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3,018 results for “aire”
Dataset for sea ice modulates air–sea methane flux in the Southern Ocean
<p>Air-sea methane (CH4) flux in marginal ice zone in the East Antarctic seas measurements on the icebreaker R/V Xuelong2 from December 2020 to January 2021</p>
Time Series Comparisons, Model Code, and a Demo Dataset for SIBaR: A New Method for Background Quantification and Removal from Mobile Air Pollution Measurements
<p>Time series comparisons between SIBaR, Brantley, and Apte background signals for all 312 time series in the Houston mobile monitoring campaign. Additionally, a R script demo (DemoData.R) of the SIBaR partitioning step on the demo datatset (DemoData.csv).</p>
Air Temperature and Relative Humidity, San Marcos, CA, USA, March 2020 - June 2021
<p>Air temperature and relative humidity collected with MX2201 and MX2301A sensors/loggers (manufactured by Onset Computer Corporation ®) in San Marcos, CA, USA. Time series span March 2020 to June 2021. Support for equipment and data collection was provided by California State University San Marcos. See metadata in files for additional information.</p>
Spatial interpolation of air pollutant and meteorological variables in Central Amazonia
<p>This dataset presents data of aerosol, trace-gases and meteorological variables from the Amazon Rainforest region, resulting from an interpolation process. The original data were collected from the GOAmazon 2014/15 project, from the Atmospheric Radiation Measurement (ARM) repository. </p>
Britain Breathing 2016-2019 Air Quality and Meteorological Dataset
<p>This data set is a collection of daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the UK for the years 2016-2019, inclusive. The dataset contains Temperature, Relative Humidity, and Pressure data, downloaded from the Met Office MIDAS archives via the MEDMI server (https://www.data-mashup.org.uk/). Also downloaded from the MEDMI server are daily pollen measurements for the UK. PM10, PM2.5, NO2, NOx (as NO2), O3, and SO2 measurements from the DEFRA AURN network, and also model forecasts of the same made using the EMEP model.</p> <p>The paper describing this dataset is available here: <a href="https://www.nature.com/articles/s41597-022-01135-6">https://www.nature.com/articles/s41597-022-01135-6</a></p> <p>The tools used to download and process these measurement datasets are available here: <a href="https://zenodo.org/record/4545257">https://zenodo.org/record/4545257</a></p> <p>The dataset is designed for use with the region estimator toolset, available in this repository: <a href="https://github.com/UoMResearchIT/region_estimators">https://github.com/UoMResearchIT/region_estimators</a></p> <p>Emissions over the UK for the EMEP model runs were generated using the NAEI 2016 UK emission dataset, available in netcdf form here: <a href="https://zenodo.org/record/3997165#.X9KUBF6nzUI">https://zenodo.org/record/3997165#.X9KUBF6nzUI</a>. The running scripts, and operation inputs for EMEP, are available here: <a href="https://zenodo.org/record/3997301#.X9KUAF6nzUI">https://zenodo.org/record/3997301#.X9KUAF6nzUI</a> and <a href="https://zenodo.org/record/3997271#.X9KV1F6nzUI">https://zenodo.org/record/3997271#.X9KV1F6nzUI</a>.</p> <p>The dataset is presented in CSV format, as three files:</p> <ol> <li>turing_aq_daily_met_pollen_pollution_original_data.csv: original data (described below)</li> <li>turing_aq_daily_met_pollen_pollution_with_imputation_data.csv: original plus imputed data (described below)</li> <li>site_location_data.csv: location metadata (site_id, latitude, longitude, postcode area)</li> </ol> <p> </p> <p>The columns intended to be used as indexes are:</p> <ul> <li>timestamp, <ul> <li>date of measurements on that row</li> </ul> </li> <li>site_id, <ul> <li>measurement site ID, corresponding to sites in the three networks: <ul> <li>AURN [indicated by AQ],</li> <li>MIDAS [indicated by WEATHER],</li> <li>or pollen [indicated by POLLEN].</li> </ul> </li> </ul> </li> </ul> <p>The data columns are:</p> <ul> <li>O3, PM10, PM2.5, NO2, NOXasNO2, SO2, <ul> <li>daily mean and maximum values in ug/m3 (all with "_max", "_mean", and "_flag" tags)</li> <li>AURN measurement data</li> </ul> </li> <li>O3_EMEP, NO2_EMEP, SO2_EMEP, NOXasNO2_EMEP, PM2.5_EMEP, PM10_EMEP, <ul> <li>daily mean and maximum values in ug/m3 (all with "_max", and "_mean" tags)</li> <li>EMEP model forecasts</li> </ul> </li> <li>alnus, ambrosia, artemisia, betula, corylus, fraxinus, platanus, poaceae, quercus, salix, ulmus, urtica, <ul> <li>daily pollen grain counts</li> </ul> </li> <li>temperature, relativehumidity, pressure, <ul> <li>daily mean and maximum values in degC, %, and hPa (all with "_max", "_mean", and "_flag" tags).</li> <li>Met Office measurement data</li> </ul> </li> </ul> <p>The "_flag" columns indicate data points which have been partially, or fully, imputed. The values for these will be in the range 0-1, and indicate the fraction of the hourly values within that day that are imputed (0 = none, 1 = all 24 hourly datapoints are imputed). No imputation is done in the original dataset, so the "_flag" data in this dataset will always be zero (the number of hourly data points used to calculate the daily mean and maximum are not recorded in this dataset).</p> <p>The station location metadata includes longitude, latitude, and UK postcode area data. Where sites lie outside of the UK the postcode is replaced with regional indicator (here: Republic of Ireland (ROI)).</p> <p> </p> <p>Please cite the following paper if you use this dataset: Reani, M., Lowe, D., Gledson, A., Topping, D., & Jay, C. (2022). UK daily meteorology, air quality, and pollen measurements for 2016–2019, with estimates for missing data. <em>Scientific Data</em>, <em>9</em>(1), 43. https://doi.org/10.1038/s41597-022-01135-6</p>
Figure 2 in Vocalizations of the Brazilian torrent frog Hylodes heyeri (Anura: Hylodidae): Repertoire and influence of air temperature on advertisement call variation
Figure 2. Territorial call of Hylodes heyeri from the Municipality of Morretes, Parana´, Brazil. Recorded on 11 January 2002, at 22.4°C. (A) Power spectrum; (B) spectrogram; (C) oscillogram.
Figure 3 in Vocalizations of the Brazilian torrent frog Hylodes heyeri (Anura: Hylodidae): Repertoire and influence of air temperature on advertisement call variation
Figure 3. Mean number of advertisement calls (bars) emitted by males of Hylodes heyeri during 5 min of monitoring each hour, and air temperature (line).
Synthetic large ensembles from four observation-based products of sea-air CO2 flux from Olivarez et al. (2021)
<p>Synthetic large ensembles from four observation-based products of sea-air CO2 flux from Olivarez et al. (2021). Observation-based products are:</p> <p>Council for Scientific and Industrial Research-Machine Learning (CSIR-ML6)<br> Max Planck Institute Self-Organizing Map-Feed-Forward Neural Network (MPI-SOMFFN)<br> Jena, Germany-Max Planck Institute for Biogeochemistry-Mixed Layer Scheme (JENA-MLS)<br> Copernicus Marine Environment Monitoring Service Feed-Forward Neural Network (CMEMS-FFNN)</p>
Text-fig. 3. Metacheiromys marshi, USNM-P 452349, coronal sections from CT scans. a – section 590 of 2020 through the anteriormost tympanic cavity showing air spaces in the entotympanic and squamosal; b – section 898 of 2020 at level of the fenestra vestibuli showing the mastoid sinus. Abbreviations: bo – basioccipital, bs – basisphenoid, cp – crista parotica, ec – ectotympanic, en – entotympanic, es – epitympanic sinus of squamosal, fv – fenestra vestibuli, hyf – hypophyseal fossa, m – malleus, ms – mastoid sinus, pr – promontorium, sq – squamosal, tc – tympanic cavity. in Skeletal Anatomy Of The Basicranium And Auditory Region In The Metacheiromyid Palaeanodont Metacheiromys (Mammalia, Pholidotamorpha) Based On High-Resolution Ct Scans
Text-fig. 3. Metacheiromys marshi, USNM-P 452349, coronal sections from CT scans. a – section 590 of 2020 through the anteriormost tympanic cavity showing air spaces in the entotympanic and squamosal; b – section 898 of 2020 at level of the fenestra vestibuli showing the mastoid sinus. Abbreviations: bo – basioccipital, bs – basisphenoid, cp – crista parotica, ec – ectotympanic, en – entotympanic, es – epitympanic sinus of squamosal, fv – fenestra vestibuli, hyf – hypophyseal fossa, m – malleus, ms – mastoid sinus, pr – promontorium, sq – squamosal, tc – tympanic cavity.
An Optimized North America MODIS Leaf Area Index (LAI) Dataset for Air Quality Modeling
<p>Air Quality Research Division, Environment and Climate Change Canada,</p> <p>4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada</p> <p>Email: Junhua.zhang@ec.gc.ca</p> <p> </p> <p>Leaf Area Index (LAI) is used in air quality models for land surface processes and for calculating biogenic emissions. MODIS LAI product provided by NASA (https://modis.gsfc.nasa.gov/data/dataprod/mod15.php) has been widely used in the air quality modeling community for such purposes. However, limitations of MODIS LAI product have been seen for some geographic areas, particularly unreasonably low LAI over the evergreen needleleaf boreal forests in the northern hemisphere during wintertime due to snow cover and low sun angle. Missing retrievals over urban areas and areas with persistent cloud cover are also seen. Considerable efforts have been made to improve the MODIS LAI product. However, some issues are still persistent, such as the very low LAI over boreal forests during wintertime. In order to solve these issues for supporting regional air quality modelling, the 8-day MODIS Collection 6 (C6) LAI product at 500m resolution (MCD15A2H) was examined for North America. Statistics were calculated by month and by land cover type defined in the “Land Cover Type 1” science data set (SDS) of the Collection 6 MODIS Land Cover (MCD12Q1) product. Comparisons with LAI calculated from the EPA’s Biogenic Emissions Landuse Database, version 4 (BELD4, https://www.epa.gov/air-emissions-modeling/biogenic-emission-sources) were also done (Zhang et al., 2020). Based on the analysis, an updated monthly LAI dataset was calculated based on 1) 17-year (2003-2019) average of MODIS summer-time peak LAI, 2) fraction of evergreen and deciduous for each pixel from BELD4, and 3) monthly profiles of LAI for evergreen and deciduous vegetation species from MODIS LAI (Zhang et al., 2021). This is the final LAI dataset for North America compiled using the 17 years of MODIS LAI product complemented by information from BELD4.</p> <p> </p> <p>REFERENCES:</p> <p>Zhang, J., M. D. Moran, P. A. Makar, and S. Kharol, 2020. Examination of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 19th CMAS Conference, 26-30 Oct., Virtual [see https://www.cmascenter.org/conference/2020/slides/ZhangJ_MODIS_LAI_CMAS_2020.pdf].</p> <p>Zhang, J., P. A. Makar, S. Kharol, M. D. Moran, and C. McLinden, 2021. Examination and Processing of MODIS Leaf Area Index (LAI) Product for Air Quality Modelling. 2021 Meteorology and Climate - Modeling for Air Quality Conference, Sep 14-17, 2021, Virtual</p> <p> </p>
Average daily air temperature, precipitation and relative sunshine duration for Vallon de Nant catchment, extracted from gridded MeteoSwiss data (1961-2020)
<p>This excel file contains time series of daily temperature, precipitation and relative sunshine duration obtained as the spatial average of gridded data sets. The underlying original gridded data sets produced by MeteoSwiss are known as RhiresD, TabsD and SrelD. All meta data are included in the excel file.</p> <p>The data can e.g. be used for hydrological modelling. Comparison to local station data is not included.</p> <p><strong>This data set as well as the original data set should be cited</strong>.</p>
Fig. 1 in Scientific note The air-breathing cycle of Hoplosternum littorale (Hancock, 1828) (Siluriformes: Callichthyidae)
Fig. 1. Air-breathing cycle of Hoplosternum littorale: 1) ascent to the water surface; 2) in contact with the surface the fish expands its oral cavity, inspiring air; 3) turning head down, with compression of the oral cavity pushing air into the intestine and causing the expiration of old air from the anus (circle, continuous line); 4) return to bottom and release of small air bubbles through the opercula (circle, dotted line). Arrows indicate the action of the buccal pump.
A Daily near-surface Air Temperature Dataset for China from 1979 - 2018
<p>The CDAT dataset contains near-surface air temperature data for China during the period of 1979-2018, in Celsius, daily temporal (T<sub>max</sub>, T<sub>min</sub>, T<sub>avg</sub>), and 0.1º spatial resolution.</p> <p>This product integrates multiple data sources such as reanalysis data (ERA5, CMFD), remote sensing data (MODIS), and in situ data, and is obtained by combining the temperature strategy to distinguish between clear sky and non-clear sky weather conditions. It is proved that this dataset has high accuracy and can be used in the further study of regional climate change.</p>
Multivariate Spatial Predictions of Air Pollutants with INLA
<p>Spatial predictions and uncertainty quantification for air pollutants: PM<sub>2.5</sub>, PM<sub>10</sub>, NO<sub>3</sub><sup>−</sup>, NH<sub>4</sub><sup>+</sup>, EC, OC, SO<sub>4</sub><sup>2− </sup>, CO, NOx, NO<sub>2</sub>, SO<sub>2</sub> and O<sub>3</sub> covering the continental US for the period 2005–2014. The daily prediction is at 12km spatial resolution.</p>
Datasets and OpenLCA foreground data processes for the article: Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment
<p>This data set contains the supplementary data sets (1-3) and exported foreground data processes from OpenLCA for the manuscript “Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment”, submitted to Nature Energy.</p> <p>This repository contains:</p> <ul> <li>Supplementary data set 1: Ancillary calculations and numerical values for HT-Aq DAC</li> <li>Supplementary data set 2: Ancillary calculations and numerical values for TSA DAC</li> <li>Supplementary data set 3: Ancillary calculations and numerical values shown in plots and table 3</li> <li>Foreground data from OpenLCA. OpenLCA process model for different cases of HT-Aq DAC and TSA DAC. To re-run the LCA calculations, OpenLCA (freeware) and the Ecoinvent 3.5 database (license required) need to be installed on a standard desktop computer or laptop with at least 8 GB RAM.</li> </ul>
Bias-corrected monthly air temperature data over South Siberia for 1979-2020 (CTSS 1.0)
<p>Bias-<strong>C</strong>orrected Air <strong>T</strong>emperature data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CTSS 1.0</strong>) contains monthly air temperature at 2m for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2020. CTSS data were combined from monthly total air temperture data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and temperature data records from ground weather stations (Bulygina et al., 2014). The ERA5 data were scaled according to the derived correction coefficient. The additive coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CTSS reproduces the spatial variability of temperature more precisely than can be done from the weather station observation network. Data provided in NetCDF (Network Common Data Form) format.</p> <p>Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at: <a href="https://cds.climate.copernicus.eu/cdsapp#!/home"><em>https://cds.climate.copernicus.eu/cdsapp#!/home</em></a></p> <p><em>Bulygina O.N., Razuvaev V.N., Trofimenko L.T., Shvets N.V., 2014. Description of the monthly air temperature data at weather stations of Russia.</em> Certificate of state registration of the database No. 2014621485 Available at: <a href="http://meteo.ru/data/156-temperature"><em>http://meteo.ru/data/156-temperature</em></a></p>
fig. 1, Plantilla venedictoƒƒae n. sp., holotype 5, habitus (G.><2,4). Fig. 2 et 3, Pl. venedictoƒƒae n. sp .. holotype 5* 2, gros plan sur l'aire striée mésonotale. le cymbacalypte métathoracique et Porgane cymbalique du côté gauche (G. x 11) 3. vue mettant en évidence les sternites membranisés (G. x 8.7). in Description d'une troisième espèce du genre Plautilla Stal [Hom. Cicadoidea Plautillidae]
fig. 1, Plantilla venedictoƒƒae n. sp., holotype 5, habitus (G.><2,4). Fig. 2 et 3, Pl. venedictoƒƒae n. sp .. holotype 5* 2, gros plan sur l'aire striée mésonotale. le cymbacalypte métathoracique et Porgane cymbalique du côté gauche (G. x 11) 3. vue mettant en évidence les sternites membranisés (G. x 8.7).
Data used in "Climatology and variability of air mass transport from the boundary layer to the Asian monsoon anticyclone"
<p>The data presented here are needed to reproduce the analyses of the publication: Nützel, M., Brinkop, S., Dameris, M., Garny, H., Jöckel, P., Pan, L. L., and Park, M.: Climatology and variability of air mass transport from the boundary layer to the Asian monsoon anticyclone, Atmos. Chem. Phys., 22, 15659–15683, https://doi.org/10.5194/acp-22-15659-2022, 2022. A short explanation of the archived data is presented in the accompanying README.</p> <p>Note: In the previous data set version (<a href="https://doi.org/10.5281/zenodo.7275804">10.5281/zenodo.7275804</a>) one file was missing and is added here.</p> <p> </p> <p> </p>
datasat literatur review online business AND air travel
<p>DATA INI DIGUNAKAN UNTUK MEMBUAT PENELITIAN BERDASARKAN TINJAUAN LITERATUR DENGAN KATA KUNCI "ONLINE BUSINESS" dan "air travel"</p>
Benefits and limitations of environmental magnetism for completing citizen science on air quality: a case study in a street canyon.
<p>Inside a street canyon in Montpellier (France) a total of 72 deposimeters were deployed in 29 households for a period of 3 months to measure local air quality. This street canyon was chosen because dwellers were already mobilized against the street traffic, and because they were in conflict on this issue with policy makers. The project aimed to include all the stakeholders through co-construction. The closure of the street during the metrological campaign and the absence of agreement curbed their involvement and motivation. However, the feedbacks from the citizen partners promote the fact that this study supported their claims and brought them a deeper understanding on the micro-scale air quality monitoring. Indeed, it is increasingly difficult for citizens, who seemed specifically interested in what is happening right outside their front door, to understand this measure with the emergence of ever more low-cost sensors. For that reason, we examined the citizen’s degree of confidence in magnetic monitoring of air quality and how can this technique be useful in their claims. The results show that magnetism can be a measurement technique favorable to citizen participation because it provides a large amount of data at the micro-scale of the street level, while the data from the certified associations for monitoring air quality requires a spatial interpolation to map variations on a neighborhood scale. In this study, we proposed a magnetic air quality index to standardize and democratize the magnetic monitoring of air quality to facilitate the dialogue with all stakeholders.</p>
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.