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Despite the increasingly digital nature of society there are some areas of research that remain firmly rooted in the past; in this case the laboratory notebook, the last remaining paper component of an experiment. Countless electronic laboratory notebooks (ELNs) have been created in an attempt to digitise record keeping processes in the lab, but none of them have become a ‘key player’ in the ELN market, due to the many adoption barriers that have been identified in previous research and further explored in the user studies presented here. The main issues identified are the cost of the current available ELNs, their ease of use (or lack of it) and their accessibility issues across different devices and operating systems. Evidence suggests that whilst scientists willingly make use of generic notebooking software, spreadsheets and other general office and scientific tools to aid their work, current ELNs are lacking in the required functionality to meet the needs of the researchers. In this paper we present our extensive research and user study results to propose an ELN built upon a pre-existing cloud notebook platform that makes use of accessible popular scientific software and semantic web technologies to help overcome the identified barriers to adoption.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-017-0221-3) contains supplementary material, which is available to authorized users.

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Self-organising multi-agent systems provide a suitable paradigm for developing autonomic computing systems that manage themselves. Towards this goal, we demonstrate a robust, decentralised approach for structural adaptation in explicitly modelled problem solving agent organisations. Based on self-organisation principles, our method enables the autonomous agents to modify their structural relations to achieve a better allocation of tasks in a simulated task-solving environment. Specifically, the agents reason about when and how to adapt using only their history of interactions as guidance. We empirically show that, in a wide range of closed, open, static and dynamic scenarios, the performance of organisations using our method is close (70 − 90%) to that of an idealised centralised allocation method and is considerably better (10 − 60%) than the current state of the art decentralised approaches.

Collaborative tagging systems are now popular tools for organising and sharing information on the Web. While collaborative tagging offers many advantages over the use of controlled vocabularies, they also suffer from problems such as the existence of polysemous tags. We investigate how the different contexts in which individual tags are used can be revealed automatically without consulting any external resources. We consider several different network representations of tags and documents, and apply a graph clustering algorithm on these networks to obtain groups of tags or documents corresponding to the different meanings of an ambiguous tag. Our experiments show that networks which explicitly take the social context into account are more likely to give a better picture of the semantics of a tag.

Twitter is a popular tool for publishing potentially interesting information about people's opinions, experiences and news. Mobile devices allow people to publish tweets during real-time events. It is often difficult to identify the subject of a tweet because Twitter users often write using highly unstructured language with many typographical errors. Structured data related to entities can provide additional context to tweets. We propose an approach which associates tweets to a given event using query expansion and relationships defined on the Semantic Web, thus increasing the recall whilst maintaining or improving the precision of event detection. In this work, we investigate the usage of Twitter in discussing the Rock am Ring music festival. We aim to use prior knowledge of the festival's lineup to associate tweets with the bands playing at the festival. In order to evaluate the effectiveness of our approach, we compare the lifetime of the Twitter buzz surrounding an event to the actual programmed event, using Twitter users as social sensors.

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Abstract. Autonomic computing is being advocated as a tool for maintaining and managing large and complex computing systems. Self-organising multi-agent systems provide a suitable paradigm for developing such autonomic systems. Towards this goal, we demonstrate a robust, decentralised approach for structural adaptation in explicitly modelled problem solving agent organisations. Our method is based on self-organisation principles and enables the agents to modify the organisational structure to achieve a better allocation of tasks across the organisation in a simulated task-solving environment. The agents forge and dissolve relations with other agents using their history of interactions as guidance. We empirically show that the efficiency of organisations using our approach is close to that of organisations having an omniscient central allocator and considerably better than static organisations or those changing the structure randomly.