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Cancer remains a major global health challenge, affecting diverse populations across various demographics. Integrating Artificial Intelligence (AI) into clinical settings to enhance disease outcome prediction presents notable challenges. This study addresses the limitations of AI-driven cancer care due to low-quality datasets by proposing a comprehensive three-step methodology to ensure high data quality in large-scale cancer-imaging repositories. Our methodology encompasses (i) developing a Data Quality Conceptual Model with specific metrics for assessment, (ii) creating a detailed data-collection protocol and a rule set to ensure data homogeneity and proper integration of multi-source data, and (iii) implementing a Data Integration Quality Check Tool (DIQCT) to verify adherence to quality requirements and suggest corrective actions. These steps are designed to mitigate biases, enhance data integrity, and ensure that integrated data meets high-quality standards. We applied this methodology within the INCISIVE project, an EU-funded initiative aimed at a pan-European cancer-imaging repository. The use-case demonstrated the effectiveness of our approach in defining quality rules and assessing compliance, resulting in improved data integration and higher data quality. The proposed methodology can assist the deployment of big data centralized or distributed repositories with data from diverse data sources, thus facilitating the development of AI tools. [ABSTRACT FROM AUTHOR]

In this study, we consider the historical climatological time series available in the meteorological yearbooks of the Royal Hungarian Central Institute of Meteorology and Earth Magnetism, first published in 1871. Data quality improvement of historical data includes the homogenization process with outlier checks and data gap filling by applying the MASH software. We investigated 13 stations from the Carpathian Region having the most complete monthly temperature and precipitation time series for the period of 1871–1918 and 8 stations with fog observations (1886–1916). First, statistical tests were conducted to compare the main statistics of the historical datasets (1871–1918) with current data (1971–2020). The sources of the current data are the National Meteorological Administration of Romania and the European Climate Assessment & Dataset. The results show significant changes between the two periods. In the whole region, the mean temperature in the last five decades (1971–2020) was 0.77 °C higher than in 1871–1918. Changes in the frequency of foggy situations were also detected. On an annual scale, in the last 31-year period (1990–2020), the number of foggy days increased by 16.2 compared with 1886–1916. Even if some local trends can be detected in the historical periods (e.g., Cluj-Napoca), significant changes are much more characteristic in the recent period. [ABSTRACT FROM AUTHOR]