in Michaelis et al. 2019), please contact one of the authors.</p>
Multimodal dataset: Protein Function Prediction using STRING data & COVID19 Mortality Model by EI
<p>The PFP.zip file contains 1. 5 well-formated GO terms dataset for EI, 2. STRING data 3. GO term annotation. The last two could be merged by the 'generate_data.py' script in https://github.com/GauravPandeyLab/ensemble_integration</p> <p>The covid19_model_built.zip contained the EI model built based on the COVID-19 Mortality dataset, the detail of usage are here:.</p>
Ensembles of knowledge graph embedding models improve predictions for drug discovery
<p>This contains data described in detail in our paper, "Ensembles of knowledge graph embedding models improve predictions for drug discovery". The metadata involves the different trained models that were used for prediction analysis as well as all the predictions from the trained models.</p>
PredictION: A predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2
<p>Dataset (1) that included 1461 samples and a dataset (2) with 471 samples that was a subset of dataset 1 that included Number of sequenced reads per genome, CT (Cycle threshold) value [N2 target gene], mean coverage depth, coverage genome (percentage), and quantification cDNA (ng/µl).</p>
Towards transferable data-driven models to predict urban pluvial flood water depth in Berlin, Germany
<p>The attached files include the predictive features and the water depth from 2D hydrodynamic simulations that were used to train data driven models to predict water depth in Berlin.</p>
Data for Developing Machine Learning Models to Predict Base Resistance of Pile Foundation
<p>This data was collected from 86 static pile load tests across 37 different high-rise buildings in Vietnam, especially soft soil region in Mekong Delta (Ho Chi Minh City). The data was used to develop machine learning models to predict base resistance of piles. Further details can be found in publication: "<strong>Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments</strong>", Link: https://www.mdpi.com/2673-7094/4/2/25.</p> <p>Recommended citation: Nguyen, Thanh T., Viet D. Le, Thien Q. Huynh, and Nhu H.T. Nguyen. 2024. "Influence of Settlement on Base Resistance of Long Piles in Soft Soil—Field and Machine Learning Assessments" <em>Geotechnics</em> 4, no. 2: 447-469. https://doi.org/10.3390/geotechnics4020025</p> <p> </p>
Streamflow Prediction in Human-Regulated Catchments Using Multiscale LSTM Modeling with Anthropogenic Similarities
<p>These codes are used in the paper entitled"Streamflow Prediction in Human-Regulated Catchments Using Multiscale LSTM Modeling with Anthropogenic Similarities" which is submitted to the Journal of Water Resources Research. The code for the differentiable parameter learning (DPL) model can be downloaded at https://doi.org/10.5281/zenodo.7091334. The code for LSTM to reproduce our analysis is available at <a href="https://github.com/neuralhydrology/neuralhydrology">https://github.com/neuralhydrology/neuralhydrology</a>. The SWORD database utilized in our study can be accessed at https://zenodo.org/records/10013982. The geometric dataset of the global river attribute information for every river reach, the values of all pressure indicators (DOF, DOR,SED, USE, RDD and URB) and the values for the CSI are available at https://doi.org/10.6084/m9.figshare.7688801.</p>
Clinical Dataset for Artificial Intelligence-Driven Predictive Modeling for Home Discharge in Neurological and Orthopedic Conditions
<p><span>In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. The Machine Learning model's application in the medical domain is one of the most compelling and challenging topics to explore, bridging the gap between Artificial Intelligence (AI) and healthcare. The combination of AI and medical information offers the possibility to create tools that can benefit both healthcare providers and physicians. This enables the enhancement of rehabilitation therapy and patient care. In the rehabilitation context, this work provides an alternative perspective: prediction of patients’ home discharge upon completing the rehabilitation protocol. Demographic and clinical data were collected from electronic Medical Record. </span></p> <p><span>The original analysis dataset includes clinical and demographic data of adults admitted to the neurology and orthopedic departments of a rehabilitation hospital in Italy from January 2015 to August 2022. The completion of patients’ ADR-r form</span><span> resulted in the collection of data for 10520 individuals, whose information is distributed across 120 initial features. We anonymized dataset rows. Clinical data, including the primary reason for rehabilitation, any associated medical conditions, impairments, and admission/discharge mBi scores were collected. </span></p> <p><span>A legend file is enclosed to explain variable labels.</span></p>
Dataset and scripts from: Predicting organismal response to marine heatwaves using dynamic thermal tolerance landscape models
<p>Marine heatwaves (MHWs) can cause thermal stress in marine organisms, experienced as extreme 'pulses' against the gradual trend of anthropogenic warming. When thermal stress exceeds organismal capacity to maintain homeostasis, organism survival becomes time-limited and can result in mass mortality events. Current methods of detecting and categorizing MHWs rely on statistical analysis of historic climatology, and do not consider biological effects as a basis of MHW severity. The reemergence of ectotherm thermal tolerance landscape models provides a physiological framework for assessing the lethal effects of MHWs by accounting for both the magnitude and duration of extreme heat events. Here, we used a simulation approach to understand the effects of a suite of MHW profiles on organism survival probability across 1) three thermal tolerance adaptive strategies, 2) interannual temperature variation, and 3) seasonal timing of MHWs. We identified survival isoclines across MHW magnitude and duration where acute (short duration-high magnitude) and chronic (long duration-low magnitude) events had equivalent lethal effects on marine organisms. While most research attention has focused on chronic MHW events, we show similar lethal effects can be experienced by more common but neglected acute marine heat spikes. Critically, a statistical definition of MHWs does not accurately categorize biological mortality. By letting organism responses define the extremeness of a MHW event, we can build a mechanistic understanding of MHW effects from a physiological basis. Organism responses can then be transferred across scales of ecological organization and better predict marine ecosystem shifts to MHWs. </p>
The energy bands of charged defect predicted by the HamGNN-Q model
<p>The dataset contains graph representations of GaAs defects for testing in the study that were not present in the training set, including single-point vacancies, interstitial atom defects, defect clusters, substitution defects, and large-sized polarons with varying background charges. charged_defect_hamiltoian.ckpt is the network weights for the HamGNN-Q model. config_charge.yaml is the input file of the model.</p>
Data: Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts
<p>Data for: <br><br>Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts</p> <p>Marian Schönauer<sup>1</sup>, Anneli M. Ågren<sup>2</sup>, Klaus Katzensteiner<sup>3</sup>, Florian Hartsch<sup>1</sup>, Paul Arp<sup>4</sup>, Simon Drollinger<sup>5</sup>, Dirk Jaeger<sup>1</sup></p> <p><sup>1</sup>Department of Forest Work Science and Engineering, University of Göttingen, Göttingen, Germany</p> <p><sup>2</sup>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden</p> <p><sup>3</sup>Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria</p> <p><sup>4</sup>Forestry and Environmental Management, University of New Brunswick, New Brunswick, Canada</p> <p><sup>5</sup>Department of Physical Geography, University of Göttingen, Göttingen, Germany</p>
Data set for study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"
<p>Input data for the study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"</p>
Figure 3 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 3. Location map of the study area.
Figure 3 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 3. Flow chart of simulation procedure the study.
Figure 1 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 1. Study area and simulation unit
Figure 2 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 2. Schematic view of Kernel Ridge Regression (KRR) model.
Figure 1 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 1. High-resolution flow chart of the Random Forest (RF) model.
Bark beetle predictive model results (FirEUrisk)
<p>This datasets contains three products developed within the FirEUrisk project. <strong>FirEUrisk</strong> is funded by the European Union as part of the Horizon 2020 framework program (Grant Agreement No. 101003890). It is focused on evaluating and promoting an integrated science-based strategy to improve forest fire risk assessments in Europe</p> <p><strong>FirEUrisk_BarkBeetleAttackPredic_50m_DE.CZ.PL_20240126_V01.tif</strong></p> <p>This product shows the probability of a bark beetle (<em>Ips typographus</em>) attack between September 2020 and October 2021. The assessment includes a set of risk factors that predispose forests to pest infestations and wildfires propagation for the FirEUrisk Central-Eastern Europe Pilot Site. This technological solution aims to identify areas prone to a bark beetle infestation using Earth Observation data and machine learning techniques. The geospatial results are provided in raster format (.tiff) to help managers optimise their resources and mitigate the negative effects of plagues. The results indicate the probability of a bark beetle attack, being ranged from 0 to 100.</p> <p> <br><strong>FirEUrisk_BarkBeetleDamage_10m_DE.CZ.PL_20240126_V01.tif</strong></p> <p>This product contains an estimate of the real bark beetle damage between September 2020 and October 2021. This estimate was derived from Sentinel-2 imagery and was used as target to train the predictive model.</p> <p> <br><strong>FirEUrisk_SpruceClassification_10m_DE.CZ.PL_20240126_V01.tif</strong></p> <p>This product contains a classification of spruce <em>(Picea abies) </em>area for September 2020. It was derived from Sentinel-2 imagery and used as input for the estimation of bark beetle damage and the predictive model.</p>
A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period
<div> <div></div> </div> <div> <div> <div> <p><span>Machine learning methodologies are increasingly popular in health care research. This shift to integrated data science approaches necessitates professional development of the existing health care data analyst workforce. To enhance a smooth transition, educational resources need to be developed. Barriers to accessing real healthcare datasets, vital for health care data analyses methodologies training purposes, include financial, ethical and patient confidentiality concerns. Synthetic datasets mimicking real-world complexities offer a simpler solution.</span></p> <p>We present a synthetic dataset which mirrors routinely collected primary care data on heart attack and stroke among the adult population. The data incorporates much of the practical challenges encountered in routinely collected primary care systems such as missing data, informative censoring, interactions, variable irrelevance, and noise and can be used for training in methods which handle these difficulties. The intent is for the user to build models of heart/stroke risk using survival-based methodologies.</p> <p>By sharing this synthetic dataset openly, our goal is to contribute a transformative asset for professional training in health and social care data analysis. The dataset covers demographics, lifestyle variables, comorbidities, systolic blood pressure, hypertension treatment, family history of cardiovascular diseases, respiratory functioning, and experience of heart-attack and/or stroke. This initiative aims to bridge the gap in sophisticated healthcare datasets for training, fostering professional development of the health and social care research workforce.</p> <p>This study is funded by the National Institute for Health and Care Research ARC Wessex and the National Centre for Research Methods. The views expressed in this summary are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.</p> <p> </p> </div> </div> </div>
Supporting data for Emerging AI-based weather prediction models as downscaling tools
<p>Supporting data for "Emerging AI-based weather prediction models as downscaling tools" by Nikolay Koldunov, T. Rackow, Christian Lessig, S. Danilov, S. Cheedela, D. Sidorenko, Irina Sandu, Thomas Jung</p> <p><a href="https://t.co/PSUCUvh9lf" target="_blank" rel="noopener noreferrer nofollow"><span>https://</span>doi.org/10.48550/arXiv<span>.2406.17977</span></a></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.