Workshop on Open & Citizen Data Science  (WOCD) Session 1

Abstract: From the courtroom to the marketplace, the parliament building to the newsroom, some of our most important institutions are adversarial. We expect that placing individuals in conflict will help solve complex problems in information-dense environments, and will drive long-term social evolution through the creation of of new questions to answer. Yet we understand very little about the general principles that make conflict creative. This leaves a major gap in our understanding of human social development, and makes it difficult for us to mitigate the negative effects of conflict while retaining its benefits. To help remedy this, I present a new framework for the quantitative study of creativity and conflict. I apply it to a new study on the relationship between conflict and information creation in the online encyclopedia Wikipedia. Fifteen years of high-resolution records allow to track the long and tumultuous process by which Wikipedia articles are written, re-written, torn-apart and reconstructed. We can see not only how the introduction of new information into an article leads to conflict, but how conflict can often precede the creation of unexpected configurations whose long-term persistence suggests adaptive success. This work contradicts simple accounts that oppose conflict and cooperation, and suggests that popular conflict-suppression mechanisms may actually undermine Wikipedia's success. It provides new evidence for the central role of conflict in creative evolution, new methods to measure and quantify it across a wide variety of systems. For those engaged in the design of systems for citizen science It suggest that the correct management ? and even the selective promotion ? of conflict is crucial to the success of citizen science. Close

Abstract: Under the name of Citizen Science, many innovative practices in which volunteers partner up with scientists to pose and answer real-world questions are growing rapidly worldwide. Citizen Science can furnish ready-made solutions with citizens playing an active role. However, this framework is still far from being well established as a standard tool for computational social science research. Here, we present our experience in bridging gap between computational social science and the philosophy underlying Citizen Science, which in our case has taken the form of what we call ?pop-up experiments.? These are non-permanent, highly participatory collective experiments which blend features developed by big data methodologies and behavioral experimental protocols with the ideals of Citizen Science. The main issues to take into account whenever planning experiments of this type are classified, discussed and grouped into three categories: infrastructure, public engagement, and the knowledge return for citizens. We explain the solutions we have implemented, providing practical examples grounded in our own experience in an urban context (Barcelona, Spain). Our aim here is that this work will serve as a guideline for groups willing to adopt and expand such in vivo practices and we hope it opens up the debate regarding the possibilities (and also the limitations) that the Citizen Science framework can offer the study of social phenomena. Close

Abstract: This manifesto aims at defining fundamental statements every Company running with the Open Science initiative in mind must respect and promote. Close

Abstract: Making available and archiving scientific results is for the most part still considered the task of classical publishing companies, despite the fact that classical forms of publishing centered around printed narrative articles no longer seem well-suited in the digital age. In particular, there exist currently no efficient, reliable, and agreed-upon methods for publishing scientific datasets, which have become increasingly important for science. Here we propose to design scientific data publishing as a Web-based bottom-up process, without top-down control of central authorities such as publishing companies. Based on a novel combination of existing concepts and technologies, we present a server network to decentrally store and archive data in the form of nanopublications, an RDF-based format to represent scientific data. We show how this approach allows researchers to publish, retrieve, verify, and recombine datasets of nanopublications in a reliable and trustworthy manner, and we argue that this architecture could be used for the Semantic Web in general. Evaluation of the current small network shows that this system is efficient and reliable. Close