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1,774 results for “Acceleration”
MPU9250 MEMS IMU Sine wave acceleration excitation along the Z axis
<p><strong>MPU9250 MEMS IMU Sine wave acceleration excitation along the Z axis</strong></p> <p>The file Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep_ADC.dump contains a dump of the ADC protbuff messages recorded by the Met4FoF dataaqusition unit during the calibration measurement. The ADC is sampled synchronously to the data ready signals of the MPU9250.</p> <p>The file Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep_Sensor.dump contains a dump of the MPU9250 protbuff messages recorded by the Met4FoF dataaqusition unit during the calibration measurement.</p> <p>Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep.xlsx contains the accelerations recorded by the PTB refferenzsystem for each measurement run. The phase is referred to the analog reference values in the channel Data_11 </p> <p>Met4FOF_mpu9250_Z_Acc_10_hz_250_hz_6rep.csv contains the values from the excel table in panda readable form.</p> <p>1FE4_AC_CAL.zip contains various measurements of the ADC transfer function as JSON files.</p>
Accelerated lignocellulosic molecule adsorption structure determination dataset
<p>Dataset containing all structures from the accelerated structure search for lignocellulosic molecules. Part of the data corresponds to DFT data, while the largest portion of structures correspond to data acquired using a machine learned interatomic potential (NequIP) trained on the former. The energies attached to each structure are atomisation energies. Contains both isolated adsorbates and adsorption structures. The dataset also contains configuration files for the NequIP training. </p>
Supplementary Materials for "Accelerating data sharing and re-use in volume electron microscopy"
<p>The deposition contains supporting materials for "Accelerating data sharing and re-use in volume electron microscopy" Comment</p> <ul> <li>Sample preparation protocol for cell monolayers optimized for serial block face scanning electron microscopy</li> <li>Supporting movies showing models of biological specimens imaged using volume electron microscopy</li> </ul>
6D phase space of charged beam in particle accelerator
<p>The dataset is collected from HPSim (https://github.com/apphys/hpsim), an advanced, open-source tool developed at LANL, enables rapid, online simulations of multipleparticle beam dynamics is used to collect data. HPSim solves Vlasov-Maxwell equations to calculate the effects of external accelerating and focusing forces on the charged particle beam as well as space charge forces within the beam. To generate the dataset from HPSim, the RF set points (amplitude and phase) for the first four modules are randomly sampled from a uniform distribution keeping the rest of the set points of 44 modules at a mean value. Other beam and accelerator parameters, like the initial beam condition, are also set to constant realistic values. Using the RF set points as inputs to the simulation, HPSim provides a six-dimensional phase space of the charged particle beam in the form of 15 unique projections at each of the 48 accelerating section/modules of LANSCE linear accelerator. </p>
Data for: Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI
<p>Magnetic Resonance Imaging data used in the work "Rational Approximation of Golden Angles: Accelerated Reconstructions for Radial MRI". The data is provided in a file format used by the BART toolbox (DOI: <a href="http://doi.org/10.5281/zenodo.592960">10.5281/zenodo.592960</a>).</p> <p> </p> <p>The *_ind.{cfl,hdr} files store the indices of the different spokes of the corresponding datasets:</p> <p> </p> <p><strong>data_res_S{1597,0987,0377,0233,0089,0055,0021}</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 3.2|2.04</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{16,15,13,12,10,9,7}^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_raga, data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi_{13}^1, 2\psi^1</p> <p>Sampling: RAGA</p> <p><br> </p> <p><strong>data_bin_ga</strong></p> <p>Type: Radial Single-Shot Dataset</p> <p>Object: Single-slice of T1 sphere of the NIST phantom (Model 106)</p> <p>Sequence: IR FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 200</p> <p>Spoke Angle: 2\psi^1</p> <p>Sampling: GA</p> <p><br><br> </p> <p><strong>data_invivo_ga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi^1</p> <p>Sampling: Golden-Ratio</p> <p><br> </p> <p> </p> <p><strong>data_invivo_raga</strong></p> <p>Type: Radial Dataset</p> <p>Object: Single-slice cardiac short-axis</p> <p>Sequence: FLASH</p> <p>TR|TE [ms]: 2.9|1.77</p> <p>FA [deg]: 8</p> <p>T_RF [ms]: 0.4</p> <p>BWTP: 1.6</p> <p>FOV [mm]: 320</p> <p>Spoke Angle: \psi_{13}^1</p> <p>Sampling: RAGA</p> <p><br> </p>
Dataset for: Smoking does not accelerate leukocyte telomere attrition: a meta-analysis of 18 longitudinal cohorts
<p>Summary dataset (.csv file) and R script (.R file) for the manuscript entitled:</p> <p>Smoking does not accelerate leukocyte telomere attrition: a meta-analysis of 18 longitudinal cohorts.</p> <p>The column names are explained at the beginning of the R script.</p> <p> </p>
A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods
<p><strong>Description</strong><br> This repository contains a comprehensive solar irradiance, imaging, and forecasting dataset. <br> The goal with this release is to provide standardized solar and meteorological datasets to the research community for the accelerated development and benchmarking of forecasting methods. <br> The data consist of three years (2014–2016) of quality-controlled, 1-min resolution global horizontal irradiance and direct normal irradiance ground measurements in California. <br> In addition, we provide overlapping data from commonly used exogenous variables, including sky images, satellite imagery, Numerical Weather Prediction forecasts, and weather data. <br> We also include sample codes of baseline models for benchmarking of more elaborated models.</p> <p><strong>Data usage</strong><br> The usage of the datasets and sample codes presented here is intended for research and development purposes only and implies explicit reference to the paper:<br> <em>Pedro, H.T.C., Larson, D.P., Coimbra, C.F.M., 2019. A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy 11, 036102. https://doi.org/10.1063/1.5094494</em></p> <p>Although every effort was made to ensure the quality of the data, no guarantees or liabilities are implied by the authors or publishers of the data.</p> <p><strong>Sample code</strong><br> As part of the data release, we are also including the sample code written in Python 3. <br> The preprocessed data used in the scripts are also provided. <br> The code can be used to reproduce the results presented in this work and as a starting point for future studies. <br> Besides the standard scientific Python packages (numpy, scipy, and matplotlib), the code depends on pandas for time-series operations, pvlib for common solar-related tasks, and scikit-learn for Machine Learning models. <br> All required Python packages are readily available on Mac, Linux, and Windows and can be installed via, e.g., pip. </p> <p><strong>Units</strong><br> All time stamps are in UTC (YYYY-MM-DD HH:MM:SS).<br> All irradiance and weather data are in SI units.<br> Sky image features are derived from 8-bit RGB (256 color levels) data.<br> Satellite images are derived from 8-bit gray-scale (256 color levels) data.</p> <p><strong>Missing data</strong><br> The string "NAN" indicates missing data</p> <p><strong>File formats</strong><br> All time series data files as in CSV (comma separated values)<br> Images are given in tar.bz2 files</p> <p><strong>Files </strong></p> <ul> <li><em>Folsom_irradiance.csv</em> Primary One-minute GHI, DNI, and DHI data.</li> <li><em>Folsom_weather.csv </em> Primary One-minute weather data.</li> <li><em>Folsom_sky_images_{YEAR}.tar.bz2</em> Primary Tar archives with daytime sky images captured at 1-min intervals for the years 2014, 2015, and 2016, compressed with bz2.</li> <li><em>Folsom_NAM_lat{LAT}_lon{LON}.csv </em> Primary NAM forecasts for the four nodes nearest the target location. {LAT} and {LON} are replaced by the node’s coordinates listed in Table I in the paper. </li> <li><em>Folsom_sky_image_features.csv </em> Secondary Features derived from the sky images.</li> <li><em>Folsom_satellite.csv </em> Secondary 10 pixel by 10 pixel GOES-15 images centered in the target location. </li> <li><em>Irradiance_features_{horizon}.csv</em> Secondary Irradiance features for the different forecasting horizons ({horizon} 1⁄4 {intra-hour, intra-day, day-ahead}). </li> <li><em>Sky_image_features_intra-hour.csv</em> Secondary Sky image features for the intra-hour forecasting issuing times. </li> <li><em>Sat_image_features_intra-day.csv</em> Secondary Satellite image features for the intra-day forecasting issuing times. </li> <li><em>NAM_nearest_node_day-ahead.csv </em> Secondary NAM forecasts (GHI, DNI computed with the DISC algorithm, and total cloud cover) for the nearest node to the target location prepared for day-ahead forecasting.</li> <li><em>Target_{horizon}.csv</em> Secondary Target data for the different forecasting horizons.</li> <li>F<em>orecast_{horizon}.py </em> Code Python script used to create the forecasts for the different horizons. </li> <li><em>Postprocess.py</em> Code Python script used to compute the error metric for all the forecasts.</li> </ul> <p> </p>
Data from: Plasma acceleration in a magnetic arch
<p><strong>Data from: Plasma acceleration in a magnetic arch</strong></p> <p>- Authors: Mario Merino, Diego García, Eduardo Ahedo</p> <p>- Contact emails: mario.merino@uc3m.es, dieggarc@ing.uc3m.es</p> <p>- Date: 2023-06-08</p> <p>- Keywords: electric propulsion, electrodeless plasma thruster, magnetic arch, plasma expansion</p> <p>- Version: 1.0.4</p> <p>- Digital Object Identifier (DOI): 10.5281/zenodo.7919577</p> <p>- License: This dataset is made available under the [Open Data Commons Attribution License](http://opendatacommons.org/licenses/by/1.0/)</p> <p> </p> <p><strong>Abstract</strong></p> <p>This dataset contains the data found in the plots of the paper:</p> <p>Mario Merino, Diego García, Eduardo Ahedo, "Plasma acceleration in a magnetic arch"</p> <p>Published in the journal Plasma Sources Science and Technology</p> <p> </p> <p><strong>Dataset description</strong></p> <p>The data in this repository has been extracted from the fluid simulations as described in the article. (https://iopscience.iop.org/article/10.1088/1361-6595/acd476).</p> <p>For further information on the setup for the simulation please refer to the article.</p> <p> </p> <p><strong>Data files</strong></p> <p>The data files are in .csv format. They were produced in numpy using the numpy.savetxt() function and can be easily read with numpy.loadtxt() or in any other language with the apropiate reader for .csv files.</p> <p>The files are organised following the order of the figures in the article. Therefore each file contains a different sized array. In the following one can find a description of all the data contained in each of the files:</p> <p>- fig2.csv</p> <p> - Applied magnetic field 'Ba/Ba0'</p> <p>- fig3.csv</p> <p> - Thermalised potential 'He'</p> <p> - Electron out of plane velocity 'uye'</p> <p>- fig4.csv</p> <p> - Plasma density 'n'</p> <p> - Electron temperature 'Te'</p> <p> - Electric potential 'phi'</p> <p> - Ion in-plane velocity 'uitilde'</p> <p> - Ion Mach number 'Mi'</p> <p>- fig5.csv</p> <p> - In-plane electric current density 'jitilde'</p> <p>- fig6.csv</p> <p> - Radial magnetic force density 'jyBz'</p> <p> - Axial magnetic force density '-jyBx'</p> <p>- fig7.csv</p> <p> - Thrust integral, beta = 0.00 case 'F_F0_beta_0.00'</p> <p> - Thrust integral, beta = 0.02 case 'F_F0_beta_0.02'</p> <p> - Thrust integral, beta = 0.04 case 'F_F0_beta_0.04'</p> <p> - Thrust integral, beta = 0.08 case 'F_F0_beta_0.08' </p> <p>- fig8.csv</p> <p> - Normalised induced magnetic field strength 'Bp_beta0_Ba0'</p> <p>- fig9.csv</p> <p> - Total magnetic field, beta = 0.00 case 'B_beta_0.00'</p> <p> - Total magnetic field, beta = 0.02 case 'B_beta_0.02'</p> <p> - Total magnetic field, beta = 0.04 case 'B_beta_0.04'</p> <p> - Total magnetic field, beta = 0.08 case 'B_beta_0.08'</p> <p>All files contain a matrix of comma separated values with 400 rows. The number of columns depends on the specific file, for the files corresponding to two dimensional maps (all files except fig7.csv) the number of columns is a multiple of 400, where the first 400 columns correspond to the Z positions values and the following 400 the X position values. These two 400 by 400 matrices correspond to a meshgrid common in Matlab and NumPy. The following columns correspond to the values of each quantity in the positions given by the grid. For example, files containing only one field such as 'fig2.csv' have 400 rows and 1200 columns with columns 801 to 1200 corresponding to the values of the given field. As an example for files containing multiple fields let us take 'fig3.csv', this file contains 400 rows and 1600 columns where columns 801 to 1200 contain the values for 'He' and columns 1201 to 1600 contain 'uye'.</p> <p>The file 'fig7.csv' contains the data for a 1D plot with multiple lines. In this case the data is matrix with 400 rows and 5 columns where column 1 contains the z axis positions column 2 contains the values for 'F_F0_beta_0.00' column 3 contains 'F_F0_beta_0.02' and so on.</p> <p>All values are normalised as explained in the article.</p> <p> </p> <p><strong>Citation</strong></p> <p>Any works using this dataset or any part of it in any form shall cite it as follows:</p> <p>The prefered means of citation is to reference the publication as soon as it is available.</p> <p>The BibTex is also provided for the sake of convinience:</p> <p>@article{Merino_2023,</p> <p>doi = {10.1088/1361-6595/acd476},</p> <p>url = {https://dx.doi.org/10.1088/1361-6595/acd476},</p> <p>year = {2023},</p> <p>month = {jun},</p> <p>publisher = {IOP Publishing},</p> <p>volume = {32},</p> <p>number = {6},</p> <p>pages = {065005},</p> <p>author = {Mario Merino and Diego García-Lahuerta and Eduardo Ahedo},</p> <p>title = {Plasma acceleration in a magnetic arch},</p> <p>journal = {Plasma Sources Science and Technology},</p> <p>abstract = {}</p> <p>}</p> <p>Optionally the dataset can be cited by referencing the DOI: 10.5281/zenodo.7919577</p> <p><strong>Acknowledgments</strong></p> <p>This dataset was created by the [ERC-ZARATHUSTRA project](https://erc-zarathustra.uc3m.es/).</p> <p>The ERC-ZARATHUSTRA project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 950466).</p>
Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 2
Open the record for dataset details and reuse information.
Whole-brain background-suppressed pCASL MRI with 1D-accelerated 3D RARE Stack-Of-Spirals Readout- Dataset 3
Open the record for dataset details and reuse information.
Dataset for AvA (Accelerated Virtualization of Accelerators) in ASPLOS'20
<p>This is the dataset used by the paper titled "AvA: Accelerated Virtualization of Accelerators" which will appear in ASPLOS'20.</p>
BOOSTR: A Dataset for Accelerator Control Systems (Partial Release 2020)
<p>BOOSTR (Booster Operation Optimization Sequential Time-Series for Regression) was created to provide cycle-by-cycle time series of readings and settings from instruments and controllable devices of the Booster, the 15~Hz Rapid-Cycling Synchrotron (RCS) at Fermilab. We are preliminarily releasing one day of it in the hopes that it— and future versions of it— can be used as a dataset to demonstrate other aspects of artificial intelligence for advanced control systems. For more information, please see our accompanying Datasheet.</p>
Accelerating Performance Inference over Closed Systems by Asymptotic Methods
<p>This archive includes the research data associated to the paper:</p> <p>Giuliano Casale. Accelerating Performance Inference over Closed Systems by Asymptotic Methods. Proc. ACM Meas. Anal. Comput. Syst., 1(1), 2017. The paper is accepted for presentation at ACM SIGMETRICS 2017.</p> <p>The research data requires MATLAB 2015a or later. Four datasets are included, each corresponding to a section of the paper:<br> - sec5.3.1: Small and medium models without infinite server nodes (Section 5.3.1)<br> - sec5.3.2: Large models without infinite server nodes (Section 5.3.2)<br> - sec5.3.3: Models with infinite server nodes (Section 5.3.3)<br> - sec5.4: Optimization programs (Section 5.4)</p> <p>A description of each dataset is included in the README.TXT file inside each folder.</p>
Multi-GeV Wakefield Acceleration in a Plasma-Modulated Plasma Accelerator
<p>Input decks for the particle-in-cell code WarpX used in a new study to simulate the accelerator stage of a recently proposed laser-plasma accelerator scheme [Phys. Rev. Lett. <strong>127</strong>, 184801 (2021)], dubbed the Plasma-Modulated Plasma Accelerator (P-MoPA).</p>
Data for: Machine-learning-accelerated simulations enable heuristic-free surface reconstruction
<p>This is the dataset for the publication "Machine-learning-accelerated simulations to enable automatic surface reconstruction", by X. Du, J.K. Damewood, J.R. Lunger, R. Millan, B. Yildiz, L. Li, and R. Gómez-Bombarelli. The repository contains the density-functional theory (DFT) data used to train the neural network force fields (NFF), selected results from our GaN(0001), Si(111), and SrTiO3(001) Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) runs, and Jupyter notebooks used for analysis and plots. To run the .ipynb's, you will need to install <a href="https://github.com/learningmatter-mit/surface-sampling">surface-sampling</a> (tested up to commit 02820d339eed6291b6af6ccb809f154ad6244110 on master) and <a href="https://github.com/learningmatter-mit/NeuralForceField">NeuralForceField</a> (tested up to commit 72d1f32f43f202c1a466116beeed15845a6456e7 on master) from the <a href="https://github.com/learningmatter-mit">Rafael Gómez-Bombarelli Group @ MIT</a>.</p>
Asaia spp. accelerate development of the yellow fever mosquito, Aedes aegypti, via interactions with the vertically transmitted larval microbiome
<p><strong><span>Background:</span></strong><em> Aedes aegypti</em> mosquitoes are the primary vectors of yellow fever, dengue, chikungunya and Zika virus. Control programs primarily rely on insecticide application, which encounter challenges related to efficacy and resistance evolution. Alternative strategies, such as the sterile insect technique, highly depend on efficient mass-rearing of healthy insects prior to mass release. Based on effects seen in other mosquito species, we tested the hypothesis that acetic acid bacteria <span>of the </span><em>Asaia</em> <span>genus are</span> mutualist<span>s</span> for developing <em>Ae. aegypti</em> larvae. We tested for beneficial interactions across three <em>Asaia </em>species and whether <em>Asaia</em> inoculation benefited both axenic and conventionally reared larvae. To better understand the underlying mechanisms, we characterized the larval microbiome<span> </span>using culture-based methods and 16S rRNA gene amplicon sequencing.</p> <p><strong>Results:</strong><span> <span>Even</span></span> though <em>Asaia </em>bacteria were transient members of the gut community in conventionally reared insects<span>, t</span>wo <em>Asaia </em>species accelerated larval development relative to controls.<span> Despite their transient nature, </span>the two mutualist <em>Asaia</em> species had lasting impacts on the larval microbiome, mostly by altering the relative abundance of the most dominant bacteria genera <em>Klebsiella</em> and <em>Pseudomonas</em> and other minor components<span>.</span> Axenic larvae that were inoculated with <em>Asaia </em>were dominated by this group, but always exhibited slower development than conventionally reared insects.</p> <p><strong>Conclusions:</strong> These results reveal <em>Asaia</em> as a poor mutualist for <em>Ae. aegypti</em>, with its<em> </em>positive effect on the host mediated by interactions with other bacteria. A practical application of <em>Asaia </em>for improving mass-rearing efficiency results from the acceleration of development time to pupation by a day.</p>
Accelerating Digital Skills for Music Researchers - Processing Text-Based Corpora for Musical Discourse Analysis - Episode 5
<p>Dataset containing four .xlsx and .csv files for the exercises in Episode 5 of the <a href="https://acceleratingdigitalskills.github.io/Processing-Text-Based-Corpora/">Processing Text-Based Corpora for Musical Discourse Analysis</a> lesson of the <a href="https://acceleratingdigitalskills.org/">Accelerating Digital Skills for Music Researchers</a> project. The original data was collected from <a href="https://boomkat.com/">Boomkat.com</a> with permission.</p>
Molecular Dynamic Simulation on the Role of CL5D in Accelerate the Product Dissociation of SIRT6
<p>The source data used to generate figures in the main text is stored in the ‘Source Data.xlsx’ file, and 'Source Data Description.docx' is a brief description of the source data table.<br>'SIRT6.prmtop' and 'SIRT6.inpcrd' are initial structure of SIRT6 system,'SIRT6-CL5D.prmtop' and 'SIRT6-CL5D.inpcrd' are initial structure of SIRT6-CL5D system.<br>'SIRT6_equ.pdb', 'SIRT6-CL5D'_equ.pdb are snapshots of the equilibrium structure of the SIRT6 system and the SIRT6-CL5D system, respectively.<br>'ramd.conf' is an example configuration file that uses RAMD simulations to obtain the AR6 dissociation path in the SIRT6 system, with the acceleration of 0.0625 kcal/Å/g and a cutoff distance of 0.005 Å.<br>'win1.conf' and 'win1.in' are example configuration files for the first window of the umbrella sampling, which calculates the dissociation energy barrier of AR6 in the SIRT6 system,'win1.in' is the parameter file for umbrella sampling, with A 2.5 kcal/mol/Ų spring constant, and window center is 9 Å.</p> <p> </p>
Data for Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX
<p>Dataset for</p> <p>M Kloewer, S Hatfield, M Croci, PD Dueben and TN Palmer, 2021. Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX by squeezing ShallowWaters.jl into Float16, in review.</p> <p>This dataset contains data from simulations with <a href="https://github.com/milankl/ShallowWaters.jl">ShallowWaters.jl</a> with varying number formats and with or without a compensated time integration. All other parameters are shared between simulations. All .tar.gz are packed folders of the same name that contain netCDF files presenting velocities u,v, sea surface height eta, and tracer sst (sea surface temperature)</p> <ul> <li>run0002. Float16 simulation with compensated summation in the time integration.</li> <li>run0003. Float16 simulation without compensated summation in the time integration.</li> <li>run0004. Float64 reference simulation (without compensated summation in the time integration).</li> <li>run0005. Float16/32 mixed-precision simulation. No compensated time integration.</li> </ul> <p>Additionally, parameter.txt in each run summarizes all model parameters and progress.txt was created to monitor the progress of the data output during simulation. The file benchmarking.jld2 stores data for the benchmarking of the different runs as Julia's <a href="https://github.com/JuliaIO/JLD2.jl">JLD2 format</a> (a subset of HDF5).</p> <p>This dataset was created using the Isambard UK National Tier-2 HPC Service operated by GW4 and the UK Met Office, and funded by the Engineering and Physical Sciences Research Council EPSRC.</p> <p>For more details, see <a href="https://github.com/milankl/ShallowWaters.jl">ShallowWaters.jl</a> or the preprint <a href="http://doi.org/10.1002/essoar.10507472.2">Kloewer et al, 2021</a>.</p>
Dataset from: 'Tropical deforestation accelerates local warming and loss of safe outdoor working hours'
<p>Abstract:</p> <p>'Climate change has increased heat exposure in many parts of the tropics, negatively impacting outdoor worker productivity and health. Although it is known that tropical deforestation causes local warming, the extent to which this warming affects people across the tropics is unknown. Here, we combine worker health guidelines with satellite, reanalysis, and population data to investigate how increases in local temperatures associated with recent deforestation (2003-2018) affects outdoor working conditions across low-latitude countries, and how future global climate change will magnify heat exposure for people in deforested areas. We find that the local warming associated with just 15 years of deforestation has caused losses in safe thermal working conditions for 2.8 million outdoor workers. We also show recent large-scale forest loss caused particularly large impacts on populations in locations such as the Brazilian states of Mato Grosso and Pará. Future global warming and additional forest loss will magnify these impacts.'</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.