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This paper reports a quantitative and structural analysis of data gathered on the food issues reported by the European Union members over the last forty years. The study applies statistical measures and network analysis techniques. For this purpose, a graph was constructed of how different contaminated products have been distributed through countries. The work aims to leverage insights into the structure formed by the involvement of European countries in the exchange of goods that can cause problems for populations. The results obtained show the roles of different countries in the detection of sensitive routes. In particular, the analysis identifies problematic origin countries, such as China or Turkey, whereas European countries, in general, do have good border control policies for the import/export of food. pre-print 1210 KB

Together with the growth in the presence of e-learning in higher education institutions, the advance of technology allows for the possibility of creating tailored institutional strategies to improv...

This paper reports a quantitative and structural analysis of data gathered from the food and feed issues reported by the European Union during the last forty years. This study includes the use of statistical measures and social network analysis techniques. For that purpose, a graph has been constructed considering how the different contaminated products have been distributed alongside countries. This work aims to leverage insights into the structure formed by the involvement of the European countries in the exchange of goods that can cause problems to the population. Results obtained show the roles of the different countries in the detection of sensitive routes. In particular, the analysis shows that there are problematic origin countries, like China or Turkey, whereas European countries, in general, do have good border control policies for the import/export of food and feed.

PurposeThe purpose of this paper is to review the current status of research on Wikidata and, in particular, of articles that either describe applications of Wikidata or provide empirical evidence, in order to uncover the topics of interest, the fields that are benefiting from its applications and which researchers and institutions are leading the work.Design/methodology/approachA systematic literature review is conducted to identify and review how Wikidata is being dealt with in academic research articles and the applications that are proposed. A rigorous and systematic process is implemented, aiming not only to summarize existing studies and research on the topic, but also to include an element of analytical criticism and a perspective on gaps and future research.FindingsDespite Wikidata’s potential and the notable rise in research activity, the field is still in the early stages of study. Most research is published in conferences, highlighting such immaturity, and provides little empirical evidence of real use cases. Only a few disciplines currently benefit from Wikidata’s applications and do so with a significant gap between research and practice. Studies are dominated by European researchers, mirroring Wikidata’s content distribution and limiting its Worldwide applications.Originality/valueThe results collect and summarize existing Wikidata research articles published in the major international journals and conferences, delivering a meticulous summary of all the available empirical research on the topic which is representative of the state of the art at this time, complemented by a discussion of identified gaps and future work.

Teams can be defined by their interactions and successful performance rests on their members’ behaviour. Although this topic has been studied both in sports and management, research on computer mediated team interactions, communication, cooperative work and efficiency in online competitive environments is scarce. In this article, networks will be used as a novel approach to understand how League of Legends professional players assist each other during a competitive match and to link their computer mediated behaviour and social interactions to their team's performance. Starting from a dataset consisting of 453.386 kill assists, the network structure and efficiency is assessed over 7.582 matches in total. After controlling for potential mixed-effects, such as the quality of the involved teams or their geography, this study reinforces previous research showing that team efficiency in the League of Legends professional scene is positively affected by the intensity of their interaction while centralization of resources is detrimental. Networks with high intensity and low inner centralization are, therefore, related to a higher performance as a team not only in traditional sports but also in computer mediated contexts.

The social media debate preceding the 2016 Brexit referendum represents a yet another instance of the growing role of social networking sites, also known as social media, in steering the dynamics of the socio-political process nowadays. Considering the scale of the phenomenon as well as the variety of concerns it raises vis-a-vis transparency, equality, representation, and legitimacy of the political process, it is imperative to query the mechanisms behind the relationship that unfolds between social media, their users and the political process. To do this, this paper employs event study analysis and recent advances in data mining and data analysis to examine how certain non-virtual political events constituent of the Brexit debate had been played out in the social media realm and influenced the social media users’ stance toward the very question of Brexit. This composite methodological approach that this study adopts allows to measure how non-virtual political events influenced the network of users who discussed the withdrawal of the UK from the EU in Twitter in the weeks prior to the Brexit referendum. The outcomes of this study suggest that social networking sites play a pivotal role not only on how information is diffused over the network but also on user’s message creation, dissemination behaviour and the shape of the social network itself.

The Web of Linked Open Data (LOD) is a decentralized effort in publishing datasets using a set of conventions to make them accesssible, notably thought RDF and SPARQL. Links across nodes in published datasets are thus critical in getting value for the LOD cloud as a collective effort. Connectivity among the datasets can occur through these links. Equivalence relationship is one of the fundamental links that connects different schemas or datasets, and is used to assert either class or instance equivalence. In this article, we report an empirical study on the equivalences found in over 59 million triples from datasets accessible via SPARQL endpoints in open source data portals. Metrics from graph analysis have been used to examine the relationships between repositories and determine their relative importance as well as their ability to facilitate knowledge discovery.

Free and open source software package ecosystems have existed for a long time, but such collaborative development practice has surged in recent years. One of the oldest and most popular pa...

Free and open source software package ecosystems have existed for a long time and are among the most sophisticated human-made systems. One of the oldest and most popular software package ecosystems is CRAN, the repository of packages of the statistical language R, which is also one of the most popular environments for statistical computing nowadays. CRAN stores a large number of packages that are updated regularly and depend on a number of other packages in a complex graph of relations; such graph is empirically studied from the perspective of complex network analysis (CNA) in the current article, showing how network theory and measures proposed by previous work can help profiling the ecosystem and detecting strengths, good practices and potential risks in three perspectives: macroscopic properties of the ecosystem (structure and complexity of the network), microscopic properties of individual packages (represented as nodes), and modular properties (community detection). Results show how complex network analysis tools can be used to assess a package ecosystem and, in particular, that of CRAN., Comment: Accepted in the Journal of Systems and Software

Data aggregators can leverage personal data for the benefit of their users in their role of mediators with enterprises that are willing to target qualified user segments with customized offerings. However, such type of transaction must comply with personal data protection regulation and provide a fair and auditable context for business partners. In this paper, the components of a solution for that problem in the context of a permissioned, private business network are described, along with an implementation using the Quorum enterprise derivative of Ethereum. The results of that design are expressed as a combination of schemas, protocols and smart contracts. The resulting model may serve as a blueprint for experimenting data market mechanisms that exploit the unique value of aggregators in a secure, transparent and auditable framework.

A considerable number of high-level quantum programming languages have been proposed and implemented in the last years. This fact opens the possibility to study the structure of the source code of quantum software, using initially the same metrics typically used in classical software. Here we report a preliminary study in module structure and use of quantum gates in the libraries of Microsoft’s quantum development platform QDK (Quantum Developer Kit) that uses a specific language, Q#. The structure of dependencies and the use of primitives is analyzed across all the source code available in the Github repositories related to the platform to date.

Online competitive gaming has become one of the largest collective human activities globally and understanding motivations and social interaction is still not fully achieved. The aim of this study is to develop a basis for a systematic classification of player-centric networks in competitive online games based on structural network criteria. Using data extracted from League of Legends players, their matches and machine learning techniques, a classification of personal player networks in League of Legends is proposed. Results show the resulting egonets can be potentially grouped in four clusters related to their egos playing habits, ranging from solo to team players.

While video games provide different Player Experiences (PE), some genres can provide particularly unique PEs driven by their particular features. Such is the case of MOBA (Multiplayer Online Battle...

This article aims to perform a literature review of the available research that focuses strictly on MOBA (multiplayer online battle arena) games. First, a review identifying the relevant papers published since 2011 is conducted, exploring them systematically to extract similarities, gaps and main findings. Results show how League of Legends is the most explored game, with player experience and toxic behaviour as popular topics for research. Second, as MOBA games remain underexplored by researchers despite their vast, enthusiast community, as well as projection and influence on contemporary game designers, a proposal on future lines for research is provided.

In the last few years, information and communication technologies (ICTs) and social media have become increasingly relevant to politicians and political parties alike, often used to issue statements or campaigning, among others. At the same time, many citizens have become more involved in politics, partly due to the highly interactive and social environments that the social networking services (SNS) provide. Political events flow through these networks, influencing their users; such events, however, often start offline (outside the online platform) and are, therefore, hard to track. Event studies, a methodology often used in financial and economic studies, can be translated to social networks to help modeling the effect of external events in the network. In the present case, the event study methodology is applied to two sample cases: the tariff war between the United States and China, with multiple responses and retaliations from both sides, and the Brexit referendum. In both cases, the Twitter social networks that arise from users who discuss the respective subjects are analyzed to examine how political events shape and modify the network. Results show how event studies, combined with the possibilities offered by the ICTs both in data retrieval and analysis, can be applied to understand the effect of external political events, allowing researchers to quantitatively track, observe, and analyze the spread of political information over social network platforms. This is a first step toward obtaining a better understanding on how political messages are diffused over social networks and their effects in the network structures and behaviors.

Ontologies are usually applied to drive intelligent applications and also as a resource for integrating or extracting information, as in the case of Natural Language Processing (NLP) tasks. Further, ontologies as the Gene Ontology (GO) are used as an artifact for very specific research aims. However, the value of ontologies for data analysis tasks may also go beyond these uses and span supporting the reuse and composition of data acquisition, integration and fusion code. This requires that both data and code artifacts support meta-descriptions using shared conceptualizations. In this paper, we discuss the different concerns in semantically describing data pipelines as a key reusable artifact that could be retrieved, compared and reused with a degree of automation if semantically consistent descriptions are provided. Concretely, we propose attaching semantic descriptions for data and analytic transformations to current backend-independent distributed processing frameworks as Apache Beam, as these already abstract out the specificity of supporting execution engines.

The widespread of data science programming languages and libraries have raised new interest in teaching computational science coding in ways that leverage the capabilities of both single-computer and cluster-based computation infrastructures. Some of the programming patterns and idioms are converging, yet there are specialized uses and cases that require learners to switch from one to another. In this paper, we report on the experience in action research with more than ten cohorts of mixed background students in postgraduate level data science classes. We first discuss the key mental models found to be essential to understanding solution design, and then review the three fundamental paradigms that students must face when coding data manipulation and their interrelation. Finally, we discuss some insights on additional elements found important in understanding the specificities of current practice in data analysis tasks.

In recent years, Twitter has become increasingly popular both as a social networking service (where users express their opinions) and as a tool for information retrieval. Many events that are commented, debated or argued online, however, are dynamic and unpredictable in nature, resulting in the need to derive the corresponding dynamic computational methodologies to track and to extract such changing topics, events and relevant content in a timely and unattended manner. In this paper, we propose a framework to accomplish two objectives: periodically obtaining the relevant topics of discussion among authorities and adapting the tracked keywords or hashtags accordingly to retrieve the most relevant possible information in each time window. The application of this framework to a case study reveals how our proposed approach is able to accurately and dynamically track the conversation around an event. Our method could therefore be applied to query Twitter in many domains, such as politics, sport events, marketing campaigns or media engagement, among others.

Traceability is considered a key requirement for trustworthy artificial intelligence (AI), related to the need to maintain a complete account of the provenance of data, processes, and artifacts involved in the production of an AI model. Traceability in AI shares part of its scope with general purpose recommendations for provenance as W3C PROV, and it is also supported to different extents by specific tools used by practitioners as part of their efforts in making data analytic processes reproducible or repeatable. Here, we review relevant tools, practices, and data models for traceability in their connection to building AI models and systems. We also propose some minimal requirements to consider a model traceable according to the assessment list of the High-Level Expert Group on AI. Our review shows how, although a good number of reproducibility tools are available, a common approach is currently lacking, together with the need for shared semantics. Besides, we have detected that some tools have either not achieved full maturity, or are already falling into obsolescence or in a state of near abandonment by its developers, which might compromise the reproducibility of the research trusted to them.

A noteworthy trend that has developed in video games in recent years is retrogaming. Retrogaming could be defined as a specific subculture of digital gaming typically characterised by playing (or collecting) obsolete personal computer, console or arcade video games in contemporary times, although it can also include playing modern games with “retro” aesthetics. However, it is not limited to that; it is also related to a wider cultural phenomenon that involves research, artistic activities, preservation and online discussions. This paper studies such activity in two separate sites, PlanetVB and AtariAge, aiming to uncover how the fan practices and the social networks that arise in these forums shape their communities, using natural language processing techniques combined with social network analysis. The results of the descriptive analysis on the language used in online forums show how most users participate in such online sites to buy and sell collectibles, to program new games for old systems or to simply socialise with like-minded people, collectively bringing new life into discontinued game systems.

Twitter is undoubtedly one of the most widely used data sources to analyze human communication. The literature is full of examples where Twitter is accessed, and data are downloaded as the previous step to a more in-depth analysis in a wide variety of knowledge areas. Unfortunately, the extraction of relevant information from the opinions that users freely express in Twitter is complicated, both because of the volume generated&mdash, more than 6000 tweets per second&mdash, and the difficulties related to filtering out only what is pertinent to our research. Inspired by the fact that a large part of users use Twitter to communicate or receive political information, we created a method that allows for the monitoring of a set of users (which we will call authorities) and the tracking of the information published by them about an event. Our approach consists of dynamically and automatically monitoring the hottest topics among all the conversations where the authorities are involved, and retrieving the tweets in connection with those topics, filtering other conversations out. Although our case study involves the method being applied to the political discussions held during the Spanish general, local, and European elections of April/May 2019, the method is equally applicable to many other contexts, such as sporting events, marketing campaigns, or health crises.

As video games grow in popularity for academic study there is also a growing need to study them by categories. Many studies focus either on a single (or a few) games or on the medium as a whole. Genres can be helpful in that regard, although they are not without caveats: genre definition is subjective and depends on multiple factors. Puzzle video games are those where puzzle-solving is the main activity and where solving them is the main purpose and source of pleasure for the player. Considering that genre is also influenced and formed by history and myths, this article aims to conduct a transhistorical perspective of the puzzle video game genre to understand what is a puzzle video game and what is the "repertoire" of elements found in them, that exhibit both their similarities and differences at the same time.

League of Legends is a multiplayer online battle arena game that follows a freemium model, but where the in-game transactions do little to impact a player's performance or ability. Although its characters, called Champions, can be purchased with actual (or ingame) money, there is a weekly rotation of ten free champions where players can test new champions before buying them. When new content is launched or a Champion is changed or reworked, players react to that by either using that Champion more or less (depending on whether they perceive it as positive or negative). Additionally, from time to time, Special Events are launched. The Bilgewater event, "Burning Tides", is one of such events, the only one that happened in season five, that aimed to merge lore with gameplay and represented Riot most ambitious story-telling experiment to date. Moreover, the seven Champions that had its background tied to Bilgewater were added to the free rotation on top of the other ten during the event, so there was no economic limiting factor that could hinder players from using them. This paper aims to understand how an event such as Bilgewater impact players in two dimensions; first, whether special events have an impact in Champion Usage and second, whether lore events can have an impact in player's motivation to read and learn in League of Legends. Results show that the effect in Champion Usage is unclear, with other factors being more relevant than Special Events, but that lore events have the potential to drive motivation to read and learn about the Champions and their story, events or optimal builds in the game, with a clear peak of interest in them.