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To advance the pace of scientific discovery we propose a conceptual format that forms the basis of a truly new way of publishing science. In our proposal, all scientific communication objects (including experimental workflows, direct results, email conversations, and all drafted and published information artifacts) are labeled and stored in a great, big, distributed data store (or many distributed data stores that are all connected). Each item has a set of metadata attached to it, which includes (at least) the person and time it was created, the type of object it is, and the status of the object including intellectual property rights and ownership. Every researcher can (and must) deposit every knowledge item that is produced in the lab into this repository. With this deposition goes an essential metadata component that states who has the rights to see, use, distribute, buy or sell this item. Into this grand (and system-wise distributed, cloud-based) architecture, all items produced by a single lab, or several labs, are stored, labeled and connected.

With the advancement of technology and the wide adoption of ontologies as knowledge representation formats, in the last decade, a handful of models were proposed for the externalization of the rhetoric and argumentation captured within scientific publications. Conceptually, most of these models share a similar representation form of the scientific publication, i.e. as a series of interconnected elementary knowledge items. The main differences are given by the terminology used, the types of rhetorical and/or argumentation relations connecting the knowledge items and the foundational theories supporting these relations. This paper analyzes the state of the art and provides a concise comparative overview of the five most prominent discourse representation models, with the goal of sketching an unified model for discourse representation.

Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area.

To manage the flood of information that threatens to engulf (life-)scientists, an abundance of computer-aided tools are being developed. These tools aim to provide access to the knowledge conveyed within a collection of research papers, without actually having to read the papers. Many of these tools focus on text mining, by looking for specific named-entities that have scientific meaning, and relationships between these. An overview of the current state of the art is given in Rebholz-Schuhmann et al. (2005) and Couto et al. (2003). Typically, these tools identify a list of sentences containing relationships between two specific named-entities that can be found using rules or a thesaurus of synonyms. These sentences represent an overview of the interactions that are known with a specific entity, thus precluding the need for an exhaustive literature study. For example, the following are a few sentences that have been found using a typical text mining tool for the relationship 'p53 activates*': 1. The p53 tumor suppressor protein exerts most of its anti-tumorigenic activity by transcriptionally activating several pro-apoptotic genes. 2. We found that p53 ... activates[,] the promoter of the myosin VI gene.

The exploration of large information spaces is a difficult task, especially if the user is not familiar with the terminology used to describe information. Conceptual models of a domain in terms of thesauri or ontologies can leverage this problem to some extend. In order to be useful, there is a need for interactive tools for exploring large information sets based on conceptual knowledge. We present a thesaurus based browser that supports a mixed-initiative exploration of large online resources that provides support for thesaurus-based search and topic-based exploration of query results. We motivate the chosen exploration strategy the browser functionality, present the results of user studies and discuss future improvements of the browser.

The dissemination of knowledge derived from research and scholarship has a fundamental impact on the ways in which society develops and progresses, and at the same time it feeds back to improve subsequent research and scholarship. Here, as in so many other areas of human activity, the internet is changing the way things work; two decades of emergent and increasingly pervasive information technology have demonstrated the potential for far more effective scholarly communication. But the use of this technology remains limited. Force11 is a community of scholars, librarians, archivists, publishers and research funders that has arisen organically to help facilitate the change toward improved knowledge creation and sharing. This document highlights the findings of the Force11 workshop on the Future of Research Communication held at Schloss Dagstuhl, Germany, in August 2011: it summarizes a number of key problems facing scholarly publishing today, and presents a vision that addresses these problems, proposing concrete steps that key stakeholders can take to improve the state of scholarly publishing.