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3,018 results for “AIR”
Air quality data created by the hackAIR Horizon2020 project
<p>The current datasets comprise of air quality data collected or created within the hackAIR project (https://platform.hackair.eu/) all around Europe from February 2018 until November 2018.</p> <p>i) "measurements_arduino.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with stationary hackAIR sensors (https://www.hackair.eu/hackair-home-v2/). These sensing devices are based either on an Arduino or a Wemos board. The first column is the unique identifier for the measurement in the hackAIR database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3.</p> <p>ii) "measurements_bleair.xlsx": PM10 and PM2.5 measurements collected by hackAIR users with mobile hackAIR sensors (https://www.hackair.eu/hackair-mobile/). The first column is the unique identifier for the measurement in the database. The date/time is in UTC timezone, while the unit of the pollutant value is μg/m3.</p> <p>iii) "measurements_sky_photos.xlsx": Air pollution estimations from photos depicting sky. The hackAIR platform estimates the particulate matter content in the air from Flickr photos, photos from webcams and sky photos that users upload on the hackAIR mobile application, based on the colour of the sky. This is expressed as Aerosol Optical Depth (AOD). In the current dataset, the timezone is UTC, while AOD is unitless.</p> <p>The pollutant index is based on a scale created for the purposes of the hackAIR project.</p>
Signalgun Signature measured in Water and Air (Tank Experiment)
<ul> <li>the experiments are performed in and above a water tank</li> <li>signatures of a Stalker R1 2.5'' gun fired at different elevations above the water surface are measured</li> <li>the Stalker R1 2.5'' gun has a caliber of 0.38 in (9 mm) and is fired with blank bullets</li> <li>signatures are measured in water and air with B&K 8105 hydrophones</li> <li>the signal recording is triggered with a hydrophone that is directly attached to the Stalker R1 2.5'' gun (it indicates when the gun is fired) - the measured signals are aligned to the main peak of the trigger signal</li> <li>recordings are averaged/stacked measurements of 4 shots with the Stalker R1 2.5'' gun at each source elevation "zs"</li> <li>the filename indicates the source type ("signalgun_"), where the signal is measured ("water_" or "air_") and the source elevation above the water surface ("zs_cm.txt")</li> <li>more information can be found in the referenced publication and the sketch of the experimental set up</li> </ul>
S15 Watergun Signature measured in Water and Air (Tank Experiment)
<ul> <li>the experiments are performed in a water tank (see photo)</li> <li>signatures of an S15 watergun fired at different water depths are measured</li> <li>S15 watergun has one cylindrical gun port and is fired at 130 bar</li> <li>signatures are measured in water and air with B&K 8105 hydrophones</li> <li>signatures in air are measured at normal incidence, directly above the source (yr = 0 cm, no "yr" in filename) and at different horizontal offsets from the source (varying "yr" as indicated in the filename)</li> <li>channel 3 is the trigger channel and indicates when the S15 watergun is fired</li> <li>recordings are averaged/stacked measurements of 3 shots with the S15 watergun at each source depth "zs"</li> <li>the filename indicates the source type ("watergun_S15_"), where the signal is measured ("water_" or "air_") and the source depth in water ("zs_cm.txt"), and the horizontal offset ("yr") for the specific experiments</li> <li>more information can be found in the referenced publication and the sketch of the experimental set up</li> </ul>
Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2017
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2017 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.</p> <p> </p>
Ambient air ozone concentrations using metal-oxide low-cost sensors: Spain and Italy, summer 2018
<p>Ozone concentrations in ambient air collected using low-cost sensor technologies, in the framework of EU project CAPTOR. Data collected during summer 2018 in NE Spain and N Italy. Sensors are metal-oxide. Data are calibrated using multiple linear regression, and validated against official reference data from each local air quality monitoring network. More details on the calibration and data validation may be found in A. Ripoll et al. / Science of the Total Environment 651 (2019) 1166–1179.</p>
Air Quality Index Scores by CBSA with Population
<p>The AQI describes the five main types of air pollution regulated by the Clean Air Act: sulfur dioxide, nitrogen dioxide, carbon monoxide, ground-level ozone, and particle pollution. The EPA and its partners take regular readings of these pollutants and converts the results into a number ranging from 0 to 500, along with a specific color corresponding to a level of health concern. Generally, if the air quality is good, the air quality index is low (0 to 50) or moderate (51-100), and the color associated with it is green or yellow. As the air quality gets worse, the numbers go up, and the color linked with it goes from orange, to red, to purple, all the way to a dark shade of maroon for hazardous (300+).</p> <p>This dataset contains the AQI scores by metropolitant area (CBSA) during 2017. I've enhanced some publically available data from the EPA's <a href="https://airnow.gov/">airnow</a> website with census data, to be able to provide context about the number of people who are actually impacted when an <a href="https://www.cleanairresources.com/resources/how-do-i-read-the-air-quality-index">AQI score</a> is high or low in a given area.</p> <p>Related datasets on <a href="https://www.cleanairresources.com/data#sources">relative composition of air pollution by source: typical distribution and during wildfire season</a> are available here.</p>
AirHeritage Datalake: Multi-site, Multi-season, Multi Unit dataset including Fixed and Mobile Citizen science data from networked Air Quality Low-Cost Multi-Sensors devices and reference stations
<p>This datalake comprises several datasets from <strong>37 networked low cost air quality multisensors</strong> (<strong>30</strong> <strong>mobile</strong> ENEA MONICA(tm) + <strong>7</strong> <strong>fixed</strong>) along with <strong>3</strong> (fixed) + <strong>1</strong> (mobile) <strong>reference stations</strong> operated by Campania Regional Envronmental Protection Agency. The datalake is organized in 3 main directories respectively related to fixed nodes, mobile nodes and nearby reference stations including a mobile laboratory used for colocation campaigns; each subdirectory include its own metadata description file.</p> <p>Data, curated by Energy and Data Science Laboratory of ENEA, include multi-weeks colocation periods when low cost devices have been colocated with reference stations as well as operational periods during which sensors are deployed for fixed or mobile monitoring campaigns. Data have been recorded during 2021 and 2022 in a<strong> pervasive, multi-site, multi-seasonal deployment</strong> in Portici, a densely populated small area city (4km2, 55k + inhabitants) located 7km south of Naples, Italy.</p> <p>The datalake consists in actual sensors and reference intrumentations timeseries along with metadata description files with deployment dates and location data. The dataset files include high sampling frequency raw sensor data of quality-controlled sensor network along with co-located reference stations data sets. Sensor data include electrochemical sensors data (intended target pollutants: NO2, O3, CO), Optical sensor data (PM2.5, PM10, PM1) readings along with meteorological parameters. .</p> <p>Further description of sensors and reference instruments are reported in the accompanying paper (see citation request).</p> <p>The dataset can be used for </p> <ul> <li> <strong>advanced (remote/universal/in field) data driven calibration strategies</strong> test or development including <strong>machine learning </strong>models</li> <li><strong>mobile opportunistic data fusion</strong> methods development</li> <li><strong>geomatics and data assimilation</strong> models studies</li> </ul> <p>as well as low cost sensor characterization performance studies. </p>
Observed and WRF-simulated air temperature and wind speed at the Czech Hydrometeorological Institute weather stations Lučina, Lysá hora and Olomouc
<p>The dataset contains two csv files with observed 2-m air temperature and 10-m wind speed data at Lučina, Lysá hora and Olomouc meteorological stations in the Czech Republic and analogical time series produced by the Weather Research and Forecasting (WRF) model. The dataset covers a period of 27 October 2010, 01:00 UTC to 01 November 2010, 00:00 UTC. WRF output is given for three model configurations:</p> <p>1) QNSE boundary layer scheme</p> <p>2) 3DTKE boundary layer scheme with Revised MM5 surface layer scheme</p> <p>3) 3DTKE boundary layer scheme with MYNN surface layer scheme</p>
Banco de dados do projeto piloto de Pagamentos por Serviços Ambientais do Arroio Grande (Venâncio Aires, RS)
<p>Banco de dados do projeto piloto de Pagamentos por Serviços Ambientais do Arroio Grande (Venâncio Aires, RS).</p> <p>Projeto em andamento.</p> <p> </p> <table> <tbody> <tr> <td><strong>Arquivo</strong></td> <td><strong>Descrição</strong></td> </tr> <tr> <td>castelhano_db.gpkg </td> <td>Banco de dados georreferenciado</td> </tr> <tr> <td>layout_pip.zip</td> <td>PDFs com mapas internos e externos do projeto</td> </tr> <tr> <td>grande_lulc.zip</td> <td>Análise de mundança de uso do solo na região de interesse</td> </tr> <tr> <td>tier2.zip</td> <td>Fotografias e mapas internos dos imóveis</td> </tr> </tbody> </table>
Measurement database and line parameter database for air- and H2O-broadened CO2 in the 4750-5175 cm-1 region including continuum
<p>Air-broadened CO2 measurements in the temperature range 200-294 K and a total pressure range 100-1000 mbar and analysis were carried out within the ESA-funded project ISOGG (Improved Spectroscopy for satellite measurements Of Greenhouse Gases). Additionally, a H2O-broadened measurement at 294 K was recorded and analyzed.</p> <p>The measurement database is in “CO2_air-_and_H2O-broadened_measurements_4750-5175cm-1.zip” and the line parameter database in “CO2_air-_and_H2O-broadening_parameters_4750-5175cm-1_V4.zip”. The CO2/air continuum is given in “CO2_2mu0_RT_foreigncont_polcoeffs_from_scaled_4mu3_N2.txt”. The readme file is “Measurement database and line parameter database for air- and H2O-broadened CO2 in the 4750-5175 cm-1 region including continuum - Readme.docx”.</p> <p>Definitions and units of line parameters are in accordance with the HITRAN database and can be found in the readme file in <a href="https://zenodo.org/records/1009126">ESA SEOM-IAS – Spectroscopic parameters database 2.3 µm region (zenodo.org)</a>. Depletions calculated with the polynomials in temperature have the unit atm<sup>-1</sup>.</p> <p>Systematic uncertainties which were common for all lines were omitted in the line parameter database and are listed here: Air-broadening parameters 0.03%, speed-dependence of air-broadening 1.5%, air Dicke narrowing 10-20%, air line mixing 0.0001 atm<sup>-1</sup>, air pressure-induced line shift 3x10<sup>-5</sup> cm<sup>-1</sup>atm<sup>-1</sup>, temperature exponent of air-broadening parameters 0.0005, temperature dependence of air line mixing 0.1 K<sup>-1</sup>, temperature dependence of air pressure-induced line shift 5x10<sup>-7</sup> cm<sup>-1</sup>atm<sup>-1</sup>K<sup>-1</sup>. In case the speed-dependence was calculated from polynomials of gamma<sub>2</sub>/gamma<sub>0</sub> vs gamma<sub>0</sub> the systematic uncertainty is estimated to be 10%.</p>
Measurement database and line parameter database for air- and H2O-broadened CO2 in the 5970-6575 cm-1 region including continuum
<p>Air-broadened CO2 measurements in the temperature range 210-294 K and a total pressure range 100-1000 mbar and analysis were carried out within the ESA-funded project ISOGG (Improved Spectroscopy for satellite measurements Of Greenhouse Gases). Additionally, a H2O-broadened measurement at 294 K was recorded and analyzed.</p> <p>The measurement database is in “CO2_air-_and_H2O-broadened_measurements_5970-6575cm-1.zip” and the line parameter database in “CO2_air-_and_H2O-broadening_parameters_5970-6575cm-1_V2.zip”. The CO2/air continuum is given in “CO2_1mu6_RT_foreigncont_polcoeffs_from_scaled_4mu3_N2.txt”. The readme file is “Measurement database and line parameter database for air- and H2O-broadened CO2 in the 5970-6575 cm-1 region including continuum - Readme_V2.docx”.</p> <p>Definitions and units of line parameters are in accordance with the HITRAN database and can be found in the readme file in <a href="https://zenodo.org/records/1009126">ESA SEOM-IAS – Spectroscopic parameters database 2.3 µm region (zenodo.org)</a>. Depletions calculated with the polynomials in temperature have the unit atm<sup>-1</sup>.</p> <p>Systematic uncertainties which were common for all lines were omitted in the line parameter database and are listed here: Air-broadening parameters 0.07%, speed-dependence of air-broadening 1.5%, air line mixing 0.0002 atm<sup>-1</sup>, air pressure-induced line shift 6x10<sup>-5</sup> cm<sup>-1</sup>atm<sup>-1</sup>, temperature exponent of air-broadening parameters 0.003, temperature dependence of air line mixing 0.03 K<sup>-1</sup>, temperature dependence of air pressure-induced line shift 2x10<sup>-7</sup> cm<sup>-1</sup>atm<sup>-1</sup>K<sup>-1</sup>. In case the speed-dependence was calculated from polynomials of gamma<sub>2</sub>/gamma<sub>0</sub> vs gamma<sub>0</sub> the systematic uncertainty is estimated to be 10%.</p>
Seefeld Cold-Air Pool Experiment (SEECAP): WRF Simulation Output with snow cover January 16 2020 0000 UTC to January 17 2020 1200 UTC
<p>The Seefeld Cold-Air Pool Experiment (SEECAP) focused on the cross-country skiing area Olympiaregion Seefeld and in particular the topographic setting in the Nordic ski arena which favors the formation of cold-air pools and took place between December 2019 and March 2020. The measurement data are described in Rudolph (2022) and Rauchöcker et al. (2024d) and meteorological measurement data associated with SEECAP are published in Rauchöcker et al. (2024c). This upload contains WRF simulation output data for the night between January 16 and January 17 2020 with snow cover and the plotting routines to reproduce the figures in Rauchöcker et al. (2024d). The night between January 16 and January 17 2020 initially featured ideal condition for cold-air pool formation followed by a disturbance around midnight. There is also an upload with simulation output for the same night, but without snow cover (Rauchöcker et al., 2024a). Also available in a different dataset are data from a simulation with snow cover for the night between January 12 and January 13 2020 (Rauchöcker et al., 2024b), which featured an undisturbed cold-air pool for almost the entire night. This case was considered to feature in Rauchöcker et al. (2024d), but a different case was chosen because some measurement data was not available during this period.</p> <h3><strong>WRF Simulation Output</strong></h3> <p>This Dataset includes data generated with WRFlux v1.4.1 (Göbel et al., 2022), a fork of the Weather Research and Forecasting model WRF (Skamarock et al. 2021). WRFlux allows to calculate the contribution of different processes to the potential temperature tendency at each grid point. The data published here is from the innermost simulation domain with 40m horizontal resolution and 10m vertical resolution close to the surface. The simulations were initialized at 00:00 UTC January 16 2020 and run until 12:00 UTC January 17 2020.</p> <p>Three different simulations were performed: two simulations with modified snow cover as described in Rauchöcker (2022), one each with the MYNN 2.5-order and the SMS-3DTKE PBL parameterizations (a scheme that blends a PBL scheme and a LES subgrid parameteriztion in the greyzone of turbulence), and one without snow cover with the MYNN 2.5-order PBL parameterization. Otherwise the simulations were identical. This dataset includes the two simulation with snow cover, where <em>jan126_sms.zip</em> contains the files relating to the simulations with the SMS-3DTKE scheme and <em>jan16.zip</em> those for the simulation with the MYNN 2.5-order PBL parameterization. A detailed description of the model setup can be found in Rauchöcker et al (2024d) and in the files <em>namelist.input</em> and <em>namelist_sms.input</em> that were used for the simulations. </p> <p>Standard WRF output can be found in <em>wrfout_40m_jan16</em> and <em>wrfout_40m_jan16_sms</em>. The mean wind speed components, which were necessary to rotate the tendencies in a coordinate system that is aligned with the valley orientation, are contained in <em>windout_40m_jan16</em> and <em>windout_40m_jan16_sms</em>. These variables were contained in the unprocessed output files produced by WRFlux; the full files were unfortunately too large to be included here. The postprocessed tendencies are stored in <em>tend_40m_jan16.nc</em> and <em>tend_40m_jan16_sms.nc</em>.</p> <h3><strong>Plotting routines</strong></h3> <p>Python scripts and environment files to reproduce most figures in Rauchoecker et al. (2024d) are included in <em>code.zip</em>. To reproduce plots involving measurement data, which is available in Rauchöcker et al. (2024c), is also needed.</p> <p>Due to conflicts between some packages, two different environment were needed. To reproduce Figure 2, install the <em>orthoplot</em> environment by running "<em>conda env create orthoplot.yml</em>" in a terminal window, activate it ("<em>conda activate orthoplot</em>") and then run <em>ortho_plot.py</em>. All other plots require the wrfstuff environent (installed by running "<em>conda env create wrfstuff.yml</em>" and activated by "<em>conda activate wrfstuff</em>") and are produced by <em>paper_plots.py</em>. Functions used to load data and plot the figures are included in <em>dataload.py</em> and <em>plotting_routines.py</em>, respectively<em>.</em></p> <p>Two variables decide which figures are plotted for which dataset: <em>dataname</em> and <em>doplot</em>. The variable <em>dataname</em> defines the path to the <em>dataset</em> that should be used to produce the figures, while the value <em>doplot</em> defines which figure to reproduce. By setting doplot="fig1", Figure 1 is reproduced, while doplot="fig7" and doplot="fig9" reproduce Figures 7 and 9, respectively. For all other values for <em>doplot</em>, Figures 4, 5, 6, 8 and 11 are reproduced. We decided not to include a script to plot Figure 3 because the data the climatology is based on is owned by GeoSphere Austria - the agency operating the permanent weather station. Further, no script for reproducing Figure 10 is included because it was not created within the framework of Python.</p> <h3><strong>Geofiles</strong></h3> <p>The files included in <em>g</em><em>eofiles.zip</em> are needed to plot Figure 1 and Figure 2, although not of particular interest on their own. Included are output files from <em>geogrid.exe</em>, whicih are necessary to plot the domains overview (Figure 1), as well as an orthophoto (<em>orthophoto.tif</em>) and high-resolution digital elevation model (<em>topo_hr.tif</em>) which are both based on data from Land Tirol.</p>
Short-lived air pollutants and climate forcers through the lens of the COVID-19 pandemic
<p>The data in this repository is part of the paper titled "Short-lived air pollutants and climate forcers through the lens of the COVID-19 pandemic". The data is required to obtain a detrended lockdown effects on air quality. The raw data was downloaded from the European Centre for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis 4 (EAC4) product portal. More details of the data are listed below:</p> <p>"ozone_data.nc": Global mixing ratio of ozone (monthly)</p> <p>"pm_data.nc": Global mass concentration of fine particulate matters, and aerosol optical depth (AOD) at 550 nm (monthly)</p> <p>"BAU_clean_latest.csv": The pollution level under a business-as-usual (BAU) scenario, inferred from the historical pollution data by Theil-Sen linear regression (monthly)</p>
Schools Weather and Air Quality (SWAQ) –Metadata – Urban Network, Sydney (NSW)
<p>Schools Weather and Air Quality (SWAQ) is a citizen science project funded by the Department of Industry, Innovation and Science as part of its Inspiring Australia - Citizen Engagement Program. SWAQ is equipping public schools across Sydney with research-grade meteorology and air quality sensors, enabling students to collect and analyse research quality data through curriculum-aligned classroom activities.</p> <p>The network includes twelve automatic weather stations and seven automatic air quality stations, stretched from -33.5995° to -34.0424° latitude and from 150.6918° to 151.2706° longitude. The average spacing is 10.2 km and the average installation height is 2.5 m above ground level. Six meteorological parameters (dry-bulb temperature, relative humidity, barometric pressure, rain, wind speed, and wind direction) and six air pollutants (SO2, NO2, CO, O3, PM2.5, and PM10) are recorded via Vaisala WXT 536 and Vaisala AQT 420 with a 20 minutes sampling frequency. </p> <p>SWAQ data provides urban canopy layer observations of the intra-urban heterogeneity and inter-parameter dependency of all major urban climate and air quality variables, valuable across diverse urban disciplines. SWAQ stations are located where there are gaps in existing government networks, and focus on Sydney’s western suburbs, where the highest urbanization rate is taking place, to better inform future urban planning. QC procedures are designed to ensure observations of extreme episodes are not excluded. Beyond research purposes, SWAQ is a citizen-centered network, conceived to promote valuable STEM (science, technology, engineering, mathematics) skills among citizens and students.</p> <p>This collection includes the metadata files for all SWAQ stations, in pdf. Metadata describe the site (type, geographic coordinates, elevation, orographic setting, representativeness, local climatic zone, dominant land use, percent land cover, mean tree and building heights, proximity to water/heat and pollutants sources/sinks, estimation of Davenport Roughness, traffic density, sky view factor), and the instrumentation (variables, models, manufacturers, calibration and installation dates). Site characteristics are described at three radial scales: 20 km, 500 m, and 50 m. Graphical representations include: satellite images, street-view maps, cardinal direction photographs, panoramic photos, and close-up photos of sensors, solar panels, and connections. Optimum site allocation was determined by undertaking a multi-criteria weighted overlay analysis to ensure data representativeness and quality. All SWAQ sensors are installed:</p> <ul> <li>in homogenous urban regions, without sections of anomalous variation in the regional urban makeup and aspect-ratio, and without large, concentrated heat/pollution sources or sinks;</li> <li>in areas falling into the WMO Class 4 with no electromagnetic sources that could have distorted the transmission;</li> <li>at a constant height of 2 - 3.5 m above ground level.</li> </ul> <p>The actual data is available from the Australian Terrestrial Ecosystem Research Network (TERN) <a href="https://https://portal.tern.org.au/schools-weather-air-sydney-nsw/22077">data portal</a> and is regularly updated. The data available from TERN has undergone a rigorous quality check routine before upload.</p> <ul> <li>Calibration: Sensors and gateways are calibrated and tested by Vaisalain controlled conditions </li> <li>Quality Assurance: Annual maintenance log</li> <li>Quality control: continuity tests, fixed range tests (on both physical and instrumental limits), dynamic range and step tests (both performed on a monthly basis), internal consistency tests (on known atmospheric relations) and persistence tests. </li> </ul> <p>The files are in csv format. On the <a href="https://www.swaq.org.au">SWAQ website</a> a non quality controlled subset is available for educational purposes only.</p> <p>This project was funded by an Australian Government Department of Industry, Innovation and Science, Inspiring Australia – Science Engagement Program: Citizen Science Grants (CSG56028).</p> <p>It was also part of the Centre of Excellence for Climate Extremes research project "Attribution & Risk".</p> <p>More information is available in the readme file, in particular a full list of the variables and a legend for the quality flags used.</p>
Britain Breathing 2016-2019 Air Quality and Meteorological Regional Estimates Dataset
<p>This data set is a collection of estimated daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for the <em>UK and crown dependencies</em> postcode districts (e.g. 'AB') for the years 2016-2019, inclusive.</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 data uses a 'concentric regions' method to estimate the measurement for all regions, as follows. If measurements exist within the region, the mean of those measurements is used, if not, then a ring of neighbouring postcode regions are selected, and the mean of their measurement values used. If no measurement sites/data are found in the first ring, the process continues, taking the next ring of postcode district regions, working outwards until one or more sensors are found in a ring. As well as the measurement estimations, the number of rings required to find site data and make the estimations is also published. <strong>As a result, please note that estimations with higher ring counts ('rings') are likely to be calculated from more distant sensors. This distance depends upon the size of the postcode regions surrounding the location being estimated. Please use the ring count ('rings') to limit/filter estimations based on your required level of confidence.</strong><br> <br> The meteorological, pollen and air quality measurement data used to make the regional estimations can be found at <a href="https://zenodo.org/record/4416028#.YABxNnX7RhF">this Zenodo archive</a>. The data there 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 code used to make the estimations is available at <a href="https://zenodo.org/record/4518866">this Zenodo archive</a>.</p> <p>The postcode data in postcode_district_data.csv are collated from several sources: </p> <ul> <li><a href="https://www.doogal.co.uk/UKPostcodes.php">https://www.doogal.co.uk/UKPostcodes.php</a> (population figures for the UK (UK Census 2011))</li> <li><a href="https://www.freemaptools.com/download-uk-postcode-outcode-boundaries.htm">https://www.freemaptools.com/download-uk-postcode-outcode-boundaries.htm</a> (postcode boundary polygons for UK and crown dependancies)</li> <li><a href="https://www.gov.gg/population">https://www.gov.gg/population</a> (Guernsey (GY) population data for end June 2020) </li> <li><a href="https://www.gov.je/Government/JerseyInFigures/Population/Pages/Population.aspx">https://www.gov.je/Government/JerseyInFigures/Population/Pages/Population.aspx</a> (Jersey (JE) population data for end 2019) </li> <li><a href="https://www.gov.im/media/1369690/isle-of-man-in-numbers-july-2020.pdf">https://www.gov.im/media/1369690/isle-of-man-in-numbers-july-2020.pdf</a> (Isle of Man (IM) population data for April 2016)</li> </ul> <p>The data-set is presented in CSV format, as six files:</p> <ol> <li>postcode_district_data.csv: location metadata (region_id, geometry, description, population, country)</li> <li>regional_site_counts.csv: a table showing the number of sites for each measurement (columns), for each region_id (rows). region_id's match those in the postcode_district_data.csv file.</li> <li>turing_regional_estimates_aq_daily_met_pollen_pollution_imputed_data.csv: uses imputed site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)</li> <li>turing_regional_estimates_aq_daily_met_pollen_pollution_original_data.csv: uses original site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)</li> <li>turing_regional_estimates_aq_loc_type_daily_imputed_data.csv: uses imputed site data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)</li> <li>turing_regional_estimates_aq_loc_type_daily_original_data.csv: uses original data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)</li> </ol> <p>* Air quality site types: </p> <ul> <li>Industrial: comprises 'urban industrial' (9 sites) and suburban industrial (2 sites)</li> <li>'Rural background' (14 sites)</li> <li>'Urban background' (48 sites)</li> <li>'Urban traffic' (47 sites)</li> </ul>
Britain Breathing 2020 Air Quality and Meteorological Regional Estimates Dataset
<p>This data set is a collection of estimated daily mean and maximum values for a range of air quality and meterological measurements and model forecasts for UK postcode districts (e.g. 'AB') for the year 2020.</p> <p>The data uses a 'concentric regions' method to estimate the measurement for all regions, as follows. If measurements exist within the region, the mean of those measurements is used, if not, then a ring of neighbouring postcode regions are selected, and the mean of their measurement values used. If no measurement sites/data are found in the first ring, the process continues, taking the next ring of postcode district regions, working outwards until one or more sensors are found in a ring. As well as the measurement estimations, the number of rings required to find site data and make the estimations is also published. <strong>As a result, please note that estimations with higher ring counts ('rings') are likely to be calculated from more distant sensors. This distance depends upon the size of the postcode regions surrounding the location being estimated. Please use the ring count ('rings') to limit/filter estimations based on your required level of confidence.</strong></p> <p>The meteorological, pollen and air quality measurement data used to make the regional estimations can be found at <a href="https://zenodo.org/record/4740965#.YPWJf3VKhhF">this Zenodo archive</a>. The data there 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 code used to make the estimations is available at <a href="https://zenodo.org/record/4518866">this Zenodo archive</a>.</p> <p>The data-set is presented in CSV format, as two files:</p> <ol> <li>turing_regional_estimates_aq_daily_met_pollen_pollution_original_data.csv: uses original site data (timestamp, region_id, ...[measurement name, rings]) ('rings' is the number of rings required to make the estimation)</li> <li>turing_regional_estimates_aq_loc_type_daily_original_data.csv: uses original data. Air quality regional estimates are calculated using specific AQ site location types* separately. (To prevent, for example, 'Traffic Urban' type sites being used to estimate 'non-traffic' or rural regions.)</li> </ol> <p>* Air quality site types: </p> <ul> <li>Industrial: comprises 'urban industrial' (9 sites) and suburban industrial (2 sites)</li> <li>'Rural background' (14 sites)</li> <li>'Urban background' (48 sites)</li> <li>'Urban traffic' (47 sites)</li> </ul>
Datasets and analysis scripts for air-sea flux study using CESM-MOM6
<p>This repository provides the fully-coupled CESM-MOM6 simulation datasets for the ocean surface and the analysis scripts for studying air-sea flux variability. This study aimed to quantify the effects of the stochastic ocean density corrections on the ocean-intrinsic component of air-sea fluctuations, which is the strongest at mesoscales, i.e., 10-1000 Km. </p>
5D-NP-FABTECH_SALV - Open Dataset for "3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction"
<p>This is the open dataset for the paper: "Omar Tricinci*, Francesca Pignatelli, Virgilio Mattoli*, 3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction, On line (2023) [DOI: 10.1002/adfm.202206946] "</p> <p>This include the Supplementary Information file ("SI-PaperSalvinia3_PostRevOKV2.pdf.pdf"), all the source material used for the paper preparation and more. </p> <p>For each folder (sub-dataset) there is a corresponding readme file describing the content and including metadata</p>
Dataset for Numerical modeling of air-vented parallel plate ionization chambers for ultra-high dose rate applications
<p>Dataset for paper: Jose Paz-Martín et al., <a href="https://www.sciencedirect.com/journal/physica-medica">Physica Medica</a> <a href="https://www.sciencedirect.com/journal/physica-medica/vol/103/suppl/C">Volume 103</a>, November 2022, Pages 147-156</p> <p><a href="https://doi.org/10.1016/j.ejmp.2022.10.006">https://doi.org/10.1016/j.ejmp.2022.10.006</a></p>
Remote Sensing based Sea Surface partial pressure of CO2 (pCO2) and air-sea CO2 flux (FCO2) in the East China Sea (2003-2019)
<p>Based on <em>in situ</em> seawater <em>p</em>CO<sub>2</sub> data collected on 51 cruises/legs over the past two decades, a satellite retrieval algorithm for seawater <em>p</em>CO<sub>2</sub> was developed by combining the semi-mechanistic algorithm and machine learning method (MeSAA-ML). MeSAA-ML introduces semi-analytical parameters, including the temperature-dependent seawater <em>p</em>CO<sub>2</sub> (<em>p</em>CO<sub>2,therm</sub> ) and upwelling index (<em>UI<sub>SST</sub></em>), to characterise the combined effect of atmospheric CO<sub>2</sub> forcing, thermodynamic effects, and multiple mixing processes on seawater <em>p</em>CO<sub>2</sub>. Additionally, considering the biological effects and various sub-regional features, multiple ocean colour parameters were also used as inputs in XGBoost, the best-selected machine learning algorithm. Independent cruise-based data were used to validate the satellite-derived <em>p</em>CO<sub>2</sub>, which achieved excellent performance in this complicated marginal sea, with low root mean square error (RMSE=19.6 μatm) and mean absolute percentage deviation (APD=4.12%). Air-sea CO2 fluxes are calculated based on retrieved seawater <em>p</em>CO<sub>2</sub>. </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.