We explore a novel ontological approach to user profiling within recommender systems, working on the problem of recommending on-line academic research papers. Our two experimental systems, Quickstep and Foxtrot, create user profiles from unobtrusively monitored behaviour and relevance feedback, representing the profiles in terms of a research paper topic ontology. A novel profile visualization approach is taken to acquire profile feedback. Research papers are classified using ontological classes and collaborative recommendation algorithms used to recommend papers seen by similar people on their current topics of interest. Two small-scale experiments, with 24 subjects over 3 months, and a large-scale experiment, with 260 subjects over an academic year, are conducted to evaluate different aspects of our approach. Ontological inference is shown to improve user profiling, external ontological knowledge used to successfully bootstrap a recommender system and profile visualization employed to improve profiling accuracy. The overall performance of our ontological recommender systems are also presented and favourably compared to other systems in the literature.

MyExperiment (http://www.myexperiment.org) is an online research environment that supports the social sharing of bioinformatics workflows. These workflows are procedures consisting of a series of computational tasks using web services, which may be performed on data from its retrieval, integration and analysis, to the visualization of the results. As a public repository of workflows, myExperiment allows anybody to discover those that are relevant to their research, which can then be reused and repurposed to their specific requirements. Conversely, developers can submit their workflows to myExperiment and enable them to be shared in a secure manner. Since its release in 2007, myExperiment currently has over 3500 registered users and contains more than 1000 workflows. The social aspect to the sharing of these workflows is facilitated by registered users forming virtual communities bound together by a common interest or research project. Contributors of workflows can build their reputation within these communities by receiving feedback and credit from individuals who reuse their work. Further documentation about myExperiment including its REST web service is available from http://wiki.myexperiment.org. Feedback and requests for support can be sent to bugs@myexperiment.org.

This paper introduces the Zooniverse citizen science project and software framework, outlining its structure from an observatory perspective: both as an observable web-based system in itself, and as an example of a platform iteratively developed according to real-world deployment and used at scale. We include details of the technical architecture of Zooniverse, including the mechanisms for data gathering across the Zooniverse operation, access, and analysis. We consider the lessons that can be drawn from the experience of designing and running Zooniverse, and how this might inform development of other web observatories.

Information security risk assessment methods have served us well over the past two decades. They have provided a tool for organizations and governments to use in protecting themselves against pertinent risks. As the complexity, pervasiveness, and automation of technology systems increases and cyberspace matures, particularly with the Internet of Things (IoT), there is a strong argument that we will need new approaches to assess risk and build trust. The challenge with simply extending existing assessment methodologies to IoT systems is that we could be blind to new risks arising in such ecosystems. These risks could be related to the high degrees of connectivity present or the coupling of digital, cyber-physical, and social systems. This article makes the case for new methodologies to assess risk in this context that consider the dynamics and uniqueness of the IoT while maintaining the rigor of best practice in risk assessment.