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Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia)
This data article describes two groups of datasets which capture, firstly - 10-minutes air temperature (T(a)) and relative humidity (RH) data from 27 urban and non-urban sites over a period of 3.5 years covering 2014–2018; and secondly - hourly T(a) data from 12 urban sites over a period of 2 years...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369387/ https://www.ncbi.nlm.nih.gov/pubmed/37501730 http://dx.doi.org/10.1016/j.dib.2023.109425 |
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author | Savić, Stevan Šećerov, Ivan Lalić, Branislava Nie, Dongyun Roantree, Mark |
author_facet | Savić, Stevan Šećerov, Ivan Lalić, Branislava Nie, Dongyun Roantree, Mark |
author_sort | Savić, Stevan |
collection | PubMed |
description | This data article describes two groups of datasets which capture, firstly - 10-minutes air temperature (T(a)) and relative humidity (RH) data from 27 urban and non-urban sites over a period of 3.5 years covering 2014–2018; and secondly - hourly T(a) data from 12 urban sites over a period of 2 years covering 2016 and 2017. Both datasets are from urban meteorological network located in the Novi Sad city (Serbia). These datasets have 2 different types of information in the collection: one type provides details about the monitoring sites at which the T(a) and RH sensors are placed, while the second type contains T(a) and RH data at all sensor locations. In all, the 10-minutes dataset contains about 185,000 instances of T(a) and RH data, and the hourly datasets contain 17,544 instances of T(a) data. The 10-minutes datasets were not quality controlled, but the hourly T(a) data has been cleaned and gap-filled so there are 24 measures at each site for each day. There are multiple potential uses, where this data can be applied. It can provide insights in understanding intra-urban and inter-urban research, urban climate modeling on local or micro scales, heat-related public health investigations and urban environment inquiries. It can also be used in machine learning experiments, for example, to test the accuracy of classification algorithms or to build and validate spatio-temporal machine learning functions, either for classification purposes or for gap filling. These datasets are directly citable through its DOIs and available for download from the Zenodo platform or from the Fair Micromet Portal. |
format | Online Article Text |
id | pubmed-10369387 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-103693872023-07-27 Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia) Savić, Stevan Šećerov, Ivan Lalić, Branislava Nie, Dongyun Roantree, Mark Data Brief Data Article This data article describes two groups of datasets which capture, firstly - 10-minutes air temperature (T(a)) and relative humidity (RH) data from 27 urban and non-urban sites over a period of 3.5 years covering 2014–2018; and secondly - hourly T(a) data from 12 urban sites over a period of 2 years covering 2016 and 2017. Both datasets are from urban meteorological network located in the Novi Sad city (Serbia). These datasets have 2 different types of information in the collection: one type provides details about the monitoring sites at which the T(a) and RH sensors are placed, while the second type contains T(a) and RH data at all sensor locations. In all, the 10-minutes dataset contains about 185,000 instances of T(a) and RH data, and the hourly datasets contain 17,544 instances of T(a) data. The 10-minutes datasets were not quality controlled, but the hourly T(a) data has been cleaned and gap-filled so there are 24 measures at each site for each day. There are multiple potential uses, where this data can be applied. It can provide insights in understanding intra-urban and inter-urban research, urban climate modeling on local or micro scales, heat-related public health investigations and urban environment inquiries. It can also be used in machine learning experiments, for example, to test the accuracy of classification algorithms or to build and validate spatio-temporal machine learning functions, either for classification purposes or for gap filling. These datasets are directly citable through its DOIs and available for download from the Zenodo platform or from the Fair Micromet Portal. Elsevier 2023-07-20 /pmc/articles/PMC10369387/ /pubmed/37501730 http://dx.doi.org/10.1016/j.dib.2023.109425 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Savić, Stevan Šećerov, Ivan Lalić, Branislava Nie, Dongyun Roantree, Mark Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia) |
title | Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia) |
title_full | Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia) |
title_fullStr | Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia) |
title_full_unstemmed | Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia) |
title_short | Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia) |
title_sort | air temperature and relative humidity datasets from an urban meteorological network in the city area of novi sad (serbia) |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369387/ https://www.ncbi.nlm.nih.gov/pubmed/37501730 http://dx.doi.org/10.1016/j.dib.2023.109425 |
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