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

doi:10.1016/j.dib.2023.109425

Cited by 2 publications

(1 citation statement)

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“…In this paper, we begin with an interpretation of the FAIR principles and articulate what is required to meet the threshold for each principle. We then use a case study from an urban micrometeorological network and perform a step through the creation of FAIR metadata through the FAIR Micromet Portal (FMP), first introduced in (Roantree et. al.…”

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confidence: 99%

Enabling FAIR Certification for Micrometeorological Datasets

Roantree,

Savić,

Scriney

et al. 2023

Preprint

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Abstract. The current state of weather-induced agricultural losses, water use for irrigation, the appearance of new invasive species and disease vectors, new environmental zoning of plant diseases and pests, deforestation, increased urbanization, rural-to-urban migration and increased urban energy consumption for cooling and heating, together impose a scientific demand for FAIR micrometeorological data. FAIR data and metadata should be easily discoverable by humans or machines, accessible under specific conditions or restrictions, conform to recognized formats and standards to be combined and exchanged, and licensed according to community norms, allowing users to know what kinds of reuse are permitted. However, the lack of FAIR data costs Europe a minimum of €10.2bn per year or approximately 78 % of the Horizon annual 2020 budget. If data met the FAIR principle, it would improve data discovery and access, enable re-use, enhance understanding, especially across domains, reach as many people as possible, be cited more often, and open new routes to build cooperation. To support owners of micrometeorological data to make their data FAIR, the FAIR Micromet Portal was developed within the CA20108 COST Action to guide owners through FAIR principles, in a step-by-step manner, with the ultimate goal of making large volumes of data FAIR. This paper provides a detailed discussion on how this is achieved by validating micrometeorological data stored on the FAIR Micromet Portal against the full set of FAIR metrics.

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confidence: 99%