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3,018 results for “aire”
Dataset for "Exposure and environmental engagement: A pilot integrating wearable sensors, air quality and citizen science"
<p>The dataset contains anonymised readings of 7 citizens taking air quality measurements using PlumeLabs Flow 2 monitor. Data is for Falmouth/Penryn, and Bristol and it was collected between January 26, 2022 and March 9, 2022.</p> <p>CSV file:</p> <ul> <li>latitude: unit degrees, positive values indicate North hemisphere.</li> <li>longitude, unit degrees, positive values indicate East.</li> <li>AQI: PlumeLabs' Air Quality Index.</li> <li>site: A refers to Falmouth/Penryn(UK), B refers to Bristol (UK).</li> <li>count: auxiliary variable that indicates that the record was comprised of a single reading.</li> </ul> <p>Jupyter notebook: The air quality analysis was conducted with Python 3.9.16 alongside numpy 1.24.3, pandas 2.0.2, matplotlib 3.7.1, and cartopy 0.21.1 (background tiles by OpenStreetMaps).</p>
Air quality source attribution and scenario analysis in the UNECE region
<p>The dataset contains the metrics of PM2.5 and ozone exposure in the UNECE region attributed to 13 activity sectors in three different ECLIPSE v6b emission scenarios (CLE BASE, MFR-BASE and SDS-MFR) used by the authors in the publication "Air quality and related health impact in the UNECE region: source attribution and scenario analysis" submitted to the Journal Atmospheric Chemistry and Physics (https://doi.org/10.5194/acp-2022-776).</p>
Figure 4 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 4. Probability for the estimated annual passenger entries with CBB-infested materials: (a) to Hawaii from CBB-occurring countries; (b) between Hawaii island and Oahu; (c) between Oahu and Maui; (d) between Oahu and Kauai; (e) between Hawaii island and Maui; (f) between Maui and Kauai; (g) between Hawaii island and Kauai; (h) between Oahu and Lanai.
Figure 1 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 1. World coffee production with CBB world distribution: a) 2019 coffee produc- tion (FAO 2020); b) CBB distribution by introduction year (Vega et al. 2015).
Figure 2 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 2. Number of months per year with optimal temperature conditions for CBB growth. The optimal temperature conditions were determined by evaluating the area where daily minimum temperature was above 18°C and daily maximum temperature was below 30°C.
Figure 3 in Pathway Analysis: Likelihood of Coffee Berry Borer (Hypothenemus hampei Ferrari) Introduction into the Hawaiian Islands by Air Passenger Travel
Figure 3. Mean annual number of air passengers traveling between the Hawaiian Islands. Dispersal pathways are shown for those islands that are confirmed to have coffee berry borer. Thicker blue lines indicate higher numbers of passengers.
assessment of aldehydes to PTR-MS m/z 69 in indoor air measurements - data set
<ul> <li>contact: Lisa Ernle (lisa.ernle@mpic.de), Nijing Wang (nijing.wang@mpic.de), Jonathan Williams (jonathan.williams@mpic.de)</li> <li>instruments: fast GC-MS SOFIA (MPIC), PTR-ToF-MS 8000 (Ionicon)</li> <li>merged dataset</li> <li>calibrated with VOC standard gas mix (Apel-Riemer Environmental Inc., Colorado, USA)</li> <li>units (filename): <ul> <li>normalized counts per second [ncps] (20210426_p_ncps.txt, bar_mean.txt, bar_std.txt)</li> <li>parts per billion [ppb] (all_sub_20210426_ppb.txt)</li> </ul> </li> <li>for information concerning updated versions, please see ReadMe.txt</li> </ul>
MERICS Podcast: China's advances in rail, sea and air transportation, with Alexander Brown and Gregor Sebastian
<p>China has extended its High-Speed Rail network from about 9.000 to 42.000 km of tracks with a fleet of Chinese made trains running on it. The first fully domestically engineered passenger aircraft C919 has started its commercial service and on June 6, the first domestically built large cruise ship, has left Shanghai for sea trials. China is undoubtedly making rapid progress in air, sea and rail transportation.</p> <p>What are China’s aims and what challenges does it face on the way? And what does this mean for European industries? To answer these questions, <strong>Alexander Brown</strong> and <strong>Gregor Sebastian</strong>, analysts in the economics team at MERICS, join <strong>Johannes Heller-John</strong> for a conversation.</p> <p>This podcast episode is part of the “Dealing with a Resurgent China” (DWARC) project, which has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101061700.</p> <p>Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.</p>
Acoustic measurements in the ancient open-air theatre of Tyndaris
<p>The data set represents the measured impulse response in the ancient Theatre of Tyndaris performed in September 2015 by the Applied Acoustics Research Group of the Department of Energy of the Politecnico di Torino.</p> <p>- Measurement set-up has been described in https://www.mdpi.com/2076-3417/10/16/5680</p> <p>- Receiver and source positions have been shown in Figure 2 in https://www.mdpi.com/2076-3417/10/16/5680</p>
On-air microcoil current density test - H2020 UWIPOM2 PROJECT
<p>On-air microcoil current density test</p> <p>https://www.youtube.com/watch?v=CkEstS5zUFQ&ab_channel=Prof.Efr%C3%A9nD%C3%ADezJim%C3%A9nez-UniversidaddeAlcal%C3%A1</p>
The Sensitivity of Southern Ocean Air-Sea Carbon Fluxes to Background Turbulent Diapycnal Mixing Variability
<p> </p> <p>The mixing map for background diapycnal diffusivity used in the paper 'The Sensitivity of Southern Ocean Air-Sea Carbon Fluxes to Background Turbulent Diapycnal Mixing Variability' in the spatially variable mixing case ExVar.</p> <p>The ExVar map is constructed as the sum of contributions from tides and topographically-generated lee waves and features horizontal and vertical variations in a background mixing rate.</p>
Dataset for "An air-stable Cu(I) metal-organic framework for hydrogen storage"
<p>Dataset for "An air-stable Cu(I) metal-organic framework for hydrogen storage"</p> <p>Raw data set of the electronic structure calculations and inputs for the GCMC calculations relating to above publication.</p>
The Air Listening Station: Bridging the gap between Sound Art and Sonification - Sonification examples
<p><strong>Example audio files:</strong></p> <p>1) aesthetic_direction_01.mp3 --> Initial aesthetic direction/proof of concept created using 1 hour of PM_10 and PM_2.5 data rendered at a 10:1 ratio (i.e. 60 minutes of air quality data results in 6 minutes of audio). Created using the Ultimate Grainer module of Ajax Sound Studio’s Cecilia 5 audio signal processing environment.</p> <p>2) sample sonification - original - good AQ - 5x bad AQ.mp3 --> contains several example outputs created using the sonification instrument created in Supercollider presented during the <a href="https://radioart.zone/sunday-28-august">radio art zone broadcast on August 28, 2022</a></p> <ul> <li>[0:00] - original field recording before processing</li> <li>[0:30] - example of "good" air quality data</li> <li>[1:02] - example of "bad" air quality data #1</li> <li>[2:10] - example of "bad" air quality data #2</li> <li>[2:45] - example of "bad" air quality data #3</li> <li>[3:08] - example of "bad" air quality data #4</li> <li>[3:38] - example of "bad" air quality data #5</li> </ul>
XXIst century projections of global forest soil and canopy air temperature offsets (CLM5.1, SSP5-8.5)
<p>The following dataset allows to reproduce the figures in <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad1f04/pdf">Hes et al 2024 </a>. It is generated by CLM5.1 simulations following the SSP5-8.5 scenario and contains the temperature offset between forest and grassland for soil temperature T<sub>g </sub> and canopy air temperature T<sub>c.</sub> The offset temperatures (∆T<sub>g</sub> and ∆T<sub>c</sub>) are defined as the difference between the internal temperature of the forest tile minus the internal temperature of the grassland tile. For each grid cell, we compute the difference between the area-weighted average temperature in forests, and the area-weighted average temperature over grassland.</p> <p>We define the offset for minimum ∆T<sub>g,min</sub>, maximum ∆T<sub>g,max</sub> and mean ∆T<sub>g,mean</sub> soil temperature. ∆T<sub>g,min</sub> (respectively, ∆T<sub>g,mean </sub>and ∆T<sub>g,max</sub>) is derived by selecting the monthly minimum (respectively, mean and maximum) of the 6-hourly forest ∆T<sub>g </sub>and subtracting the simultaneous T<sub>g</sub> over grassland within a grid cell. We consider all grid cells that contain both more than 0% of tree PFT cover (forest) and more than 0% of grass PFT cover (grassland). T<sub>c</sub> offsets (∆T<sub>c,min</sub>,∆T<sub>c,max</sub> and ∆T<sub>c,mean</sub>) are generated using the same method although we note that unlike for ∆T<sub>g</sub>, the differentiation between forest and grassland T<sub>c</sub> also encapsulates a difference in height.<br><br>Here, we provide monthly data for minimum, maximum and mean soil and canopy air temperature for 3 forest PFT-based biomes (boreal, temperate and tropical) and for all forest PFTs (global).<br><br>For more information, refer to the Methods is <a href="https://iopscience.iop.org/article/10.1088/1748-9326/ad1f04/pdf">Hes et al 2024</a></p>
The global marine free air gravity anomaly model SDUST2022GRA
<p>SDUST2022GRA.nc is the global marine free air gravity anomaly model covering 80°S~82°N and 0~360°E on 1′×1′ grids. SDUST2022GRA is recovered from multi-radar and ICESat-2 laser altimeter data to investigate the contribution of ICESat-2.</p>
Phtometry measurements using spectroscopic ellipsometer of PAAO containing samples in air and water (AoI 45 deg., 60 deg., varying polarizer and analyser angles)
<p>The file contains raw intensity data measured in reflection mode for various material combinations: porous anodized aluminium oxide (PAAO), gold nanoparticles (Au NPs), diamond-like carbon with silver nanoparticles composite (DLC:Ag), quartz, and silicon.</p> <p>List of investigated samples:</p> <table> <thead> <tr> <th scope="col">Sample name</th> <th scope="col">Sample description</th> </tr> </thead> <tbody> <tr> <td>AJ1</td> <td>Aluminium substrate, ~241 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 16 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ2</td> <td>Aluminium substrate, ~259 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 37 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ3</td> <td>Aluminium substrate, ~293 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 8 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ4</td> <td>Aluminium substrate, ~317 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 30 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ5</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 57 s), 60 nm diameter Au NPs (dip-coated at a speed of 1 μm/s)</td> </tr> <tr> <td>AJ6</td> <td>Aluminium substrate, ~276 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 3 min 42 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ7</td> <td>Aluminium substrate, ~316 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 4 min 25 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>AJ8</td> <td>Aluminium substrate, ~345 nm thickness PAAO (anodized in 0.3 mol/L oxalic acid at 40 V for 5 min 4 s), ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Q140</td> <td>Quartz substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> <tr> <td>Si140</td> <td>Silicon substrate, ~55 nm thickness, 20 V.% Ag concentration DLC:Ag (magnetron sputtered using silver target, 80 sccm argon gas flow, 5.4 sccm C<sub>2</sub>H<sub>2</sub> gas flow, 405 V voltage, 0.09-0.10 A current, 7·10<sup>-3</sup> mbar work pressure, 2 minutes 32 seconds process duration)</td> </tr> </tbody> </table> <p>Ellipsometer: rotating compensator GES5-E (Semilab).</p> <p>Light source: 75 W xenon short arc lamp with 185-2000 nm wavelength spectrum.</p> <p>Detector: UV-Vis CCD with 0.8 nm resolution.</p> <p>Spectral range: approximately 230-960 nm.</p> <p>Mode: photometry.</p> <p>Light incidence angles: 45°, 60°.</p> <p>Polarizer angles: 0°, 90°.</p> <p>Analyzer angles: 0°, 45°, 90°.</p> <p>Light beam size: microspot (365 × 470 μm<sup>2</sup> at 75° angle of incidence).</p> <p>The reference was measured from aluminum mirror provided with the ellipsometer for each variation of measurement set-up.</p> <p>Each sample was first measured in air and then after immersion in water. Focusing was performed before each measurement.</p> <p>.smdx and .mup files are generated by the ellipsometer and can be opened using Semilab software. Their file names contain sample name, polarizer angle (Pol), analyzer angle (Ana), incidence angle (ang), and surrounding medium (air/water). .csv files are exported data related to each sample. .jpg files are for illustrative purposes to show the spectra. .rar file contains all the files in this upload.</p> <p> </p>
GIS and Pollution Data: Designating Regional Airsheds for Air Quality Management in India
<p>Full journal article published here<br><strong>Designating Airsheds in India for Urban and Regional Air Quality Management<br></strong><a href="https://doi.org/10.3390/air2030015" target="_blank" rel="noopener">https://doi.org/10.3390/air2030015</a><strong><br></strong></p> <p>[Summary presentation <a href="https://urbanemissions.info/wp-content/uploads/docs/UEinfo-Designating-Airsheds-in-India.pptx">download</a>]</p> <p>Datasets used for proposing India's 15 regional airsheds for air quality management are the following</p> <p>PM2.5 Datasets<br>Raw data source: <a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5">https://sites.wustl.edu/acag/datasets/surface-pm2-5</a></p> <ul> <li>Gridded 0.1 degree resolution source apportionment results from WUSTL's global model simulations<br>File: india_data_pm25_wustl_source_cont_0p1deg.xlsx<br>Aggregated Source definitions used in this presentation <ul> <li>1. DUST = Anthropogenic dust = AFCID</li> <li>2. WINDUST = Wind erosion (dust storms) = WDUST</li> <li>3. WASTE = Waste burning = WST</li> <li>4. RESI = All commercial and residential cooking, lighting, and heating = RCOC + RCOO + RCORbiofuel + RCORcoal + RCORother</li> <li>5. TRANS = All transport (excluding aviation) = ROAD + NRTR + SHP</li> <li>6. POWER = Energy generation = ENEcoal + ENEother</li> <li>7. INDUS = All industries and product use = INDcoal + INDother + SLV</li> <li>8. BIOB = Biomass burning, including forest fires and agricultural waste burning = GFEDoburn + GFEDagburn</li> <li>9. AGR = Agricultural activities (excluding agricultural waste burning) = AGR</li> <li>10. OTHER = All others = OTHER</li> </ul> </li> <li>Gridded 0.1 degree resolution, reanalysis data from WUSTL's global model simulations<br>File: india_data_pm25_wustl_reanalysis_0p1deg.xlsx<br>Time period: 1998 to 2022, annual averages</li> <li>Gridded 0.1 degree achive for monthly averages from WUSTL's global model simulations<br>File: <a href="https://www.urbanemissions.info/wp-content/uploads/misc/IndiaSubcontinent-Gridded-Monthly-WUSTL-v4.rar">Download-44MB</a></li> </ul> <p>Population Datasets<br>Raw data source: <a href="https://landscan.ornl.gov">https://landscan.ornl.gov</a></p> <ul> <li>Gridded 0.1 degree resolution population density data<br>File: india_data_population_2021_0p1deg.xlsx</li> </ul> <p>GIS databases used in this study</p> <ul> <li>ESRI shapefile of 0.1 x 0.1 degree mesh file for the Indian Subcontinent covering longitudes from 67E to 99E and latitudes from 7N to 39N<br>File: india_gis_grids-0.1x0.1deg.rar</li> <li>ESRI shapefile of India administrative level 2 data - 28 states and 8 union territories (as of December 2023)<br>File: india_gis_states28+8_2023.rar</li> <li>ESRI shapefile of India administrative level 3 data - 755 districts (as of December 2023): district23 and states23 codes are re-designed for emissions and pollution mapping and data tracking purposes<br>File: India_gis_districts755_2023.rar (original source: <a href="https://projects.datameet.org/maps">https://projects.datameet.org/maps</a>)</li> <li>ESRI shapefile of India's Agro-Climatic zones<br>File: india_gis_agroclimatic_zones.rar (original source: <a href="https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions">https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions</a> </li> <li>ESRI shapefile of India's meteorological sub-divisions<br>File: india_gis_meteo_subdivisions.rar (original source: <a href="https://mausam.imd.gov.in/">https://mausam.imd.gov.in</a>)</li> </ul>
An Anesthesia-Centered Bundle to Reduce Postoperative Pulmonary Complications: The PRIME-AIR Study
ClinicalTrials.gov study NCT04108130. IPD Sharing: YES. Countries: 1. Publications: 11.
Air-Q® SP Versus Air-Q® and I-gel: A Randomized Clinical Trial
ClinicalTrials.gov study NCT01592760. IPD Sharing: NO. Countries: 1. Publications: 1.
Home Air Quality Impact for Adults With Asthma
ClinicalTrials.gov study NCT05224076. IPD Sharing: YES. Countries: 1. Publications: 6.
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.