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The digital age allows data collection to be done on a large scale and at low cost. This is the case of genealogy trees, which flourish on numerous digital platforms thanks to the collaboration of a mass of individuals wishing to trace their origins and share them with other users. The family trees constituted in this way contain information on the links between individuals and their ancestors, which can be used in historical demography, and more particularly to study migration phenomena. This article proposes to use the family trees of 238, 009 users of the Geneanet website, or 2.5 million (unique) individuals, to study internal migration. The case of 19th century France is taken as an example. Using the geographical coordinates of the birthplaces of individuals born in France between 1800 and 1804 and those of their descendants, we study migration between generations at several geographical scales. We start with a broad scale, that of the departments, to reach a much finer one, that of the cities. Our results are consistent with those of the literature traditionally based on parish or civil status registers. The results show that the use of collaborative genealogy data not only makes it possible to recover known facts in the literature, but also to enrich them.
Physical science has changed in the century since Lord Kelvins celebrated essay on Nineteenth Century Clouds over the Dynamical Theory of Heat and Light, but some things are the same. Analogs in what was happening in physics then and what is happenin
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We study rare events in networks with both internal and external noise, and develop a general formalism for analyzing rare events that combines pair-quenched techniques and large-deviation theory. The probability distribution, shape, and time scale o