What do biomarkers add: Mapping quantitative imaging biomarkers research.

PURPOSE: To understand the contribution of the concept of "biomarker" to quantitative imaging research. METHOD: The study consists of a bibliometric and a network analysis of quantitative imaging biomarkers research based on publication data retrieved from the Web of Science (WoS) for the period 1976-2017. Co-authorship is used as a proxy for scientific collaboration among research groups. Research groups are disambiguated and assigned to an institutional sector and to a medical specialty or academic discipline. Co-occurrence maps of specialties are built to delineate the collaborative network structure of this emerging field. RESULTS: Two very distinct growth patterns emerged from the 5432 publications retrieved from WoS. Scientific production on «quantitative imaging biomarkers¼ (QIB) began 20 years after the first publications on «quantitative imaging¼ (QI). The field of QIB has exhibited rapid growth becoming the most used term since 2011. Among the 12,882 institutions identified, 56% include the term QIB and 44% include the term QI; among the 14,734 different research groups identified, 60% include the term QIB and 40% the term QI. QIB is characterized by a well-established community of researchers whose largest contributors are in medical specialties (radiology 17%, neurology 16%, mental 10%, oncology 10%), while QI shows a more fragmented and diverse community (radiology 13%, engineering 13%, physics 10%, oncology 9%, neurology 6%, biology 4%, nuclear 3%, computing 3%). This suggests a qualitative difference between QIB and QI networks. CONCLUSIONS: Adding biomarkers to quantitative imaging suggests that medical imaging is rapidly evolving, driven by the efforts to translate quantitative imaging research into clinical practice.

Correction: Do women in science form more diverse research networks than men? An analysis of Spanish biomedical scientists.

[This corrects the article DOI: 10.1371/journal.pone.0238229.].

Do women in science form more diverse research networks than men? An analysis of Spanish biomedical scientists.

This paper examines the role of gender in the formation of research collaboration networks, by investigating the composition of networks through connections to diverse professional communities. Drawing on an ego network approach, we examine gender differences among researchers' networks in terms of partner diversity, openness and brokerage roles. We use data from 897 valid responses to a questionnaire administered to biomedical scientists in Spain, which enquired into multiple aspects of personal research networks. Our findings show that women form more diverse networks and brokerage triads than men. This result is reinforced if we consider the most heterogeneous brokerage triads in terms of professional differences among network partners (i.e., consultant and liaison). Our results suggest that women are more likely to access non-redundant knowledge and richer research perspectives via their knowledge-flow intermediary roles. This research suggests the need for analyses of gender and networks that go beyond a gender-to-gender approach.

Towards a second generation of 'social media metrics': Characterizing Twitter communities of attention around science.

'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.

Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences.

For the past 50 years, acknowledgments have been studied as important paratextual traces of research practices, collaboration, and infrastructure in science. Since 2008, funding acknowledgments have been indexed by Web of Science, supporting large-scale analyses of research funding. Applying advanced linguistic methods as well as Correspondence Analysis to more than one million acknowledgments from research articles and reviews published in 2015, this paper aims to go beyond funding disclosure and study the main types of contributions found in acknowledgments on a large scale and through disciplinary comparisons. Our analysis shows that technical support is more frequently acknowledged by scholars in Chemistry, Physics and Engineering. Earth and Space, Professional Fields, and Social Sciences are more likely to acknowledge contributions from colleagues, editors, and reviewers, while Biology acknowledgments put more emphasis on logistics and fieldwork-related tasks. Conflicts of interest disclosures (or lack of thereof) are more frequently found in acknowledgments from Clinical Medicine, Health and, to a lesser extent, Psychology. These results demonstrate that acknowledgment practices truly do vary across disciplines and that this can lead to important further research beyond the sole interest in funding.