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Quantum Moves is a citizen science game that investigates the ability of humans to solve complex physics challenges that are intractable for computers. During the launch of Quantum Moves in April 2016 the games leaderboard function broke down resulting in a no leaderboard game experience for some players for a couple of days (though their scores were still displayed). The subsequent quick fix of an all-time Top 5 leaderboard, and the following long-term implementation of a personalized relative-position (infinite) leaderboard provided us with a unique opportunity to compare and investigate the effect of different leaderboard implementations on player performance in a points-driven citizen science game. All three conditions were live sequentially during the games initial influx of more than 150.000 players that stemmed from global press attention on Quantum Moves due the publication of a Nature paper about the use of Quantum Moves in solving a specific quantum physics problem. Thus, it has been possible to compare the three conditions and their influence on the performance (defined as a players quality of game play related to a high-score) of over 4500 new players. These 4500 odd players in our three leaderboard-conditions have a similar demographic background based upon the time-window over which the implementations occurred and controlled against Player ID tags. Our results placed Condition 1 experience over condition 3 and in some cases even over condition 2 which goes against the general assumption that leaderboards enhance gameplay and its subsequent overuse as a an oft-relied upon element that designers slap onto a game to enhance said appeal. Our study thus questions the use of leaderboards as general performance enhancers in gamification contexts and brings some empirical rigor to an often under-reported but overused phenomenon.
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The application of Network Science to social systems has introduced new methodologies to analyze classical problems such as the emergence of epidemics, the arousal of cooperation between individuals or the propagation of information along social networks. More recently, the organization of football teams and their performance have been unveiled using metrics coming from Network Science, where a team is considered as a complex network whose nodes (i.e., players) interact with the aim of overcoming the opponent network. Here, we combine the use of different network metrics to extract the particular signature of the F.C. Barcelona coached by Guardiola, which has been considered one of the best teams along football history. We have first compared the network organization of Guardiolas team with their opponents along one season of the Spanish national league, identifying those metrics with statistically significant differences and relating them with the Guardiolas game. Next, we have focused on the temporal nature of football passing networks and calculated the evolution of all network properties along a match, instead of considering their average. In this way, we are able to identify those network metrics that enhance the probability of scoring/receiving a goal, showing that not all teams behave in the same way and how the organization Guardiolas F.C. Barcelona is different from the rest, including its clustering coefficient, shortest-path length, largest eigenvalue of the adjacency matrix, algebraic connectivity and centrality distribution.
Large-scale research endeavors can be hindered by logistical constraints limiting the amount of available data. For example, global ecological questions require a global dataset, and traditional sampling protocols are often too inefficient for a small research team to collect an adequate amount of data. Citizen science offers an alternative by crowdsourcing data collection. Despite growing popularity, the community has been slow to embrace it largely due to concerns about quality of data collected by citizen scientists. Using the citizen science project Floating Forests (http://floatingforests.org), we show that consensus classifications made by citizen scientists produce data that is of comparable quality to expert generated classifications. Floating Forests is a web-based project in which citizen scientists view satellite photographs of coastlines and trace the borders of kelp patches. Since launch in 2014, over 7,000 citizen scientists have classified over 750,000 images of kelp forests largely in California and Tasmania. Images are classified by 15 users. We generated consensus classifications by overlaying all citizen classifications and assessed accuracy by comparing to expert classifications. Matthews correlation coefficient (MCC) was calculated for each threshold (1-15), and the threshold with the highest MCC was considered optimal. We showed that optimal user threshold was 4.2 with an MCC of 0.400 (0.023 SE) for Landsats 5 and 7, and a MCC of 0.639 (0.246 SE) for Landsat 8. These results suggest that citizen science data derived from consensus classifications are of comparable accuracy to expert classifications. Citizen science projects should implement methods such as consensus classification in conjunction with a quantitative comparison to expert generated classifications to avoid concerns about data quality.
Individual performance metrics are commonly used to compare players from different eras. However, such cross-era comparison is often biased due to significant changes in success factors underlying player achievement rates (e.g. performance enhancing drugs and modern training regimens). Such historical comparison is more than fodder for casual discussion among sports fans, as it is also an issue of critical importance to the multi-billion dollar professional sport industry and the institutions (e.g. Hall of Fame) charged with preserving sports history and the legacy of outstanding players and achievements. To address this cultural heritage management issue, we report an objective statistical method for renormalizing career achievement metrics, one that is particularly tailored for common seasonal performance metrics, which are often aggregated into summary career metrics -- despite the fact that many player careers span different eras. Remarkably, we find that the method applied to comprehensive Major League Baseball and National Basketball Association player data preserves the overall functional form of the distribution of career achievement, both at the season and career level. As such, subsequent re-ranking of the top-50 all-time records in MLB and the NBA using renormalized metrics indicates reordering at the local rank level, as opposed to bulk reordering by era. This local order refinement signals time-independent mechanisms underlying annual and career achievement in professional sports, meaning that appropriately renormalized achievement metrics can be used to compare players from eras with different season lengths, team strategies, rules -- and possibly even different sports.
We investigate the development of scientific content knowledge of volunteers participating in online citizen science projects in the Zooniverse (www.zooniverse.org), including the astronomy projects Galaxy Zoo (www.galaxyzoo.org) and Planet Hunters (www.planethunters.org). We use econometric methods to test how measures of project participation relate to success in a science quiz, controlling for factors known to correlate with scientific knowledge. Citizen scientists believe they are learning about both the content and processes of science through their participation. Wont dont directly test the latter, but we find evidence to support the former - that more actively engaged participants perform better in a project-specific science knowledge quiz, even after controlling for their general science knowledge. We interpret this as evidence of learning of science content inspired by participation in online citizen science.
Quantum computers are on the verge of becoming a commercially available reality. They represent a paradigm shift in computing, with a steep learning gradient. The creation of games is a way to ease the transition for beginners. We present a game similar to the Poker variant Texas hold em with the intention to serve as an engaging pedagogical tool to learn the basics rules of quantum computing. The concepts of quantum states, quantum operations and measurement can be learned in a playful manner. The difference to the classical variant is that the community cards are replaced by a quantum register that is randomly initialized, and the cards for each player are replaced by quantum gates, randomly drawn from a set of available gates. Each player can create a quantum circuit with their cards, with the aim to maximize the number of $1$s that are measured in the computational basis. The basic concepts of superposition, entanglement and quantum gates are employed. We provide a proof-of-concept implementation using Qiskit. A comparison of the results for the created circuits using a simulator and IBM machines is conducted, showing that error rates on contemporary quantum computers are still very high. For the success of noisy intermediate scale quantum (NISQ) computers, improvements on the error rates and error mitigation techniques are necessary, even for simple circuits. We show that quantum error mitigation (QEM) techniques can be used to improve expectation values of observables on real quantum devices.
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