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374 results for “Power Data”
Supporting data for Loik et al. 2017 Wavelength-Selective Solar Photovoltaic Systems: Powering greenhouses for plant growth at the food-energy-water nexus. Earth's Future
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Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify differences in DNA methylation [Array]
GEO Series GSE169218. Mus musculus; Rattus norvegicus; Homo sapiens. 80 samples. Type: Methylation profiling by genome tiling array.
Theoretical and experimental data for stopping power of Hf by proton impact
<p>This release contains the theoretical and experimental data for stopping power of Hf from Montanari et al. 2020.</p>
Supporting data for "Powering the Galilean Satellites with Moon-Moon Tides'
<p>Data files accompanying the submitted manuscript "Powering the Galilean Satellites with Moon-Moon Tides" for GRL.</p>
Data from: Is your phylogeny informative? Measuring the power of comparative methods
Phylogenetic comparative methods may fail to produce meaningful results when either the underlying model is inappropriate or the data contain insufficient information to inform the inference. The ability to measure the statistical power of these methods has become crucial to ensure that data quantity keeps pace with growing model complexity. Through simulations, we show that commonly applied model choice methods based on information criteria can have remarkably high error rates; this can be a problem because methods to estimate the uncertainty or power are not widely known or applied. Furthermore, the power of comparative methods can depend significantly on the structure of the data. We describe a Monte Carlo based method which addresses both of these challenges, and show how this approach both quantifies and substantially reduces errors relative to information criteria. The method also produces meaningful confidence intervals for model parameters. We illustrate how the power to distinguish different models, such as varying levels of selection, varies both with number of taxa and structure of the phylogeny. We provide an open-source implementation in the pmc ("Phylogenetic Monte Carlo") package for the R programming language. We hope such power analysis becomes a routine part of model comparison in comparative methods.
Data from: Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits
Plant architecture, phenology and yield components of cultivated plants have repeatedly been shaped by selection to meet human needs and adaptation to different environments. Here we assessed the genetic architecture of 24 correlated maize traits that interact during plant cycle. Overall, 336 lines were phenotyped in a network of 9 trials and genotyped with 50K single-nucleotide polymorphisms. Phenology was the main factor of differentiation between genetic groups. Then yield components distinguished dents from lower yielding genetic groups. However, most of trait variation occurred within group and we observed similar overall and within group correlations, suggesting a major effect of pleiotropy and/or linkage. We found 34 quantitative trait loci (QTLs) for individual traits and six for trait combinations corresponding to PCA coordinates. Among them, only five were pleiotropic. We found a cluster of QTLs in a 5 Mb region around Tb1 associated with tiller number, ear row number and the first PCA axis, the latter being positively correlated to flowering time and negatively correlated to yield. Kn1 and ZmNIP1 were candidate genes for tillering, ZCN8 for leaf number and Rubisco Activase 1 for kernel weight. Experimental repeatabilities, numbers of QTLs and proportion of explained variation were higher for traits related to plant development such as tillering, leaf number and flowering time, than for traits affected by growth such as yield components. This suggests a simpler genetic determinism with larger individual QTL effects for the first category.
Data from: Demographic inferences using short-read genomic data in an Approximate Bayesian Computation framework: in silico evaluation of power, biases, and proof of concept in Atlantic walrus
Approximate Bayesian Computation (ABC) is a powerful tool for model-based inference of demographic population histories from large genetic data sets. For most organisms its implementation has been hampered by the lack of sufficient genetic data. Genotyping-by-sequencing (GBS) provides cheap genome-scale data to fill this gap, but its potential has not fully been exploited. Here, we explored power, precision and biases of a coalescent-based ABC approach where GBS data were modeled with either a population mutation parameter (θ) or with a fixed sites (FS) approach, allowing single or several segregating sites per locus. With simulated data ranging from 500 to 50,000 loci a variety of demographic models could be reliably inferred across a range of timescales and migration scenarios. Posterior estimates were informative with 1,000 loci for migration and split time in simple population divergence models. In more complex models posterior distributions were wide and almost reverted to the uninformative prior even with 50,000 loci. ABC parameter estimates, however, were generally more accurate than an alternative composite-likelihood method. Bottleneck scenarios proved particularly difficult and only recent bottlenecks without recovery could be reliably detected and dated. Notably, minor allele frequency filters – usual practice for GBS data – negatively affected nearly all estimates. With this in mind, we used a combination of FS and θ approaches on empirical GBS data generated from the Atlantic walrus (Odobenus rosmarus rosmarus), collectively providing support for a population split before the last glacial maximum followed by asymmetrical migration and a range-wide bottleneck. Overall, this study evaluates the potential and limitations of GBS data in an ABC-coalescence framework and proposes a best-practice approach.
Main code and data for manuscript - Global Coal Power Plants Efficiency Analysis
<p>This repository contains the datasets and MATLAB code utilized in the study Revisiting Global Coal Power Plants Efficiency Gains in Delivering Climate Goals. The research integrates artificial neural networks (ANN), optimization models, and Monte Carlo-based sensitivity analyses to assess efficiency improvements in global coal-fired power plants and their potential contributions to climate objectives.</p>
Raw and Preprocessed BMRA Wind Power Data
<p>Dataset of metered energy generation and Bid Acceptance Volume (i.e., observational dataset) used as input for the probabilistic wind power forecasting tool developed for the paper: <strong>Seamless short- to mid-term probabilistic wind power forecasting</strong>.</p>
Data and code for 'Global disparity in synergy of solar power and vegetation growth'
<p>The 'stepwisefit' requires Statistics and Machine Learning Toolbox installed in the MATLAB to run the code. The import data are attached.</p>
Data on IEEE and Synthetic Test Power Systems for Oriol Cartiel's PhD
<p>The data used in my PhD thesis comes from internet repositories (see within files). It is based on detailed information from both IEEE n-bus test power systems and synthetic power grid test cases developed by the scientific community. The dataset, stored in spreadsheet format (.xlsx), includes comprehensive technical details such as line impedances, equivalent internal impedances, locations of generators, load demands and their locations, and shunt element specifications. All values are normalized to per-unit (pu), assuming a base power of 100 MVA. Additionally, the reference to the internet repository is available in the same file for potential further consultation.</p>
Data article: Distributed PV power data for three cities in Australia.
<p>This dataset is as presented in the paper titled "Data article: Distributed PV power data for three cities in Australia." in the Journal of Renewable and Sustainable Energy, volume 11 by Jamie M, Bright, Sven Killinger and Nicholas A. Engerer.<br> </p> <p><strong>Abstract:</strong><br> We present a publicly available dataset containing photovoltaic (PV) system power measurements and metadata from 1,287 residential installations across three states/territories in Australia--- though mainly for the cities of Canberra, Perth and Adelaide. <br> The data is recorded between September 2016 and March 2017 at 10-min temporal resolution and consists of real inverter reported power measurements from PV systems that are well distributed throughout each city. The dataset represents a considerably valuable resource as public access to spatio-temporal PV power data is almost non-existent; this dataset has been used in numerous articles already by the authors. The PV power data is free to download and is available in its raw, quality controlled (QC) and `tuned' formats. Each PV system is accompanied by individual metadata including geolocation, user reported metadata and simulated parameterisation. Data provenance,download, usage rights and example usage are detailed within. <br> Researchers are encouraged to leverage this rich spatio-temporal dataset of distributed PV power data in their research.</p> <p>Further information is available at <a href="https://dx.doi.org/10.25911/5ca6a0640869a">ANU Data Commons</a> and <a href="https://solcast.com.au/rooftop-solar/publication-of-a-research-grade-solar-pv-power-dataset/">Solcast</a>. <br> This dataset has an embargo period for 3 years after the ARENA funded ANU project closure, though data is always available through <a href="https://solcast.com.au/rooftop-solar/publication-of-a-research-grade-solar-pv-power-dataset/">Solcast</a>.</p> <p><br> <strong>Usage rights:</strong></p> <p>There is a non-standard data usage rights agreement for this data. In the uploads is a 'license and metadata.txt' file that details the usage rights and metadata of the data. The exact agreement is reproduced here:<br> <br> <em>The data is released with bespoke terms. We state the crucial elements of these terms here. The dataset is freely provided to researchers as is with no guarantee of support. The dataset is not for commercial usage, but for research only. You are empowered to use this dataset however you wish in your research, through direct usage, adaptation, or improvements to the data itself. The data must not be redistributed, the access point for the data is exclusively through the website as described in Sec.III of the manuscript. Should you make significant changes to the data and wish to redistribute the new data, explicit permission must be obtained from the authors. Finally, appropriate accreditation to the creators must be made in all publications and outputs that arise from using this dataset in any way. To appropriately accredit the creators, we require that this exact data article (Bright et al., 2019) is referenced alongside its DOI: https://dx.doi.org/10.25911/5ca6a0640869a. Additionally, if using the QC version of the data, we also require a citation for the original papers detailing QCPV (Killinger et al., 2016a, 2016a). Furthermore, if using the tuned PV version of this data, we also require a citation for both the QCPV papers above and the PV tuning papers (Killinger et al., 2016b, 2017b) for full visibility of the data provenance. Lastly, the original hosts of this data PVoutput.org should be recognised for their efforts.</em></p> <p><em>References:</em></p> <p><em> Bright, Jamie M.; Killinger, Sven; and Engerer, Nicholas A. 2019. Data article: Distributed PV power data for three cities in Australia. Journal of Renewable and Sustainable Energy. Vol 11. See online for full details.<br> <br> Killinger, Sven; Braam, Felix; Muller, Bjorn; Wille-Haussmann, Bernhard and McKenna, Russell, 2016a. Projection of power generation between differently-oriented PV systems. Solar Energy. 136, 153-165.</em></p> <p><em> Killinger, Sven; Muller, Bjorn; Saint-Drenan, Yves Marie and McKenna, Russell. 2016b. Towards an improved nowcasting method by evaluating power profiles of PV systems to detect apparently atypical behavior. Conference Record of the IEEE Photovoltaic specialists Conference, pages 980-985.10.1109/PVSC.2016.7749757</em></p> <p><em> Killinger, Sven; Engerer, Nicholas and Müller, Björn. 2017a. QCPV: A quality control algorithm for distributed photovoltaic array power output. Solar Energy. 143, 120-131.</em></p> <p><em> Killinger, Sven; Bright, Jamie M.; Lingfors, David and Engerer, Nicholas A. 2017b. A tuning routine to correct systematic influences in reference PV systems’ power outputs. Solar Energy. 157, 6.</em></p> <p> </p>
Data for "Resolving Structures of Paramagnetic Systems in Chemistry and Materials Science by Solid-State NMR: the Revolving Power of Ultra-Fast MAS"
<p>Raw NMR data</p>
Data from: Long-term monitoring dataset of fish assemblages impinged at nuclear power plants in northern Taiwan
The long-term species diversity patterns in marine fish communities are garnering increasing attention from ecologists and conservation biologists. However, current databases on quantitative abundance information lack consistent long-term time series, which are particularly important in exploring the possible underlying mechanism of community changes and evaluating the effectiveness of biodiversity conservation measures. Here we describe an impinged fish assemblage dataset containing 1, 283, 707 individuals from 439 taxa. Once a month over 19 years (1987–1990 and 2000–2014), we systematically collected the fish killed by impingement upon cooling water intake screens at two nuclear power plants on the northern coast of Taiwan. Because impingement surveys have low sampling errors and can be carried out over many years, they serve as an ideal sampling tool for monitoring how fish diversity and community structure vary over an extended period of time.
Data from: QTL detection power of multi-parental RIL populations in Arabidopsis thaliana
A major goal of today's biology is to understand the genetic basis of quantitative traits. This can be achieved by statistical methods that evaluate the association between molecular marker variation and phenotypic variation in different types of mapping populations. The objective of this work was to evaluate the statistical power of QTL detection of various multi-parental mating designs as well as to assess the reasons for the observed differences. Our study was based on empirical data of 20 Arabidopsis thaliana accessions which have been selected to capture the maximum genetic diversity. The examined mating designs differed strongly with respect to the statistical power to detect QTL. We observed the highest power to detect QTL for the diallel cross with random mating design. The results of our study suggested that performing sibling mating within subpopulations of joint linkage mapping populations has the potential to considerably increase the power for QTL detec tion. Our results, however, revealed that using designs in which more than two parental alleles segregate in each subpopulation increases the power even more.
Inspiration From Eye-tracking Data: Investigating the Impact of Combining Specific Environmental Features and Power Mobility Training
ClinicalTrials.gov study NCT06928077. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Association mapping for phenology and plant architecture in maize shows higher power for developmental traits compared with growth influenced traits
Open the record for dataset details and reuse information.
Data from: Assessment of a storage system to deliver uninterrupted therapeutic oxygen during power outages in resource-limited settings
Open the record for dataset details and reuse information.
Data from: Limitations of rotational manoeuvrability in insects and hummingbirds: evaluating the effects of neuro-biomechanical delays and muscle mechanical power
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Data from: Demographic inferences using short-read genomic data in an Approximate Bayesian Computation framework: in silico evaluation of power, biases, and proof of concept in Atlantic walrus
Open the record for dataset details and reuse information.
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.