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3,018 results for “aire”
Data from: dispersal sweepstakes: biotic interchange propelled air-breathing fishes across the globe
<p>Synbranchiformes is a phenotypically diverse and species rich clade of freshwater acanthomorph fishes, which include eel- and perch-like, air-breathing and non-air-breathing fishes. The ability to breathe out of water has presumably aided lineages of Synbranchiformes in dispersing across all southern continents except Antarctica. The lack of a well-resolved, time-calibrated phylogeny of Synbranchiformes limits our understanding of the timing and geographic patterns of diversification of these anatomically and ecologically diverse fishes. As a consequence, contemporary interpretations of synbranchiform biogeography invoke scenarios as disparate as Gondwana vicariance and pan-global rafting to explain their modern-day geographic distribution. In this study, we use high-throughput sequencing of ultra-conserved elements (UCEs) to infer a phylogeny for all major synbranchiform lineages. We combine this dataset with existing Sanger sequenced genes and fossil calibrations to infer a comprehensive time-calibrated phylogeny of Synbranchiformes. Then, we use Bayesian methods of biogeographical reconstruction to document the history of dispersal of synbranchiforms, finding support for Southeast Asia as the likely ancestral area of all major lineages. Our results reject the hypothesis of Gondwanan vicariance explaining synbranchiform biogeography, and instead the historical biogeographic analyses support a hypothesis of independent continental invasions by snakeheads, anabantids, and spiny eels. However, there is no signal of elevated lineage diversification rates after these invasions. Instead, higher rates of lineage diversification in spiny eels pre-dates their arrival to Africa, while the high levels of lineage diversification observed in <em>Betta</em> were initiated prior to the flooding of insular Sundaland in SE Asia.</p>
Air Quality
<p>NO2 and O3 air concentration data aquired as baseline before the NBS implementation. The data were recorded in June 2019 in the proximity of the NBS and in a control site. In total 74 passive diffusion tubes (Gradko International Ltd) were installed in selected NBS of the three Front Runner Cities (Dortmund, Turin and Zagreb), in particular 16 in Dortmund (NBS3), 28 in Zagreb (NBS3 and NBS5) and 30 in Turin (NBS2, NBS3, NBS5). Half of the tubes monitored NO2 while the other half O3. The placed samplers were removed after 21 days and sent for analysis.</p> <p>More details are reported in Baldacchini, C. (2019): Monitoring and Assessment Plan, Deliverable No. 4.1, proGIreg. Horizon 2020 Grant Agreement No 776528, European Commission, 124.</p>
Data for Airlines network analysis on an air-rail multimodal system. Journal of Open Aviation Science, 1(2)
<h2>About</h2> <p>This dataset contains all the input data required to generate the analysis and results of the article Delgado, L., Trapote-Barreira, C., Montlaur, A., Bolić, T., & Gurtner, G. (2023). <em>Airlines’ network analysis on an air-rail multimodal system</em>. Journal of Open Aviation Science, 1(2). <a href="https://doi.org/10.59490/joas.2023.7223" rel="nofollow">https://doi.org/10.59490/joas.2023.7223</a></p> <p>The code used is available on GitHub: <a href="https://github.com/UoW-ATM/joas_air_rail_network_analysis">https://github.com/UoW-ATM/joas_air_rail_network_analysis</a></p> <p>The path to the input data can be modified in the scripts provided in the GitHub repository. With the default setting, the input is in a folder called data.</p> <h2>Dataset structure</h2> <ul> <li>data_computed <ul> <li><em>rail_used_emissions.csv</em></li> <li><em>rail_used_emissions_2.csv</em></li> <li><em>routes_emissinos_v2.csv</em></li> </ul> </li> <li>flights_data4 <ul> <li>year=2023 <ul> <li>month=05 <ul> <li><em>1st_week_0523.csv</em></li> </ul> </li> </ul> </li> </ul> </li> <li>renfe <ul> <li>renfe_mid_long <ul> <li><em>agency.txt</em></li> <li><em>calendar.txt</em></li> <li><em>calendar_dates.txt</em></li> <li><em>routes.txt</em></li> <li><em>stops.txt</em></li> <li><em>stop_times.txt</em></li> <li><em>trips.txt</em></li> </ul> </li> </ul> </li> <li><em>aircraftDatabase.csv</em></li> <li><em>airport_static.csv</em></li> <li><em>code_seats.csv</em></li> <li><em>manual_fixed_airports.csv</em></li> <li><em>mat_corrected.csv</em></li> <li><em>type_code_missing.csv</em></li> </ul> <h2>Data description</h2> <h3>data_computed</h3> <p>This folder contains pre-computed values by the authors on rail and air emissions. These are estimated:</p> <ul> <li>For rail using <a href="http://ecopassenger.hafas.de/" rel="nofollow">EcoPassenger</a>.</li> <li>For flights based on the emission model from Montlaur, A., Delgado, L., & Trapote-Barreira, C. (2021). <a href="https://doi.org/10.3390/su131810401" rel="nofollow"><em>Analytical Models for CO<sub>2</sub> Emissions and Travel Time for Short-to-Medium-Haul Flights Considering Available Seats</em></a>. Sustainability 13.18 (2021) and from some specific flights using EUROCONTROL's <a href="https://www.eurocontrol.int/platform/integrated-aircraft-noise-and-emissions-modelling-platform">IMPACT </a>model.</li> </ul> <h3>flights_data4</h3> <p>Information from flights_data4 table from <a href="https://opensky-network.org/data/impala">OpenSky</a>. Please refer to the <a href="https://opensky-network.org/about/terms-of-use">terms of use of OpenSky</a> for the restrictions on the further use of these data.</p> <p>The file <em>1st_week_0523.csv </em>contains the information of the table flights_data4 from OpenSky for the week of the 1st May 2023 (01/05/2023 to 07/05/2003). This was downloaded with the SQL query </p> <p>SELECT * FROM flights_data4 WHERE lastseen >= 1682899200 AND firstseen <= 1683504000;</p> <p>Note that the lastseen and firstseen are in UnixTime and correspond to 2023-05-01 00:00:00 UTC and 2023-05-08 00:00:00 UTC respectively. This ensures capturing all flights landing on 01/05/2023 even if they departed the day before and departing on 07/05/2023 even if landing the day after.</p> <h3>renfe</h3> <p>renfe folder contains the General Transit Feed Specification (GTFS) data from the <a href="https://data.renfe.com/dataset/horarios-de-alta-velocidad-larga-distancia-y-media-distancia">Renfe</a> rail operator with the high-speed, long and medium distances timetables. Note that Renfe provides the data under a <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Creative Commons Attribution 4.0</a> license. </p> <h3>Other datasets</h3> <ul> <li><em>aircraftDatabase.csv </em>: Database containing information on aircraft information (type, manufacturer, license, etc.) as a function of their transponder icao24 code. Obtained from <a href="https://opensky-network.org/aircraft-database">OpenSky</a>.</li> <li><em>airport_static.csv</em>: Airport ICAO code, latitude and longitude.</li> <li><em>code_seats.csv</em>: allows each aircraft model to be related to the seats in the cabin. It has been extracted from airline websites and other sources.</li> <li><em>manual_fixed_airports.csv</em>: List of manually modified airports for arrival/departure to fix wrong rotations from OpenSky data. For each airport ICAO code, it provides the one that should be used instead and some information on that airport (e.g., name)</li> <li><em>mat_corrected.csv</em>: identifies the rotation of each aircraft in the week of study. This is especially relevant for fleet analysis as it is necessary to know the start and end airport of each rotation for each day. It has been identified with an ad-hoc algorithm, and some data has been corrected with <a href="https://www.flightradar24.com/">FlightRadar24</a> data support.</li> <li><em>type_code_missing.csv</em>: relates the transponders' icao24 identifiers missing from OpenSky to the aircraft type (complement aircraftDatabase), compiled from <a href="https://www.flightradar24.com/" rel="nofollow">FlightRadar24</a>.</li> </ul>
Plant-flower visitor interactions recorded in Argentina (Buenos Aires: Carlos Casares county)
<p>Data set 1</p> <table> <tbody> <tr> <td>FIELD NAME</td> <td>VALUE</td> </tr> <tr> <td>Dataset Title</td> <td>Plant-flower visitor interactions recorded in 21 sites in Argentina (Buenos Aires: Carlos Casares county) by Tania Palacios in 2018</td> </tr> <tr> <td>Abstract</td> <td>522 records of plant-flower visitor interactions, recorded in 21 sites in Argentina (Buenos Aires: Carlos Casares county). The records were taken in the field by Tania Palacios, curated by Julia Tavella, and transformed to meet FAIR principles by Rocío Ana González Vaquero. Morphospecies are detailed in identificationRemarks. Data collection was supported by grants UBACyT 20020170100778BA and PICT 2017-4111, Argentina. Data standardization was supported by grant European Commission-EU 101058393.</td> </tr> <tr> <td>Classification System</td> <td>GBIF</td> </tr> <tr> <td>Sampling Description</td> <td>Two 100 m-long “variable transects” in field margins or 50/100 m from the field margin, were walked simultaneously by two trained observers for 20 minutes.</td> </tr> </tbody> </table> <p> </p> <p>Data set 2</p> <table> <tbody> <tr> <td>FIELD NAME</td> <td>VALUE</td> </tr> <tr> <td>Dataset Title</td> <td>Plant-flower visitor interactions recorded in 49 sites in Argentina (Buenos Aires: Carlos Casares county) by Marcos Monasterolo (2013-15) and Antonio López Carretero (2016)</td> </tr> <tr> <td>Abstract</td> <td>2471 records of plant-flower visitor interactions, recorded in 49 sites in Argentina (Buenos Aires: Carlos Casares county). The records were taken in the field by Marcos Monasterolo and Antonio López Carretero, curated by Julia Tavella, and transformed to meet FAIR principles by Rocío Ana González Vaquero. Morphospecies are detailed in identificationRemarks. Data collection was supported by FONCyT, UBA, CONICET, Fundación Banco Galicia, and Syngenta Agro, Argentina. Data standardization was supported by grant European Commission-EU 101058393.</td> </tr> <tr> <td>Classification System</td> <td>GBIF</td> </tr> <tr> <td>Sampling Description</td> <td>One 100 m-long “variable transect” was walked by one trained observer for 40 minutes (Monasterolo et al. 2020).</td> </tr> </tbody> </table> <p> </p> <p> </p>
Application of regional meteorology and air quality models based on MIPS and LoongArch CPU Platform
<p>bcfile.zip: the clean boundary condition files.</p> <p>emisfile.zip: the emission files.</p> <p>icfile.zip: the clean initial condition files.</p> <p>tuvfile.zip and o3mapfile.zip: the photolysis files.</p> <p>outputfile.zip: the computation results outputted by CAMx model for MIPS and X86 platforms.</p> <p>wrfcamx.zip: meteorological files.</p> <p>outputfile_LoongArch_platform.tar.gz: the computation results outputted by CAMx model for LoongArch platforms.</p> <p>bin_executable_on_LoongArch_platform.tar.gz: The executable files of CAMx model which can run stably on LoongArch platform, including noMPI, MPICH and OpenMP version.</p> <p>bin_executable_on_MIPS_platform.tar.gz: The executable files of CAMx model which can run stably on MIPS platform, including noMPI, MPICH and OpenMP version</p>
A detectable change in the air-sea CO2 flux estimate from sailboat measurements
<p>This repository contains files and scripts used to generate figures for the paper titled "A detectable change in the air-sea CO2 flux estimate from sailboat measurements". The study quantifies the impact of pCO2 observations measured by the sailboat "Seaexplorer" on the air-sea CO2 flux estimate.</p><p><strong> Overview:</strong></p><p>We used pCO2 measurements sourced from SOCAT (www.socat.info), along with other environmental variables. We applied the 2-step neural network method SOM-FFN (Landschützer et al., 2013) to reconstruct air-sea CO2 flux estimates.</p><p>Four sets of air-sea CO2 flux estimates were reconstructed, each with 40 ensemble members to account for random errors in the neural network. These are the four sets:</p><p>E1) fluxesA - Based on SOCATv2022 including Seaexplorer data</p><p>E2) fluxesB - Based on SOCATv2022 excluding Seaexplorer data.</p><p>E3) fluxesC - Based on SOCATv2022 data, including Seaexplorer data, but introducing a random uncertainty of ± 5 μatm to the Seaexplorer data.</p><p>E4) fluxesD - Based on SOCATv2022 data, including Seaexplorer data, but introducing a constant measurement offset of 5 μatm to the Seaexplorer data.</p><p>These air-sea CO2 flux density estimates are 4-dimensional, represented in mol C m-2 yr-1, with the following dimensions:</p><p>- Ensemble size of flux reconstructions (40)</p><p>- Time (months from 1982 to 2021, totaling 480 months)</p><p>- Latitude (1-degree intervals, totaling 180 latitudes)</p><p>- Longitude (1-degree intervals, totaling 360 longitudes)</p>
A comparative study between air cooling and liquid cooling thermal management systems for a high-energy lithium-ion battery module
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A New Concept of Air Cooling and Heat Pipe for Electric Vehicles in Fast Discharging
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Strategies of air space acquisition in Neotropical savanna trees differing in leaf habit
<p>Strategies of air space acquisition in Neotropical savanna trees differing in leaf habit - Manuscript submitted to the Australian Journal of Botany</p>
Data from: Interactive effects of soil moisture, air temperature and litter nutrient diversity on soil microbial communities and Folsomia candida population
<p>Soil organisms play a key role in carbon and nutrient cycling in forest ecosystems. While soil organisms are strongly influenced by litter chemistry and are highly sensitive to abiotic conditions, little is known about how the interactive effects of these two factors. To address this gap in knowledge, we conducted a 10-week microcosm experiment in which we simulated the effects of climate change on soil ecology. More specifically, we studied relationships among litter nutrient concentration, microbial biomass, Collembola demographic parameters, and litter decomposition, exploring the potential impacts of increasing air temperature and decreasing soil moisture. To develop a gradient of nutrient concentrations, we created six tree litter mixtures with materials gathered from <em>Quercus pubescens</em> and its companion species. In contrast to microbes, we observed that Collembola abundance and litter decomposition were interactively affected by soil moisture and air temperature: the negative effect of increasing air temperature on Collembola abundance was amplified by reduced soil moisture, whereas the positive effect of increasing air temperature on litter decomposition disappeared under reduced soil moisture conditions. In contrast to fungi, the response of bacterial biomass and Collembola abundance to litter nutrient concentration was dependent on abiotic conditions. More specifically, the relationships between nutrients, especially calcium and magnesium, and bacterial biomass and Collembola abundance were less robust or disappeared under drier or warmer conditions. In conclusion, our findings underscore that ongoing climate change could affect soil organisms directly as well as indirectly, by altering their responses to litter nutrient concentrations. In addition, we found that nutrient-rich habitats might be more affected than nutrient-poor habitats by altered climatic conditions.</p>
Leaf traits and leaf-to-air temperature differences in tropical plants suggest variability in thermoregulatory capacities across elevations
<p>Understanding thermoregulation in plants is critical to predicting the consequences of global warming on terrestrial ecosystems. I present empirical evidence that leaf traits and leaf-to-air temperature differ among plants from a tropical elevation gradient. These findings suggest the existence of specific plant adaptations for thermoregulation across varying elevational zones.</p>
All-sky daily mean ambient air temperature datasets at 1-km resolution from 2003-2012 in China
<ul> <li>All-sky daily maximum, minimum, and mean ambient air temperature datasets at 1-km resolution over two decades (2003-2022) in China have been generated by the four-dimensional spatiotemporal deep forest (4D-STDF) model. The overall RMSE values for estimates are 1.49°C, 1.53°C, and 1.18°C.</li> <li>These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. They are presented in GeoTIFF format with WGS84 projection, with data measured in 0.1 degrees Celsius (°C).</li> <li>These datasets have divided for six parts to upload Zenodo, the links as follow: <ol> <li>Daily Tmax from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>,</li> <li>Daily Tmax from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>,</li> <li>Daily Tmin from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>,</li> <li>Daily Tmin from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>,</li> <li>Daily Tmean from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>,</li> <li>Daily Tmean from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177</a></li> </ol> </li> <li>This dataset is daily Tmean from 2003-2012 (<a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>).</li> </ul>
All-sky daily min ambient air temperature datasets at 1-km resolution from 2003-2012 in China
<ul> <li>All-sky daily maximum, minimum, and mean ambient air temperature datasets at 1-km resolution over two decades (2003-2022) in China have been generated by the four-dimensional spatiotemporal deep forest (4D-STDF) model. The overall RMSE values for estimates are 1.49°C, 1.53°C, and 1.18°C.</li> <li>These datasets cover mainland China, featuring high spatial resolution (1km), long temporal sequences (2003-2022), and increased accuracy. They are presented in GeoTIFF format with WGS84 projection, with data measured in 0.1 degrees Celsius (°C).</li> <li>These datasets have divided for six parts to upload Zenodo, the links as follow: <ol> <li>Daily Tmax from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10983219">https://doi.org/10.5281/zenodo.10983219</a>,</li> <li>Daily Tmax from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983207">https://doi.org/10.5281/zenodo.10983207</a>,</li> <li>Daily Tmin from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>,</li> <li>Daily Tmin from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983199">https://doi.org/10.5281/zenodo.10983199</a>,</li> <li>Daily Tmean from the years 2003-2012: <a href="https://doi.org/10.5281/zenodo.10947354">https://doi.org/10.5281/zenodo.10947354</a>,</li> <li>Daily Tmean from the years 2013-2022: <a href="https://doi.org/10.5281/zenodo.10983177">https://doi.org/10.5281/zenodo.10983177</a></li> </ol> </li> <li>This dataset is daily Tmin from 2003-2012 (<a href="https://doi.org/10.5281/zenodo.10951765">https://doi.org/10.5281/zenodo.10951765</a>).</li> </ul>
Solid-state NMR data for the publication "Bonding of Polyethylenimine in Covalent Organic Frameworks for CO2 Capture from Air"
<p>This upload contains the solid-state NMR of COF-709 series obtained by using Bruker Avance IV NEO 400 MHz NMR instrument. The instrument used in this work was supported by the National Science Foundation under Grant No. 2018784. Per NSF requirements, the data will be publicly available at the time of publication. This dataset can be opened and processed by using MestreNova NMR or similar softwares.</p>
DatabaseNH3 : EOM ammonia emission factor measured with INRAE Caract'Air device (under controlled conditions)
<p>This dataset includes ammonia volatilization measurements led by ECOSYS INRAE with Caract’Air device. Ammonia measurements, based on the principle of a mass balance in dynamic chambers are performed under thoroughly controlled and replicative conditions. Caract’Air was designed to be as close as possible to field conditions (in situ soil cores) while optimizing ambient conditions (temperature, air humidity and soil water content) and exchange conditions (flow rate, head volume, air circulation conditions) (Génermont et al., 2021; Décuq et al., 2023). A variety of EOMs are represented ranging from historic livestock effluents and manure to emerging biowastes produced by human urban and agro-industrial activities, all these biowastes having undergone a variety of treatments: raw, separated, composted, anaerobically stored slurries, farm yard manure, sewage sludges, municipal and domestic wastes; various digestats from mechanization; urine based fertilizers; etc. The types and origins of the EOM are reported. The physico-chemical properties of the EOM as well as soils on which the EOM were applied are detailed, leading to 20 and 30 parameters accompanied by information on analytical methods. Measurement conditions are also described including the application dose, the experimental set duration, the ambient conditions, and also the reproductibily conditions, etc. Finally, ammonia volatilization data are compiled, in terms of total cumulative loss (kg N/ha) or volatilization rates (% N and % N-NH4 applied).</p>
MuAP Spatial distribution of various air pollutants in China at 1 km(SO2 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(SO2)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <p>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</p> <p>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</p> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.PM2.5:https://zenodo.org/records/8093749</p> <p>O3:https://zenodo.org/records/8180923</p> <p>SO2:https://zenodo.org/records/8093749</p> <p>NO2:Please contact the author at fjcyfeng@qq.com.</p> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
MuAP Spatial distribution of various air pollutants in China at 1 km(O3 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(O3)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <ol> <li>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</li> <li>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</li> </ol> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.</p> <ul> <li>PM2.5:https://zenodo.org/records/8093749</li> <li>O3:https://zenodo.org/records/8180923</li> <li>SO2:https://zenodo.org/records/8093749</li> <li>NO2:Please contact the author at fjcyfeng@qq.com.</li> </ul> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
MuAP Spatial distribution of various air pollutants in China at 1 km(PM2.5 2021-01-01:2023-12-31) (Version1.1)
<p>MuAP Spatial distribution of various air pollutants in China at 1 km(PM2.5)</p> <p>Multiple air pollutions dataset (MuAP) </p> <p>Time frame: 2021-2023<br>Area: Most of China<br>Resolution: about 1km<br>File storage format: .xz and GeoTIFF<br>Spatial projection: WGS84<br>Daily file name: year_doy.tif (Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.)</p> <p>Monthly file name: year_month.tif</p> <p>Yearly file name: year_month.tif</p> <p>Unit: Please divide by 10 when using. (ug/m3)</p> <p>When you download and use our data, please cite:</p> <ol> <li>Chi, Y., Zhan, Y., Wang, K., and Ye, H.: Sequential spatiotemporal distribution of PM<sub>2.5</sub>, SO<sub>2</sub> and Ozone in China from 2015 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-76, in review, 2023.</li> <li>Chi, Y., Zhan, Y., Wang, K., & Ye, H. (2023). Spatial Distribution of Multiple Atmospheric Pollutants in China from 2015 to 2020. Remote Sensing, 15(24). doi:10.3390/rs15245705</li> </ol> <p>Note: The MuAP for 2015-2020 can be obtained by:</p> <p>1.</p> <ul> <li>PM2.5:https://zenodo.org/records/8093749</li> <li>O3:https://zenodo.org/records/8180923</li> <li>SO2:https://zenodo.org/records/8093749</li> <li>NO2:Please contact the author at fjcyfeng@qq.com.</li> </ul> <p> </p> <p>2. Daily MuAP data volume exceeds Zenodo platform limits. Please contact the author at fjcyfeng@qq.com.</p> <p> </p>
Dataset: Prevalence of Antibodies Against Dengue Virus in Buenos Aires
<p>This anonymized dataset was collected as part of the study titled "Prevalence of Antibodies Against Dengue Virus in Buenos Aires: Results of a Probabilistic Population Survey." It contains data on the prevalence of antibodies against dengue virus in the general population of Buenos Aires, obtained through a representative probabilistic survey. The dataset includes basic demographic variables, serological results (IgG and IgM), and other related characteristics. All personal identifiers have been removed to ensure participant privacy. This dataset aims to support analysis and reproducibility of the published findings</p>
Dataset for nanoparticles and air ions of six Chinese cities
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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.