There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

To gain insight into changes of scholarly journals’ recommendations, we conducted a systematic review of studies that analysed journals’ Instructions to Authors (ItAs). We summarised results of 153 studies, and meta-analysed how often ItAs addressed: 1) authorship, 2) conflicts of interest, 3) data sharing, 4) ethics approval, 5) funding disclosure, and 6) International Committee of Medical Journal Editors’ Uniform Requirements for Manuscripts. For each topic we found large between-study heterogeneity. Here, we show six factors that explained most of that heterogeneity: 1) time (addressing of topics generally increased over time), 2) country (large differences found between countries), 3) database indexation (large differences found between databases), 4) impact factor (topics were more often addressed in highest than in lowest impact factor journals), 5) discipline (topics were more often addressed in Health Sciences than in other disciplines), and 6) sub-discipline (topics were more often addressed in general than in sub-disciplinary journals).

The ICT revolution of the last decades impacted scientific communication as it has impacted many other forms of communications, changing the way in which articles are delivered and how they can be discovered. However, the impact of ICT on the research itself has been much more profound, introducing digital tools to the way in which researchers gather data, perform analyses, and exchange results. This brought new, digital forms of research output, and disseminating those calls for changes deeply impact the core format of the scientific article.In 2009, Elsevier introduced the “Article of the Future” project to define an optimal way for the dissemination of science in the digital age, and in this paper we discuss three of its key dimensions. First we discuss interlinking scientific articles and research data stored with domain-specific data repositories — such interlinking is essential to interpret both article and data efficiently and correctly. We then present easy-to-use 3D visualization tools embedded in online articles: a key example of how the digital article format adds value to scientific communication and helps readers to better understand research results. The last topic covered in this paper is automatic enrichment of journal articles through text-mining or other methods. Here we share insights from a recent survey on the question: how can we find a balance between creating valuable contextual links, without sacrificing the high-quality, peer-reviewed status of published articles?

Although relevance feedback techniques have been investigated for more than 20 years, hardly any of these techniques has been implemented in a commercial full-text document retrieval system. In addition to pure performance problems, this is due to the fact that the application of relevance feedback techniques increases the complexity of the user interface and thus also the use of a document retrieval system. In this paper we concentrate on a relevance feedback technique that allows easily understandable and manageable user interfaces, and at the same time provides high-quality retrieval results. Moreover, the relevance feedback technique introduced unifies as well as improves other well-known relevance feedback techniques.