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78 results for “generation time”
Data archive for "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network"
<p>This datasets supports the paper "Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial Network" submitted to IEEE Transactions in Geoscience and Remote Sensing. A preprint of the paper can be found here: <a href="https://arxiv.org/abs/2005.10374">https://arxiv.org/abs/2005.10374</a>. The code that uses these data is available at <a href="https://github.com/jleinonen/downscaling-rnn-gan">https://github.com/jleinonen/downscaling-rnn-gan</a>.</p> <p>The file "goes-samples-2019-128x128.nc" contains the training dataset called "GOES-COT" in the paper, consisting of cloud optical depth measurements from the GOES-16 satellite. The files "gen_weights*.nc" contain the generator weights saved at different time steps during training for the two different datasets described in the paper.<br> </p>
Synthetic time series data generation for edge analytics
<p>In this research, we create synthetic data with features that are like data from IoT devices. We use an existing air quality dataset that includes temperature and gas sensor measurements. This real-time dataset includes component values for the Air Quality Index (AQI) and ppm concentrations for various polluting gas concentrations. We build a JavaScript Object Notation (JSON) model to capture the distribution of variables and structure of this real dataset to generate the synthetic data. Based on the synthetic dataset and original dataset, we create a comparative predictive model. Analysis of synthetic dataset predictive model shows that it can be successfully used for edge analytics purposes, replacing real-world datasets. There is no significant difference between the real-world dataset compared the synthetic dataset. The generated synthetic data requires no modification to suit the edge computing requirements. The framework can generate correct synthetic datasets based on JSON schema attributes. The accuracy, precision, and recall values for the real and synthetic datasets indicate that the logistic regression model is capable of successfully classifying data</p>
More social species live longer, have longer generation times, and longer reproductive windows
<p>Data and scripts required to reproduce the results of the manuscript "More social species live longer, have longer generation times, and longer reproductive windows"</p>
Additional results for article "A new approach for the generation of real-time GNSS low-latitude ionospheric scintillation maps"
<p>Complete set of interpolation error and correlation metrics for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options using the SSS cross-validation scheme for the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Complete table of interpolation errors and correlation.csv”.</p> <p>Complete set of scintillation maps for the approaches GDA, IDW, RBF and GPR for all the 12 pre-processing options covering the 10-hour dataset (40 maps with 16-minute interval for each approach and each pre-processing options) - file “Scintillation maps for the 10-hour dataset.zip”.</p> <p>Comparison plots of the scintillation maps generated by the approaches GDA, IDW, RBF and GPR with the pre-processing options SAR, SMR and VQI for each of the 40 intervals of time of 16 minutes covering the 10-hour dataset - file “Set of maps for all 4 approaches with the SAR, SMR and VQI sets of options.zip”.</p> <p>Sequence of scintillation maps for the 8-hour dataset generated by the GPR(VQI) approach for the three time resolutions (1, 2, and 16-minute) - file “Scintillation maps for the 8-hour comparison dataset.zip”.</p> <p>Animations corresponding to the sequence of maps generated by the GPR(VQI) approach for the 8-hour dataset, and for the three time resolutions (1, 2, and 16-minute) - file “Animations of GPR(VQI) maps for the 8-hour comparison dataset.zip”.</p>
Using Blender EEVEE for the Generation of Real Time Background Plate in Green Screen Movie Shots
<p>This talk will introduce you to the use of blender EEVEE for green screen shots for a short movie. Green (and blue) screen shots are notoriously difficult to get the lighting condition right, since the image from the camera is dominated by the bright green background. It is very helpful on the set to see in real time the final composition of the scene with the proper background to make adjustments of the camera and the lighting position and the lighting intensity and color.<br> For the shots a large volume mocap solution (Optitrack) was used to track the movie camera (Arri Alexa) and the transformation data was sent to Blender to animate the virtual camera. The previously laser scanned background was rendered in real time in EEVEE. To combine the camera image and the rendered image a dedicated live-keying hardware was used. The described set-up was used in connection with the research project Virtually Real – Aesthetics and Perception of Virtual Spaces in Film by the Zurich University of the Arts and the University of Bern, funded by the Swiss National Science Foundation. <br> </p>
Data for article: Time-Resolved Spectroscopic Investigation of Charge Trapping in Carbon Nitrides Photocatalysts for Hydrogen Generation
<p>This is the data presented in the article titled 'Time-Resolved Spectroscopic Investigation of Charge Trapping in Carbon Nitrides Photocatalysts for Hydrogen Generation', published in the Journal of the American Chemical Society. DOI:10.1021/jacs.7b01547</p> <p>http://pubs.acs.org/doi/abs/10.1021/jacs.7b01547</p> <p> </p>
Time dependent interaction modification generated from plant-soil feedback
<p>Pairwise interactions between species can be modified by other community members, leading to emergent dynamics contingent on community composition. Despite the prevalence of such higher-order interactions, little is known about how they are linked to the timing and order of species' arrival. We generate population dynamics from a mechanistic plant-soil feedback model, then apply a general theoretical framework to show that the modification of a pairwise interaction by a third plant depends on its germination phenology. These time-dependent interaction modifications emerge from concurrent changes in plant and microbe populations and are strengthened by higher overlap between plants' associated microbiomes. The interaction between this overlap and the specificity of microbiomes further determines plant coexistence. Our framework is widely applicable to mechanisms in other systems from which similar time-dependent interaction modifications can emerge, highlighting the need to integrate temporal shifts of species interactions to predict the emergent dynamics of natural communities.</p>
Time series generated by nonlinear Langevin equation
<p>Datasets used in papers:</p> <p>Telesca L. and Z. Czechowski, Fisher–Shannon Investigation of the Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series, Entropy 2023, 25, 1650.</p> <p>Czechowski Z. and L. Telesca, Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series, Chaos, Solitons and Fractals 183 (2024), 114927</p>
TIMES-Sweden (Industrial) Heat generation technologies database
<p>This is a database containing techno-economic data for heat & power technologies, primarily focusing on technologies for heat generation in industry or district heating. The database is a compilation of information from literature, specifically tailored for use in TIMES models. Even though this specific database has been developed for TIMES-Sweden, the data can also be applied for other regions. The database is continuously updated as work progresses with the TIMES-Sweden model.</p> <p>Preferably to be used in combination with TIMES-Sweden Industry database (<a href="https://doi.org/10.5281/zenodo.4139800">10.5281/zenodo.4139800</a>), and TIMES-Sweden Fuel production technologies database (<a href="https://doi.org/10.5281/zenodo.6372926">10.5281/zenodo.6372926</a>).</p> <p>This Database is also a part of the IEA ETSAP SubRES project, with the aim to make techno-economic data more accessible. More information about ETSAP can be found here: <a href="https://iea-etsap.org/">https://iea-etsap.org/</a></p> <p>More information about TIMES-Sweden and the modelling team can be found here: <a href="http://www.ltu.se/TIMES-Sweden">http://www.ltu.se/TIMES-Sweden</a></p>
Generation time in stage-structured populations under fluctuating environments (Simulations)
<p>The study is about the computation of the generation time when individuals are classified based on their stage and the environment stochastically fluctuates with time. This folder contains simulation code in R to plot Figure 2 of the manuscript.</p>
Fig 6. A Bayesian time-tree generated from mitochondrial 16S in A New Species of Microhyla (Anurα: Microhylidαe) from Nilphαmαri, Bαnglαdesh
Fig 6. A Bayesian time-tree generated from mitochondrial 16S gene fragment for all known species in the genus Microhyla. Calibration points are indicated with arrows. Numbers are in million years, and the light blue colored bars indicate 95% confidence intervals for divergence time estimates. doi:10.1371/journal.pone.0119825.g006
Fig. 2. Standard curve generated using the log10 in A novel quantitative real-time PCR diagnostic assay for seal heartworm (Acanthocheilonema spirocauda) provides evidence for possible infection in the grey seal (Halichoerus grypus)
Fig. 2. Standard curve generated using the log10 of the ng of input A. spirocauda DNA plotted against Ct value. Unknown values are displayed as stars. For unknown samples, the total input DNA was 1 ng, which contains a mixture of seal DNA from the blood and A. spirocauda DNA. R2 = 0.985 for linear fit of standards. Curve is described by the equation y = −5.63x + 17.16, where y is the log (ng) and x is the Ct value. 95% confidence intervals are denoted by the dotted lines.
Bias-corrected simluated wind power generation time series for Brazil
<p>Simulated and bias corrected wind power generation time series data sets for Brazil, its North-East and South, seven states and seven wind parks.</p> <p>The data sources, generation and validation of the datasets are described in the article "Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil", preprint available on arXiv: arxiv.org/abs/1904.13083, final version DOI: <a href="https://doi.org/10.1016/j.energy.2019.116212">10.1016/j.energy.2019.116212</a></p> <p>Code for generating the datasets is available at github.com/KatharinaGruber/BrazilWindpower_biascorr</p> <p> </p> <p>The files "comp_*" contain comparisons of simulated and observed wind power generation time series with daily resolution for all regions.</p> <p>"comp_noc.RData" is for comparison of interpolation methods and contains time series generated with Nearest Neighbour interpolation (NN), Bilinear Interpolation (BLI) and Inverse Distance Weighting (IDW).</p> <p>"comp_wmsa.RData" is for comparison of wind speed mean approximation methods and contains time series generated with Nearest Neighbour interpolation (NN - no correction applied), mean approximation with measured data (IN) and mean approximation with the Global Wind Atlas (GWA).</p> <p>"comp_wsc.RData" is for comparison of spatiotemporal wind speed correction methods and contains time series generated with mean approximation with the Global Wind Atlas (wmsa) and combined mean approximation with the Global Wind Atlas and hourly and monthly mean approximation with measured data (wschm).</p> <p> </p> <p>The files "statpowlist_*" contain hourly simulated wind power generation time series for three interpolation methods (NN, BLI, IDW), two mean approximation methods (wsmaIN - measured data (INMET), wsmaWA - Global Wind Atlas) as well as for spatiotemporal (hourly and monthly) wind speed bias correction (wschm) for each wind park available in The Wind Power dataset.</p>
Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses - Supporting Information for Trajectories
<p>We provide videos of trajectories for a test charge, bound by Lennard-Jones potential. First video, titled "PW Trajectory - Point 2 " refers to the particle under the effect of a plane wave pulse. The other video , titled "Emitter Trajectory - Point 2" refers to the particle affected by an orbital angular momentum carrying pulse.</p> <p>These videos are supplementary materials for the article titled "Time-Resolved Plasmon-Assisted Generation of Arbitrary Optical-Vortex Pulses".</p>
Forestation at the right time with the right species can generate persistent carbon benefits in China
<p>This collection contains the datasets used in our study ‘<strong>Forestation at the right time with the right species can generate persistent carbon benefits in China</strong>’.</p> <p><em><strong>Part A: Data</strong></em></p> <p>Most of the data presented here are after pre-processing, such as transforming the projection, extracting variables, clipping to the study region (70<sup>o</sup>E-140<sup>o</sup>E,15<sup>o</sup>N-55<sup>o</sup>N), and resampling to 1-km.</p> <p>The original source of these data sets (usually global, at different resolutions) is given in 'data_original.txt' as well as in the 'Data availability' of the main text.</p> <p>1. potential_china_forest_1km.rar: contains the potential forest distributions for China at 1-km resolution from multiple source (Random Forest, WRI and ORCHIDEE). Note: the forest distribution is in the form of logical variables in the .mat file, where a value of 1 or true means that the grid point is potentially forestable, and a value of 0 or false means this grid is not suitable for forest.</p> <p>2. ori_carbon_all_grid_1km.mat: Living biomass carbon densities in 2010 for China at 1-km (unit: Mg C ha-1). Both aboveground and belowground biomass carbon are included. The original biomass map is from Spawn et al. 2020.</p> <p>3. Forest_inventory_data_5th_9th.xlsx: 1) The forest area reported in 5th to 9th national forest inventory 2) The forest area of different age classes derived form the 9th national forest inventory.</p> <p>4. data_original.txt: the original source of these data sets</p> <p><strong>Part B: MATLAB Code</strong></p> <p>This file (Matlab_code.rar) contains the code, functions and parameters for our analysis of the data, mainly MATLAB files (.m or mat)</p> <p><strong>Part C: Demo/Example data and code</strong></p> <p>This file (Demo.rar) contains the demo of our code running, which includes the demo code along with code comments, input data for the demo, and the expected output results.</p> <p><strong>Part D: Docs</strong></p> <p>Reference and guidelines (Docs.rar).</p> <p>If you have any questions or suggestions, please contact xuhaotony@pku.edu.cn</p>
Time dependent interaction modification generated from plant-soil feedback
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Sexual selection matters in genetic rescue, but productivity benefits fade over time: A multi-generation experiment to inform conservation
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Generation time in stage-structured populations under fluctuating environments (Simulations)
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High-resolution wind power generation time series for Germany in the period 2000-2015
<p>High-resolution wind power generation time series for Germany in the period 2000-2015. A paper describing the applied methodology can be found in this repository as well.</p> <p>The final temporal resolution is hourly and the spatial resolution NUTS 3. The data is stored in csv and hdf5 files.</p> <p>More information on the data set, e.g. missing time stamps and versioning, can be found in readme.txt.</p>
Genomic vulnerability to rapid climate warming in a tree species with a long generation time
<p><span><span><span><span><span><span><span><span><span><span><span>The ongoing increase in global temperature affects biodiversity, especially in mountain regions where climate change is exacerbated. As sessile, long-lived organisms, trees are especially challenged in terms of adapting to rapid climate change. Here, we show that low rates of allele frequency shifts in Swiss stone pine (<i>Pinus cembra</i>) occurring near the treeline result in high genomic vulnerability to future climate warming, presumably due to the species' long generation time. Using exome sequencing data from adult and juvenile cohorts in the Swiss Alps, we found an average rate of allele frequency shift of 1.23×10<sup>-2</sup>/generation (i.e. 40 years) at presumably neutral loci, with similar rates for putatively adaptive loci associated with temperature (0.96×10<sup>-2</sup>/generation) and precipitation (0.91×10<sup>-2</sup>/generation). These recent shifts were corroborated by forward-in-time simulations at neutral and adaptive loci. Additionally, in juvenile trees at the colonisation front we detected alleles putatively beneficial under a future warmer and drier climate. Notably, the observed past rate of allele frequency shift in temperature-associated loci was decidedly lower than the estimated average rate of 6.29×10<sup>-2</sup>/generation needed to match a moderate future climate scenario (RCP4.5). Our findings suggest that species with long generation times may have difficulty keeping up with the rapid climate change occurring in high mountain areas and thus are prone to local extinction in their current main elevation range.</span></span></span></span></span></span></span></span></span></span></span></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.