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Dataset results
44 results for “predictive power”
Data from: Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement
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Codes and datasets associated with the paper "Day-ahead Wind Power Predictions at Regional Scales: Post-processing Operational Weather Forecasts with a Hybrid Neural Network"
<p>The jupyter notebooks and datasets associated with the EEM20 forecasts are available here. More details will be provided shortly. </p> <p>Please check the EEM20 website (<a href="https://eem20.eu/forecasting-competition/">https://eem20.eu/forecasting-competition/</a>) for the details of the forecasting competition. </p>
Data from: Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution
A central prediction of much theory on adaptive radiations is that traits should evolve rapidly during the early stages of a clade's history and subsequently slowdown in rate as niches become saturated – a so-called "Early Burst". Although a common pattern in the fossil record, evidence for early bursts of trait evolution in phylogenetic comparative data has been equivocal at best. We show here that this may not necessarily be due to the absence of this pattern in nature. Rather, commonly used methods to infer its presence perform poorly when when the strength of the burst - the rate at which phenotypic evolution declines - is small, and when some morphological convergence is present within the clade. We present two modifications to existing comparative methods that allow greater power to detect early bursts in simulated datasets. First, we develop posterior predictive simulation approaches and show that they outperform maximum likelihood approaches at identifying early bursts at moderate strength. Second, we use a robust regression procedure that allows for the identification and down-weighting of convergent taxa, leading to moderate increases in method performance. We demonstrate the utility and power of these approach by investigating the evolution of body size in cetaceans. Model fitting using maximum likelihood is equivocal with regards the mode of cetacean body size evolution. However, posterior predictive simulation combined with a robust node height test return low support for Brownian motion or rate shift models, but not the early burst model. While the jury is still out on whether early bursts are actually common in nature, our approach will hopefully facilitate more robust testing of this hypothesis. We advocate the adoption of similar posterior predictive approaches to improve the fit and to assess the adequacy of macroevolutionary models in general.
Data from: Predictive power of food web models based on body size decreases with trophic complexity
Food web models parameterized using body size show promise to predict trophic Interaction Strengths (IS) and abundance dynamics. However, this remains to be rigorously tested in food webs beyond simple trophic modules, where indirect and intraguild interactions could be important and driven by traits other than body size. We systematically varied predator body size, guild composition and richness in microcosm insect webs and compared experimental outcomes with predictions of IS from models with allometrically scaled parameters. Body size was a strong predictor of IS in simple modules (r2=0.92), but with increasing complexity the predictive power decreased, with model IS being consistently overestimated. We quantify the strength of observed trophic interaction modifications, partition this into density-mediated vs. behaviour-mediated indirect effects and show that model shortcomings in predicting IS is related to the size of behaviour-mediated effects. Our findings encourage development of dynamical food web models explicitly including and exploring indirect mechanisms.
What is the best fitness measure in wild populations? A case study on the power of short-term fitness proxies to predict reproductive value
<p>Fitness is at the core of evolutionary theory, but it is difficult to measure accurately. One way to measure long-term fitness is by calculating the individual's reproductive value, which represents the expected number of allele copies an individual passes on to distant future generations. However, this metric of fitness is scarcely used because the estimation of individual's reproductive value requires long-term pedigree data, which is rarely available in wild populations where following individuals from birth to death is often impossible. Wild study systems therefore use short-term fitness metrics as proxies, such as the number of offspring produced. This study obtained three frequently used short-term proxies for fitness obtained at different offspring life stages (eggs, hatchlings, fledglings and recruits), and compared their ability to predict reproductive values derived from the genetic pedigree of a wild passerine bird population. We used twenty years of precise field observations and a near-complete genetic pedigree to calculate reproductive success, individual growth rate and de-lifed fitness as lifetime fitness measures, and as annual de-lifed fitness. We compared the power of these metrics to predict reproductive values and lineage survival to the end of the study period. The three short-term fitness proxies predict the reproductive values and lineage survival only when measured at the recruit stage. There were no significant differences between the different fitness proxies at the same offspring stages in predicting the reproductive values and lineage survival. Annual fitness at one year old predicted reproductive values equally well as lifetime de-lifed fitness. However, none of the short-term fitness proxies was strongly associated with the reproductive values. In summary, the commonly short-term fitness proxies capture long-term fitness with intermediate accuracy at best, if measured at recruitment stage. As lifetime fitness measured at recruit stage and annual fitness in the first year of life were the best proxies of long-term fitness, we encourage their future use.</p>
Thermal Power Prediction Data set
<p>Haoning Jia 's graduation thesis chapter three raw data.</p>
IS Three d Power Doppler of the Endometrial and Subendometrial Regions Effective in Predicting Endometrial Implantation?
ClinicalTrials.gov study NCT04081870. IPD Sharing: UNDECIDED. Countries: 0. Publications: 1.
Data from: Predictive power of food web models based on body size decreases with trophic complexity
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Data from: Phylogenetic signal and noise: predicting the power of a data set to resolve phylogeny
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What is the best fitness measure in wild populations? A case study on the power of short-term fitness proxies to predict reproductive value
Open the record for dataset details and reuse information.
Data from: Robust regression and posterior predictive simulation increase power to detect early bursts of trait evolution
Open the record for dataset details and reuse information.
Genome-Wide Association Study Towards Genomic Predictive Power for High Production and Quality of Milk in American Alpine Goats
GEO Series GSE145419. Capra hircus. 276 samples. Type: Genome variation profiling by SNP array.
Evaluation of Mechanical Power and Ventilator Parameters to Predict Weaning Success in the Intensive Care Unit
ClinicalTrials.gov study NCT07268989. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Hypertension Related Damage to the Microcirculation in South Asian: Emergence, Predictive Power and Reversibility
ClinicalTrials.gov study NCT00331370. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Development, Safety, and Feasibility of an Artificial Intelligence-Powered Platform (NodeAI) for Real-Time Prediction of Mediastinal Lymph Node Malignancy During Endobronchial Ultrasound Staging f
ClinicalTrials.gov study NCT06540196. IPD Sharing: NO. Countries: 1. Publications: 0.
Analysis of the Loss of Muscle Force, Muscle Power and Motor Control Degradation to Predict the Risk of Falls in Patients With Knee Osteoarthritis
ClinicalTrials.gov study NCT06611618. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Role of Elastic Power in Predicting the Severity and Mortality in Adult Patients With ARDS Due to Pneumonia
ClinicalTrials.gov study NCT06477861. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Evaluation of a Mobile AI-powered Decision Support System for Insulin Dosing and Glucose Prediction in Type 1 Diabetes: The glUCModel Clinical Trial Protocol
ClinicalTrials.gov study NCT07304778. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
To Compare Predictive Power of End-tidal Carbon Dioxide Between Different Time Line During Resuscitation
ClinicalTrials.gov study NCT03345888. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Prediction Of Worldwide Energy Resources (POWER)
The POWER Project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly (by year 12 months + annual averages), and climatology. The POWER Data Archive provides data at the native resolution of the source data products. The data is updated nightly to maintain Near Real Time (NRT) availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER Project targets three specific user communities: Renewable Energy (RE), Sustainable Buildings (SB), and Agroclimatology (AG). The POWER Projects provides community specific parameters, output formats, naming conventions, and units that are commonly employed by each user community. The POWER Services Catalog consists of a series of RESTful Application Programming Interfaces (API), geospatial enabled image services, and a web mapping Data Access Viewer (DAV). These three different service offerings support data discovery, access, and distribution to our user base as ARD and as direct application inputs to decision support tools.
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.