https://collections.enfance-jeunesse.fr/files/original/d5bf1064d87c4ac7f9e3fd250854b8bd f2c3a5bdf71f52978f4e8bfe539d316b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource arXiv.org Snapshot https://collections.enfance-jeunesse.fr/files/original/3673a78aff852167def1bd39e7cb4108 cdb1df39e7da3f6a8de9c098aa5444eb Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource arXiv:1311.4276 PDF Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Bibliographie sur la généalogie et les pratiques généalogiques dans le monde Description An account of the resource Bibliographie sur la généalogie et les pratiques généalogiques dans le monde, réalisée dans le cadre du programme EnJeux, sous la direction de Patrice Marcilloux, professeur d'archivistique, et Bénédicte Grailles, maîtresse de conférences en archivistique (CERHIO-Angers CNRS UMR 6258, Université d'Angers), par Adelaïde Laloux, ingénieure d'étude, en décembre 2015, complétée par Verónica Zurita, étudiante-stagiaire du master Dynamiques et actions sociales territoriales (Université d'Angers) et Bénédicte Grailles en février 2016 Creator An entity primarily responsible for making the resource Adelaïde Laloux Contributor An entity responsible for making contributions to the resource Adélaïde Laloux Bénédicte Grailles Verónica Zurita Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population Type The nature or genre of the resource Journal Article Creator An entity primarily responsible for making the resource Michael Fire Yuval Elovici Date A point or period of time associated with an event in the lifecycle of the resource 2013 Zotero Title Data Mining of Online Genealogy Datasets for Revealing Lifespan Patterns in Human Population Item Type Journal Article Author Michael Fire Yuval Elovici URL http://arxiv.org/abs/1311.4276 Pages 1-24 Publication Title arXiv Date 2013 Extra arXiv: 1311.4276 Access Date 2015-07-07 12:21:46 Library Catalog arXiv.org Abstract Note Online genealogy datasets contain extensive information about millions of people and their past and present family connections. This vast amount of data can assist in identifying various patterns in human population. In this study, we present methods and algorithms which can assist in identifying variations in lifespan distributions of human population in the past centuries, in detecting social and genetic features which correlate with human lifespan, and in constructing predictive models of human lifespan based on various features which can easily be extracted from genealogy datasets. We have evaluated the presented methods and algorithms on a large online genealogy dataset with over a million profiles and over 9 million connections, all of which were collected from the WikiTree website. Our findings indicate that significant but small positive correlations exist between the parents' lifespan and their children's lifespan. Additionally, we found slightly higher and significant correlations between the lifespans of spouses. We also discovered a very small positive and significant correlation between longevity and reproductive success in males, and a small and significant negative correlation between longevity and reproductive success in females. Moreover, our machine learning algorithms presented better than random classification results in predicting which people who outlive the age of 50 will also outlive the age of 80. We believe that this study will be the first of many studies which utilize the wealth of data on human populations, existing in online genealogy datasets, to better understand factors which influence human lifespan. Understanding these factors can assist scientists in providing solutions for successful aging. Attachment Title arXiv.org Snapshot arXiv:1311.4276 PDF Attachment URL [No URL] [No URL] Computer Science - Social and Information Networks Quantitative Biology - Populations and Evolution Statistics - Applications