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• W3204442365 abstract "Since the beginning of the COVID-19 pandemic in March 2020, national and international authorities started to develop and update datasets to provide data to researchers, journalists and health care providers as well as public opinion. These data became one of the most important sources of information, which are updated daily and analysed by scientists in order to investigate and predict the spread of this epidemic. Despite this positive reaction from both national and international authorities in providing aggregated information on the diffusion of COVID-19, different challenges have been underlined in previously published studies. Different papers have discussed strengths and weaknesses of these types of datasets by focusing on different quality perspectives, which include the statistical methods adopted to analyse them; the lack of standards and models in the adoption of data for their management and distribution; and the analysis of different data quality characteristics. These studies have analysed datasets at the general level or by focusing the attention on specific indicators such as the number of cases or deaths. This paper further investigates issues and opportunities in the diffusion of these datasets under two main perspectives. At the general level, it analyses how data are organized and distributed to scientific and non-scientific communities. Moreover, it further explores the indicators adopted to describe the spread of the COVID-19 epidemic while also highlighting the level of detail used to describe them in terms of gender, age ranges and territorial units. The paper focuses on six European countries: Belgium, France, Germany, Italy, Spain and UK." @default.
• W3204442365 created "2021-10-11" @default.
• W3204442365 creator A5010067486 @default.
• W3204442365 creator A5061292206 @default.
• W3204442365 date "2021-10-06" @default.
• W3204442365 modified "2023-09-24" @default.
• W3204442365 title "Open Data Resources on COVID-19 in Six European Countries: Issues and Opportunities" @default.
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• W3204442365 doi "https://doi.org/10.3390/ijerph181910496" @default.
• W3204442365 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8507931" @default.
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