1,721,151 research outputs found
Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, PAP 2017
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Going beyond GDP to nowcast Well-Being using retail market data
Full text linkOne of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory
Correction to: An ethico-legal framework for social data science (International Journal of Data Science and Analytics, (2021), 11, 4, (377-390), 10.1007/s41060-020-00211-7)
No full textThe article ‘‘An ethico-legal framework for social data science’’, written by Nikolaus Forgó, Stefanie Hänold, Jeroen van den Hoven, Tina Krügel, Iryna Lishchuk, René Mahieu, Anna Monreale, Dino Pedreschi, Francesca Pratesi, David van Putten originally published electronically on the publisher’s internet portal (currently SpringerLink) on April 10, 2021 without open access. The copyright of the article changed t
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Correction to: Human migration: The big data perspective
No full textS.77The article "Human migration: the big data perspective", written by Alina Sîrbu, Gennady Andrienko, Natalia Andrienko, Chiara Boldrini, Marco Conti, Fosca Giannotti, Riccardo Guidotti, Simone Bertoli, Jisu Kim, Cristina Ioana Muntean, Luca Pappalardo, Andrea Passarella, Dino Pedreschi, Laura Pollacci, Francesca Pratesi, Rajesh Sharma originally published electronically on the publisher's internet portal (currently SpringerLink) on April 10, 2021 without open access. The copyright of the article changed to © The Author(s) 2021 and the article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The original article has been updated.12Nr.
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