Search

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]

The recovery of early records of maximum, minimum, and mean temperatures; pressure; and relative humidity measurements in Puerto Madryn for the period 1902–1915 is presented. A careful evaluation of the quality of the data was performed using internal coherence, tolerance, and temporal consistency tests. The monthly mean series of all the variables, constructed from daily raw data, were subject to several homogeneity tests, and only discontinuities in pressure and relative humidity were found. The homogenized monthly mean series were compared with the Twentieth Century Reanalysis series in annual and seasonal time steps. In addition, the trends of each variable were assessed using the Mann–Kendall procedure, and correlations between relative humidity and the other variables were examined. The results show a remarkably good agreement between the temperature measurements and reanalysis values with a Spearman correlation coefficient of 0.94. The raw data for minimum and maximum temperatures represent a very good upper and lower bound for the mean temperature values of both observational and reanalysis data. Agreement was found to be lower for relative humidity and pressure with the correlation coefficients being close to 0.6 in both cases. No trends were found for the variables. The correlation analysis of the humidity measurements with the other variables shows an inverse dependence of the temperatures and no relatedness with the pressure values. [ABSTRACT FROM AUTHOR]

Long‐term variations in maximum gust speeds have been analyzed using wind measurements recorded at St Mary's, Isles of Scilly, UK, and possible drivers of recent changes have been investigated. A rigorous homogenization procedure has been applied to the wind records to remove discontinuities mainly caused by non‐climatic changes. Trends in magnitude of the homogenized annual and monthly maximum gust speeds have been subsequently estimated. A significant (p < 0.10) decreasing trend of −0.102 m s−1/10 years is exhibited by the annual maximum gust speeds over the period 1928–2011, while a non‐significant (p > 0.10) positive trend characterizes the entire period 1928–2020 of wind measurements. A turning point has been detected in the monthly maximum gust speed series in 2012, followed by a trend reversal. Distinctive differences in the gust trend pattern have been observed among the seasons, with increasing trend in summer, and winter gust speeds exhibiting decline in 1969–2011 and strengthening since 2012. Variations in the monthly maximum gust speeds appear closely related to changes in air temperature and latitudinal pressure gradients. Furthermore, the monthly maximum gust speeds show significant correlation with the main oceanic‐atmospheric circulation patterns in the North Atlantic region. A synergistic effect among the phases of the dominant patterns of climate variability appears to explain the observed decline and recovery trends in winter gusts. Summer gust speeds show a positive correlation with the global land‐ocean temperature deviations from the corresponding 1951–1980 base period mean. Key Points: Homogenization of historical gust speeds at St Mary's, Isles of Scilly, has been performed to remove discontinuities caused by non‐climatic changesSummer gust speeds have significantly increased, while winter gust speeds have declined in 1969–2011 and strengthened since 2012Statistically significant correlations are found between monthly maximum gust speeds and main climate indices which might explain the observed variability [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]

ABSTRACT In situ climatological observations are essential for studies related to climate trends and extreme events. However, in many regions of the globe, observational records are affected by a large number of data quality issues. Assessing and controlling the quality of such datasets is an important, often overlooked aspect of climate research. Besides analysing the measurement data, metadata are important for a comprehensive data quality assessment. However, metadata are often missing, but may partly be reconstructed by suitable actions such as station inspections. This study identifies and attributes the most important common data quality issues in Bolivian and Peruvian temperature and precipitation datasets. The same or similar errors are found in many other predominantly manned station networks worldwide. A large fraction of these issues can be traced back to measurement errors by the observers. Therefore, the most effective way to prevent errors is to strengthen the training of observers and to establish a near real-time quality control ( QC) procedure. Many common data quality issues are hardly detected by usual QC approaches. Data visualization, however, is an effective tool to identify and attribute those issues, and therefore enables data users to potentially correct errors and to decide which purposes are not affected by specific problems. The resulting increase in usable station records is particularly important in areas where station networks are sparse. In such networks, adequate selection and treatment of time series based on a comprehensive QC procedure may contribute to improving data homogeneity more than statistical data homogenization methods. [ABSTRACT FROM AUTHOR]

ABSTRACT Long-term time series of climatological data measured at meteorological stations provide one of the most authentic annals of climate in the past. However, there are a host of factors that affect measurements of climate parameters and make the data unsuitable for direct use and analysis. Serial completion and homogenization thus have to be undertaken in order to draw valid conclusions about the climate, or to apply the time series in impact studies. This study reports on gap filling and homogenization results of climatological elements in the headwater region of the Upper Blue Nile Basin from 1980 to 2013. Firstly, approaches for reconstruction of the time series from neighboring stations using different techniques were compared and selected. Neighboring stations were selected based on horizontal distance and coefficient of correlation. Secondly, the reconstructed time series were homogenized using Multiple Analysis of Series for Homogenization ( MASH). The results show improved spatial coherence of the final data series. Higher spatial coherence was revealed for maximum temperature than for minimum temperature and rainfall. For rainfall and minimum and maximum temperatures, the coefficient of correlation weighting method outperforms other candidate methods, such as the normal ratio method (NRM), the modified NRM, and the inverse distance weighting method. Rainfall series for half of the stations considered were found to be homogeneous, and thus inhomogeneity corrections were not applied. Inhomogeneity in the remaining stations either underestimates or overestimates annual rainfall series. All stations revealed inhomogeneity in the mean annual maximum and minimum temperature series. These serially complete and homogenized data on rainfall, and minimum and maximum temperatures of the present study can be used for climate change and hydrological studies in the basin. [ABSTRACT FROM AUTHOR]

Purpose: Although there are several options for improving the generalizability of learned models, a data instance-based approach is desirable when stable data acquisition conditions cannot be guaranteed. Despite the wide use of data transformation methods to reduce data discrepancies between different data domains, detailed analysis for explaining the performance of data transformation methods is lacking., Approach: This study compares several data transformation methods in the tuberculosis detection task with multi-institutional chest x-ray (CXR) data. Five different data transformations, including normalization, standardization with and without lung masking, and multi-frequency-based (MFB) standardization with and without lung masking were implemented. A tuberculosis detection network was trained using a reference dataset, and the data from six other sites were used for the network performance comparison. To analyze data harmonization performance, we extracted radiomic features and calculated the Mahalanobis distance. We visualized the features with a dimensionality reduction technique. Through similar methods, deep features of the trained networks were also analyzed to examine the models' responses to the data from various sites., Results: From various numerical assessments, the MFB standardization with lung masking provided the highest network performance for the non-reference datasets. From the radiomic and deep feature analyses, the features of the multi-site CXRs after MFB with lung masking were found to be well homogenized to the reference data, whereas the others showed limited performance., Conclusions: Conventional normalization and standardization showed suboptimal performance in minimizing feature differences among various sites. Our study emphasizes the strengths of MFB standardization with lung masking in terms of network performance and feature homogenization., (© 2023 The Authors.)

A near-continuous time series of global mean, skin SST anomaly has been calculated for 1995 to 2011, based on the first consistently processed data set for the Along Track Scanning Radiometer(s) or ATSRs. The ATSR multi-mission Version 2.0 (ESA_V2) archive is used to derive level 3 averaged skin SSTs for ATSR-2 and AATSR consistently for both sensors using a standard operational-like processor. Data are then gridded, aligned between sensors and a final anomaly time series derived relative to a climatological average calculated, for each month, from the standard ATSR data for the same period; the process strongly reduces global mean and spatially varying biases. The ATSR-2 to AATSR SST relative bias is obtained by analysing the overlap period (July 2002 to June 2003) between the two sensors. Before alignment, the average relative bias between ESA_V2 SST data sets from the two instruments is +0.03K for the dual-view, three channel SST, with a small but linear relation to water vapour in the differences between ATSR-2 and AATSR. Post-alignment, the residual average relative bias is +0.007K. The sampled area for the final time series using the ESA_V2 data extends from 60° N to 65° S; it is recommended that sampling areas be always provided for time series of data for climate. The uncertainty on the monthly mean global SST anomaly is estimated to be 0.014K for ATSR-2 and 0.011K for AATSR which are comparable to the uncertainties on monthly global average in situ data sets in the equivalent time period. [ABSTRACT FROM AUTHOR]

An objective cyclone tracking algorithm is applied to twentieth century reanalysis (20CR) 6-hourly mean sea level pressure fields for the period 1871-2010 to infer historical trends and variability in extra-tropical cyclone activity. The tracking algorithm is applied both to the ensemble-mean analyses and to each of the 56 ensemble members individually. The ensemble-mean analyses are found to be unsuitable for accurately determining cyclone statistics. However, pooled cyclone statistics obtained by averaging statistics from individual members generally agree well with statistics from the NCEP-NCAR reanalyses for 1951-2010, although 20CR shows somewhat weaker cyclone activity over land and stronger activity over oceans. Both reanalyses show similar cyclone trend patterns in the northern hemisphere (NH) over 1951-2010. Homogenized pooled cyclone statistics are analyzed for trends and variability. Conclusions account for identified inhomogeneities, which occurred before 1949 in the NH and between 1951 and 1985 in the southern hemisphere (SH). Cyclone activity is estimated to have increased slightly over the period 1871-2010 in the NH. More substantial increases are seen in the SH. Notable regional and seasonal variations in trends are evident, as is profound decadal or longer scale variability. For example, the NH increases occur mainly in the mid-latitude Pacific and high-latitude Atlantic regions. For the North Atlantic-European region and southeast Australia, the 20CR cyclone trends are in agreement with trends in geostrophic wind extremes derived from in-situ surface pressure observations. European trends are also consistent with trends in the mean duration of wet spells derived from rain gauge data in Europe. [ABSTRACT FROM AUTHOR]

A homogenized 76-year daily-mean temperature series is presented for Moor House, an upland peatland meteorological station in northern England, which provides the longest instrumental temperature record in the UK uplands. This paper assesses trends in the temperature record over time compared to the nearby lowland temperature record at Durham. Overall warming for 1991-2006 compared with 1931-1960 and 1961-1990 was approximately the same at the upland site (0.53 °C and 0.73 °C for the two time periods, respectively) as at Durham (0.61 °C and 0.71 °C), maintaining a similar lapse rate. However, winter warming is dominant at Moor House and more pronounced and concentrated over a shorter period than at Durham; summer warming is more prominent at Durham. Therefore, seasonal lapse rates have changed and become more divergent between winter and summer half-years. Rather than changes in the dominance of particular air-masses over the study area accounting for seasonal lapse rate changes, there had been significant steepening of lapse rates over time for some air flow types. Minimum temperatures have increased more than maximum temperatures at Moor House with a consequent significant reduction in diurnal range. At Durham minimum and maximum temperatures have changed in approximate tandem so that there is no significant change in diurnal temperature range. The frequency of air frosts reduced by approximately 23% (from 129 days per year to 99) at Moor House from 1991-2006 compared to 1953-1980, and this may have major implications for the functioning of terrestrial and aquatic systems in the region. These results demonstrate that while overall annual warming in the uplands and lowlands within particular regions may be similar, even where annual lapse rates have been maintained, the seasonal distribution of that warming may be quite different even over short distances and so lowland records are not a reliable indicator of regional upland temperature change. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Although there are still slight differences during some periods, the global surface air temperature (SAT) change series developed by different groups are generally very consistent with each other. However, there are still considerable uncertainties in the analysis of air temperature series at the regional scale. At the slightly larger sub-continental scale, to understand the trend and magnitude of regional climate warming in China, many teams of scientists have tried to establish a series of air temperature changes across the country from the 1880s onwards. However, until recently, the conclusions reached by these different teams remained markedly different, implying clear uncertainties. The reasons for the uncertainties are inhomogeneities in some SAT series, incomplete data and the biased nature of the station distribution. In addition, many studies have attributed the rapid warming rates in China to contributions from urbanization. However, additional research has shown that the characteristics of the periodic variations in temperature change across China in recent decades indicate that China has warmed faster than other areas at the same latitude and the average for the Northern Hemisphere. Although rapid urbanization in some parts of China has led to local-scale warming, due to the relatively small overall sizes of urbanized areas, the latest conclusions confirm that urbanization contributed less than 5 % of the regional air temperature changes in China over the past century. Since approximately 1998, the general characteristics of air temperature changes in China have also shown warming, but more in extremes than average values. These phenomena can be explained by the physical mechanisms of atmospheric circulation changes. This paper discusses all these issues including summarizing the process and experience of establishing a series of air temperature changes at the sub-continental scale and estimating the magnitude of climate change by developing a series of regional temperature changes throughout the 20th century. [ABSTRACT FROM AUTHOR]