Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
3,018
datasets available to search
ShareScore release 0.9.0
Dataset results
3,018 results for “AIR”
The features of the selected papers in the field of air quality prediction
<p>The table is a part of a submitted manuscript (Iskandaryan, D., Ramos, F., & Trilles, S. The Role of Datasets in Air Quality Prediction. Submitted to Atmosphere.) and includes the following features extracted from the selected papers: <em>Year, Case Study, Prediction Target, Dataset Type, Data Rate, Period (Days), Open Data, Algorithm, Time Granularity and Evaluation Metric</em>. The relevant papers were selected from a systematic review in <em>Air Quality Prediction Using Machine Learning Technologies. </em>The works were queried in Association for Computing Machinery, IEEE Xplore, Scopus and Web of Science databases using the following query: ("machine learning") AND ("prediction"OR "forecast") AND ("air quality" OR "air pollution"), which was being applied to title, abstract and keywords. After filtering the results guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, ninety-three papers were selected. The goal of this review is to understand which features are used in the field, in particular to answer the following questions: 1) What types of datasets are used to improve air quality predictions?; and 2) What characteristics of the dataset are important for efficient and effective air quality forecasting? <br> Twenty-six datasets were used by the authors as supplemental air quality data in order to predict air quality more accurately. Those datasets are: "MET"- meteorological data; "Spatial"- topographical characteristics, the locations of the stations; "Temporal"-includes the day of the month, day of the week, the hour of the day; "AOD"- aerosol optical depth; "Social Media"- microblog data; "Traffic"; "PBL Height"- planetary boundary layer height; "Land Use"; "BEV"- Built Environment Variables; "UV Index"; "SP"- Sound Pressure; "PD"-Population Density; "Human Movements"- floating population and estimated traffic volume; "Altitude"; "OMI-SO2"-Satellite-retrieved SO2 from Ozone Monitoring Instrument-SO2; "PPS"- Pollution Point Source; "TS"-Transportation Source; "WFD’"- weather forecast data; "POI Distribution"; "FAPE"- factory air pollution emission; "RND"- Road Network Distribution; "Elevation"; "AEI"- Anthropogenic Emission Inventory; "NDVI"; "Chemical"- chemical component forecast data (organic carbon, black carbon, sea salt, etc.); "Emission".</p>
Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors
<p>This repository contains data for the manuscript: "Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors."</p> <p> </p> <p>This includes:</p> <p>Raw data from the low-cost prototype EarthSense Zephyrs, as well as raw data from reference instrumentation.</p> <p>SC stands for "Summer Campaign" and WC stands for "Winter Campaign", denoting the two different campaigns assessed in this study.</p> <p> </p> <p><strong>Abstract</strong></p> <p>The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensors using reference instrumentation with user-friendly guidelines, open access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO<sub>2</sub>) or ozone (O<sub>3</sub>). A full example of the application of this methodology to a case study in an urban environment using both Multiple Linear Regression (MLR) and the Random Forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOS). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit, transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensors, we suggest a number of best practices for future studies using low-cost sensors to ensure greater comparability of research.</p>
MFS-M-00001 Air temperature at +2 m, raised bog-ridge, Thermochron (DS1921G-F5)
<p>Air temperature at 2m measured in a raised bog ecosystem (ridge) by Thermochron logger, 2009-present (with several breaks) as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
MFS-M-00002 Air temperature at 2m measured in a raised bog-ridge, DS18B20 (APIK)
<p>Air temperature at 2m measured in a raised bog ecosystem by DS18B20 (temperature logger), 2018-2019, 30 min frequency, N60.89494 E68.66999, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).</p>
Dataset from "Quantifying air-sea gas exchange using noble gases in a coastal upwelling zone"
<p>Dataset of dissolved noble gas (He, Ne, Ar, Kr, and Xe) measurements in Monterey Bay, CA. Published as a supplement to: Manning, C.C., R.H.R. Stanley, D.P. Nicholson, and M.E. Squibb (2016). Quantifying air-sea gas exchange using noble gases in a coastal upwelling zone. <em>IOP Conference Series: Earth and Environmental Science</em>, 35, 012017 (13 pp). doi: 10.1088/1755-1315/35/1/012017</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bioindicators in San Jose, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Gravity, Free-Air and Bouguer Anomaly Data in the Ivrea-Verbano Zone (Western Alps, Italy)
<p>Gravity dataset collected in the Ivrea-Verbano Zone (IVZ, Western Alps, Italy). </p><p>The data was collected in the frame of a gravity-based investigation and modelling of the Ivrea Geophysical Body. </p><p>For citation and further details on the work see Scarponi et al. (2020, GJI): <a href="https://doi.org/10.1093/gji/ggaa263">https://doi.org/10.1093/gji/ggaa263</a></p><p>The file contains the gravity data collected in the IVZ region, including free-air anomaly and Bouguer gravity anomaly (in mGal).</p><p>Longitude, Latitude coordinates are in degrees, elevation in meters.</p><p>Uncertainty on the final gravity data products and gravity data is 1 mGal.</p><p>---</p><p>Data collection, as well as the associated research, were supported by the Swiss National Science Foundation (SNF) (grant numbers PP00P2_157627 and PP00P2_187199).</p>
Reconstruction of atmospheric H2 from Greenland and Antarctic firn air
<p>This data set contains:</p> <ol> <li>firn air depth profiles of H<sub>2</sub> from 4 polar sites (South Pole and Megadunes in Antarctica) and NEEM and Summit in Greenland used to by Patterson et al., in press to reconstruct the history of atmospheric H<sub>2</sub> </li> <li>Matlab code for the UCI firn air model and data used in the reconstructions </li> </ol> <p>Reference: Patterson, J. D., Aydin, M., Crotwell, A. M., Pétron, G., Severinghaus, J. P., Krummel, P. B., Langenfelds, R. L., Petrenko, V. V., and Saltzman, E. S.: Reconstructing atmospheric H<sub>2</sub> over the past century from bi-polar firn air records, Clim. Past, https://doi.org/10.5194/cp-2023-27, in press.</p> <p>Note: The firn air depth profiles archived here are processed to remove outliers and average replicates. For raw data, please contact the laboratories at which the measurements were made (G. Petron, GML/NOAA; P. Krummel; CSIRO). </p>
CLaMS results used for age of air analysis in the report of assessment of the ESA Earth Explorer candidate mission CAIRT
<p>Results of the Chemical Lagrangian Model of the Stratosphere (CLaMS) used for age of air analysis in the report of assessment of the ESA Earth Explorer candidate mission CAIRT. Days of results: 2011-01-01, 2011-04-01, 2011-07-01, 2011-10-01 and 2019-09-23. Included trace gases: SF6, CFC-11, CFC-12, HCFC-22, N2O and CH4. The results also include the clock tracer BA, which can be converted into the precise mean age of or air of the model environment with the attached python script 'AOA2age_years.py'.</p>
Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022
<p>Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm. </p>
SAQI: An Ontology based Knowledge GraphPlatform for Social Air Quality Index
<p>This dataset consists of all contributions made by Social AQI (SAQI) project. The description of dataset is as below -</p> <p>Local Sensor Data (hyperlocal-air-quality-sensor-data) - contains all sensors values recorded through local neighbourhood sensors throught the length of the project <br> Locations for all these sensors are as below - In Najafgarh, Delhi, India : Jharoda Kalan, Nangli Dairy and DTC Bus terminal.<br> In Okhla : Sanjay Colony, Tekhand, Shaheen Bagh.</p> <p>Data from <a href="https://cpcb.nic.in/">Central Pollution Control Board</a> (central-air-quality-sensor-data) - Najafgarh_CPCB.csv, Okhla_CPCB.csv : Contains data provided by CPCB from Najafgarh,Delhi and Oklha, Delhi</p> <p><br> PollutionODP.owl : Ontology Design Pattern for pollution - http://ontologydesignpatterns.org/wiki/Submissions:Pollution.<br> <br> Ontology : SAQI ontology as triples (ttl), xml (rdf) and json-ld (json) serialization format<br> Ontology documentation : ontology/diagram contains figures describing ontology, ontology/documentation/saqi.html contains LODE documentation for the ontology</p> <p><br> ethnographic-survey-data - anonymized survey responses for initial pollution perception and literacy survey as well as SAQI app feedback survey.</p> <p>SHACL-shapes - for validating against SAQI ontology.</p> <p> sparql-queries - sample queries to run on our ontology.</p> <p>setup-rdf-store-script - script to setup rdf store with given data using rml mapper.</p> <p> </p> <p><br> </p>
Data used in "The Complex Role of Storms in Modulating Air-Sea CO2 Fluxes in the sub-Antarctic Southern Ocean"
<p>The data included in this repository were used to generate the figures for the paper "The Complex Role of Storms in Modulating Air-Sea CO2 Fluxes in the sub-Antarctic Southern Ocean" in Geophysical Research Letter.</p> <p>Abstract:</p> <p>"The intra-seasonal CO<sub>2</sub> flux (FCO<sub>2</sub>) variability across the Southern Ocean is poorly understood due to sparse observations at the required temporal and spatial scales. Twinned Waveglider-Seaglider experiments were used to investigate how storms influence FCO<sub>2</sub> through both the gas transfer velocity (k<sub>w</sub>) and the air-sea gradient in partial pressure of CO<sub>2</sub> (ΔpCO<sub>2</sub>) in the sub-Antarctic zone. Winter-spring storms caused ΔpCO<sub>2</sub> to weaken (by 15-55 μatm) due to mixing/entrainment and weaker stratification. This response in ΔpCO<sub>2</sub> was in phase with k<sub>w</sub> resulting in a counteractive weakening in FCO<sub>2</sub> (by 6.6 - 26.5% per storm), despite the wind-driven increase in k<sub>w</sub>. Stronger stratification during summer explained the weaker sensitivity of ΔpCO<sub>2</sub> to storms, instead its thermal drivers dominated the ΔpCO<sub>2 </sub>variability. These results highlight the importance of observing synoptic-scale variability in ΔpCO<sub>2</sub>, the absence of which may propagate significant biases to the mean annual FCO<sub>2</sub> estimates from large-scale observing programmes and reconstructions."</p> <p>The data collected from the Wave Glider, such as the concentration of CO<sub>2</sub> in the atmosphere (xCO<sub>2air</sub>) and in the ocean (xCO<sub>2sea</sub>), surface temperature and salinity were used to calculate the different parameters of the bulk CO<sub>2</sub> flux formula (FCO<sub>2</sub> = k<sub>w</sub> x ko x ΔpCO<sub>2</sub>). Note that the meteorological weather station of one of the Wave Gliders was faulty and the wind speed, wind direction and wind stress data was replaced by hourly ERA5 data provided by ECMWF available at <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>. </p> <p>The temperature, pressure and salinity data collected by the Seaglider were used to calculate the Mixed Layer Depth and the Brunt Vaisala Frequency of the first 300m of the ocean.</p>
Heat pumps for all? Distributions of the costs and benefits of residential air-source heat pumps in the United States
<p>Dataset for the "<span>Heat pumps for all? Distributions of the costs and benefits of residential air-source </span><span>heat pumps in the United States" paper</span></p>
Bottom-illuminated photothermal nanoscale chemical imaging with a flat silicon ATR in air and liquid (data evaluation)
<p>This record contains a docker image for data evaluation of AFM-IR data for our publication 'Bottom-illuminated photothermal nanoscale chemical imaging with a flat silicon ATR in air and liquid'. The evaluations can be accessed by running the container and accessing the contained Jupyter Lab via a browser. The calculations are contained in 'Bottom_illuminated_PTIR.ipynb'.</p> <p>To run the container (requires docker):</p> <p>1.download 'container.tar.gz'</p> <p>2. in the command line, execute 'docker load -i container.tar.gz'. This will return something like 'Loaded image: <image_name>'</p> <p>3. then 'docker run -p 8888:8888 <image_name>' replacing the brackets with the actual name of the image, such as paper_bottom_illuminated:submission</p> <p>4.In your command line a link starting in 'http://127.0.0.1:8888/lab?token=...' will appear. Open this link in your browser to access the evaluation.</p>
Global present-day air-conditioning adoption rate
<p>This dataset contains the present-day, global, survey-based, and spatially explicit air-conditioning adoption rate dataset developed in Li et al. (2024), “Enhancing Urban Climate-Energy Modeling in the Community Earth System Model (CESM) through Explicit Representation of Urban Air-conditioning Adoption”, published in <em>Journal of Advances in Modeling Earth Systems</em>. It also contains the simulation results analyzed in the article. Details about this dataset (data sources, data collection and processing methods, simulation setup, etc.) are described in the article. The air-conditioning adoption rate dataset is publicly available in tabular, vector, and gridded formats. It is compatible with CESM, and can also be leveraged in other climate and energy modeling applications and socioeconomic or integrated assessment analyses. This dataset may be useful for multiple scientific communities regarding urban climate and energy, impacts, vulnerability, risks, and adaptation applications. </p> <p>For more detailed description, please refer to the README file (<em>global_AC_adoption_rate_README.txt</em>) included in the dataset.</p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Data for the submitted paper by Yasunari et al., "Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments"
<p>The dataset contains some of the outputs from the global climate model experiments by MIROC5 on changing Siberian wildfire severities, the other data used in the paper (see READ_ME files on the data sources), the analyzed data, and the scripts for analyses, which were used in the following submitted paper. Note that this dataset also includes unused data for the paper:</p> <p><br>Yasunari, T. J., D. Narita, T. Takemura, S. Wakabayashi, and A. Takeshima, Comprehensive Impact of Changing Siberian Wildfire Severities on Air Quality, Climate, and Economy: MIROC5 Global Climate Model's Sensitivity Assessments, submitted.</p> <p>Please read the READ_ME files for detailed information in each directory (especially see the "about_figures_and_tables/" directory first). Because of their large sizes, the data were separated into three zipped files.</p>
Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet. in Reproduction of Zingel asper (Linnaeus, 1758) in controlled conditions: an assessment of the experiences realized since 2005 at the Besançon Natural History Museum
Operating diagram of larvae hatching module, this installation was used to determine the optimum larvae load during the rearing process and provided additional space for rearing several thousand larvae. It consists of nine 20-litre tanks with a glass panel along the front. They are fitted with an inlet supplying filtrated water at a rate of 100 l/h and an individual air inlet.
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.