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3,018 results for “aire”
Derived environmental temperatures at Jezero crater from Air Temperature Sensors' measurements on the Perseverance rover.
<p><strong>Material from Version 2</strong> extends derived Air Temperature Sensor data to the first 700 sols of the Mars 2020 mission used in the analysis of <em>Munguira et al. (2024). "One Martian Year of Near-Surface Temperatures at Jezero from MEDA measurements on Mars2020/Perseverance". Journal of Geophysical Research: Planets. [in revision]. </em>We also include the tables needed to generate and reproduce the figures in the paper. Most importantly, the tables include the results from different analyses of temperatures through Fourier series and Reynolds averaging. </p>
Data archive for paper "Energy and environmental impacts of air-to-air heat pumps in a mid-latitude city"
<p><strong>Overview</strong></p> <p>This is the data archive for paper "<a href="https://www.nature.com/articles/s41467-024-49836-3" target="_blank" rel="noopener">Energy and environmental impacts of air-to-air heat pumps in a mid-latitude city</a>". It contains the paper's data archive with model outputs (see <code>notebooks</code> folder) and the Singularity image for (optionally) re-running experiments.</p> <p>For the standalone models to model air conditioners and heat pumps please refer to <a href="https://github.com/dmey/minimal-dx">MinimalDX</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux with Bash shell.</li> <li><a>Git</a> version >= 2.</li> <li><a href="https://sylabs.io/">Singularity</a> version >= 3.</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a>*.</li> <li><a href="https://en.wikipedia.org/wiki/Intel_Fortran_Compiler">Intel Fortran Compiler</a> [<em>Required for MesoNH simulations</em>].</li> <li>A compatible version of the MPI library implementation version 3 [<em>Required for MesoNH simulations</em>].</li> </ul> <p>Please note that most steps require <a href="https://sylabs.io/">Singularity</a>. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the hpc directory.</p> <p><strong>Simulations</strong></p> <p><em><strong>Offline</strong></em></p> <ol> <li>Build Surfex with <code>qsub hpc/surfex_build.pbs</code>.</li> <li>Run scenarios with the following commands: <pre><code> qsub -v case_name=fincap hpc/surfex_run.pbs qsub -v case_name=fincap_extended_autosize hpc/surfex_run.pbs qsub -v case_name=minidx_cop=2.5 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=3.0 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=3.5 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=4.0 hpc/surfex_run.pbs<br> qsub -v case_name=minidx_cop=4.5 hpc/surfex_run.pbs </code></pre> </li> </ol> <p><em><strong>Online</strong></em></p> <p>To run MesoNH simulations, follow the three steps outlined below in the same order. Note: Depending on your system and configuration, submit scripts may require change.</p> <ol> <li>Build MesoNH with <code>qsub hpc/build_mnh_intel.pbs</code>.</li> <li>Run the preprocessing step with <code>qsub hpc/submit_mnh_prep.pbs</code>.</li> <li>Finally run MesoNH cases with the following commands for <code>fincap</code> and <code>minidx</code> simulations respectively: <pre><code> hpc/submit_mnh.sh fincap 20050120 12 1 toulouse hpc/submit_mnh.sh minidx 20050120 12 1 toulouse </code></pre> </li> <li>Post-process the results with <code>qsub hpc/submit_mnh_post.pbs</code></li> </ol> <p><strong>Analyses</strong></p> <pre><code>qsub hpc/postprocess_results.pbs # Plots in notebooks/ </code></pre>
Data from: Neuroprotection provided by hypothermia initiated with high transnasal flow with ambient air in a model of pediatric cardiac arrest
<p>Clinical trials of hypothermia after pediatric cardiac arrest have not seen robust improvement in functional outcome, possibly because of the long delay in achieving target temperature. Previous work in infant piglets showed that high nasal airflow, which induces evaporative cooling in the nasal mucosa, reduced regional brain temperature uniformly in half the time needed to reduce body temperature. The mouth is kept open to allow the high nasal airflow to easily exit. Here, we evaluated whether initiation of hypothermia with high transnasal airflow (32 L/min) provides neuroprotection without adverse effects in the setting of asphyxic cardiac arrest. Anesthetized, mechanically ventilated piglets (approximately 2-weeks-old) underwent sham-operated procedures (Group 1) or asphyxic cardiac arrest (Groups 2-6). The asphyxic insult consisted of reducing the inspired oxygen from 30% to 9.5-10% for 45 minutes (hypoxia period), then briefly increasing the inspired oxygen to 21% for 5 min (to improve the later success of cardiac resuscitation), and then completely stopping ventilation for 7 minutes. Cardiopulmonary resuscitation (CPR) commenced by re-establishing ventilation, performing chest compression, and injecting epinephrine as needed. The five cardiac arrest groups were further divided into those with normothermic recovery (38.5°C; Group 2), with mild hypothermia (34°C) initiated by surface cooling at 10 minutes (Group 3) or 120 minutes (Group 5) after resuscitation, or with mild hypothermia (34°C) initiated by transnasal cooling initiated at 10 minutes (Group 4) or 120 minutes (Group 6) after resuscitation. In the two transnasal cooling groups, the high nasal airflow continued for 2 hours and was then stopped; thereafter, surface cooling was used to maintain hypothermia. In all four groups with induced hypothermia, rectal temperature was sustained at the targeted temperature of 34°C with surface cooling until 20 hours after resuscitation, followed by 6 hours of gradual rewarming and cessation of fentanyl/70% nitrous oxide anesthesia.<strong> </strong>At four days of recovery, the piglets were euthanized and their brains were analyzed for the density of morphologically intact neurons in putamen, sensorimotor cortex, ventrolateral thalamus, and prefrontal cortex. The data sheet shows the density of viable neurons in these 4 brain regions for the 45 piglets that completed the study. The data sheet also shows the serial measurements of rectal temperature, mean arterial blood pressure, heart rate, and arterial blood measurements of the partial pressure of oxygen (PO2) and carbon dioxide (PCO2), oxyhemoglobin saturation, and pH obtained at baseline, during the period of hypoxia, at 4 minutes of ventilation with 21% O2, during the period of asphyxia, and during the first 24 hours of recovery. The piglets are assigned the same unique identifier number, labelled 1-45, for each set of measurements. Transnasal cooling initiated at 10 minutes after resuscitation was able to significantly rescue neurons in the highly vulnerable putamen without adverse effects.</p>
Data for "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations"
<p>These data are shown in the figures included with the article "Hunting for gravity waves in non-orographic winter storms using 3+ years of regional surface air pressure networks and radar observations," submitted to Atmospheric Chemistry and Physics.</p>
Drainage network for a region in central Buenos Aires province, 30m resolution raster, EPSG:5347
<p>This drainage network was obtained using the Copernicus-30 Global Digital-Surface-Model, tiles (S37-38, W60) with QGIS and PCraster plugin: local direction drainage and flow accumulation algorithms. It helps to clarify the surrounding drainage pattern at cities like Azul, Cacharí and Tandil, for flood alleviation schemes.</p>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Kampala
<p>This paper is under review</p> <p>Abstract:</p> <p>Kampala, the political and economic capital of Uganda and one of the fastest urbanising cities in sub-Saharan Africa, is experiencing a deteriorating trend in air quality with emissions from multiple diffused local sources like transportation, domestic and outdoor cooking, and industries, and sources outside the city airshed like seasonal open fires in the region. PM2.5 (particulate matter under 2.5um size) is the key pollutant of concern in the city with monthly spatial heterogeneity of 60-100 ug/m3. Outdoor air pollution is distinctly pronounced in the global south cities and lack the necessary capacity and resources to develop integrated air quality management programmes including ambient monitoring, emissions and pollution analysis, source apportionment, and preparation of clean air action plans. This paper presents an integrated assessment of air quality in Kampala drawing from ground measurements (from a hybrid network of stations), satellite observations (from NASA’s MODIS and OMI), global reanalysis fields (from GEOS-chem and CAMS simulations), high resolution (~1km) multi-pollutant emissions inventory for the airshed, WRF-CAMx based PM2.5 pollution analysis, and a qualitative review of institutional and policy environment for air quality management in Kampala. The proposed clean air action plans aim for better air quality in the region using a combination of short-, medium-, and long-term emission control measures for all the dominate sources and institutionalize pollution tracking mechanisms (like emissions and pollution monitoring and reporting) for effective management of air pollution.</p> <p>This data archive serves as a supplemenary to the journal article and with a short description of the files below:</p> <ul> <li>File: AQ-Kampala-Analysis-Summary.pptx (Caution: large 60MB) <br>A composite presentation including the following<br> <ul> <li>Grid summaries</li> <li>Snapshots of airshed GIS files, emission activities</li> <li>Summary of meteorology from WRF simulations and historical synoptics</li> <li>Summaries of ambient monitoring data</li> <li>CAMS reanalysis summary</li> <li>Summaries of Emission inventory and WRF-CAMx modelling (annual and monthly)</li> <li>Summaries of PM2.5 Source apportionment (annual and monthly)</li> </ul> </li> <li>File: grids_kampala.rar<br>Grid file (KML and ESRI shapefiles format) for the airshed spanning 0.0N to 0.6N and 32.3E to 32.9E with a spatial resolution of 0.01deg (~1km)</li> <li>File: gis_roads_from_opensteetmaps.rar<br>ESRI shapefiles of primary roads and all roads, extracted from the openstreetmaps<br>Raw data archive @ https://download.geofabrik.de/index.html</li> <li>File: gis-scanned2021image-quarries.kml<br>KML file of quarries scanned using the imagery on Google Earth platform</li> <li>File: population_kampala_2000-2022.csv<br>Gridded population data 2000 to 2022<br>Raw data archive is from LANDSCAN - https://landscan.ornl.gov</li> <li>File: Monitoring-Kampala_USEmbassy_2017-2024.xlsx<br>Summary of monitoring data collected at the US Embassy in Kampala<br>Raw data archive is @ https://www.airnow.gov/international/us-embassies-and-consulates/#Uganda$Kampala</li> <li>File: meteo_wrf_stats.xlsm<br>Summary of output of WRF simulations for the Kampala region. Have to activiate macros to summarize the results by month and update the charts. The tool can be used to other cities also by changing the input data.</li> <li>File: meteo_precip-era5-reanalysis.csv<br>Summary of monthly precipitation date (mm/day) from ERA5 reanalysis fields<br>Raw data archive is @ https://psl.noaa.gov/data/atmoswrit/timeseries</li> <li>File: TROPOMI_EastAfrica_NO2_Maps.zip<br>Images of monthly average TROPOMI NO2 extracts covering East Africa (Uganda and Ethiopia)<br>Extracted from Google Earth Engine, using 10% cloud fraction</li> <li>File: TROPOMI_EastAfrica_CSVs.zip<br>CSV files of gridded monthly average NO2, SO2, HCHO, and Ozone columnar densities<br>Extracted from Google Earth Engine, using 10% cloud fraction<br>Read the data descriptions and applicability of the data for analysis before using (for example, negative numbers in the SO2 file). <br>NO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_NO2 <br>SO2 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_SO2<br>HCHO - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_HCHO<br>O3 - https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S5P_OFFL_L3_O3</li> <li>File: composite_emisson_factors_gains.xlsx<br>A composite library of emission factors for reference</li> <li>File: kampala_gridded_emissions_2018.rar<br>Gridded emissions inventory for Kampala - PM25, PM10, SO2, NO, NO2, and CO<br>PM25 is speciated into FPRM, BC, and OC (sum all for PM25)<br>PM10 is speciated into FPRM, CPRM, BC, OC (sum all for PM10)<br>All emissions in tons/year/grid<br>Emissions are seggragted into sectors and fuels - included in the filenames</li> <li>File: kampala_gridded_modelled_monthavgp25.csv<br>Gridded PM2.5 concentrations for 2018, from WRF-CAMx modelling system<br>Monthly averages in ug/m3</li> </ul>
Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy
<p>Model Output supporting the paper "Coastal Upwelling Modulates Winds and Air-Sea Fluxes, Impacting Offshore Wind Energy"</p> <p>The dataset includes four WRF runs, with upwelling (labeled 'operational') and with upwelling removed (labeled 'experimental'). Two of the runs have parameterized wind turbines, labeled "Fitch". </p> <p>This work was supported by NJ Board of Public Utilities. </p> <p> </p>
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Addis Ababa
<p>This paper is under review. For additional information or queries, send email to sguttikunda@urbanemissions.info</p> <p>****</p> <p>ABSTRACT: Ground measurements and satellite observations over Addis Ababa airshed show a deteriorating trend of air pollution, especially for PM2.5 (all particulate matter under 2.5um). In this paper, we present a review of available monitoring data; a model-ready multi-pollutant (PM10, PM2.5, SO2, NOx, CO, non-methane VOCs, and CO2) emissions inventory at 0.01º resolution for the designated airshed; a heatmap of PM2.5 concentrations and an estimate of source contributions constructed using WRF-CAMx chemical transport modelling system; and a discussion on proposed actions towards establishing an air quality management plan for the city. Emissions from road transport; residential and commercial cooking; resuspended dust on roads and from construction activities; residential and industrial heating; lighting; open waste burning; and other industrial activities contributed the most to ambient PM2.5 pollution. Particularly, vehicle exhaust is estimated to contribute up to 29% of total PM2.5, followed by biomass combustion in the residential and industrial sectors.</p> <p>File included in this dataset:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Report-Addis-Data-Summary.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Ambient monitoring data<br>File: AddisAbaba_AllEmbassy_Data.xlsx (summary of data till May2024)<br>File: monitoring_addisair_cleaned.xlsx (summary of sensor data)</li> <li>Gridded emissions inventory<br>File: addis_gridded_emissions.rar<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_addis_grids.rar</li> <li>TROPOMI UVAI and MODIS AOD<br>Data files extracted from google earth engine as TIF and CSV, for the designated airshed<br>File: ADDISABABA_yearly_tifs.zip</li> <li>Meteorlogical data summary for the airshed, extracted from the WRF sumulations<br>File: meteo_addis.houravg_summary.csv</li> <li>Reference reports<br>File: Report-C40-2016-Addis-Ababa-GHG-Emssions.pdf<br>File: Report-CSE-Ethiopia-Urban-AQM-Guidance.pdf<br>File: Report-WB-Ethiopia-motorization-management.pdf<br>File: Report-UNEP-Addis-AQM-Plan-Draft.pdf</li> <li>GIS files<br>File: gis_addis_grids.rar (shapefile and KML file for the airshed grid)<br>File: gis_addis_osm_roads.rar (shapefiles extracted from openstreetmaps)<br>File: pop_extracts_4selection_domain.xlsm (gridded population data, with macros can be used to extract population totals around a monitoring station)<br>File: gis_addis_multiple_layers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> </ol>
Dataset for Interannual and seasonal variability of the air-sea CO2 exchange at Utö in the coastal region of the Baltic Sea
<p>Uto Atmospheric and Marine Research Station<br>Finnish Meteorological Institute and Finnish Environment Institute</p> <p>Data Jan 2017 - Dec 2021</p> <p> </p> <p>Data used in:<br>Honkanen, M., Aurela, M., Hatakka, J., Haraguchi, L., <br>Kielosto, S., Mäkelä, T., Seppälä, J., Siiriä, S.-M., <br>Stenbäck, K., Tuovinen, J.-P., Ylöstalo, P., and Laakso, L.: <br>Interannual and seasonal variability of the air-sea CO2 exchange at Utö in the coastal region of the Baltic Sea, <br>EGUsphere [preprint], https://doi.org/10.5194/egusphere-2024-628, 2024.</p> <p> </p> <p>This research has been supported by the Research Council of Finland project SEASINK (Evolving carbon sinks and sources in coastal<br>seas – will ecosystem response temper or aggravate climate change? project nos. 317297 and 317298), and the JERICO-NEXT and JERICO-S3 projects which have received funding from the European Union Horizon 2020 Research and Innovation Program under grant agreement nos. 654410 and 871153, respectively.</p>
Fig. 3 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania
Fig. 3. The correlation between the forest coverage and air pollution indicators.
Fig. 2 in The Relation Of Forest And Air Pollution With Human Health In Urban Territories Of Lithuania
Fig. 2. The correlation between the forest and human health indicators.
Fig. 4. Annual air temperature over Lake Onega catchment area for 1951–2014 in Phytoplankton Responses To Climate Change In The Large Lakes Of The Baltic Sea Basin
Fig. 4. Annual air temperature over Lake Onega catchment area for 1951–2014.
A Multi-Pollutant Emissions Inventory for Air Pollution Modeling and Supporting Information for Bishkek
<p>Full paper is published here<br><a href="https://doi.org/10.3390/air2040021">https://doi.org/10.3390/air2040021</a><br>Mapping PM2.5 Sources and Emission Management Options for Bishkek, Kyrgyzstan<br><br></p> <p>****</p> <p>Harsh winters, aging infrastructure and control technologies, and the increasing demand for urbanization and modernization of amenities are major factors contributing to the deteriorating air quality in Bishkek, the capital city of Kyrgyzstan and a burgeoning economic hub in Central Asia. The heating energy needs are met via combustion of coal at the central heating plant, heat only boilers, and in-situ heating equipment and the mobility needs via combustion of diesel and petrol. Other mapped sources contributing to daily air pollution levels in Bishkek’s airshed include 30 km2 of industrial area, 16 large open combustion brick kilns, a vehicle fleet with average age more than 10 years, 7.5 km2 of quarries, and one landfill. Annual PM2.5 emission load for the airshed is approximately 5,500 tons, resulting in an annual average concentration of 48 ug/m3, which is 9-10 times higher than the World Health Organization (WHO) guideline of 5 ug/m3. Wintertime daily averages range from 200-300 ug/m3. Proposed emissions management policies for the city include shift to clean fuels like gas and electricity at the heating plants and households, restricting the secondhand vehicle imports and incentivizing newer standard vehicles, promotion of public transport system with newer buses, at least doubling of the waste collection efficiency and landfill management capacity and encouraging greening and maintaining road infrastructure to control dust emissions. PM2.5 levels from mid- to long-term implementation of these options is expected to drop by 50-70%. A long-term plan for Bishkek must include an expansion of the ambient monitoring network using a combination of reference-grade and low-cost sensors to track progress of air quality management efforts and to support information dissemination for public awareness.</p> <p>Files included here:</p> <ol> <li>Composite presentation of supporting information and analysis results<br>File: Bishkek_AQ_Analysis_Composite.pptx<br>This presentation includes summary images of<br> <ul> <li>monitoring data</li> <li>the GIS fields</li> <li>google earth scans </li> <li>annual and monthly emissions</li> <li>annual and monthly PM2.5 concentrations</li> <li>annual and monthly source apportionment</li> </ul> </li> <li>Meteorological data summary for the airshed, extracted from the WRF sumulations<br>File: Bishkek_Met_Summary.pptx<br>File: bishkek_meteorology_stats.xlsm (activate macros for stats and making images for final use)</li> <li>GIS files<br>File: gis_bishkek-grids-pop.rar (shapefile and KML file for the airshed grid, csv file for gridded population 20 years, and images)<br>File: gis_bishkek_roads.rar (shapefiles extracted from openstreetmaps)<br>File: gis_bishkek_adm0.rar (shapefiles of administrative boundaries)<br>File: gis_bishkek_multiplelayers.rar (as KML files - districts, townships, main_roads, water_bodies, quarries, landfill, industrial_areas)</li> <li>CAMx output (units is ug/m3)<br>File: bishkek_camx_pm25_monthlyavg.csv (see the grid file for mapping)</li> <li>Gridded model-ready emissions inventory<br>File: bishkek_gridded_emissions_2018.rar (see the grid file for mapping)<br>Format: ix,iy,midlong,midlat,FPRM,CPRM,BC,OC,NO,NO2,CO,SO2<br>Units: tons/grid/year<br>PM2.5 emissions = FPRM + BC + OC<br>PM10 emissions = FRPM + BC + OC + CPRM<br>midlong and midlat are midpoints of grids - see gis_bishkek-grids-pop.rar</li> <li>Ambient monitoring data<br>File: bishkek_monitoring_claritysensor_2021.xlsx<br>File: bishkek_monitoring_us.embassy2019-2024.rar</li> </ol>
Fig. 2 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 2. Monthly relative frequency (%) of gonad phases for females of Anchoa marinii.
Fig. 5 in Reproductive studies of Anchoa marinii Hildebrand, 1943 (Actinopterygii: Engraulidae) in the nearby-coastal area of Mar Chiquita coastal lagoon, Buenos Aires, Argentina
Fig. 5. Oocyte diameter distribution in spawning capable phase of Anchoa marinii. N= 183.
Data on the acoustic field in experiments with an acoustic vortex in the air
Open the record for dataset details and reuse information.
Data from: Moist heatwaves intensified by entrainment of dry air that limits deep convection
<p>Moist heatwaves in the tropics and subtropics pose substantial risks to society, yet the dynamics governing their intensity are not fully understood. The onset of deep convection arising from hot, moist near-surface air has been thought to limit the magnitude of moist heatwaves. Here, we use reanalysis data, and output from the Coupled Model Intercomparison Project Phase 6 and model entrainment perturbation experiments, to show that entrainment of unsaturated air in the lower-free troposphere (roughly 1--3 km above the surface) limits deep convection, thereby allowing much higher near-surface moist heat. Regions with large-scale subsidence and a dry lower-free troposphere, such as coastal areas adjacent to hot and arid land, are thus particularly susceptible to moist heat waves. Even in convective regions such as the northern Indian Plain, southeast Asia, and interior South America, the lower-free tropospheric dryness strongly affects the maximum surface wet-bulb temperature. As the climate warms, the dryness (relative to saturation) of the lower-free tropospheric air increases; this allows for a larger increase of extreme moist heat, further elevating the likelihood of moist heatwaves.</p>
The dataset of the manuscript "GPU-HADVPPM4HIP V1.0: higher model accuracy on China's domestically GPU-like accelerator using heterogeneous compute interface for portability (HIP) technology to accelerate the piecewise parabolic method (PPM) in an air quality model (CAMx V6.10)"
<p><strong>bcfile.zip:</strong> the clean boundary condition files.</p> <p><strong>CAMxv6x_cpp.zip: </strong>the source code of CAMx-HIP version which coupled with HIP-HADVPPM scheme.</p> <p><strong>data.zip:</strong> final data tables used to plot figures.</p> <p><strong>emisfile.zip: </strong>the emission files.</p> <p><strong>icfile.zip:</strong> the clean initial condition files.</p> <p><strong>tuvfile.zip </strong>and <strong>o3mapfile.zip:</strong> the photolysis files.</p> <p><strong>outputfile.zip:</strong> the computation results outputted by CAMx model for Fortran version on the Intel Xeon E5-2682 v4 CPU, CUDA version on the NVIDIA K40m and V100 clusters, and HIP version on the China' s domestically heterogeneous cluster A.</p> <p><strong>wrfcamx.zip:</strong> the meteorological files.</p> <p><strong>offline_test_cuda.zip: </strong>the advection module code written in CUDA C language</p> <p><strong>offline_test_fortran.zip:</strong> the advection module code written in Fortran language</p> <p><strong>offline_test_hip.zip: </strong>the advection module code written in HIP C language</p>
EAHE-D: a Dataset for Modeling and Performance Evaluation of Earth to Air Heat Exchangers
<p><strong>The dataset is available in its original form, allowing users to perform data preprocessing according to their specific needs. Additionally, we offer a processed version of the dataset, which can be used to further investigate the effectiveness of EAHE deployment in buildings and its integration with other passive and active systems, aiming to enhance energy efficiency while maintaining occupant comfort.</strong></p>
Smogseer: A Convolutional LSTM for forecasting air quality from Sentinel-5P data
<h1><strong>Training and checkpoint datasets </strong></h1> <h2><a href="#h_1501865622611722161313186" target="_blank" rel="noopener">S5PL2_5D.nc</a></h2> <p>This is the Sentinel-5P traning dataset for the Smogseer ConvLSTM model. The dataset was created using the xcube Sentinel Hub data store from the <a href="https://deepesdl.readthedocs.io/en/latest/datasets/datastores/#xcube-sentinel-hub-data-store">Deep Earth System Data Lab</a>.</p> <p>The dataset has the following characteristics:</p> <ul> <li>bbox= [68.137207,24.886436,84.836426,34.379713] #WGS84 // lon,lat,lon,lat</li> <li>res = (bbox[2]-bbox[0])/512 # ~3629m</li> <li>date_range = ['2019-01-01', '2023-12-31']</li> <li> <div>timesteps = '5D'</div> </li> </ul> <h2><a href="#h_4062378263291722161323566">X_val.npy</a></h2> <p>Validation feature data with shape: (74, 1, 291, 512, 6)</p> <ul> <li>74: dates</li> <li>1: time steps</li> <li>291: latitudes</li> <li>512: longitudes</li> <li>6: Features ['SO2', 'NO2', 'CH4', 'O3', 'CO', 'HCHO']</li> </ul> <h2><a href="#h_9861938573981722161330905">Y_val.npy</a></h2> <p>Validation target data with shape: (74, 1, 291, 512, 1)</p> <ul> <li>74: dates</li> <li>1: time steps</li> <li>291: latitudes</li> <li>512: longitudes</li> <li>6: Features ['SO2', 'NO2', 'CH4', 'O3', 'CO', 'HCHO']</li> </ul> <h2><a href="#h_321677095651722161353847">smogseer50.keras</a></h2> <p>Model weights for training the ConvLSTM with 50 epochs.</p> <h2><a href="#h_5931467847131722161401921">smogseer100.keras</a></h2> <p>Model weights for training the ConvLSTM with 10 epochs.</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.