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3,018 results for “AIR”
Crowdsourced air traffic data from The OpenSky Network 2020 [CC-BY]
<p><strong>WARNING! </strong>This dataset is no longer updated after January 2022. Refer to the <a href="https://doi.org/10.5281/zenodo.3737101">original dataset</a> with different license terms for an up to date version.</p> <p><strong>Motivation</strong></p> <p>The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic.</p> <p><strong>License</strong></p> <p>Creative Commons CC-BY</p> <p>The only difference with the <a href="https://doi.org/10.5281/zenodo.3737101">original dataset</a> comes from anonymised aircraft information.</p> <p><strong>WARNING:</strong>This dataset is now longer updated after January 2022. The original dataset is still updated.</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <ul> <li>Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found.</li> <li>Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database.</li> </ul> <p><strong>Description of the dataset</strong></p> <p>One file per month is provided as a csv file with the following features:</p> <ul> <li><strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)</li> <li><strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API)</li> <li><strong>aircraft_uid</strong>: a unique anonymised identifier for aircraft;</li> <li><strong>typecode</strong>: the aircraft model type (when available);</li> <li><strong>origin</strong>: a four letter code for the origin airport of the flight (when available);</li> <li><strong>destination</strong>: a four letter code for the destination airport of the flight (when available);</li> <li><strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network;</li> <li><strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network;</li> <li><strong>day</strong>: the UTC day of the last message received by the OpenSky Network;</li> <li><strong>latitude_1</strong>, <strong>longitude_1</strong>, <strong>altitude_1</strong>: the first detected position of the aircraft;</li> <li><strong>latitude_2</strong>, <strong>longitude_2</strong>, <strong>altitude_2</strong>: the last detected position of the aircraft.</li> </ul> <p><strong>Examples</strong></p> <p>Possible visualisations and a more detailed description of the data are available at the following page:<br> <<a href="https://traffic-viz.github.io/scenarios/covid19.html">https://traffic-viz.github.io/scenarios/covid19.html</a>></p> <p><strong>Credit</strong></p> <p>Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders<br> <strong>"</strong>Crowdsourced air traffic data from the OpenSky Network 2019–2020<strong>"</strong><br> <em>Earth System Science Data</em> 13(2), 2021<br> <a href="https://doi.org/10.5194/essd-13-357-2021">https://doi.org/10.5194/essd-13-357-2021</a></p> <p> </p>
Air exchange rate dataset of the BELUVA project
<p>This dataset is extracted from the DFG (German Research Foundation) funded project BELUVA. The project is about the determination of air exchange rates in naturally ventilated barns and validation of prediction model.</p> <p>Series of numerical air flow simulations inside and around naturally ventilated barns were undergone for five different parameter, the length to width ratio of the barn (LW), the side curtains position (Curt), the incoming air temperature (T), the incoming air velocity magnitude (Vel) and the incoming air velocity direction (Theta), see the figures “parameters_tree” and “Convection_and_Temp-Vel-pair”. More details can be found in the following papers:</p> <p>On Finding the Right Sampling Line Height through a Parametric Study of Gas Dispersion in a NVB (<a href="https://doi.org/10.3390/app11104560">https://doi.org/10.3390/app11104560</a>)</p> <p>A Parametric Model for Local Air Exchange Rate of Naturally Ventilated Barns (<a href="https://doi.org/10.3390/agronomy11081585">https://doi.org/10.3390/agronomy11081585</a>)</p> <p>50 planes of equal distance inside the barn from 0.39 m to 11.66 m height have been designed. Each plan contains a grid of points 100 along the width of the barn and 2, 3 or 4 x 100 points along the length L=2, 3 or 4 x W respectively.</p> <p>Each file contains the coordinates of the grid points of one plan as well as the corresponding, ammonia concentration, carbon dioxide concentration and the Cartesian velocity components. The nomenclature of the file is as follows:</p> <p>Yplane_LW_Curt_Conv_Temp_Vel_Theta_Yx-yz</p> <p>LW: the length to width ratio, LW2, LW3 or LW4 corresponding to L=2, 3, 4 x W</p> <p>Curt: the curtain position, Open (without curtain), OpenUp (curtain from floor to middle opening height) or OpenDown (curtain from roof to middle opening height)</p> <p>Conv: the flow convection type, For (forced convection), Mix (mixed convection), Nat (natural convection)</p> <p>Temp: the temperature, written x-y meaning x.y in Celsius (°C), for example 30-0 means 30°C</p> <p>Vel: the air velocity magnitude, written x-y meaning x.y in m/s, for example 2-7 means 2.7 m/s</p> <p>Theta: the air inlet angle, 0, 45 or 90 deg, see figure “Inlet-angle_and_Length-to-width-ratio”</p> <p>Yx-yz: x-yz referring to the height of the plane x.yz, for example 3-15 means 3.15 m</p> <p>The python script “contour.py” represents an example of use of the dataset. Here it serves to obtain contour plots of the temperature, velocity magnitude and ammonia and carbon dioxide concentrations from any case LW_Curt_Conv_Temp_Vel_Theta_Yx-yz. Once the command “python contour.py” is given in the terminal, the user just needs to follow the instructions and the contour plots will be created in the folder “Plots”.</p>
Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic
<ul> <li>Supporting datasets for paper "Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic". </li> <li>Those are a subset of the (analyzed) datasets from WRF control simulation "ERA5" in netcdf format. See manuscript for more details. <ul> <li>cld_size.nc: cloud object size</li> <li>cld_ort_2020-03-01_15_00_00.nc: cloud object at 15:00 UTC</li> <li>hydro-02-2020-03-01_15/00/00.nc: water path sample data at 15:00 UTC</li> <li>wrfout_d02_2020-03-01_15/00/00: wrf output sample data at 15:00 UTC</li> </ul> </li> </ul>
Dataset for "LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225"
<p>This excel file contains the raw data used in the paper " LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events; DOI: 10.3390/s20216225 " In particular it comprises sensor measurements and pollutant data from the automated air quality monitoring stations in the Tarragona area.</p>
Data for "Schlieren and BOS velocimetry of a round turbulent helium jet in air"
<p>This is a subset of data used in our publication <a href="https://arxiv.org/abs/2202.04122">Schlieren and BOS velocimetry of a round turbulent helium jet in air</a> </p> <p> </p> <p>In the four helium-jet schlieren-image datasets given below, the nozzle diameter is 1.4 mm, the camera frame rate is 6000 fps and the individual image exposure is 0.0001667 s. 3000 images are provided for each dataset, or 1/2 s in real-time, which we determined to provide statistically-adequate data. Please see the <a href="https://arxiv.org/abs/2202.04122">paper</a> for more detail.</p> <p><strong>20210125-Run1</strong>: traditional mirror-schlieren, $Re_d = 5,890$, $U_j = 436$ m/s, and the scale is 0.29 mm/pixel.</p> <p><strong>20210206-Run1</strong>: traditional mirror-schlieren, $Re_d = 11,300$, $U_j = 682$ m/s, and the scale is 0.29 mm/pixel.</p> <p><strong>20210419-Run1</strong>: background-oriented schlieren (BOS), $Re_d = 5,890$, $U_j = 436$ m/s, and the scale is 0.26 mm/pixel. These are raw BOS images that must be processed with a reference image in order to yield pseudo-schlieren results. The reference (flow-off, tare) image is the first image in the sequence and is so named.</p> <p><strong>20210420-Run1</strong>: background-oriented schlieren (BOS), $Re_d = 11,300$, $U_j = 682$ m/s, and the scale is 0.26 mm/pixel. These are raw BOS images that must be processed with a reference image in order to yield pseudo-schlieren results. The reference (flow-off, tare) image is the first image in the sequence and is so named.</p>
Data used to create figures and tables in the ACP manuscript "Two-way coupled meteorology and air quality models in Asia: a systematic review and meta-analysis of impacts of aerosol feedbacks on meteorology and air quality" by Gao et al. (2022)
<p>This dataset contains the original data that extracted from all collected papers refering applications of two-way coupled models in Asia. It is supplied to the review paper, which titled as "Review on two-way coupled meteorology and air quality models in Asia: impacts of aerosol feedbacks on meteorology and air quality". The dataset includes three excel files (in the format of xlsx) as follows:</p> <p>1. Basic information of literatures (Table S1.xlsx)</p> <p>2. Model performance metrics (Table S2.xlsx)</p> <p>3. Quantitative results of aerosol effects on meteorological and air quality variables (Table S3.xlsx)</p> <p>4. Basic information of model setup for two-way coupled model applications in Asia (Table S4.xlsx)</p> <p>5. Summary of aerosol-induced variations of simulated shortwave and longwave radiative forcing at the bottom and top of atmosphere and in the atmosphere in Asia (Table S5.xlsx)</p> <p>.</p>
Ambient air sensor data for 2022-01-17
<p>Raw data from ambient air sensors BME680 for 2022-01-17 at</p> <p>CIE: Centro de Intercambios Escolares (28049 Madrid, Spain)</p> <p>CFA Taller de Naturaleza "Villaviciosa de Odón" (28300 Villaviciosa de Odón, Spain)</p> <p>IES "Luis Cobiella Cuevas" (38700 Santa Cruz de La Palma, Spain)</p> <p>Data begin at 08:00 because station at La Palma is connected daily at that time</p> <p>UNITS:</p> <p>Madrid time (CET - GMT +1)</p> <p>Air temperature T / ºC</p> <p>Relative Humidity RH / %</p> <p>Barometric pressure P / hPa</p>
Air stagnation index
<p>Daily air stagnation index datasets in China (2010-2021).</p> <p>Please refer to the study "An air stagnation index to qualify extreme haze events in northern China"(10.1175/JAS-D-17-0354.1).</p>
Estimating historical air-sea CO2 fluxes: Incorporating physical knowledge within a data-only approach
<p>Reconstructed surface ocean pCO2 and air-sea CO2 fluxes for 1990-2019 using the pCO2-Residual Approach (JAMES 2021MS002960, in review)</p> <p>Surface ocean pCO2 (spo2) and resulting estimates of the air-sea CO2 flux (fCO2) are included in the netcdf file at monthly temporal resolution and for 1x1 grid cell spatial resolution. SeaFlux (https://zenodo.org/record/5482547#.YlT72y-B0_U) variables are used to calculate the fluxes from surface ocean pCO2.</p>
ERA5-Land weekly: Air temperature at 2 meter above surface, weekly time series for Europe at 1 km resolution (2016 - 2020)
<p>Overview:<br> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.</p> <p>Air temperature (2 m):<br> Temperature of air at 2m above the surface of land, sea or in-land waters. 2m temperature is calculated by interpolating between the lowest model level and the Earth's surface, taking account of the atmospheric conditions.</p> <p>Processing steps:<br> The original hourly ERA5-Land data has been spatially enhanced from 0.1 degree to 30 arc seconds (approx. 1000 m) spatial resolution by image fusion with CHELSA data (V1.2) (https://chelsa-climate.org/). For each day we used the corresponding monthly long-term average of CHELSA. The aim was to use the fine spatial detail of CHELSA and at the same time preserve the general regional pattern and fine temporal detail of ERA5-Land. The steps included aggregation and enhancement, specifically:<br> 1. spatially aggregate CHELSA to the resolution of ERA5-Land<br> 2. calculate difference of ERA5-Land - aggregated CHELSA<br> 3. interpolate differences with a Gaussian filter to 30 arc seconds<br> 4. add the interpolated differences to CHELSA</p> <p>The spatially enhanced daily ERA5-Land data has been aggregated on a weekly basis starting from Saturday for the time period 2016 - 2020.<br> Data available is the weekly average of daily averages, the weekly minimum of daily minima and the weekly maximum of daily maxima of air temperature (2 m).</p> <p>File naming:<br> Average of daily average: <code>era5_land_t2m_avg_weekly_YYYY_MM_DD.tif</code><br> Max of daily max: <code>era5_land_t2m_max_weekly_YYYY_MM_DD.tif</code><br> Min of daily min: <code>era5_land_t2m_min_weekly_YYYY_MM_DD.tif</code></p> <p>The date in the file name determines the start day of the week (Saturday).</p> <p>Pixel value:<br> °C * 10<br> Example: Value 44 = 4.4 °C</p> <p>The QML or SLD style files can be used for visualization of the temperature layers.</p> <p>Coordinate reference system:<br> ETRS89 / LAEA Europe (EPSG:3035) (EPSG:3035)</p> <p>Spatial extent:<br> north: 82:00:30N<br> south: 18N<br> west: 32:00:30W<br> east: 70E</p> <p>Spatial resolution:<br> 1km</p> <p>Temporal resolution:<br> weekly</p> <p>Time period:<br> 01/01/2016 - 12/31/2020</p> <p>Format: GeoTIFF</p> <p>Representation type: Grid</p> <p>Software used:<br> GDAL 3.2.2 and GRASS GIS 8.0.0 (r.resamp.stats -w; r.relief)</p> <p>Lineage:<br> Dataset has been processed from original Copernicus Climate Data Store (ERA5-Land) data sources. As auxiliary data CHELSA climate data has been used.</p> <p>Original ERA5-Land dataset license:<br> <a href="https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf">https://cds.climate.copernicus.eu/api/v2/terms/static/licence-to-use-copernicus-products.pdf</a></p> <p>CHELSA climatologies (V1.2):<br> Data used: Karger D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E, Linder, H.P., Kessler, M. (2018): Data from: Climatologies at high resolution for the earth's land surface areas. Dryad digital repository. <a href="http://dx.doi.org/doi:10.5061/dryad.kd1d4">http://dx.doi.org/doi:10.5061/dryad.kd1d4</a><br> Original peer-reviewed publication: Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, P., Kessler, M. (2017): Climatologies at high resolution for the Earth land surface areas. Scientific Data. 4 170122. <a href="https://doi.org/10.1038/sdata.2017.122">https://doi.org/10.1038/sdata.2017.122</a></p> <p>Other resources:<br> <a href="https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b">https://data.mundialis.de/geonetwork/srv/eng/catalog.search#/metadata/601ea08c-0768-4af3-a8fa-7da25fb9125b</a></p> <p>Processed by:<br> mundialis GmbH & Co. KG, Germany (<a href="https://www.mundialis.de/">https://www.mundialis.de/</a>)</p> <p>Contact:<br> mundialis GmbH & Co. KG, info@mundialis.de</p>
PALEODEM/ Supplementary materials of the manuscript "Unraveling Early Holocene occupation patterns at El Arenal de la Virgen (Alicante, Spain) open-air site: an integrated palimpsest analysis"
<p>This repository hosts the R code scripts and datasets that allow reproducibility and replicability of the intra-site spatial analyses implemented in the paper:</p> <p>Rabuñal, J.R., Gómez-Puche, M., Polo-Díaz, A., Fernández-López de Pablo, J., 2022. Unraveling Early Holocene occupation histories at open-air sites through integrated chronological, archaeostratigraphical, lithic refitting and spatial analyses: the Arenal de la Virgen (Villena, Alicante) study case. SocArXiv.</p> <p>Contents:</p> <p>AV_Spatial_database.xlsx: main dataset for the intra-site spatial analysis.</p> <p>AV_Lcross_database.xlsx: dataset for the implementation of the cross-type L function.</p> <p>AV_2clusters.rds: dataset for the calculation of the artifact metrics.</p> <p>AV_MovingWindow_Results.csv: dataset with the results of the calculation of the Burnt Microdebris Index and its spatial autocorrelation analysis.</p> <p>AV_DBSCAN_Separation.R: R file containing the code used for the separation of the lithic spatial distribution using the DBSCAN automated density-based clustering algorithm.</p> <p>AV_Spatial_analysis.R: R file containing the code used for the intra-site spatial analysis.</p> <p>AV_MWA_Moran.R: R file containing the code for implementing the calculation and spatial autocorrelation analysis of the Burnt Microdebris Index.</p>
Air pollution, atmospheric and local meteorological data for Graz, Austria from 2014 to end of 2021
<p>The data covers a timeframe from January 2014 to November 2021 in a daily frequency, and covers two sources:</p> <ul> <li>The environmental and pollutant data was provided by the Austrian government under the following license: CC-BY-4.0: Land Steiermark - <a href="http://data.steiermark.gv.at">data.steiermark.gv.at</a> <ul> <li>Air quality (<em>Lovric_et_al_air_pollutants.csv</em>) by means of NO<sub>2</sub>, NO, NO<sub>x</sub>, PM<sub>10</sub> and O<sub>3</sub> was measured at five sites in Graz, Austria (Süd (<em>eng. South</em>) - S, Nord (<em>eng. North</em>) - N, West (<em>eng. West</em>) - W, Don Bosco – D, Ost (<em>eng. East</em>) – O). In addition weather conditions like temperature, percipitation, relative humidity, pressure, wind speed and direction are added (<em>Lovric_et_al_local_meteorology.csv</em>)</li> </ul> </li> <li>The ERA5-Land data (<em>Lovric_et_al_era5_recalculated.csv</em>) is subject to the Copernicus licence from following source <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcds.climate.copernicus.eu%2Fcdsapp%23!%2Fdataset%2F10.24381%2Fcds.e2161bac%3Ftab%3Doverview&data=05%7C01%7Cmlovric%40know-center.at%7C2ba06457329349623a5608da631632c9%7C0d3c92e977ae4f49bd126ff29e8f1c37%7C0%7C0%7C637931244242754711%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=lt5NcIfbIRGse01Naha8bolxEkdtLmyp2VNcrz38Rk8%3D&reserved=0">https://cds.climate.copernicus.eu/cdsapp#!/dataset/10.24381/cds.e2161bac?tab=overview</a> <ul> <li>it includes following variables : <ul> <li>Cloud_Cover_Mean</li> <li>Temperature_Air_2m_Max_Day_Time</li> <li>Temperature_Air_2m_Min_Night_Time</li> <li>Wind_Speed_10m_Mean</li> </ul> </li> </ul> </li> </ul>
Air_production_machine_09/29/22
Documentation material from the Mastic pilot of the Mingei project
Classification of Artificial Intelligence and eXplainable Artificial Intelligence publications in Air Traffic Management
<p>v1.0 version used and partially published in "A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory". In this version, it references mainly Transportation Reasearch Part C, ICRAT, Journal of ATM, and ATM Seminar, IEEE transaction on ITS, but not only</p>
Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak
<p>Dataset of model simulations discussed in paper "Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak", <a href="https://doi.org/10.1016/j.atmosres.2022.106302">https://doi.org/10.1016/j.atmosres.2022.106302</a></p>
Challenges of high-fidelity air quality modeling in urban environments - PALM sensitivity study during stable conditions (TURBAN)
<h3>Introduction</h3> <p>This dataset contains the PALM model inputs and the source code used to create the simulations for Prague-Legerova scenarios performed in the scope of the <strong>TURBAN</strong> project (<a href="https://www.project-turban.eu/">https://www.project-turban.eu/</a>). Detailed description of the simulations is provided in the referencing scientific paper.</p> <h3>List of simulations</h3> <table> <tbody> <tr> <td><strong>Scenario name</strong></td> <td><strong>Days simulated</strong></td> <td><strong>IBC</strong></td> <td><strong>Configuration changes</strong></td> </tr> <tr> <td>legerovas_s6_sens_base</td> <td>13–15 February 2023</td> <td>ICON</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_dtmax</td> <td>13 February 2023</td> <td>ICON</td> <td>dt_max=0.2</td> </tr> <tr> <td>legerovas_s6_sens_heat</td> <td>13 February 2023</td> <td>ICON</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_sgs</td> <td>13 February 2023</td> <td>ICON</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_stg</td> <td>13 February 2023</td> <td>ICON</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_alad</td> <td>13–15 February 2023</td> <td>ALADIN</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_alad_heat</td> <td>13 February 2023</td> <td>ALADIN</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_alad_sgs</td> <td>13 February 2023</td> <td>ALADIN</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_alad_stg</td> <td>13 February 2023</td> <td>ALADIN</td> <td>STG_PROFILES added</td> </tr> <tr> <td>legerovas_s6_sens_wrf</td> <td>13–15 February 2023</td> <td>WRF</td> <td>-</td> </tr> <tr> <td>legerovas_s6_sens_wrf_heat</td> <td>13 February 2023</td> <td>WRF</td> <td>car anthropogenic heat (custom code)</td> </tr> <tr> <td>legerovas_s6_sens_wrf_sgs</td> <td>13 February 2023</td> <td>WRF</td> <td>e_min=0.02</td> </tr> <tr> <td>legerovas_s6_sens_wrf_stg</td> <td>13 February 2023</td> <td>WRF</td> <td>STG_PROFILES added</td> </tr> </tbody> </table> <h3>Directory structure</h3> <p>The directory inputs contains the model inputs and it is further divided into these subdirectories:</p> <p>- inputs/common: The PALM static driver and the emission drivers for the parent and child domains. These files are common to all simulations</p> <p>- inputs/dynamic/*: These directories contain the dynamic drivers for the parent and child domanis, which contain the initial and boundary conditions (IBC) as well as external radiation data. The three subdirectories aladin, icon and wrf contain IBCs created from the respective mesoscale model outputs. </p> <p>- inputs/legerovas_s6_sens_*: These directories contain the PALM model configuration (p3d) for both domains for each simulation.</p> <p>- inputs/build_config: The included .palm.iofiles configuration file ensures that the files STG_PROFILES are correctly copied from the input directory.</p> <p>The directory palm_sources contains the exact model source used for the simulations. It is derived from the PALM model release 23.04 with additional bugfixes. There are two source archives:</p> <p>- heat.tar.gz: PALM source further modified to include anthropogenic heat from cars, used for the simulations legerovas_s6_sens_*_heat</p> <p>- standard.tar.gz: PALM source used for all other included simulations.</p> <h3>Reproducing the simulations</h3> <p>In order to reproduce the simulations, unpack the respective source code archive and follow the standard installation, configuration and build procedures described in the README.md file within the archive and on the PALM model website http://www.palm-model.org/. Then copy the input files for the respective simulation in the JOBS directory. The common files and the dynamic driver files need to be renamed so that they match the prefix given by the name of the simulation, as is described in the PALM model documentation.</p>
Plasmids Identified in Air Metagenomes
<p> Metagenomic data were selected in Web of Science (Clarivate) on October 2022 using keywords: txid655179[Organism:noexp] AND metagenome [Filter]; AIR Metagenome; Air microbiome; Troposphere; Aerosol; Atmosphere. Data were manually curated to remove sequencing originated from metabarcoding data (i.e., 16S). The assembled data supplied by MetaSUB consortium (Danko et al., 2021) when available was used for air metagenome in the built environments. </p> <div> <p>Plasmid contents were predicted using the assembled data. Metagenomes sequencing by Illumina (paired-illumina reads) were assembled by using megahit 1.2.9 with metalarge option (Li et al., 2015) after cleaning data with bbduk2 (qtrim=rl trimq=28 minlen=25 maq=20 ktrim=r k=25 mink=11 and a list of adaptators to remove) from bbtools suite (<a href="https://jgi.doe.gov/data-and-tools/software-tools/bbtools/" target="_blank" rel="noreferrer noopener">https://jgi.doe.gov/data-and-tools/software-tools/bbtools/</a>) </p> <div> <p><span><span>Plasmids were predicted for each assembling by using scripts describing in-depth in Hilpert et al. (Hilpert </span></span><span><span>et al.</span></span><span><span>, 2021; </span><span>Hennequin</span> </span><span><span>et al.</span></span><span><span>, 2022) and available in </span><span>github</span><span> website (</span></span><span><span><span>https://github.com/meb-team/PlasSuite/</span></span></span><span><span>). Briefly, contigs were analyzed using both reference-based and reference-free approaches.</span></span><span><span> The databases employed included those for chromosomes (archaea and bacteria) and plasmids from NCBI, as well as the MOB-suite tool (Robertson and Nash, 2018</span><span>) ,</span><span> SILVA (Quast </span></span><span><span>et al.</span></span><span><span>, 2013) and phylogenetic markers harbored by chromosomes (Wu </span></span><span><span>et al.</span></span><span><span>, 2013). Two reference-free methods were applied to contigs that were not affiliated with chromosomes (discarded) or plasmids (</span><span>retained</span><span> in the first step): </span><span>PlasFlow</span><span> (Krawczyk et al., 2018) and </span><span>PlasClass</span><span> (Pellow </span></span><span><span>et al.</span></span><span><span>, 2020). Viruses were removed by using </span><span>viralVerify</span><span> (</span></span><span><span><span>https://github.com/ablab/viralVerify</span></span></span><span><span>) (Antipov </span></span><span><span>et al.</span></span><span><span>, 2020) that provides in parallel provide plasmid/non-plasmid classification</span><span>. </span><span> </span><span>The database built for this purpose is available at this address </span></span><span><span><span>https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases</span></span></span> <span><span> Eukaryotes contaminants were removed by aligning the sequences against NT databases and human chromosomes (GRCh38) with minimap2 with -x asm5 </span><span>option (Li, 2018)</span><span>. Contigs mapping with an identity of 95% and a coverage of 80% were removed.</span></span><span> the final plasmidome set was clustered by mmseqs (Mirdita, Steinegger and Söding, 2019) with 80% of coverage and 90% of identity (--min-seq-id 0.90 -c 0.8 --cov-mode 1 --cluster-mode 2 --alignment-mode 3 --kmer-per-seq-scale 0.2). </span></p> </div> </div>
Air Quality and Exposure Disparity Results for the Bronx, New York City
<p>This is the dataset accompanying the publication "Big Mobility Data Reveals Hyperlocal Air Pollution Exposure Disparities in the Bronx, New York". It contains mainly three parts: 1. day-to-day air quality prediction maps for exposure estimation; 2. street-level PM2.5 exposure and its disparity modeling results for all populations and for socio-demographic groups; 3. residence- and mobility-based exposure calculation for a sample of Bronx residents.</p>
Minimal data set for: Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend
<p>This minimal data set presents the values behind the means and standard deviation for the publication entitled: "Air-liquid interface exposure of A549 human lung cells to characterize the hazard potential of a gaseous bio-hybrid fuel blend"</p>
UK shale gas air and water quality data
<p>Datasets for UK coal bed methane compositions (Airth field), shale gas composition from Bowland shale operations and produced water composition from UK Airth field.</p>
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