Dataset Information

Constructing a meteorological indicator dataset for selected European NUTS 3 regions.

ABSTRACT: The harmonization of data granularity in spatial and temporal terms is an important pre-step to any econometric and machine learning applications. Researchers, who wish to statistically test hypotheses on the relationship between agro-meteorological and European policy outcomes, often observe that agro-meteorological data is typically stored in gridded and temporally detailed form, while many relevant policy outcomes are only available on an aggregated level. This dataset intends to aid empirical investigations by providing a dataset with monthly meteorological indicators on a European Nomenclature of Territorial Units for Statistics level 3 (NUTS 3) regional level for 13 countries for the period from 1989 to 2018. The data we provide allows researchers to investigate hypothesis related to weather volatility and the probability of extreme weather events. We created this dataset from the daily data in grids of 25 km x 25 km provided by the Joint Research Centre of the European Commission. We matched the map with the raw data to a map with the administrative boundaries of European NUTS 3 regions. After appropriately weighting, we calculated the monthly, regional mean, variance and kurtosis of the following variables: maximum, minimum, average air temperature in degrees Centigrade, sum of precipitation in mm and snow depth in cm. We report the covariance between the average temperature and the precipitation as well.

SUBMITTER: Angelova D

PROVIDER: S-EPMC7289765 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Constructing a meteorological indicator dataset for selected European NUTS 3 regions.

Angelova Denitsa D Lupio Norman Blanco NB

Data in brief 20200529

The harmonization of data granularity in spatial and temporal terms is an important pre-step to any econometric and machine learning applications. Researchers, who wish to statistically test hypotheses on the relationship between agro-meteorological and European policy outcomes, often observe that agro-meteorological data is typically stored in gridded and temporally detailed form, while many relevant policy outcomes are only available on an aggregated level. This dataset intends to aid empirica ...[more]

PMID: 32551351

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Project description:When a selective sweep occurs in the chromosomal region around a target gene in two populations that have recently separated, it produces three dramatic genomic consequences: 1) decreased multi-locus heterozygosity in the region; 2) elevated or diminished genetic divergence (F(ST)) of multiple polymorphic variants adjacent to the selected locus between the divergent populations, due to the alternative fixation of alleles; and 3) a consequent regional increase in the variance of F(ST) (S(2)F(ST)) for the same clustered variants, due to the increased alternative fixation of alleles in the loci surrounding the selection target. In the first part of our study, to search for potential targets of directional selection, we developed and validated a resampling-based computational approach; we then scanned an array of 31 different-sized moving windows of SNP variants (5-65 SNPs) across the human genome in a set of European and African American population samples with 183,997 SNP loci after correcting for the recombination rate variation. The analysis revealed 180 regions of recent selection with very strong evidence in either population or both. In the second part of our study, we compared the newly discovered putative regions to those sites previously postulated in the literature, using methods based on inspecting patterns of linkage disequilibrium, population divergence and other methodologies. The newly found regions were cross-validated with those found in nine other studies that have searched for selection signals. Our study was replicated especially well in those regions confirmed by three or more studies. These validated regions were independently verified, using a combination of different methods and different databases in other studies, and should include fewer false positives. The main strength of our analysis method compared to others is that it does not require dense genotyping and therefore can be used with data from population-based genome SNP scans from smaller studies of humans or other species.

Project description:Background: The objective of this study was to evaluate the impact of weather factors on stroke parameters. Methods: This retrospective study analyzed the records of stroke patients concerning the influence of meteorological conditions and moon phases on stroke parameters. Results: The study group consisted of 402 patients aged between 20 and 102; women constituted 49.8% of the subjects. Ischaemic stroke was diagnosed in 90.5% of patients and hemorrhagic stroke was diagnosed in 9.5% of patients. The highest number of hospitalizations due to stroke was observed in January (48 events); the lowest number was observed in July (23 events). There was no statistically significant correlation between the meteorological parameters on the day of onset and the preceding day of stroke and the neurological status (NIHSS) of patients. Mean air temperature on the day of stroke and the day preceding stroke was significantly lower in the group of patients discharged with a very good functional status (≤2 points in modified Rankin scale (mRS)) compared to the patients with a bad functional status (>2 points in mRS); respectively: 7.98 ± 8.01 vs. 9.63 ± 7.78; p = 0.041 and 8.13 ± 7.72 vs. 9.70 ± 7.50; p = 0.048). Humidity above 75% on the day of stroke was found to be a factor for excellent functional state (RR 1.61; p = 0.016). The total anterior circulation infarcts (in comparison with stroke in the other localization) were more frequent (70%) during a third quarter moon (p = 0.011). The following parameters had a significant influence on the number of stroke cases in relation to autumn having the lowest number of onsets: mean temperature (OR 1.019 95% CI 1.014-1.024, p < 0.000), humidity (OR 1.028, CI 1.023-1.034, p < 0.0001), wind speed (OR 0.923, 95% CI 0.909-0.937, p < 0.0001), insolation (OR 0.885, 95% CI 0.869-0.902, p < 0.0001), precipitation (OR 0.914, 95% CI 0.884-0.946, p < 0.0001). Conclusion: Air humidity and air temperature on the day of stroke onset as well as air temperature on the day preceding stroke are important for the functional status of patients in the acute disease period. A combination of the following meteorological parameters: lowered mean temperature and low sunshine, high humidity and high wind speed all increase the risk of stroke during the winter period. High humidity combined with high precipitation, low wind speed and low sunshine in the autumn period are associated with the lowest stroke incidence risk. A possible relationship between phases of the moon and the incidence requires further investigation.

Dataset Information

Constructing a meteorological indicator dataset for selected European NUTS 3 regions.

Publications

Constructing a meteorological indicator dataset for selected European NUTS 3 regions.

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