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3,018 results for “AIR”
Data from: Do the health benefits of boiling drinking water outweigh the negative impacts of increased indoor air pollution exposure?
<p><strong>Background: </strong>Billions of the world's poorest households are faced with the lack of access to both safe drinking water and clean cooking. One solution to microbiologically contaminated water is boiling, often promoted without acknowledging the additional risks incurred from indoor air degradation from using solid fuels.</p> <p><strong>Objectives: </strong>This modeling study explores the tradeoff of increased air pollution from boiling drinking water under multiple contamination and fuel use scenarios typical of low-income settings.</p> <p><strong>Methods: </strong>We calculated the total change in disability-adjusted life years (DALYs) from indoor air pollution (IAP) and diarrhea from fecal contamination of drinking water for scenarios of different source water quality, boiling effectiveness, and stove type. We used Uganda and Vietnam, two countries with a high prevalence of water boiling and solid fuel use, as case studies. </p> <p><strong>Results: </strong>Boiling drinking water reduced the diarrhea disease burden by a mean of 1110 DALYs and 368 DALYs per 10,000 people for adults and children <5 years in Uganda, respectively, for high-risk water quality and the most efficient (lab-level) boiling scenario, with smaller reductions for less contaminated water and ineffective boiling. Similar results were found in Vietnam, apart from fewer avoided DALYs in children due to different demographics. In both countries, for households with high baseline IAP from existing solid fuel use, adding water boiling to cooking on a given stove was associated with a limited increase in IAP DALYs due to the log-linear dose-response curves. Boiling, even at low effectiveness, was associated with <em>net </em>DALY reductions for medium- and high-risk water, even if using unclean stoves/fuels. Replacing traditional stoves with improved stoves coupled with effective boiling practices significantly reduced total DALYs. </p> <p><strong>Discussion: </strong>Boiling water generally resulted in a net decrease in DALYs. Future efforts should empirically measure health outcomes from IAP vs. diarrhea associated with boiling drinking water using field studies with different boiling methods and stove types.</p>
Data for "The role of ocean mesoscale variability in air-sea CO2 exchange: a global perspective"
<p>Processed model data for article "The role of ocean mesoscale variability in air-sea CO2 exchange: a global perspective"</p>
Particulate atmospheric concentrations of trace metals and leachable nutrients in air at the Southeastern Mediterranean Sea (1994-1999)
<p><span>Table 1 dataset contains</span><span> aerosol concentrations of trace metals (Cd, Pb, Cu, Zn, Cr, Mn, Fe and Al)</span><span> </span><span>at the SE Mediterranean coast of Israel, </span><span>collected</span><span> between 1994 and </span><span>1999</span><span>. Total suspended particles (TSP) in air were collected</span><span> </span><span>on Whatman QM-A quartz micro</span><span>fi</span><span>bre </span><span>filters </span><span>and on Whatman 41 </span><span>fil</span><span>ters (both 20.3</span><span> cm x </span><span>25.4</span><span> </span><span>cm), by high-volume sampler</span><span> (HVS). </span><span><span> </span></span><span>The HVS was </span><span>located on the roof of the</span><span> </span><span>National Institute of Oceanography (NIO) at Tel-Shikmona</span><span>, Israel </span><span>(located on the shore, 22</span><span> </span><span>m above sea</span><span> </span><span>level) and at Maagan Michael</span><span>, Israel</span><span> (about 900m from shore, 13 m above sea level). Analyses were carried out after total digestion with HF following the procedure of ASTM (1983).</span><span> <span>Further details in Herut et al., 2001.</span></span></p> <p><span>Table 2 dataset contains leachable</span><span> inorganic</span><span> </span><span>nitrogen (NO</span><sub><span>3</span></sub><span> </span><span>+ NO<sub>2</sub></span><span>, NH</span><sub><span>4</span></sub><span>) and phosphorus (PO<sub>4</sub>)</span><span> concentrations in</span><span> aerosol</span><span> <span>(</span></span><span>total suspended particles </span><span>in air</span><span>)</span><span> </span><span>samples collected on Whatman 41 filters between</span><span> </span><span>April 1996 and January 1999</span><span>. </span><span>The atmospheric</span><span> </span><span>sampling was performed</span><span> </span><span>on the roof of the National Institute of Oceanography</span><span> </span><span>(NIO) at Tel-Shikmona (TS)</span><span>, Israel</span><span> (located on the shore and inside</span><span> </span><span>the sea, 22 m above sea level). Leaching experiments were performed to evaluate the amount of seawater leachable nitrate, ammonium, and phosphate from the TSP</span><span> using </span><span>SE</span><span> </span><span>Mediterranean </span><span>low nutrient low chlorophyll </span><span>surface seawater</span><span>. Further details in Herut et al., 2002.</span></p> <p><span>Herut, B., Nimmo<span>, M., Medway, A., Chester, R., & Krom, M. D. (2001). Dry atmospheric inputs of trace metals at the Mediterranean coast of Israel (SE Mediterranean): sources and fluxes. <em>Atmospheric Environment</em>, <em>35</em>(4), 803-813.</span><span><span>‏</span></span></span></p> <p><span>Herut, B., Collier, R., & Krom, M. D. (2002). The role of dust in supplying nitrogen and phosphorus to the Southeast Mediterranean. <em>Limnology and Oceanography</em>, <em>47</em>(3), 870-878.</span><span><span>‏</span></span></p> <p><span>Herut, B., Krom, M. D., Pan, G., & Mortimer, R. (1999). Atmospheric input of nitrogen and phosphorus to the Southeast Mediterranean: Sources, fluxes, and possible impact. <em>Limnology and Oceanography</em>, <em>44</em>(7), 1683-1692.</span><span><span>‏</span></span></p>
Análisis de la dispersión de contaminantes PM 2.5 y su impacto en la salud: Identificación de puntos críticos y evaluación de la Calidad del Aire en Mochuelo, Bogotá
<p>La contaminación del aire es una amenaza para la salud humana y para el ambiente. Por ello es crucial el monitoreo de PM 2.5. Entidades como la CAR dan seguimiento a la calidad del aire, identificando concentraciones preocupantes de PM 2.5 en el sector de Mochuelo. Por esto se determinó el nivel de riesgo real generado por el PM2.5 en este sector, mediante el análisis de correlación entre datos obtenidos de estaciones de calidad del aire, muestreos puntuales y resultados proporcionados por los modelos de dispersión de contaminantes. Para estudiar la dispersión de contaminantes se empleó el modelo gaussiano AERMOD. De acuerdo con los resultados obtenidos se efectuaron mediciones puntuales en zonas de riesgo. Para analizar el efecto del contaminante se calculó el valor del ICA. Se obtuvo que las mediciones directas en las zonas de riego a nivel del suelo mostraron discrepancias con las estaciones de calidad del aire, posiblemente debido a diferencias en la altura de las mediciones y la influencia de factores locales. De igual manera, las alertas moderadas a altas, no tienen correlación clara entre los datos, por lo cual se destacó la necesidad de evaluar el efecto de las fuentes fijas no consideradas.</p>
Raw data: Nanogels with tailored hydrophobicity and their behavior at air water interfaces
<p>Raw data for Journal article: "Nanogels with tailored hydrophobicity and their behavior at air/water interfaces"</p> <p>Abstract:</p> <p>The interfacial behavior of micro-/nanogels is governed to a large extent by the hydrophobicity of their polymeric network. Prevailing studies to examine this influence mostly rely on external stimuli like temperature or pH to modulate the colloidal hydrophobicity. Here, a sudden transition between hydrophilic and hydrophobic state prevents systematic and gradual modulation of hydrophobicity. This limits important correlations between interfacial behavior and quantitative physicochemical measures for network hydrophobicity. To address this challenge, we introduce a nanogel platform that allows accurate tuning of hydrophobicity on a molecular level. For this, via post-functionalization of active ester-based particles, we prepare poly(N-(2-hydroxypropyl)methacrylamide) (PHPMA) nanogels as a hydrophilic benchmark and introduce gradually varied amounts of hydrophobic propyl or dodecyl moieties to increase the nanogel hydrophobicity. We study the deformation and arrangement of these particles at an air/water interface and correlate the results with quantitative measures for nanogel hydrophobicity. We observe that increasing hydrophobicity of nanogels, either by increasing the hydrophobic moiety ratio or the alkyl chain length, leads to decreased particle deformability and aggregation of an interfacially-adsorbed monolayer. Contrary to what may be intuitively assumed, these changes are not gradual, but rather occur suddenly above a threshold in hydrophobicity. Our study further shows that the effect of hydrophobicity affects the nanogel properties differently in bulk and when adsorbed at liquid interfaces. Thus, this study establishes the transition of interfacial behavior between soft gel-like particles to a solid spherical morphology triggered by the increase in hydrophobicity.</p>
Multi-milliwatt average power two-color air-plasma terahertz source at 100 kHz repetition rate
<p>These data sets are associated with the aforementioned paper and can be used to reproduce the figures of our article. An example for reading and plotting of the data sets is provided as a <em>jupyter notebook</em>.</p>
ChinaHighTEMmin: Daily Seamless 1 km Minimum Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>minimum air temperature</strong> (TEMmin) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.98 and a root-mean-square error (RMSE) of 1.53 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMmin dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
ChinaHighTEMavg: Daily Seamless 1 km Average Air Temperature Dataset for China (2003–Present)
<p>ChinaHighTEM is part of a series of long-term, seamless, high-resolution, and high-quality datasets of air pollutants for China (i.e., ChinaHighAirPollutants, CHAP). It is generated from big data sources (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence, taking into account the spatiotemporal heterogeneity of air pollution.</p> <p>Here is the big data-derived seamless (spatial coverage = 100%) daily 1 km (i.e., D1K) <strong>average air temperature</strong> (TEMavg) dataset for China <strong>from 2003 to the present</strong>. This dataset exhibits high quality, with a cross-validation coefficient of determination (CV-R<sup>2</sup>) of 0.99 and a root-mean-square error (RMSE) of 1.18 ℃ on a daily basis.</p> <p>If you use the ChinaHighTEMavg dataset in your scientific research, please cite the following reference (Wang et al., SD, 2024):</p> <ul> <li>Wang, M., Wei, J., Wang, X., Luan, Q., and Xu, X. <a href="https://weijing-rs.github.io/publications/Wang_et_al-SD-2024.pdf" target="_blank" rel="noopener">Reconstruction of all-sky daily air temperature datasets with high accuracy in China from 2003 to 2022</a>. <em>Scientific Data</em>, 2024, 11, 1133. https://doi.org/10.1038/s41597-024-03980-z</li> </ul> <p><strong>More CHAP datasets for different air pollutants are available at: </strong><a href="https://weijing-rs.github.io/product.html"><strong>https://weijing-rs.github.io/product.html</strong></a></p>
Air Quality Stripes
<p>Timeseries of annual mean particulate matter (PM2.5) concentrations (in micrograms per meter cubed) from 1850 to 2021 in 177 cities around the globe. </p> <p>Updated 10/12/2024 to include data for more cites.</p>
NAHosMIP - monthly surface air temperature and precipitation v3
<p>This dataset is for data generated from the North Atlantic Hosing Model Intercomparison Project (NAHosMIP), which is documented in <a href="https://gmd.copernicus.org/articles/16/1975/2023/gmd-16-1975-2023.html" target="_blank" rel="noopener">Jackson et al, 2023</a>. </p> <p>Data used in that paper (including AMOC streamfunctions) can be found <a href="https://zenodo.org/records/7643437">here</a> </p> <p><strong>Experiments</strong></p> <ul> <li>picon - preindustrial control which was run as part of CMIP6 (<a href="https://gmd.copernicus.org/articles/9/1937/2016/">Eyring et al., 2016</a>),</li> <li>u03-hos - constant uniform hosing of 0.3 Sv. </li> <li>u03-r50 - experiment with no hosing initialised 50 years into u03-hos</li> <li>u03-r100 - experiment with no hosing initialised 100 years into u03-hos</li> </ul> <p><strong>Models</strong></p> <p>Eight CMIP6 models took part: CanESM5, CESM2, EC-Earth3, HadGEM3-GC3-1LL, HadGEM3-GC3-1MM, IPSL-CM6A-LR, MPI-ESM1-2-HR, MPI-ESM1-2-LR </p> <p><strong>Variables</strong></p> <ul> <li>tas - surface air temperature (monthly resolution)</li> <li>pr - precipitation (monthly resolution)</li> <li>evspsbl - surface evaporation (including sublimation and transpiration)</li> <li>psl - sea level pressure</li> </ul> <p><strong>File name convention</strong></p> <p>We use the CMIP file naming convention, so for example:</p> <p>tas_Amon_HadGEM3-GC31-LL_u03-r100_r1i1p1f1_gn_215001-215912.nc</p> <pre><code>$variable_$timeresolution_$model_$experiment_$version_$grid_$date.nc</code></pre> <p>Files with the same variable and model are combined in a tar file:</p> <pre><code>$variable_$timeresolution_$model_$version_$grid.tar</code><br><br><strong>AMOC timeseries<br></strong><br>These are included in the file M26.tar. This is the maximum streamfunction at 26.5N<strong><br><br>Additional precip and wind files<br><br></strong>Also included are files used by <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF003959">Ben-Yami et al, 2024</a> who examined the impacts of an AMOC <br>collapse on monsoons. The files contain monthly mean (mmean) or annual mean (ymean) of <br>precipitation (prcp) and surface winds (ua and va) for the experiments <br>u03-r50 (for HadGEM3-GC31-MM, CanESM5, CESM2) or u03-r100 (for IPSL-CM6A-LR)<br><br>Files are named<br><br></pre> <pre><code>BY24_$model_$exp.tar</code></pre> <pre><br><br></pre>
Data supporting "A comprehensive analysis of air-sea CO2 flux uncertainties constructed from surface ocean data products"
<p>Changelog</p> <p>v2: Fixes an identified issue in FluxEngine v4.0.7 that affects the calculation of fCO2atm. Fluxes have been recalculated using FluxEngine v4.0.9.1, and the analysis regenerated. The intergrated air-sea CO2 flux (or ocean sink) has reduced by ~0.2-0.3Pg C yr-1 but uncertainties are unchanged. </p> <p>v1: Initial dataset released along with the supporting manuscript</p> <p> </p> <p>Data included in this repository supports the manuscript "A comprehensive analysis of air-sea CO<sub>2</sub> flux uncertainties constructed from surface ocean data products".</p> <p>Two files are present:</p> <ol> <li>A Python config file used to run the software developed for the analysis (Ford et al., 2024)</li> <li>A ZIP file containing the input, neural network, and output files for the analysis.</li> </ol> <p>Within the ZIP file, multiple folders are present:</p> <ol> <li>Decorrelation contains .csv files that contain the annual estimates of the decorrelation lengths for the parameters requiring these (SST, sea ice, wind, fCO<sub>2</sub> and fCO<sub>2</sub> network).</li> <li>Flux contains the individual FluxEngine output files that provide all the flux calculations, and auxillary data to the flux calculations.</li> <li>Fluxengine_input contains the input files to FluxEngine, which specifies the fCO<sub>2 (sw), </sub>xCO<sub>2 (atm)</sub> and the temperature, salinities for the skin and subskin layers.</li> <li>Inputs contains all the monthly 1 degree input data used. Many of the data used are not native monthly 1 deg, and so these are generated from the higher resolution data. These are all combined into the neural_network_input.nc file, so a single file can be distributed with all the inputs used.</li> <li>Networks contains the TensorFlow neural network (FNN) files, where each province has 10 folders (one for each ensemble).</li> <li>Plots contains output plots for debugging and final plots of uncertainties</li> <li>Scalars contains the scalars used to normalise the data before input into the neural network. These are saved as Python pickle files, as they are needed if the neural network is used on other data.</li> <li>Unc_lut contains the look up tables to generate the parameter uncertainty as described in the manuscript. These are Python pickle files.</li> <li>Validation contains a csv file with the independent test RMSD, along with Python Pickle files of the validation data.</li> </ol> <p>In the main folder, three files are present:</p> <ol> <li>Annual_flux.csv contains the annual air-sea CO<sub>2</sub> flux (or ocean sink estimate) estimated from the fCO<sub>2 (sw)</sub> fields. This also contains the annual integrated uncertainties for each component in the uncertainty flow chart in the manuscript.</li> <li>Output.nc contrains the gridded global fields of the fCO<sub>2 (sw)</sub>, the air-sea CO<sub>2</sub> flux, and the uncertainties for all the individual components. Metadata within the file should provide all the information required.</li> <li>Training.tsv contains the training/validation data alongside the input parameters for neural network training</li> </ol> <p> </p> <p>Please contact Daniel J. Ford (<a href="mailto:d.ford@exeter.ac.uk">d.ford@exeter.ac.uk</a>) if you have any questions.</p> <p><strong>Acknowledgements</strong></p> <p>This work was funded by the Convex Seascape Survey (https://convexseascapesurvey.com/) and the European Union under grant agreement no. 101083922 (OceanICU; https://ocean-icu.eu/) and UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee [grant number 10054454, 10063673, 10064020, 10059241, 10079684, 10059012, 10048179]. The views, opinions and practices used to produce this dataset/software are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.</p> <p>The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT.</p> <p> </p> <p><strong>References</strong></p> <p>Ford, D. J., Blannin, J., Watts, J., Watson, A. J., Landschutzer, P., Jersild, A., & Shutler, J. D. (2024, June 30). OceanICU Neural Network Framework with per pixel uncertainty propagation (v1.1) (Version v1.1). Zenodo. https://doi.org/10.5281/ZENODO.12597803</p>
Upcycling food ingredients from orange by-products by hot air-microwave drying. Impact on energy consumption.
<p>Currently industrial citrus by-products represent a relevant environmental issue. The main aim of this work was the chemical characterization of the different bioactive compounds obtained after hot air-microwave drying (HAD+MW) of orange by-products, and their further conversion into three <strong>upcycled </strong>ingredients with health-related benefits: aqueous extract, ethanolic extract and <strong>dietary fibre</strong>. Total phenolics, antioxidant capacity, individual phenolic acids, flavonoids, limonin and carotenoids were monitored during blanching and colour extraction steps by analysing fresh by-products and process co-products: an aqueous extract rich in polyphenols and an ethanolic extract rich in carotenoids. After drying, the resulting fibre was characterized in terms of chemical composition, soluble and insoluble dietary fibre content and particle size. Technological properties and colour were compared to those of commercial citrus fibre. Energy and time consumption were compared with conventional hot air drying (HAD). Most polyphenols (50-65 %) and limonin (70 %) were extracted during the blanching step. 86 % of carotenoids were removed by soaking in ethanol. The orange fibre obtained had 71.9 g DF/ 100 g and antioxidant properties (205 mg TE/ Kg<sub>dm</sub>). Whiteness, water retention capacity and oil retention capacity were similar to commercial citrus fibre. HAD+MW reduced drying time and energy consumption by up to 50 % compared to HAD.</p>
Outdoor air pollution impacts chronic obstructive pulmonary disease deaths in South Asia and China: a systematic review and meta-analysis
<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) is among leading causes of death globally. Exposure to outdoor pollution is an important cause for increased mortality and morbidity. This study presents a systemic review regarding the impact of outdoor pollution on COPD mortality in South Asia and China.</p> <p><strong>Methods: </strong>A systematic search was conducted from 1990 to June 30<sup>th</sup> 2020 in English electronic databases: PubMed, Google Scholar and CDSR (Cochrane Database of Systematic Reviews) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The following terms were used: Chronic Obstructive Pulmonary disease OR COPD OR Chronic Bronchitis OR Emphysema OR COPD Deaths OR Chronic Obstructive Lung Disease OR Airflow Obstruction OR Chronic Airflow Obstruction OR Airflow Obstruction, Chronic OR Bronchitis, Chronic AND Mortality OR Death OR Deceased AND Outdoor pollution, ambient pollution was conducted.</p> <p><strong>Results:</strong> Out of 1899 papers screened only 17 were found eligible to be included. Subjects with COPD exposed to higher levels of outdoor air pollution had a 49% higher risk of death as compared to COPD subjects exposed to lower levels of outdoor air pollution. When taking common air pollutants individually into consideration, PM10 had an odds ratio (OR) of 1.99 respectively at CI 95%, whereas SO2 had OR of 1.8 at 95% CI, and NO2 had an OR of 1.23 OR at 95% CI. These values suggest that there is an effect of outdoor pollution on COPD but not to a significant level.</p> <p><strong>Conclusion: </strong>Despite heterogeneity across selected studies, individuals exposed to outdoor pollutants were found to be at risk of COPD mortality. Though it appears to have risk, COPD mortality was not significantly associated with outdoor pollutants. Controlling air pollution can substantially decrease the risk of COPD in South Asia and China. Further researches including more prospective and longitudinal studies are urgently needed in COPD sub-groups.</p>
Data for figures in Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_
<p>Tar files containing gridded metrics, domain-wide metric means and confidence intervals, and rain-gauge reports used to generate figures in Kemp et al (2021).<br> <br> Citation:<br> </p> <p>Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_.</p>
Deep-Learning-Based Harmonization and Super-Resolution of Near-Surface Air Temperature from CMIP6 Models (1850-2100)
<p>A long-term (1850-2100) monthly air temperature (tas) product with a spatial resolution of 0.5 degree. This is a merged product from 31 CMIP6 models using the Deep-learning model which reduce bias, spatial downscaling and data merge at the same time,. To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain one historical data (1850-2014) , four future scenarios data during 2015-2100 (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) and four future scenarios data in Australia . The dataset is stored in NetCDF format, containing the variable tas, representing air temperature, produced in centigrade (℃) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.75E to 179.75E, the latitude from -89.75N to 89.75N. </p>
Particulate methylsulfonic acid (MSA), sodium and chloride concentrations from high-volume air filter samples over the Southern Ocean during austral summer of 2016/2017 on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>Aerosol particles come from a variety of sources: a look at the chemical composition gives insights on the particle origin. Ion chromatography was performed for aerosol particles smaller than 10 micrometers (PM10 inlet), giving concentrations of sodium and chloride, as well as particulate methylsulfonic acid (MSA). For this, aerosol particles where sampled on quartz fibre filters for 24 hours each. The sampled filters were stored at -20 degrees C on the research vessel, transported frozen back to the chemistry lab of TROPOS and analysed for main ions. Temporal coverage is from December 20, 2016 to March 20, 2017. We give 24-hour quality controlled particulate MSA, sodium and chloride concentrations in microgram per cubic meter for the Antarctic Circumnavigation Expedition (ACE) cruise over the Southern Ocean, as part of the ACE-SPACE project.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ACESPACE_ particulate_MSA_Sodium_Chloride_PM10, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul>
Data Repository for "Single-particle characterization of polycyclic aromatic hydrocarbons in background air in Northern Europe", Atmos. Chem. Phys.
<p>Data Repository for <br> Passig et al., "Single-particle characterization of polycyclic aromatic hydrocarbons<br> in background air in Northern Europe", Atmospheric Chemistry and Physics, 2021/22</p> <p>Details in Readme.txt</p> <p> </p>
CO2 NEE and ER + air and soil meteorological and climate parameters in Arctic tundra, Ny Ålesund (Svalbard, NO) - summer 2019
<p>The dataset “fluxes_meteoclimate_NyAlesund” is a .csv file reporting CO2 fluxes and basic meteoclimatic variables measured in the Bayelva Basin near Ny Ålesund, in the Brøgger peninsula, Spitsbergen, Norway (78°55’24’’ N, 11°55’15’’E) during the 2019 growing season peak (July-August). Average coordinates of the measuring site are: 78°55’25.7” N,11°53’29.4” E. Fluxes were measured using the flux chamber method: the Net Ecosystem Exchange (NEE) was measured with a transparent flux chamber, while the Ecosystem Respiration (ER) with a shaded chamber. Three types of sampling were performed: at a fixed point during 24h ('point' in column sampling); in points randomly distributed over a site ('site' in column sampling); and in points covered with specific species ('species' in column sampling). Flux data are complemented by measurements of soil temperature (Ts, in Celsius degrees), soil volumetric water content (VWC, in %), atmospheric pressure (Pr, in hPa), air temperature (Ta, in Celsius degrees), air moisture (RH, in %), and solar radiance (rs , in W/m2). The Green Fractional Cover (GFC, between 0 and 1) of the vegetation inscribed within the sampling surface was estimated from digital RGB pictures taken at nadir. Measurements were divided into 4 classes, depending on the prevailing cover type: bare soil (BS), vascular vegetation (V), non-vascular vegetation (NV, including lichens, mosses and bacterial soil crust) and mix of vascular and non-vascular vegetation (MIX). Class V was further split into 5 subclasses: Carex spp. (CX), Dryas octopetala (DR), Salix Polaris (SL), Saxifraga oppostifolia (SX) and Silene acaulis (SI). </p>
Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities
<p>These datasets were generated to assess linear and nonlinear Granger causalities in the submitted manuscript, Global Soil Moisture-Air Temperature Interactions from Linear and Nonlinear Granger Causalities by Bhatti et al. submitted to AGU-GRL. Nonlinear GC here is achieved with the Kernel Granger causality by Marinazzo et al. (2008). The data was used to develop theoretical experiments that help validate the strengths and limitations of both the linear Granger causality and the Kernel Granger causality before applying to real world datasets</p>
Supporting data and software for: Low-temperature open-air synthesis of PVP-coated NaYF4:Yb,Er,Mn upconversion nanoparticles with strong red emission
<p>Upconversion nanoparticles (UCNPs) have unique photonic properties that make them ideally suited for many applications. They are excited by low-energy near-infrared photons and emit at higher energy (typically visible) wavebands. However, synthesis of UCNPs requires either high pressure reaction chambers or inert atmospheres. Combined with the requirements for high-temperatures (200 to 400 °C) and long reaction times (e.g. up to 24 hours), these place barriers to entry for UCNP research, in terms of both financial barriers and knowledge/"know how". These constraints may also limit the scale of UCNP production for end-user applications.</p> <p>We adapted and further developed a method for producing UCNPs with simple laboratory equipment, i.e. a hot-plate and beakers. No pressure vessel or inert atmosphere is required. The UCNPs produced have a<span> polyvinylpyrrolidone (PVP) polymer coating, with strong red emission due to Mn<sup>2+</sup> co-doping within the UCNP crystal lattice. It was found that UCNPs of composition NaYF<sub>4</sub>:Yb,Er,Mn (Yb = 20 mol %, Er = 2 mol%, Mn = 35 mol%) maximised the red emission whilst also minimising the diameter of the UCNPs to </span> 36 ± 15 nm. These combination of optical and physical properties should make these UCNPs ideal for further development and exploitation, particularly for biological applications where red emission can penetrate over a centimetre of tissue.</p> <p>This dataset and software accompanies the manuscript <em>'Low-temperature open-air synthesis of PVP-coated NaYF<sub>4:</sub>Yb,Er,Mn upconversion nanoparticles with strong red emission</em>', which was published in Royal Society Open Science on 19th January 2022. https://doi.org/10.1098/rsos.211508</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.