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FIG. 1 in The Walker Scale: Details of a Method for Assessing Beach-Spawning Runs of California Grunion Leuresthes tenuis (Atheriniformes: Atherinopsidae)
FIG. 1. (A) California Grunion spawn out of water on beaches in southern California. Photo by Arnold Liu (used with permission of Grunion.org). (B) Grunion Greeters at a spawning run, scored as W-4. Photo by Harris Lakisic (used with permission of Grunion.org).
Additional code and data for running the simulations presented in the article "Using observations of surface fracture to address ill-posed ice softness estimation over Pine Island Glacier"
<p>The data required to run the inverse problems described in the article "Using observations of surface fracture to address ill-posed ice softness estimation over Pine Island Glacier" along with code specific to the modified inverse problem described therein, additional to that downloadable from BISICLES is downloadable from https://commons.lbl.gov/display/bisicles/BISICLES.</p> <p>These data are restricted to those involved in reviewing the aforementioned article.</p>
FIGURE 24. Hydriastele montana. A. Stem basal portion with running rhizomes and roots. B. Leaf diagram. C in A monograph of Hydriastele (Areceae, Arecaceae) in New Guinea and Australia
FIGURE 24. Hydriastele montana. A. Stem basal portion with running rhizomes and roots. B. Leaf diagram. C. Apical and mid-leaf portion. D. Inflorescences with pistillate flowers. E. Portion of rachilla with pistillate flowers. F. Pistillate flower whole and in longitudinal section. Scale bar: A, C = 4 cm; B = 18 cm; D = 2 cm; E = 7 mm; F = 4 mm. All from Baker et al. 1380. Drawn by Lucy T. Smith.
Lotus2 on Mycorrhizal Fungi in the Galaxy Training Network - Example Run Output
Open the record for dataset details and reuse information.
Example view data for running M-VIDIA
We provide three example data for running MVIDIA-DEA (views.zip), MVIDIA-Patient Diagnosis (train.zip, test.zip) and for MVIDIA-Cell Clustering (sc.zip). If each data can only be used for its corresponding task as the data should be organized in the correct formats. Please visit our webserver (http://www.ai4pro.tech:3838/) to learn more about our M-VIDIA.
DO UNITED STATES STOCK INDICES AND GOLD PRICES DETERMINE BITCOIN PRICES? EVIDENCE FROM NONLINEAR SHORT- AND LONG-RUN ASYMMETRIC APPROACHES
<p>Prices of Bitcoin, US stock indices, Gold and crude oil</p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_0001-0500
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p> <p> </p>
Neural-Parareal dataset JOREK blob runs with RMHD model
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the RMHD model, model-ID "model600", with option</p> <p>with_vpar = .false.</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p>psi = poloidal magnetic flux</p> <p>zj = toroidal current</p> <p> </p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_0501-1000
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_1501-2000
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p>
Neural-Parareal dataset JOREK blob runs with electrostatic model batch_1001-1500
<p>Please refer to journal paper from S.J.P.Pamela, titled</p> <p>"Neural-Parareal: Self-improving acceleration of fusion MHD simulations using time-parallelisation and neural operators"</p> <p>Available on ArXiV and on Comp.Phys.Comm.: https://doi.org/10.1016/j.cpc.2024.109391</p> <p> </p> <p>Data produced by the JOREK code, https://jorek.eu</p> <p>All runs created using the electrostatic model, model-ID "model003"</p> <p>Data downsampled by saving every 10th timestep, on a regular 2D grid of 100x100.</p> <p>To reproduce full runs, use corresponding input files.</p> <p> </p> <p>Note: variables names are the same as in the JOREK code:</p> <p>u = electric potential</p> <p>omega = toroidal vorticity</p> <p>rho = density</p> <p>T = temperature</p> <p> </p> <p>The create_gif.py can be used to convert data into movies.</p> <p> </p>
Battery Pack Temperature Change Prediction for Running BLDC 1500 W Motor using Artificial Neural Network
<p><span>The study explores the prediction of battery temperature using an artificial neural network (ANN) model, trained with experimental data from a brushless DC (BLDC) motor setup. The ANN model, with a 15-14-1 architecture, successfully predicted battery temperature change based on various input parameters, including RPM, load and voltage change of thirteen series of battery. The ANN predictions aligned closely with experimental results, demonstrating the model's effectiveness in capturing the nonlinear behavior of battery temperature changes. These findings highlight the potential of deep learning techniques to improve real-time thermal management in BMS, offering a promising approach for extending battery life and optimizing performance in electric vehicles and energy storage systems.</span></p>
Bayesian Analysis of Quasar Lightcurves with a Running Optimal Average: PyROA Fits to COSMOGRAIL Data
<p>Available as .zip files are the data/results of using PyROA to fit to the COSMOGRAIL gravitationally lensed quasar data. In each .zip are the individual lightcurves for each image as a .dat file, a plot of the results, a corner plot of the sampled parameters and three pickle objects. These are samples.obj, samples_flat.obj and X_t.obj.</p> <ul> <li>The first of these, samples.obj, contains all the MCMC samples in an array with shape (Nsamples, Nwalkers, Ndim).</li> <li>The second, samples_flat.obj, contains the flattened samples, where the burn-in has been removed and is an array with shape (Nsamples_final, Ndim), where Nsamples_final is the number of samples with burn-in removed. This was used to generate the corner plot and obtain the best fit parameters.</li> <li>The last, X_t.obj, contains the driving lightcurve, X(t), as described in Donnan et al. 2021. This is an array of the form [t, X, X_errs], where X is the value of the driving lightcurve at time, t, with errors, X_errs.</li> </ul> <p>For objects with more than two images, folders where other images were the reference are included. The sigma parameter in the corner plot is the extra error added to the flux data which is labelled as <em>s<sub>i</sub></em> in the paper. The original data file downloaded from the COSMOGRAIL website is also included as a csv file.</p>
Run, Beep, Breathe: Exploring the Effects on Adherence and User Experience of 5 Breathing Instruction Sounds while Running
<p>Five Breathing Instruction Sounds used in the experiment. </p>
Knee extension moment arm variations relate to mechanical function in walking and running
<p>The patellofemoral joint plays a crucial mechanical role during walking and running. It increases the knee extensor mechanism's moment arm and reduces required knee extension muscle forces to generate the extension moment that supports body weight, prevents knee buckling and propels the centre-of-mass. However, the mechanical implications of moment arm variation caused by patellofemoral and tibiofemoral motion remain unclear. We used a data-driven musculoskeletal model with a 12-degree-of freedom knee to simulate the knee extension moment arm during walking and running. Using a geometric method to calculate the moment arm, we found smaller moment arms during running than walking during the swing phase. Overall, knee flexion causes differences between running and walking moment arms as increased flexion causes a posterior shift in the tibiofemoral rotation axis and patella articulation with the distal femur. Moment arms were also affected by knee motion direction and best predicted by separating by direction instead of across the entire gait cycle. Furthermore, we found high inter-subject variation in the moment arm that was largely explained by out-of-plane motion. Our results are consistent with the concept that shorter moment arms increase the effective mechanical advantage of the knee and may contribute to increased running velocity.</p>
Figure 36. Strict consensus trees from the taxon addition–deletion runs. A in Craniodental characters and the relationships of Procyonidae (Mammalia: Carnivora)
Figure 36. Strict consensus trees from the taxon addition–deletion runs. A, consensus tree of the five most parsimonious trees recovered by an exhaustive search of extant taxa only; B, most parsimonious tree recovered by a heuristic search of taxa with more than 50% of the characters scorable; C, consensus tree of the 11 most parsimonious trees recovered by a heuristic search of taxa with more than 25% of the characters scorable; D, consensus tree of the 342 most parsimonious trees recovered by a heuristic search of all taxa scored. Numbers below the nodes indicate node number. †, extinct taxa.
CESM1 preindustrial run SST
<p>The data is downloded from https://www.earthsystemgrid.org/dataset/ucar.cgd.ccsm4.cesmLE.ocn.proc.monthly_ave.SST/file.html with log-in. It is 1800-year monthly sea surface temperature data from Community Earth System Model 1 (CESM1) preindustrial control run (Kay et al., 2015), with fixed radiative forcing at the level of year 1850.<br> Kay, J. E., Deser, C., Phillips, A., Mai, A., Hannay, C., Strand, G., et al. (2015). The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bulletin of the American Meteorological Society, 96(8), 1333–1349. https://doi.org/10.1175/BAMS-D-13-00255.1</p>
Replication Package for: "Pre-Colonial Warfare and Long-Run Development in India"
<p>Data and code to replicate all the results in the article Dincecco, Mark; Fenske, James; Menon, Anil; and Mukherjee, Shivaji "Pre-Colonial Warfare and Long-Run Development in India," Economic Journal.</p>
Performance data for GPU runs of the Islet SL method described in the Islet paper
<p>Performance data for GPU runs of the Islet SL method described in the Islet paper. The code that created this data is https://doi.org/10.5281/zenodo.5595508.</p>
Running data for SymbointScreener
<p>Running data for SymbointScreener</p>
ScienceDex guides
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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.