a b s t r a c tResearch networks play a crucial role in the production of new knowledge since collaboration contributes to determine the cognitive and social structure of scientific fields and has a positive influence on research. This paper analyses the structure of co-authorship networks in three different fields (Nanoscience, Pharmacology and Statistics) in Spain over a three-year period (2006)(2007)(2008) and explores the relationship between the research performance of scientists and their position in co-authorship networks. A denser co-authorship network is found in the two experimental fields than in Statistics, where the network is of a less connected and more fragmented nature. Using the g-index as a proxy for individual research performance, a Poisson regression model is used to explore how performance is related to different co-authorship network measures and to disclose interfield differences. The number of co-authors (degree centrality) and the strength of links show a positive relationship with the g-index in the three fields. Local cohesion presents a negative relationship with the g-index in the two experimental fields, where open networks and the diversity of co-authors seem to be beneficial. No clear advantages from intermediary positions (high betweenness) or from being linked to well-connected authors (high eigenvector) can be inferred from this analysis. In terms of g-index, the benefits derived by authors from their position in co-authorship networks are larger in the two experimental fields than in the theoretical one.

‘Social media metrics’ are bursting into science studies as emerging new measures of impact related to scholarly activities. However, their meaning and scope as scholarly metrics is still far from being grasped. This research seeks to shift focus from the consideration of social media metrics around science as mere indicators confined to the analysis of the use and visibility of publications on social media to their consideration as metrics of interaction and circulation of scientific knowledge across different communities of attention, and particularly as metrics that can also be used to characterize these communities. Although recent research efforts have proposed tentative typologies of social media users, no study has empirically examined the full range of Twitter user’s behavior within Twitter and disclosed the latent dimensions in which activity on Twitter around science can be classified. To do so, we draw on the overall activity of social media users on Twitter interacting with research objects collected from the Altmetic.com database. Data from over 1.3 million unique users, accounting for over 14 million tweets to scientific publications, is analyzed. Based on an exploratory and confirmatory factor analysis, four latent dimensions are identified: ‘Science Engagement’, ‘Social Media Capital’, ‘Social Media Activity’ and ‘Science Focus’. Evidence on the predominant type of users by each of the four dimensions is provided by means of VOSviewer term maps of Twitter profile descriptions. This research breaks new ground for the systematic analysis and characterization of social media users’ activity around science.

The acknowledgments in scientific publications are an important feature in the scholarly communication process. This research analyzes funding acknowledgment presence in scientific publications and introduces a novel approach for discovering text patterns by discipline in the acknowledgment section of papers. First, the presence of acknowledgments in 38,257 English‐language papers published by Spanish researchers in 2010 is studied by subject area on the basis of the funding acknowledgment information available in the Web of Science database. Funding acknowledgments are present in two thirds of Spanish articles, with significant differences by subject area, number of authors, impact factor of journals, and, in one specific area, basic/applied nature of research. Second, the existence of specific acknowledgment patterns in English‐language papers of Spanish researchers in 4 selected subject categories (cardiac and cardiovascular systems, economics, evolutionary biology, and statistics and probability) is explored through a combination of text mining and multivariate analyses. “Peer interactive communication” predominates in the more theoretical or social‐oriented fields (statistics and probability, economics), whereas the recognition of technical assistance is more common in experimental research (evolutionary biology), and the mention of potential conflicts of interest emerges forcefully in the clinical field (cardiac and cardiovascular systems). The systematic inclusion of structured data about acknowledgments in journal articles and bibliographic databases would have a positive impact on the study of collaboration practices in science.