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17 results for “meteorological factor”
Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic
<ul> <li>Supporting datasets for paper "Impact of Meteorological Factors on the Mesoscale Morphology of Cloud Streets during a Cold Air Outbreak over the western North Atlantic". </li> <li>Those are a subset of the (analyzed) datasets from WRF control simulation "ERA5" in netcdf format. See manuscript for more details. <ul> <li>cld_size.nc: cloud object size</li> <li>cld_ort_2020-03-01_15_00_00.nc: cloud object at 15:00 UTC</li> <li>hydro-02-2020-03-01_15/00/00.nc: water path sample data at 15:00 UTC</li> <li>wrfout_d02_2020-03-01_15/00/00: wrf output sample data at 15:00 UTC</li> </ul> </li> </ul>
Data files for: Meteorological factors in the production of Gigantic Jets by tropical thunderstorms in Colombia
<p>Data includes:</p> <ul> <li>Gigantic jet locations and times</li> <li>Vertical profiles for GJ and null cases</li> <li>CSV files with meteorological variables per GJ event and null case</li> </ul>
Association between meteorological factors and the number of tuberculosis notifications: a time-series study in Hong Kong
<p> Using a 22-year consecutive surveillance data in Hong Kong, including monthly averages of meteorological factors, air pollution concentrations , total number of TB cases notified, to analyze the association of monthly average temperature and relative humidity with temporal dynamics of monthly total number of TB cases notified. </p>
Fig.11 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.11. Ranking of meteorological factors by the quantity of significant positive or negative correlations with the number of sun-basking Emys orbicularis in the interval 8d"Nsbd"21.
Fig.3 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.3. Basic forms of sun-basking activity of Emys Fig.4. Basic forms of sun-basking activity of Emys orbicularis registered in the study: lying in the orbicularis registered in the study: heating under shadow. the sun in the shoal.
Ground obeservation data (meteorological factors and air pollution) in Greater Bay Area, 2015 - 2021
<p>Ground observation data used in the paper <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement</em>.</p>
Fig.6 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.6. Daily dynamics of sun-basking activity of Emys orbicularis.
Fig.1 in The Experimental Data On Sun-Basking Activity Of European Pond Turtle Emys Orbicularis In Natural Climate In Latvia: Dynamics And Correlation With The Meteorological Factors
Fig.1. The schema of the experimental out-door terrarium.
Airborne pollen patterns and their relationship with meteorological factors in the Betula microphylla-dominated wetland of Ebinur Lake, Xinjiang, China
<p>Airborne pollen concentrations and meteorological data.</p>
Datasets for "Meteorological factors associated with the timing and abundance of Hymenoscyphus fraxineus spore release" by Burns, Timmermann and Yearsley.
<p>======++++++++++++++++++++++++++++++==============<br> <br> # Data Files:<br> <br> File: burns_etal_preprocessed_data.Rdata<br> <br> This file contains the pre-processed spore count data and the cleaned meteorological data<br> The file contains:<br> <br> stations The longitude and latitude of the two weather stations used for the metro data<br> varStr_mean Names of the meteorological variables<br> windowStr Names of the three time windows<br> <br> emission The main data frame containing the spore and meteorological data<br> date Date of a spore count recording. (POSIXlt)<br> year Year of spore count recording<br> month Month of spore count recording <br> day Day of year of spore count recording<br> total The total daily spore count<br> peak The maximum spore count each day <br> peak_time The time (hours after midnight) of the maximum spore count each day<br> peak_time_raw Raw value for time of maximum spore count each day<br> peak_time_date Date and time (POSIXct) for maximum spore count each day<br> ===================================================<br> <br> File: results_burns_etal_daily_emission_analysis_2010_2011_prop0.8.Rdata<br> <br> This file gives the results for the total daily emission of spores<br> <br> File: results_burns_etal_daily_peaktime_analysis_2010_2011_prop0.8.Rdata<br> <br> This file gives the results for the time of the daily per in spore counts<br> <br> ================<br> Both files have the same variables, which are listed below.<br> <br> # Setup parameters<br> use.prop Proportion of the data to use for fitting model<br> colinear_threshold The correlation threshold to identify collinear covariates<br> frost_var The name of the variable to use as a frost covariate (three possible windows)<br> k.use The dimension of the basis for the smoothing thin-plate splines in the GAM<br> nIter Number of Monte-Carlo random subsamples of the data<br> seed The random number seed at the start of the analysis<br> years The years of data to use for fitting the GAM models.<br> Leaving a year out allows it to be used as independent validation data<br> <br> # Outputs from the analysis<br> var.use The names of covariate used in the final analysis after removing collinear covariates<br> models A list (of length nIter) giving all the fitted models<br> d A data frame with a summary of the nIter model results.<br> There are nIter rows. Each row summarises the results from one GAM<br> The data frame contains:<br> r2 r-squared between the model and the validation data.<br> Validation data are the (1-use.prop) proportion not used for fitting<br> r2_fitted. r-squared for the data used to fit the model<br> dev.exp. The explained deviance from the fitted GAM<br> nTerm. The number of smooth terms in the fitted GAM<br> term1 The smooth term with the smallest p-value (number is an index for var.use)<br> term2 The smooth term with the second smallest p-value (number is an index for var.use)<br> term3 The smooth term with the third smallest p-value (number is an index for var.use)<br> termF The smooth term with the largest F-value (number is an index for var.use)<br> pValues p values for each of the smooth terms (columns) for each of the nIter models (rows)<br> FValues F values for each of the smooth terms (columns) for each of the nIter models (rows)<br> edf Estimated degrees of freedom for each of the smooth terms (columns) for each of the nIter models (rows)<br> pValue_param p values for each of the parametric terms (columns) in each of nIter models (rows)<br> tVal_param. t statistics for each of the parametric terms (columns) in each of nIter models (rows)<br> </p>
Ozone concentrations and meteorological factors in coastal areas of China in July of 2015-2022
<p>Ozone concentrations and meteorological factors in four coastal areas of China (the coast of Fujian Province (c-FJ), the coast of Yangtze River Delta region (c-YRD), the coast of Shandong Peninsula (c-SD) and Beijing-Tianjin-Hebei (c-BTH) in July of 2015-2022. Totally 25 coastal cities were picked as typical cities representative of the studied regions (Ningde, Fuzhou, Putian, Quanzhou, Xiamen and Zhangzhou in c-FJ; Wenzhou, Taizhou, Ningbo, Zhoushan, Jiaxing, Shanghai, Nantong, Yancheng and Lianyungang in c-YRD; Rizhao, Qingdao, Weifang, Weihai, Yantai and Dongying in c-SD; Cangzhou, Tianjin, Tangshan and Qinhuangdao in c-BTH) and the data was averaged to obtain the O<sub>3</sub> concentrations and meteorological conditions regionally.</p>
Acute Effects of Meteorological Factors and Air Pollution on Cardiovascular Health
ClinicalTrials.gov study NCT06663267. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Socio-economic, Meteorological and Environmental Factors Associated With the Incidence of COVID-19
ClinicalTrials.gov study NCT05379621. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Based on the Data from the Zhejiang Emergency Center, Explore the Independent and Interactive Effects of the Association Between Meteorological Factors and Air Pollution and the Number of Emergency Pe
ClinicalTrials.gov study NCT06798987. IPD Sharing: Not stated. Countries: 1. Publications: 16.
Fig. 3 in The population dynamics of three polyphagous owlet moths (Lepidoptera: Noctuidae) and the influence of meteorological factors and ENSO on them
Fig. 3. Average number of Anicla infecta (continuous line), Elaphria agrotina (dashed line) and Spodoptera frugiperda (dotted line) in four crop seasons. Oceanic Niño Index - ONI; the white bars (left) represents six months before the beginning of the collections (gray bars). Values over 0,5 correspond to El Niño and under to −0.5 correspond to La Niña (NOAA, 2018).
Fig. 2 in The population dynamics of three polyphagous owlet moths (Lepidoptera: Noctuidae) and the influence of meteorological factors and ENSO on them
Fig. 2. Average number of moths (a) Anicla infecta, (b) Elaphria agrotina and (c) Spodoptera frugiperda in four crop seasons according to meteorological variables: humidity - dotted line; maximum temperature - long dashed line; minimum temperature - continuous line and precipitation - short dashed line. Left columns have different scales and * [first column of E. agrotina (b)] represents 106.3 moths.
Fig. 1 in The population dynamics of three polyphagous owlet moths (Lepidoptera: Noctuidae) and the influence of meteorological factors and ENSO on them
Fig. 1. Annual average abundance of Anicla infecta (black columns), Elaphria agrotina (grey columns) and Spodoptera frugiperda (white columns); cumulative precipitation in cubic millimeters per quadratic meter (black bars) in four Crop Seasons and expected precipitation according to Climatological Normal 1974–2004 (Silva et al., 2014). Moths captured with light traps at Estação Experimental da Embrapa Cerrados, Federal District, Brazil.
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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.