03888nas a2200589   4500008004100000020001400041245010600055210006900161260001500230520215400245100001702399700002302416700002802439700002102467700002202488700001802510700002702528700002302555700001702578700001602595700001802611700002102629700002602650700002102676700002102697700002302718700001802741700001702759700002102776700001802797700001702815700001902832700001702851700002902868700001902897700002102916700001802937700002302955700002202978700002003000700001903020700001903039700002303058700001803081700002403099700001703123700001803140700002203158700002603180700002703206856006503233       2021                        eng d  a1572-843900aGive more data, awareness and control to individual citizens, and they will help COVID-19 containment0 aGive more data awareness and control to individual citizens and   c2021/02/023 aThe rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.1 aNanni, Mirco1 aAndrienko, Gennady1 aBarabasi, Albert-Laszlo1 aBoldrini, Chiara1 aBonchi, Francesco1 aCattuto, Ciro1 aChiaromonte, Francesca1 aComandé, Giovanni1 aConti, Marco1 aCoté, Mark1 aDignum, Frank1 aDignum, Virginia1 aDomingo-Ferrer, Josep1 aFerragina, Paolo1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aHelbing, Dirk1 aKaski, Kimmo1 aKertész, János1 aLehmann, Sune1 aLepri, Bruno1 aLukowicz, Paul1 aMatwin, Stan1 aJiménez, David, Megías1 aMonreale, Anna1 aMorik, Katharina1 aOliver, Nuria1 aPassarella, Andrea1 aPasserini, Andrea1 aPedreschi, Dino1 aPentland, Alex1 aPianesi, Fabio1 aPratesi, Francesca1 aRinzivillo, S1 aRuggieri, Salvatore1 aSiebes, Arno1 aTorra, Vicenc1 aTrasarti, Roberto1 avan den Hoven, Jeroen1 aVespignani, Alessandro  uhttps://link.springer.com/article/10.1007/s10676-020-09572-w01999nas a2200181   4500008004100000245011100041210006900152300001100221490000700232520140700239100002101646700001801667700002101685700002301706700002001729700002101749856004701770       2019                        eng d00aPlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach0 aPlayeRank datadriven performance evaluation and player ranking i  a1–270 v103 aThe problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players’ evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank—i.e. searching players and player versatility—showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.1 aPappalardo, Luca1 aCintia, Paolo1 aFerragina, Paolo1 aMassucco, Emanuele1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://dl.acm.org/doi/abs/10.1145/334317201689nas a2200193   4500008004100000245007700041210006900118300001100187490000600198520109400204100002101298700001801319700001901337700002301356700002101379700002001400700002101420856005401441       2019                        eng d00aA public data set of spatio-temporal match events in soccer competitions0 apublic data set of spatiotemporal match events in soccer competi  a1–150 v63 aSoccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.1 aPappalardo, Luca1 aCintia, Paolo1 aRossi, Alessio1 aMassucco, Emanuele1 aFerragina, Paolo1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.nature.com/articles/s41597-019-0247-7