Research Objects (ROs) are semantically enhanced aggregations of resources associated to scientific experiments, such as data, provenance of these data, the scientific workflow used to run the experiment, intermediate results, logs and the interpretation of the results. As the number of ROs increases, it is becoming difficult to find ROs to be used, reused or re-purposed. New search and retrieval techniques are required to find the most appropriate ROs for a given researcher, paying attention to provide an intuitive user interface. In this paper we show CollabSpheres, a user interface that provides a new visual metaphor to find ROs by means of a recommendation system that takes advantage of the social aspects of ROs. The experimental evaluation of this tool shows that users perceive high values of usability, user satisfaction, usefulness and ease of use. From the analysis of these results we argue that users perceive the simplicity, intuitiveness and cleanness of this tool, as well as this tool increases collaboration and reuse of research objects.
The rationale behind the Collaboration Spheres is closely related with circles of collaborative search introduced by Russell-Rose @cite_16 . The definition of the circles of collaborative search provides a holistic view of collaboration in search by defining a three-circle model composed by the inner and intermediate social circles; and the outer circle. The inner and intermediate circles represent and intentional collaboration between individuals who share some degree of social connectedness, and form the nucleus of collaboration. The outer circle comprises , unintentional collaboration. This is the case in our Collaboration Spheres for the statistical data extracted from the preferences of the whole research community in collaborative filtering. However, our approach goes further, because we show an additional circle of recommendations based on the context (explicit preferences) of the user.
@cite_16
2169942798 1987498565 2021418345 1717267627
Web search is often viewed as a solitary task; however, there are many situations in which groups of people gather around a single computer to jointly search for information online. We present the findings of interviews with teachers, librarians, and developing world researchers that provide details about users' collaborative search habits in shared-computer settings, revealing several limitations of this practice. We then introduce CoSearch, a system we developed to improve the experience of co-located collaborative Web search by leveraging readily available devices such as mobile phones and extra mice. Finally, we present an evaluation comparing CoSearch to status quo collaboration approaches, and show that CoSearch enabled distributed control and division of labor, thus reducing the frustrations associated with shared-computer searches, while still preserving the positive aspects of communication and collaboration associated with joint computer use. We examine the problem of identifying social circles, or sets of cohesive and mutually aware nodes surrounding an initial query set, in directed graphs where the complete graph is not known beforehand. This problem differs from local community mining, in that the query set defines the circle of interest. We explicitly handle edge direction, as in many cases relationships are not symmetric, and focus on the local context because many real-world graphs cannot be feasibly known. We outline several issues that are unique to this context, introduce a quality function to measure the value of including a particular node in an emerging social circle, and describe a greedy social circle discovery algorithm. We demonstrate the effectiveness of this approach on artificial benchmarks, large networks with topical community labels, and several real-world case studies. We study collaboration networks in terms of evolving, self-organizing bipartite graph models. We propose a model of a growing network, which combines preferential edge attachment with the bipartite structure, generic for collaboration networks. The model depends exclusively on basic properties of the network, such as the total number of collaborators and acts of collaboration, the mean size of collaborations, etc. The simplest model defined within this framework already allows us to describe many of the main topological characteristics (degree distribution, clustering coefficient, etc.) of one-mode projections of several real collaboration networks, without parameter fitting. We explain the observed dependence of the local clustering on degree and the degree‐degree correlations in terms of the “aging” of collaborators and their physical impossibility to participate in an unlimited number of collaborations. In a search task, a group of agents compete to be the first to find the solution. Each agent has different private information to incorporate into its search. This problem is inspired by settings such as scientific research, Bitcoin hash inversion, or hunting for some buried treasure. A social planner such as a funding agency, mining pool, or pirate captain might like to convince the agents to collaborate, share their information, and greatly reduce the cost of searching. However, this cooperation is in tension with the individuals' competitive desire to each be the first to win the search. The planner's proposal should incentivize truthful information sharing, reduce the total cost of searching, and satisfy fairness properties that preserve the spirit of the competition. We design contract-based mechanisms for information sharing without money. The planner solicits the agents' information and assigns search locations to the agents, who may then search only within their assignments. Truthful reporting of information to the mechanism maximizes an agent's chance to win the search. e-voluntary participation is satisfied for large search spaces. In order to formalize the planner's goals of fairness and reduced search cost, we propose a simplified, simulated game as a benchmark and quantify fairness and search cost relative to this benchmark scenario. The game is also used to implement our mechanisms. Finally, we extend to the case where coalitions of agents may participate in the mechanism, forming larger coalitions recursively.