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1,574 results for “atmospheres”

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zenodo32/100

Figure data for "From the top of Martian Olympus to Deep Craters and Beneath: Mars Radiation Environment under Different Atmospheric and Regolith Depths"

<p>Include data underlying figures of the article &quot;From the top of Martian Olympus to Deep Craters and Beneath: Mars Radiation Environment under Different Atmospheric and Regolith Depths&quot; by Zhang &amp; Guo et. al in 2022 at Journal of Geophysical Research - Planets.&nbsp;</p>

opencc-by-4.0Feb 2022View details →
zenodo32/100

Data for "Machine Learning Parameterization of Subgrid-Scale Orographic Gravity Wave Drag in a Middle-Atmosphere General Circulation Model" by Lu et al., submitted to JAMES, 2022.

<p>The NetCDF data file involving the decision tree strucutre attributes of the random forest emulator.</p> <p>gcm_regressors/<br> &nbsp; &nbsp;The data file involving the decision tree strucutre attributes (in NetCDF format)</p>

opencc-by-4.0Mar 2022View details →
zenodo32/100

Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".

<p>The model codes, data, and plot scripts used in the paper, &quot;Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)&quot;.</p> <ul> <li>7_experiments.zip contains modified model code and output data of each&nbsp;experiment&nbsp;in this study.</li> <li>off-line test.zip contains off-line test code and output data.</li> <li>plot_scripts.zip are&nbsp;the NCL scripts used for figures in the paper.</li> </ul>

opencc-by-4.0Mar 2022View details →
zenodo32/100

Implications of emission sources and biosphere exchange on temporal variations of CO2 and δ13C using continuous atmospheric measurements at Shadnagar (India)

<p>The data contains daily atmospheric CO2 mixing ratios and 13C of CO2 over the Shadnagar region of India during November 2018 to October 2019.</p>

opencc-by-4.0Mar 2022View details →
zenodo32/100

Dataset and scripts for manuscript "OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3"

<p>This repository contains model output datasets and plotting scripts as used for evaluations and figures presented in the manuscript &quot;OpenIFS/AC: atmospheric chemistry and aerosol in OpenIFS 43r3&quot;, submitted to Geosci. Model Dev.</p>

opencc-by-4.0Mar 2022View details →
zenodo32/100

Model code, data, and plot scripts for the paper "Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)".

<p>The model codes, data, and plot scripts used in the paper, &quot;Impacts of Ice-Particle Size Distribution Shape Parameter on Climate Simulations with the Community Atmosphere Model Version 6 (CAM6)&quot;.</p> <ul> <li>Figs&amp;Table are&nbsp;the NCL scripts used for figures and table&nbsp;in the paper.</li> <li>Model_Results&nbsp;contains&nbsp;output data of each&nbsp;experiment&nbsp;in this study.</li> <li>Mods_Scripts&nbsp;contains modified model code.</li> <li>Offline_Code&nbsp;contains off-line test code.</li> </ul>

opencc-by-4.0Apr 2022View details →
zenodo32/100

First Detection of the Pekeris Internal Global Atmospheric Resonance: Evidence from the 2022 Tonga Eruption and from Global Reanalysis Data

<p>The numerical model simulation data necessary to&nbsp;reproduce figures in our paper&nbsp;submitted to the Journal of the Atmospheric Sciences.</p>

opencc-by-4.0Mar 2022View details →
zenodo32/100

Pyroconvection Classification based on Atmospheric Vertical Profiling Correlation with Extreme Fire Spread Observations

<p>1. Isochrones for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca and Sierra Bermeja fires, in shapefile format. Each file has associated an attribute table identifying the hour (in UTC), the affected area, the rate of spread, and direction. Source: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p> <p>2. ERA5 reanalysis data obtained for each fire, hourly and at different pressure levels (37) from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The files are in netCDF format, and the variables requested were temperature, relative humidity, U-component of wind, V-component of wind. Source: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Hor&aacute;nyi, A., Mu&ntilde;oz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Th&eacute;paut, J-N. (2018): ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.bd0915c6</p> <p>3. Data from sondes launched in fires during the 2021 Spain wildfire campaign. The files are in CSV format, and there are two per fire: the sounding data corrected and the raw flight history. The information provided is Hour (UTC), Wind speed (m/s), Wind direction (true deg), Dew point (C), Latitude, Longitude, Altitude (in m MSL and m AGL), Pressure (Pascal), Speed (m/s), Heading (degrees), Temperature (C), Relative humidity (%), Internal temperature (C), Latitude, Longitude, Rise speed (m/s). Source: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p> <p>4. Data from the closest weather station to each fire. The file is an Excel file. The table fields are: fire name, weather station name, day, hour, average temperature (&deg;C), maximum temperature(&deg;C), minimum temperature (&deg;C), average relative humidity (%), precipitation (mm), wind speed (10 m, km/h), wind direction (10 m, degrees), wind gusts (10 m, km/h), pressure (hPa), radiation (W/m). Source: Meteo.cat, Servei Meteorol&ograve;gic de Catalunya</p> <p>5. Fire behavior resume for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca, Llan&ccedil;&agrave;, Alfarr&agrave;s and Sierra Bermeja fires (Spain). The differences in the data shown respond to the possibility of launching sondes, recreating isochrones, and observing the plume column during each fire. In those cases where the information was obtained through these three ways, the variables available are: column type, ABL and LCL height (m), sonde ID, rate of spread (km/h), ROS observed / ROS expected ratio, fireline intensity expected and observed (kW/m), and affected area (ha).</p> <p>6. Photographic registry of the fire plume evolution and a brief description of the pyroconvective moments in the Alfarr&agrave;s, Martorell, Llan&ccedil;&agrave;,&nbsp;Torroella,&nbsp;Santa Coloma de Queralt, Pobla Massaluca, and Sierra Bermeja&nbsp;fires (Spain). Pictures&nbsp;sources: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p>

opencc-by-4.0Dec 2021View details →
zenodo32/100

Code for Atmospheric Research publication - Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields

<p>In this repository, we include the source codes for WRF namelist, figures, and height correction used in the Atmospheric Research publication &quot;Height correction method based on the Monin&ndash;Obukhov similarity theory for better prediction of near-surface wind fields&quot;</p> <p>The namelist.wps and namelist.input in WRF namelist are using for making input and running simulation, and Fig scripts in Figure scripts are using for plotting the figures in the paper.</p> <p>hgt_corr in Height correction method is a code to correct&nbsp;the disparity of the 10-m height definition between the model and observation by applying the developed the height correction algorithm based on the Monin-Obukhov similarity theory.</p>

opencc-by-4.0Apr 2022View details →
zenodo32/100

The role of ocean and atmospheric dynamics in the marine-based collapse of the last Eurasian Ice Sheet

<p><strong>EIS_reconstruction.zip:&nbsp;</strong>&nbsp;.shp files of Eurasian Ice Sheet reconstruction 20-14 ka (1 ka time step)</p> <p><strong>EIS_thickness.zip</strong>:&nbsp; .shp files of Eurasian Ice Sheet thickness&nbsp;for 19 ka, 18 ka, 16 ka and 15 ka.</p> <p><strong>Supplementary Data 1:</strong>&nbsp;An Excel spreadsheet containing radiocarbon dates from the North Sea</p> <p><strong>Supplementary Data 2</strong>:&nbsp; An Excel spreadsheet containing radiocarbon dates from the Mid Norwegian margin</p> <p><strong>Supplementary Data</strong> <strong>3:&nbsp;</strong>An Excel spreadsheet&nbsp;containing radiocarbon dates from the Svalbard-Kara Sea-Barents Sea</p> <p><strong>Supplementary Data 4:&nbsp;</strong>&nbsp; An Excel spreadsheet&nbsp;containing model output GIA adjusted</p> <p><strong>Supplementary Data 5:&nbsp;</strong> An Excel spreadsheet&nbsp;containing Model output GIA adjusted -20%</p> <p><strong>Supplementary Data 6:</strong>&nbsp; An Excel spreadsheet&nbsp;containing Model output GIA adjusted +20%</p>

opencc-by-4.0Mar 2022View details →
zenodo32/100

contribution of atmospheric transport to phosphorus in the East China Sea in summer

<p>For the analysis of the main sources of phosphate in the&nbsp;East China Sea&nbsp;during summer, we choose the area between 120.93 ˚ E-125.9 ˚ E and 26.08˚ N-32.35 ˚ N as research sites . The samples were all from the voyage of the research ship (Xiangyanghong 18). Surface seawater was collected at 49 stations for &delta;<sup>18</sup>O<sub>p</sub> analysis by a conductivity-temperature-depth profiler. Then we utilized &delta;<sup>18</sup>O<sub>p</sub> to trace the source of phosphate in the&nbsp;East China Sea.&nbsp;</p> <p>The &delta;<sup>18</sup>O<sub>p</sub> values in seawater are affected by water masses with different phosphate concentrations.We need the two-component mixing model for the analysis of water masses that control phosphate.In this research, atmospheric deposition and adjacent sea transportation were used as terrestrial and seawater end-members respectively to construct the &delta;<sup>18</sup>O<sub>p</sub> end-member mixing model.</p> <p>In order to quantify the source of phosphate in the sea, a Bayesian isotope mixing model run in the R software package (Stable Isotope Analysis in R, SIAR) was selected for analysis.</p>

opencc-by-4.0May 2022View details →
zenodo32/100

Dataset for "Observational evidence for the non-suppression effect of atmospheric chemical modification on the ice nucleation activity of East Asian dust"

<p>These are datasets for the manuscript titled &quot;Observational evidence for the non-suppression effect of atmospheric chemical modification on the ice nucleation activity of East Asian dust&quot;.</p>

opencc-by-4.0May 2022View details →
zenodo32/100

High resolution opacities for H2/He atmospheres

<p>This set of molecular and atomic opacities has been built based on the same opacity sources used for calculating the correlated-k coefficients in the following repositories:</p> <p>10.5281/zenodo.5590997 (11 windows)</p> <p>10.5281/zenodo.5590995 (30 windows)</p> <p>10.5281/zenodo.5590986 (180 windows)</p> <p>10.5281/zenodo.5590989 (196 windows)</p> <p>These opacities can be used to generate high resolution spectra for the atmospheric structures computed with the&nbsp;correlated-k coefficients&nbsp;above.</p> <p>The set includes C2H2, C2H4, C2H6, CH4, CO, CO2, CrH, Fe, FeH, H2, H3+, H2O, H2S, HCN, LiCl, LiF, LiH, MgH, N2, NH3, OCS, PH3, SiO, TiO, and VO, in addition to alkali metals (Li, Na, K, Rb, Cs). The opacities are calculated for a grid of 1460 pressure-temperature points, from10^&minus;6 to 3000 bar and from 75 to 4000 K, listed in the file 1460_layer_list. Each *.zip file contains one opacity file for each pressure-temperature layer, with the species&rsquo;&nbsp;name, the pressure, and the temperature given in the filename. The opacity is given in units of cm^2/molecule. For completeness, each *.zip file also contains a file named wavelengths.txt, listing the wavelengths corresponding to each opacity point, in units of microns. All the opacities are calculated on the same wavelength grid.&nbsp;</p> <p>The references for the line lists used in these opacity calculations can be found in the file Opacity_references_2021.pdf. Please include these references, as well as the reference to this Zenodo repository when publishing your paper.&nbsp;</p> <p><em>Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.</em></p>

opencc-by-4.0May 2022View details →
dryad32/100

Carbon absorption capacity determines the response of algal competitiveness to elevated atmospheric CO2

<p><span>Although many studies have focused on </span><span>the effects of elevated </span><span>atmospheric</span> <span>CO<sub>2</sub></span><span> on algal growth</span><span>, few studies have demonstrated how </span><span>CO2</span><span> interacts with carbon absorption capacity to determine the algal competition at the population level</span><span>.</span><span> We conducted a pairwise competition experiment of <em>Phormidium</em></span><span><em> </em>sp., <em>Scenedesmus quadricauda</em>, <em>Chlorella vulgaris, </em>and <em>Synedra ulna</em>. The results showed that when the </span><span>CO<sub>2</sub></span><span> concentration increases from 400 to 760 ppm, the competitiveness of <em>S. quadricauda</em> increased, the competitiveness of <em>Phormidium </em>sp. and <em>C. vulgaris</em> decreased, and the competitiveness of <em>S. ulna</em> was always the lowest. we constructed a model</span><span> to explore whether interspecific differences in affinity and flux rate for CO<sub>2</sub> and HCO<sub>3</sub><sup>−</sup> could explain changes of </span><span>competitiveness</span><span> between algae species along the gradient of atmospheric </span><span>CO<sub>2</sub></span><span> concentration.</span><span> Affinity and flux rate are the capture capacity and transport capacity of substrate, respectively, which are inversely proportional to each other. Low resource concentration is beneficial to the growth and reproduction of algae with high affinity. The simulation results showed that when the atmospheric </span><span>CO<sub>2</sub></span><span> concentration was low</span><span>, </span><span>species with high affinity for both </span><span>CO<sub>2</sub> and </span><span>HCO<sub>3</sub><sup>−</sup> (HCHH) had the highest </span><span>competitiveness, followed by the species with high affinity for </span><span>CO<sub>2</sub> and low affinity for </span><span>HCO<sub>3</sub><sup>−</sup> (HCLH), </span><span>the species with low affinity for </span><span>CO<sub>2</sub> and high affinity for </span><span>HCO<sub>3</sub><sup>−</sup> (LCHH) and the species </span><span>with low affinity for both </span><span>CO<sub>2</sub> and </span><span>HCO<sub>3</sub><sup>−</sup> (LCLH); when the CO2 concentration was high, the species were ranked according to the competitive ability: LCHH &gt; LCLH &gt; HCHH &gt; HCLH</span><span>. Thus, with the increase of atmospheric </span><span>CO<sub>2</sub> concentration, the competitive advantage changed from </span><span>HCHH</span> <span>species to </span><span>LCHH</span> <span>species.</span><span> These results indicate the important species types contributing to water bloom under the background of increasing global atmospheric </span><span>CO<sub>2</sub>, highlighting the importance of carbon absorption characteristics in understanding, predicting and regulating population dynamics and community composition of algae.</span></p>

opencc-zeroJun 2022View details →
zenodo32/100

Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 1)

<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the &lsquo;New Arctic&rsquo;.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>

opencc-by-4.0Jun 2022View details →
zenodo32/100

Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 3)

<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the &lsquo;New Arctic&rsquo;.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>

opencc-by-4.0Jun 2022View details →
zenodo32/100

Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 2)

<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the &lsquo;New Arctic&rsquo;.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>

opencc-by-4.0Jun 2022View details →
zenodo32/100

Daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions (Part 2a)

<p>A database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions to study the fate of sea ice in the &lsquo;New Arctic&rsquo;.</p> <p>Files are multi-part zip files containing trajectory and ancillary data on an annual basis over a sea ice year.</p>

opencc-by-4.0Jun 2022View details →
zenodo32/100

Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model

<p>This dataset includes a complete set of raw data, metadata and saved session data which is&nbsp;necessary for re-producing&nbsp;figures in a&nbsp;paper entitled &quot;Gravity Wave Morphology During the 2018 Sudden Stratospheric Warming Simulated by a Whole Neutral Atmosphere General Circulation Model&quot; submitted to the Journal of Geophysical Research - Atmosphere.</p>

opencc-by-4.0Feb 2022View details →
zenodo32/100

data for the manuscript entitled Survival probability of atmospheric new particles: closure between theory and measurements from 1.4 to 100 nm

<p>data for figures.</p> <p>Please contact runlong.cai@helsinki.fi if needed</p>

opencc-by-4.0Jun 2022View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record