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3,018 results for “aire”
A synchronized estimation of hourly ground-level concentrations of six criteria air pollutants in China using data from the first geostationary air-quality monitoring satellite
<p>This dataset provides the ground-level concentrations of six criteria air pollutants estimated from the first geostationary air quality monitoring satellite GEMS with a multi-output random forest model.</p>
A Comprehensive Analysis of Air Quality in the NYC Subway System
<p>Average PM2.5 concentration in the subway stations and inside the train of nine subway lines in NYC. </p>
Air mass exposure to chlorophyll a (AEC) over the eastern China seas during 2009 to 2020
<p>This dataset contains (1) air mass exposure to chlorophyll <em>a</em> (AEC) values, (2) air mass retention ratio over the land (<em>R<sub>L</sub></em>), (3) marine air mss retention ratio within the boundary layer (<em>R<sub>MBL</sub></em>), and (4) harmonic means of boundary layer height along the air mass trajectory (BLH_traj) over the eastern China seas spanning from 2009 to 2020. The domain (23−37° N, 117−130° E) includes the south Yellow Sea (YS), the East China Sea (ECS), and part of Northwest Pacific, which is divided into 145 grids with a spatial resolution of 1° × 1°. The time period is 1 January 2009 − 31 December 2020, and the time resolution is 6 hours. The tracking time of initial trajectory is 72 hours.</p> <p>The Matlab scripts for calculating the abovementioned indices are also given. The main code includes "AEC_Cal_Multi.m", "AirmasstoChla.m", and "BLH_traj_multi.m".</p>
Solid-sorbent direct air capture global performance data
<p>Global performance data for solid-sorbent direct air capture utilizing amine-functionalized sorbent (i.e., Lewatit VP OC 1065) in a steam-assisted vacuum-pressure temperature swing adsorption cycle.</p>
Figure 1 in Helvetia cf. cancrimana (Araneae: Salticidae: Chrysillini) from Buenos Aires
Figure 1. Female Helvetia cf. cancrimana on a tree in the Reserva Ecológica Costanera Sur in the city of Buenos Aires, 20 March 2018.. Body length of this spider was 3.13 mm. 2, Note the glabrous, rugose anterolateral carapace below the lateral eyes, part of a stridulatory apparatus that opposes setal sockets inside of the ipsilateral femur (Ruiz & Brescovit 2008).
Figure 2 in Helvetia cf. cancrimana (Araneae: Salticidae: Chrysillini) from Buenos Aires
Figure 2. Distribution of Helvetia species, all endemic to South America. Localities shown here are listed
A synopsis of AIRS global-mean clear-sky radiance trends from 2003 to 2020
<p>Here archived the time series of global-mean clear-sky BTs from AIRS observation, GEOS-5, and ERA5 simulations used for trend analysis.</p>
Thermal conductivity of air at different temperatures
<p><strong>Thermal conductivity of air at different temperatures</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>The thermal conductivity of a material is a measure of its ability to a particular material conduct heat. Heat transfer occurs at a lower rate in materials of low thermal conductivity than in materials of high thermal conductivity. For instance, metals typically have high thermal conductivity and are very efficient at conducting heat, while the opposite is true for insulating materials. Correspondingly, materials of high thermal conductivity are widely used in heat sink applications, and materials of low thermal conductivity are used as thermal insulation. The reciprocal of thermal conductivity is called thermal resistivity. There are several ways to measure thermal conductivity; each is suitable for a limited range of materials. Broadly speaking, there are two categories of measurement techniques: steady-state and transient. Steady-state techniques infer the thermal conductivity from measurements on the state of a material once a steady-state temperature profile has been reached, whereas transient techniques operate on the instantaneous state of a system during the approach to steady state. Lacking an explicit time component, steady-state techniques do not require complicated signal analysis. The disadvantage is that a well-engineered experimental setup is usually needed, and the time required to reach steady state precludes rapid measurement. In comparison with solid materials, the thermal properties of fluids are more difficult to study experimentally. This is because in addition to thermal conduction, convective and radiative energy transport are usually present unless measures are taken to limit these processes. The formation of an insulating boundary layer can also result in an apparent reduction in the thermal conductivity.</p> <p>Thermal conductivity (watts per meter-kelvin), Temperature (degrees kelvin)</p> <table> <tbody> <tr> <td> <p>0.00922</p> </td> <td> <p>100</p> </td> </tr> <tr> <td> <p>0.01375</p> </td> <td> <p>150</p> </td> </tr> <tr> <td> <p>0.0181</p> </td> <td> <p>200</p> </td> </tr> <tr> <td> <p>0.02226</p> </td> <td> <p>250</p> </td> </tr> <tr> <td> <p>0.02614</p> </td> <td> <p>300</p> </td> </tr> <tr> <td> <p>0.0297</p> </td> <td> <p>350</p> </td> </tr> <tr> <td> <p>0.03305</p> </td> <td> <p>400</p> </td> </tr> <tr> <td> <p>0.03633</p> </td> <td> <p>450</p> </td> </tr> <tr> <td> <p>0.03951</p> </td> <td> <p>500</p> </td> </tr> <tr> <td> <p>0.0456</p> </td> <td> <p>600</p> </td> </tr> <tr> <td> <p>0.0513</p> </td> <td> <p>700</p> </td> </tr> <tr> <td> <p>0.0569</p> </td> <td> <p>800</p> </td> </tr> <tr> <td> <p>0.0625</p> </td> <td> <p>900</p> </td> </tr> <tr> <td> <p>0.0672</p> </td> <td> <p>1000</p> </td> </tr> <tr> <td> <p>0.0717</p> </td> <td> <p>1100</p> </td> </tr> <tr> <td> <p>0.0759</p> </td> <td> <p>1200</p> </td> </tr> <tr> <td> <p>0.0797</p> </td> <td> <p>1300</p> </td> </tr> <tr> <td> <p>0.0835</p> </td> <td> <p>1400</p> </td> </tr> <tr> <td> <p>0.087</p> </td> <td> <p>1500</p> </td> </tr> </tbody> </table>
Adiabatic flame temperatures of common fuels in air at constant pressure
<p><strong>Adiabatic flame temperatures of common fuels in air</strong> <strong>at constant pressure</strong></p> <p>Junjie Chen</p> <p>Department of Energy and Power Engineering, School of Mechanical and Power Engineering, Henan Polytechnic University, 2000 Century Avenue, Jiaozuo, Henan, 454000, P.R. China</p> <p>Contributor: Junjie Chen, ORCID: 0000-0002-5022-6863, E-mail address: koncjj@gmail.com</p> <p> </p> <p>In the study of combustion, the adiabatic flame temperature is the temperature reached by a flame under ideal conditions. It is an upper bound of the temperature that is reached in actual processes. There are two types adiabatic flame temperature: constant volume and constant pressure, depending on how the process is completed. The constant volume adiabatic flame temperature is the temperature that results from a complete combustion process that occurs without any work, heat transfer or changes in kinetic or potential energy. Its temperature is higher than in the constant pressure process because no energy is utilized to change the volume of the system.</p> <p>Fuel, Oxidizer, Adiabatic flame temperature (degrees Celsius)</p> <p>Acetylene Air 2500</p> <p>Butane Air 4074</p> <p>Ethane Air 1955</p> <p>Ethanol Air 2082</p> <p>Gasoline Air 2138</p> <p>Hydrogen Air 2254</p> <p>Magnesium Air 1982</p> <p>Methane Air 1963</p> <p>Methanol Air 1949</p> <p>Naphtha Air 4591</p> <p>Natural gas Air 1960</p> <p>Pentane Air 1977</p> <p>Propane Air 1980</p> <p>Methylacetylene Air 2010</p> <p>Toluene Air 2071</p> <p>Kerosene Air 2093</p> <p>Bituminous Coal Air 2172</p> <p>Anthracite Air 2180</p>
Open-air museum ''Vēveri'' pt2
"VĒVERI" RURAL EXHIBITION OF THE ETHNOGRAPHIC OPEN AIR MUSEUM OF LATVIA IN VECPIEBALGA The history of "Vēveri" in Vecpiebalga dates as far back as 400 years. It is mentioned for the first time in Swedish accountant documents in 1601 as one household and at the end of the 17th century it was marked on the map. http://brivdabasmuzejs.lv/en/museum/museum-veveri/ Source: Objaverse 1.0 / Sketchfab
Air – Sea Rescue Craft (ASR-10)
This craft, known as ASR-10, is a rare example of a type once stationed in the North Sea and English Channel and played an important role during World War II. Its role was to provide emergency shelter for the crews of downed aircraft, and it contained vital equipment and supplies, including food, drinking water, bunks, towels, washing gear, books and playing cards. These comforts were more to reduce the shock of their ordeal than to prepare them for a long stay. Stranded men were able to radio for assistance ensuring that a fast rescue vessel would be sent out. ASR-10 was built by Carrier Engineering of Wembley in 1941. Its career after the war is not documented, but it may have ended its working life as a towed naval target on the Clyde. The Museum received ASR-10 after it lay derelict on the slipway at Battery Park, Gourock, for many years. It remains a representative of an ongoing challenge to save lives at sea and a reminder of the importance of the sea to Britain during wartime. Source: Objaverse 1.0 / Sketchfab
Air Raid Shelter Parliament Square
The entrance to a WW2 air raid shelter in the side of the Treasury Building on Parliament Square, London. This is one of five along this road. This is the furthest east. 158 photos taken in July 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Open-air museum ''Vēveri'' pt1
"VĒVERI" RURAL EXHIBITION OF THE ETHNOGRAPHIC OPEN AIR MUSEUM OF LATVIA IN VECPIEBALGA The history of "Vēveri" in Vecpiebalga dates as far back as 400 years. It is mentioned for the first time in Swedish accountant documents in 1601 as one household and at the end of the 17th century it was marked on the map. http://brivdabasmuzejs.lv/en/museum/museum-veveri/ Source: Objaverse 1.0 / Sketchfab
Beechcraft King Air Plane, Rockingham Dive Trail
479 images recorded on the 16th of January 2021. Camera: Sony RX100IV with Fantasea UWL-400Q Wide Angle Wet Lens. Processed in Agisoft Metashape. This is one of two planes intentionally sunk as a part of the trail. Source: Objaverse 1.0 / Sketchfab
A Numerical Study of Tropical Cyclone and Ocean Responses to Air-sea Momentum Flux at High Winds
<p>The simulation data output from FIO-AOW for tropical cyclone study</p>
Dataset for "Disproportionate Clean Air Act violations occur in communities of color throughout the United States"
<div> <p>This is the dataset (ver 2024.04.25) supporting the manuscript "Disproportionate Clean Air Act violations occur in communities of color throughout the United States" in the journal <em>Environmental Research Letters </em>(DOI: 10.1088/1748-9326/ad416d). A description of the data files and scripts can be found in the README. </p> </div>
Smart Cable Air (SCA) and Smart Cable Water (SCW) public data sets, april 2024
<p>These datasets are supporting material for the publication<br>"An ASIC-based system-in-package MEMS gas sensor with impedance spectroscopy readout and AI-enabled identification capabilities"<br>by CNR-IMM Bologna, Univerisity of Pisa and Sensichips.</p> <p>These are some data sets as acquired to date using the Smart Cable Air and Smart Cable Water devices, for public use by data scientists and AI developers.</p> <p>SCA.zip contains datasets for the "Smart Cable Air" sensor: <a href="https://sensichips.com/air-sensor/">https://sensichips.com/air-sensor/</a></p> <p>SCW.zip contains datasets for the "Smart Cable Water" sensor: <a href="https://sensichips.com/water-sensor/">https://sensichips.com/water-sensor/</a></p> <p>The contents are described in the readme files contained in the subdirectories.</p>
Data and code AEM article: Catching some air: A method to spatially quantify aerial triazole resistance in Aspergillus fumigatus
<h2>Name</h2> <p>Catching_some_air</p> <h2><a href="#description"></a>Description</h2> <p>This script project was written to visualise and analyse the data used in the manuscript: Catching some air: A method to spatially quantify aerial triazole resistance in <em>Aspergillus fumigatus</em>.</p> <p>In the R script we load and clean the data from the international air sampling pilot, analyse it, generate figures of the sampled regions, the CFU totals and resistance fractions. The genotyping and phenotyping data of isolated resistant strains.</p> <p>The following files are required to run this R script:</p> <ul> <li>RF_air_IP_cleaned.csv This fine contains total and resistance counts as well as metadata on samples from international air sampling pilot and includes the following variables:</li> </ul> <p>Sample ID: an arbitrary number given to the packages prior to them being handed out</p> <p> </p> <p>Country: Country in which sample was taken</p> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <p>City/Town: City/Town in which the sample was taken</p> <p>Start date: date on which the trap was deployed and the stickers exposed to the air</p> <p>End date: date on which the trap was taken down and the stickers were re-covered and no longer exposed to the air</p> <p>Total.ITR: A. fumigatus CFU count in the permissive layer of the itraconazole-treated plate</p> <p>Res.ITR: CFU count of colonies that had breached the surface of the itraconazole-treated layer after incubation and were visually (with the unaided eye) sporulating.</p> <p>RF.ITR: The itraconazole (~4 mg/L) resistance fraction = Res.ITR/Total.ITR</p> <p>Total.VOR: A. fumigatus CFU count in the permissive layer of the voriconazole-treated plate</p> <p>Res.VOR: CFU count of colonies that had breached the surface of the voriconazole-treated layer after incubation and were visually (with the unaided eye) sporulating.</p> <p>RF.VOR: The voriconazole (~2 mg/L) resistance fraction = Res.VOR/Total.VOR</p> <p>Total control: CFU count on the untreated growth control plate</p> <p>Date.Batch: The date on which proccessing of the sample was started. To be more specific, the date at which Flamingo medium was poured over the seals of the sample and incubation was started.</p> <p>Note: note on the sample based on either information given the participant or observations in the lab.</p> <p>Exclude: Binary to quickly filter out samples that were considered unsuitable for further analysis either because low or high CFU counts. See manuscript for rationale.</p> <p>Lat: Latitude at the centre of the sampled region, does not relate to sample-specific location.</p> <p>Long: Longitude at the centre of the sampled region, does not relate to sample specific location.</p> <ul> <li>Weather_data_IP_study_nov_dec_jan22_23.csv : contains raw weather data of the sampled regions during the sampling interval of the pilot downloaded from: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview" target="_blank" rel="nofollow noreferrer noopener">https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview</a> (see link for full description of the data and the units). Contains the following variables:</li> </ul> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <p>Wind Nov : 10 m Wind speed (m/S) for the month november 2022 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>Wind Dec : 10 m Wind speed (m/S) for the month december 2022 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>Wind Jan : 10 m Wind speed (m/S) for the month januari 2023 This parameter is the horizontal speed of the wind, or movement of air, at a height of ten metres above the surface of the Earth.</p> <p>UV Nov: UV radiation at the surface (J/m^2) for the month november 2022. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>UV Dec: UV radiation at the surface (J/m^2) for the month december 2022. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>UV Jan: UV radiation at the surface (J/m^2) for the month januari 2023. This parameter is the amount of ultraviolet (UV) radiation reaching the surface. It is the amount of radiation passing through a horizontal plane.</p> <p>Temp Nov: Temperature (K) for the month november 2022. This parameter is the temperature of air at 2m above the surface of land, sea or inland 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>Temp Dec: Temperature (K) for the month december 2022. This parameter is the temperature of air at 2m above the surface of land, sea or inland 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>Temp Jan: Temperature (K) for the month januari 2023. This parameter is the temperature of air at 2m above the surface of land, sea or inland 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>Precipitation Nov: Total precipitation (m) for the month november 2022. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>Precipitation Dec: Total precipitation (m) for the month december 2022. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>Precipitation Jan: Total precipitation (m) for the month januari 2023. This parameter is the accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth's surface. It is the sum of large-scale precipitation and convective precipitation.</p> <p>lat_rep: Latitude at the centre of the sampled region, does not relate to sample specific location.</p> <p>lon_rep: Longitude at the centre of the sampled region, does not relate to sample specific location.</p> <ul> <li>Genotyping_IP_cleaned.csv : contains the TR-type genotypes of the isolated resistant strains Contains the following variable:</li> </ul> <p>Order: Ordering variable included in the file to readily be able to order the isolates by the order in which they were isolated. Contains the following variables:</p> <p>Strain: Strain code with "I" for strains isolated from itraconazole and V for strains isolated from voriconazole followed by a number indicating the order in which they were isolated from the air sample plate.</p> <p>Air sample: The plate/air sample from which the isolate originates</p> <p>Triazole: The triazole treatment the resistant strain grew on can be ITRA (itraconazole) or VORI (voriconazole)</p> <p>Country: Country in which sample was taken</p> <p>Region: Circular area with a 50 km radius within which the samples were clustered for analysis</p> <h2><a href="#project-status"></a>Project status</h2> <p>The manuscript has been published in AEM under the DOI: https://doi.org/10.1128/aem.00271-24</p>
Scanning electron microscopy (SEM) images of particulate matter collected on air filters
<p>Airborne PM sampling was conducted within a larger study on the PM composition of different areas in Santa Rosa, La Pampa, Argentina by Prof. Dr. Mendez Mariano. Airborne PM10 samples were collected on commercial 47mm diameter PTFE membrane filters (Image 1-blank) and Nylon filters (Image 2-blank). The PM10 was collected using an electrostatic precipitator coupled with the Easy Dust Generator (EDG). Filters were analysed using a Scanning Electron Microscope (Phenom™ ProX Desktop, Thermofisher). Images were taken using an accelerating voltage of 15 kV. SEM-EDX results are presented in this dataset.</p>
OceanSODA-ETHZ-v2: Surface ocean sea-air CO2 fluxes from 1982 to 2022 (8-day by 0.25° x 0.25°)
<div> <h2>Product information</h2> <table> <tbody> <tr> <td><strong>Product name</strong></td> <td>OceanSODA-ETHZ-v2</td> <td> </td> </tr> <tr> <td><strong>SOCOM-style name</strong></td> <td>OSETHZv2-NN</td> <td> </td> </tr> <tr> <td><strong>Product version</strong></td> <td>v2024r01</td> <td> </td> </tr> <tr> <td><strong>Coverage</strong></td> <td>1982-01-01 — 2022-12-31</td> <td>Global ice free ocean</td> </tr> <tr> <td><strong>Resolution</strong></td> <td>8-day</td> <td>0.25° x 0.25°</td> </tr> <tr> <td><strong>Contact</strong></td> <td><a href="mailto:nicolas.gruber@env.ethz.ch">nicolas.gruber@env.ethz.ch</a></td> <td>OR <a href="mailto:luke.gregor@usys.ethz.ch">luke.gregor@usys.ethz.ch</a></td> </tr> </tbody> </table> <h2>Variables</h2> <p>Each netCDF contains only one variable. The metadata also has a start and end time as well as min/max lat/lon.<br>The information about the settings used in PyCO2SYS is also included in each file's metadata.</p> <table> <tbody> <tr> <th>Folder</th> <th>Variable name</th> <th>Units</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td><code>fgco2</code></td> <td><em>F</em><em>C</em><em>O</em><sub>2</sub><sup>MBL</sup></td> <td>mmol m<sup>−2</sup> day<sup>−1</sup></td> <td>Sea-air <em>C</em><em>O</em><sub>2</sub> flux computed using Marine Boundary Layer <em>x</em>CO<sub>2</sub></td> </tr> <tr> <td><code>fgco2CT</code></td> <td><em>F</em><em>C</em><em>O</em><sub>2</sub><sup>CT</sup></td> <td>mmol m<sup>−2</sup> day<sup>−1</sup></td> <td>Sea-air <em>C</em><em>O</em><sub>2</sub> flux computed using CarbonTracker <em>x</em>CO<sub>2</sub></td> </tr> <tr> <td><code>fgco2_global</code></td> <td>globally integrated <em>F</em><em>C</em><em>O</em><sub>2</sub></td> <td>PgC yr<sup>−1</sup></td> <td>Surface — atmosphere ocean fugacity of <em>C</em><em>O</em><sub>2</sub></td> </tr> <tr> <td><code>dfco2</code></td> <td><em>Δ</em><em>f</em><em>C</em><em>O</em><sub>2</sub></td> <td><em>μ</em>atm</td> <td>Surface — atmosphere ocean fugacity of <em>C</em><em>O</em><sub>2</sub></td> </tr> <tr> <td><code>sfco2</code></td> <td><em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup></td> <td><em>μ</em>atm</td> <td>Surface ocean fugacity of <em>C</em><em>O</em><sub>2</sub></td> </tr> <tr> <td><code>sfco2_uncert</code></td> <td><em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup> uncertainty</td> <td><em>μ</em>atm</td> <td>Uncertainty of surface ocean fugacity of <em>C</em><em>O</em><sub>2</sub> based on 5.7 ⨉ ensemble spread</td> </tr> <tr> <td><code>kw</code></td> <td><em>k</em><sub><em>w</em></sub></td> <td>cm hr<sup>−1</sup></td> <td>Gas transfer velocity</td> </tr> <tr> <td><code>sol</code></td> <td><em>K</em><sub>0</sub></td> <td>mol L<sup>−1</sup> atm<sup>−1</sup></td> <td>Solubility of <em>C</em><em>O</em><sub>2</sub> in seawater</td> </tr> <tr> <td><code>ice</code></td> <td><em>i</em><em>c</em><em>e</em></td> <td>fraction</td> <td>Sea-ice fraction</td> </tr> </tbody> </table> <h2>File structure</h2> <p>The file naming structure is <code><variable>_<decade>_OceanSODAETHZv2.2024rXX.nc</code>. Data are grouped by decade, with each file being about 800 MB. Not all files contain the same number of years (e.g., 1982-1989). The size for each variable (1982-2022) is between 2.5 and 3.5 GB.</p> <h2>Product description</h2> <p>This dataset contains a global gridded dataset of sea-air carbon dioxide fluxes (<em>F</em><em>C</em><em>O</em><sub>2</sub>) for subseasonal to decadal studies. The product is available from 1982 through 2022 and is at an 8-daily by 0.25° by 0.25° resolution. FCO2 is calculated using the bulk formulation of flux:</p> <p>(<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup>−<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>a</em><em>t</em><em>m</em></sup>) ⋅ <em>k</em><sub><em>w</em></sub> ⋅ <em>K</em><sub>0</sub> ⋅ (1−<em>i</em><em>c</em><em>e</em>)</p> <p>where (<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup> − <em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>a</em><em>t</em><em>m</em></sup>) is the sea-air difference in fugacity of carbon dioxide, <em>k</em><sub><em>w</em></sub> is the gas transfer velocity, <em>K</em><sub>0</sub> is the solubility of CO2 in seawater, and ice is the sea-ice fraction.</p> <p>The surface ocean fugacity of carbon dioxide (<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup>) is calculated using the surface Ocean Carbon dioxide Neural Network (OceanCarbNN). This two-step approach first estimates an 8-day climatology of <em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup> based on climatological drivers. The second step uses the output of the first step as a prior estimate of <em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup> which is then adjusted to a time-varying product. An important novelty of this method is that the first step uses Gradient Boosted Trees (low bias, low variance, but unable to extrapolate) while the second step uses Neural Networks (able to extrapolate, but higher variance). We use 35 ensemble members to make these estimates. The average of the ensemble members is used as the final estimate of <em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>s</em><em>e</em><em>a</em></sup>. Further, the standard deviation of the ensemble members can be scaled (× 5.7) to be used as an analog of the uncertainty.</p> <p>The atmospheric fugacity of carbon dioxide (<em>f</em><em>C</em><em>O</em><sub>2</sub><sup><em>a</em><em>t</em><em>m</em></sup>) is calculated using the NOAA Marine Boundary Layer (MBL) product OR the NOAA CarbonTracker 2022 product (CT), ERA5 sea level pressure. <em>k</em><sub><em>w</em></sub> is calculated using the Wanninkhof (2014) formulation, but we adjust the coefficient of gas transfer for the ERA5 wind reanalysis product (0.274) so that the global mean kw = 16.5 cm/hr. <em>K</em><sub>0</sub> is calculated using the Weiss (1974) formulation.</p> </div>
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.