Browsing by Author "Della Briotta Parolo, Pietro"
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- Analysis of Cumulative and Temporal Patterns in Science
School of Science | Doctoral dissertation (article-based)(2017) Della Briotta Parolo, PietroThe goal of science has always been to investigate the world and its phenomena, by collecting data from all possible events that take place around us, breaking them down into their most simple elements and trying to come up with models able to explain and predict the outcome of these events. For centuries, the primary focus of science was mainly on natural events, but as the new technologies allowed to gather data from human interactions, it was natural for scientists to use this new information in order to apply the same logic to social systems, including science itself. Since the late 19th century, when the first modern scientific journals were published, science has seen a constant rise in both its size and productivity, thanks to the standardization of research practices and the building of an international community that actively helps to push forward the limits of human knowledge. As science itself went from being a purely intellectual endeavor to a complex social, economical and political system, it is no surprise that a lot of attention has been dedicate in recent years to the study of the underlying mechanisms of science, aided by the explosion of means of communication that allow collaborations and exchange of information at instant speed across the globe, leaving behind digital traces that provide valuable data to study. The continuous exponential growth of science however, causes also difficulties in analyzing objectively the patterns and statistics that scientific data can reveal: for example a paper from the early 20th century would rarely get more than 100 citations, while now it is not uncommon for publications to pass the 10 thousand citation mark. This thesis follows these attempts in trying to grasp how science works, by investigating the connections, i.e. citations, that exists between scientific publications and how these connections create structures and patterns. It shows that typical patterns in citation count and diffusion of information between fields is heavily influenced by the rate of growth of science, thus suggesting to use the number of publications as a better measure of time. It shows that there is a lag between breakthrough discoveries and the time when they are recognized, thus suggesting that we might be either running out of discoveries or rather having too much of them, in either case an extreme phenomenon. It shows that the community of publications which builds around an original successful paper has a typical life cycle, with an initial clustering, followed by an inevitable breaking down. Finally, it offers a new way of quantifying the impact of publications across time based on their cumulative impact on the overall corpus of scientific material. - Prediction and impact of personalized donation intervals
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä(2022-04) Toivonen, Jarkko; Koski, Yrjö; Turkulainen, Esa; Prinsze, Femmeke; Della Briotta Parolo, Pietro; Heinonen, Markus; Arvas, MikkoBackground and Objectives Deferral of blood donors due to low haemoglobin (Hb) is demotivating to donors, can be a sign for developing anaemia and incurs costs for blood establishments. The prediction of Hb deferral has been shown to be feasible in a number of studies based on demographic, Hb measurement and donation history data. The aim of this paper is to evaluate how state-of-the-art computational prediction tools can facilitate nationwide personalized donation intervals. Materials and Methods Using donation history data from the last 20 years in Finland, FinDonor blood donor cohort data and blood service Biobank genotyping data, we built linear and non-linear predictors of Hb deferral. Based on financial data from the Finnish Red Cross Blood Service, we then estimated the economic impacts of deploying such predictors. Results We discovered that while linear predictors generally predict Hb relatively well, they have difficulties in predicting low Hb values. Overall, we found that non-linear or linear predictors with or without genetic data performed only slightly better than a simple cutoff based on previous Hb. However, if any of our deferral prediction methods are used to assign temporary prolongations of donation intervals for females, then our calculations indicate cost savings while maintaining the blood supply. Conclusion We find that even though the prediction accuracy is not very high, the actual use of any of our predictors in blood collection is still likely to bring benefits to blood donors and blood establishments alike.