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1,574 results for “atmospheres”
Statistical trends in JWST transiting exoplanet atmospheres
<p>Spectra shown in Figure 1</p> <p>Columns are: Wavelength(micron), Wavelength err(micron), Transit Depth, Transit Depth err</p>
Quality Assessment of YUNYAO GNSS-RO Refractivity Data in the Neutral Atmosphere
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Supplement videos for manuscript Impacts of storm "Zyprian" on middle and upper atmosphere observed from Central European stations
<p>Supplement video material for the manuscript Impacts of storm “Zyprian” on middle and upper atmosphere observed from Central European stations. It contains loop of MSG satellite observation and infrasound animations.</p>
Dataset supporting "Predictable Patterns of Seasonal Atmospheric River Variability Over North America During Winter"
<p>A dataset containing selected outputs from real time forecasts and hindcasts produced by the Seamless System for Prediction and Earth System Research (SPEAR). Variables in the dataset include atmospheric river frequency, surface temperature, 500 hPa geopotential height and precipitation. The period covers 1991 through 2023 averaged seasonally in the months December through February. Forecasts initialized in a given month "MM" are stored in subdirectories and files with the indicator "iMM," where MM varies from 03 to 12 (March through December). A pair of python scripts demonstrating average predictability time analysis are also included. This data is associated with the study "Predictable Patterns of Seasonal Atmospheric River Variability over North America during Winter," by Clark et al. </p>
Time-efficient atmospheric water harvesting using Fluorophenyl oligomer incorporated MOFs
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Dataset for "Rapid initial growth of new atmospheric particles by large nanoparticle concentration gradient"
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Is there a scalar atmospheric surface layer within a convective boundary layer? Implications for flux measurements
<p>This dataset contains the data used in the submitted manuscript of Liu, Liu, Zhou, Zhang, Desai, Ghannam, Huang, and Katul 2024. Please refer to the manuscript for the detailed description of the dataset.</p>
Atmospheric Turbulence Dataset.
<h2><strong>This dataset comprises 38 video sequences that contain various levels of atmospheric turbulence.</strong></h2> <h3>Paul Hill, The University of Bristol</h3> <div>Please contact Paul Hill for any further information </div> <div>Paul.Hill@bristol.ac.uk</div> <h3>Sequences</h3> <ul> <li>The sequences were captured to give a range of</li> <li>Distances</li> <li>Turbulence levels</li> <li>Content (mainly aligning with discussed content of interest e.g. vehicles, buildings, roads etc.)</li> <li>The camera used was a Cannon R5, with a Canon 100-400mm zoom lens</li> <li>The sequences were captured over a period of three days in different locations</li> </ul> <h3>Preprocessing</h3> <ul> <li>All the sequences were cropped in time to. </li> <li>Get manageable / easily processable sequences of length of approximately 20 seconds</li> <li>To remove unwanted artefacts (hands in front of lenses, people walking through the shot etc.)</li> <li>Cropped referred (in the Excel file) that they were spatially cropped from higher resolution sequences (to focus on more interesting / coherent content)</li> </ul> <div> </div> <h3>Sequence Descriptions</h3> <div> <ul> <li>The included Excel file: Heathaze_datasets_descriptions_2024.xlsx gives a detailed descriptiong each sequence.</li> </ul> </div> <div>Directory Structure</div> <div>.</div> <div>├── CLEAR1 # Static PNG image results of the CLEAR1 method</div> <div>├── CLEAR2 # Example AVI CLEAR2 sliding window processed sequences</div> <div>├── Original_Seqs # All Original Sequences in MP4 format</div> <div> │ └── PNGs # The raw original sequences in directories named after the MP4 seq files. </div> <div> </div> <ul> <li>CLEAR1: Region and Pixel refer to the pixel by pixel and region-based fusion defined in the original CLEAR paper: Anantrasirichai et al. "Atmospheric Turbulence Mitigation Using Complex Wavelet-Based Fusion," Image Processing, IEEE Transactions on , vol.22, no.6, pp.2398-2408, June 2013 </li> <li>CLEAR2: The outputs of an example CLEAR2 method output on the input sequences: Anantrasirichai et al. Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion", ICIP, pp. 2895-2899 2018.</li> <li>Original_Seqs: The PNGs are numbered in temporal order in each directory (named as per the MP4 seq files).</li> </ul> <p> </p>
Impact of oxygen fugacity on the atmospheric structure and emission spectra of ultra-hot rocky exoplanets
<p>Data and simulation results for the paper "Impact of oxygen fugacity on the atmospheric structure and spectra of ultra-hot rocky exoplanets", Seidler et al. 2024, A&A. This project contains the HELIOS output files, some auxillary information as well as the python scripts used to generate the figures.</p>
Data Behind the Figures: No Thick Atmosphere on the Terrestrial Exoplanet Gl 486b
<p>This repository includes all the data to create the figures in "No Thick Atmosphere on the Terrestrial Exoplanet Gl 486b" by Megan Weiner Mansfield et al. (2024).</p>
RegCM5 monthly mean differences in atmospheric state variables over Europe, FOR - CTL
<p>This dataset contains monthly means of RegCM5 model output underlying the main results of the following publication:</p> <p><br>Bright, R. M., Caporaso, L., Duveiller, G., Piccardo, M., Cescatti, A., "Biogeophysical radiative forcings of large-scale afforestation in Europe are highly localized and dominated by surface albedo change". Geophysical Research Letters, Under review. </p> <p>RegCM5 outputs were applied directly to assess the statistical robustness of remote adjustments as well as serve as inputs to radiative kernels to diagnose individual TOA forcings and adjustments. </p>
Figure data for "Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds"
<p>Data supporting figures in the paper "Antarctic sea ice surface temperature bias in atmospheric reanalyses induced by the combined effects of sea ice and clouds" published at <em>Communications Earth & Environment. </em></p>
Data for high-yield atmospheric water capture via bioinspired material segregation
<p>This dataset includes raw data logs of indoor and outdoor water capture tests. The dataset also includes Mathematica code to calculate and generate convection-limited water capture fluxes.</p> <p>The data logs are TSV text files where the columns correspond to time (seconds), wind tunnel temperature (C), wind tunnel humidity (%), humidity setpoint (%), liquid desiccant chamber temperature (C), liquid desiccant chamber humidity (%), measured volume (mL).</p> <p>Links to this data are included in our paper.</p> <div> <div> <div> <p>The Mathematica code relies on external datasets listed in the mapping_plots.nb file</p> </div> </div> </div> <div> <div></div> </div>
Data used in "Land use change influence on atmospheric organic gases, aerosols, and radiative effects" (Vella et al., 2024)
<p>Model outputs used in the study: "Land use change influence on atmospheric organic gases, aerosols, and radiative effects"</p>
X-Shooting ULLYSES: Massive Stars at low metallicity VI. Atmosphere and mass-loss properties of O-type giants in the SMC
<p>Figures from X-Shooting ULLYSES: Massive Stars at low metallicity VI. Atmosphere and mass-loss properties of O-type giants in the SMC. The individual fits of the stars and their explored parameter space. </p> <p>Caption:</p> <p>Overview of the fitting results of AV\,80. The \textit{top} part shows the line profiles included in the fitting process, with the name of each feature indicated in the bottom left. The green line shows the best fit, with the shaded region showing the 1$\sigma$ uncertainty on the model fit. The black vertical bars indicate the observed flux, with the length indicating the uncertainty. The horizontal axis shows the wavelength in \AA. The \textit{bottom} part shows the distribution of $1/\chi^2_{\rm r}$ for each parameter (indicated on the top left), with $\chi^2_{\rm r}$ the reduced $\chi^2$ value. Each scatter point indicates one {\sc Fastwind} model calculated by Kiwi-GA. The color indicates the generation in which the model was computed, with light gray the first generation and black the last. The vertical yellow line indicates the best fit value for each parameter, and the shaded regions are the 1 and 2 $\sigma$ confidence intervals. The distributions in the red shaded area are from the optical only GA fit.</p>
Sand transport processes change in response to changing atmospheric properties at higher elevations
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Data and Code for "Updraft Width Modulates Ambient Atmospheric Controls on Convective Cloud Depth" by Varble et al. (2024)
<p>The zip file contains additional data and revised code used in the following paper: Varble, A. C., Feng, Z., Marquis, J. N., Zhang, Z., Geiss, A., Hardin, J. C., & Jo, E. (2024), Updraft Width Modulates Ambient Atmospheric Controls on Deep Convection Depth. Under review in J. Geophys. Res. Atmos.</p> <p>Datasets used in analyses that are derived from observations and model output can be found in the data folder. Python code and notebooks used to derive these datasets and to make all plots in the paper are in the code folder. The cell tracking was performed using PyFLEXTRKR software available here: https://github.com/FlexTRKR/PyFLEXTRKR.</p> <p>Parallax corrected GOES-16 satellite retrievals used in the study are downloadable at doi.org/10.5439/2008448. Taranis C-band radar retrievals are a very large dataset and in the process of being uploaded to the DOE ARM data archive. Interpolated sonde data is downloadable at doi.org/10.5439/1095316. Raw WRF model output is a very large dataset and currently stored at NERSC. To access raw model output or Taranis radar retrievals (before being available via ARM), please contact Adam Varble (adam.varble@pnnl.gov).</p> <p>If you have any further questions, please contact Adam Varble at adam.varble@pnnl.gov.</p>
High quality figures of "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations"
<p>This repository provides the figures for the publication "Global Assessment of Atmospheric Forcing Uncertainties in The Common Land Model 2024 Simulations" in their original resolution, ensuring clarity the high-quality visual representations for readers.</p>
Dataset for light absorption parameter and molecular composition of atmospheric brown carbon in Xi'an
<p>This is the raw data for light absorption parameter and molecular composition of atmospheric brown carbon in Xi'an.</p>
ClimateNet Dataset as used in "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data"
<p>ClimateNet dataset as it was used by us for the study: "Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data" (https://gmd.copernicus.org/preprints/gmd-2024-60/).</p> <p> </p> <p>For the original dataset refer to: https://portal.nersc.gov/project/ClimateNet/</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.