Astrophysicists on Twitter: An in-depth analysis of tweeting and scientific publication behavior

This paper analyzes the tweeting behavior of 37 astrophysicists on Twitter and compares their tweeting behavior with their publication behavior and citation impact to show whether they tweet research-related topics or not. Astrophysicists on Twitter are selected to compare their tweets with their publications from Web of Science. Different user groups are identified based on tweeting and publication frequency. A moderate negative correlation (p=-0.390*) is found between the number of publications and tweets per day, while retweet and citation rates do not correlate. The similarity between tweets and abstracts is very low (cos=0.081). User groups show different tweeting behavior such as retweeting and including hashtags, usernames and URLs. The study is limited in terms of the small set of astrophysicists. Results are not necessarily representative of the entire astrophysicist community on Twitter and they most certainly do not apply to scientists in general. Future research should apply the methods to a larger set of researchers and other scientific disciplines. To a certain extent, this study helps to understand how researchers use Twitter. The results hint at the fact that impact on Twitter can neither be equated with nor replace traditional research impact metrics. However, tweets and other so-called altmetrics might be able to reflect other impact of scientists such as public outreach and science communication. To the best of our knowledge, this is the first in-depth study comparing researchers' tweeting activity and behavior with scientific publication output in terms of quantity, content and impact.Comment: 14 pages, 5 figures, 7 table

Tweets as impact indicators: Examining the implications of automated bot accounts on Twitter

This brief communication presents preliminary findings on automated Twitter accounts distributing links to scientific papers deposited on the preprint repository arXiv. It discusses the implication of the presence of such bots from the perspective of social media metrics (altmetrics), where mentions of scholarly documents on Twitter have been suggested as a means of measuring impact that is both broader and timelier than citations. We present preliminary findings that automated Twitter accounts create a considerable amount of tweets to scientific papers and that they behave differently than common social bots, which has critical implications for the use of raw tweet counts in research evaluation and assessment. We discuss some definitions of Twitter cyborgs and bots in scholarly communication and propose differentiating between different levels of engagement from tweeting only bibliographic information to discussing or commenting on the content of a paper.Comment: 9 pages, 4 figures, 1 tabl

Social media in scholarly communication : a review of the literature and empirical analysis of Twitter use by SSHRC doctoral award recipients

This report has been commissioned by the Social Sciences and Humanities Research Council (SSHRC) to analyze the role that social media currently plays in scholarly communication as well as to what extent metrics derived from social media activity related to scholarly content can be applied in an evaluation context. Scholarly communication has become more diverse and open with research being discussed, shared and evaluated online. Social media tools are increasingly being used in the research and scholarly communication context, as scholars connect on Facebook, LinkedIn and Twitter or specialized platforms such as ResearchGate, Academia.edu or Mendeley. Research is discussed on blogs or Twitter, while datasets, software code and presentations are shared on Dryad, Github, FigShare and similar websites for reproducibility and reuse. Literature is managed, annotated and shared with online tools such as Mendeley and Zotero, and peer review is starting to be more open and transparent. The changing landscape of scholarly communication has also brought about new possibilities regarding its evaluation. So-called altmetrics are based on scholarly social media activity and have been introduced to reflect scholarly output and impact beyond considering only peer-reviewed journal articles and citations within them to measure scientific success. This includes the measurement of more diverse types of scholarly work and various forms of impact including that on society. This report provides an overview of how various social media tools are used in the research context based on 1) an extensive review of the current literature as well as 2) an empirical analysis of the use of Twitter by the 2010 cohort of SSHRC Doctoral Award recipients was analyzed in depth. Twitter has been chosen as one of the most promising tools regarding interaction with the general public and scholarly communication beyond the scientific community. The report focuses on the opportunities and challenges of social media and derived metrics and attempts to provide SSHRC with information to develop guidelines regarding the use of social media by funded researchers as well support the informed used of social media metrics

The academic advantage : gender disparities in patenting

We analyzed gender disparities in patenting by country, technological area, and type of assignee using the 4.6 million utility patents issued between 1976 and 2013 by the United States Patent and Trade Office (USPTO). Our analyses of fractionalized inventorships demonstrate that women’s rate of patenting has increased from 2.7% of total patenting activity to 10.8% over the nearly 40-year period. Our results show that, in every technological area, female patenting is proportionally more likely to occur in academic institutions than in corporate or government environments. However, women’s patents have a lower technological impact than that of men, and that gap is wider in the case of academic patents. We also provide evidence that patents to which women—and in particular academic women—contributed are associated with a higher number of International Patent Classification (IPC) codes and co-inventors than men. The policy implications of these disparities and academic setting advantages are discussed

Ensembles of probability estimation trees for customer churn prediction

Customer churn prediction is one of the most, important elements tents of a company's Customer Relationship Management, (CRM) strategy In tins study, two strategies are investigated to increase the lift. performance of ensemble classification models, i.e (1) using probability estimation trees (PETs) instead of standard decision trees as base classifiers; and (n) implementing alternative fusion rules based on lift weights lot the combination of ensemble member's outputs Experiments ale conducted lot font popular ensemble strategics on five real-life chin n data sets In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion tides However: the effect vanes lot the (Idol cut ensemble strategies lit particular, the results indicate an increase of lift performance of (1) Bagging by implementing C4 4 base classifiets. (n) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (in) AdaBoost, by implementing both

The KRESCENT Program (2005-2015) : an evaluation of the state of Kidney Research Training in Canada

Background: The Kidney Research Scientist Core Education and National Training (KRESCENT) Program was launched in 2005 to enhance kidney research capacity in Canada and foster knowledge translation across the 4 themes of health research. Objective: To evaluate the impact of KRESCENT on its major objectives and on the careers of trainees after its first 10 years. Methods: An online survey of trainees (n = 53) who had completed or were enrolled in KRESCENT was conducted in 2015. Information was also obtained from curriculum vitae (CVs). A bibliometric analysis assessed scientific productivity, collaboration, and impact in comparison with unsuccessful applicants to KRESCENT over the same period. The analysis included a comparison of Canadian with international kidney research metrics from 2000 to 2014. Results: Thirty-nine KRESCENT trainees completed the survey (74%), and 44 trainees (83%) submitted CVs. KRESCENT trainees had a high success rate at obtaining grant funding from the Canadian Institutes of Health Research (CIHR; 79%), and 76% of Post-Doctoral Fellows received academic appointments at the Assistant Professor level within 8 months of completing training. The majority of trainees reported that KRESCENT had contributed significantly to their success in securing CIHR funding (90%), and to the creation of knowledge (93%) and development of new methodologies (50%). Bibliometric analysis revealed a small but steady decline in total international kidney research output from 2000 to 2014, as a percentage of all health research, although overall impact of kidney research in Canada increased from 2000-2005 to 2009- 2014 compared with other countries. KRESCENT trainees demonstrated increased productivity, multiauthored papers, impact, and international collaborations after their training, compared with nonfunded applicants. Conclusions: The KRESCENT Program has fostered kidney research career development and contributed to increased capacity, productivity, and collaboration. To further enhance knowledge creation and translation in kidney research in Canada, programs such as KRESCENT should be sustained via long-term funding partnerships.Mise en contexte: Le programme KRESCENT (Kidney Research Scientist Core Education and National Training) a été lancé en 2005 pour augmenter la capacité de la recherche sur les maladies du rein à travers le Canada, et pour encourager la transmission des connaissances au sein des quatre axes de recherche en santé. Objectifs de l’étude: Cette étude avait pour but d’évaluer les répercussions du programme KRESCENT sur ses principaux objectifs ainsi que des retombées sur la carrière des stagiaires participants, dix ans après sa création. Méthodologie: Un sondage en ligne a été mené en 2015 auprès des stagiaires (n = 53) ayant été admis ou ayant complété le programme KRESCENT. Des renseignements ont également été obtenus par la consultation de curriculum vitae (CV). Une analyse bibliométrique a évalué la productivité scientifique et la collaboration des participants ainsi que les répercussions de leur participation à KRESCENT sur leur carrière. Les données de cette analyse ont été comparées à celles des candidats n’ayant pas été retenus au cours de la même période. L’analyse comprenait également une comparaison des données canadiennes avec celles obtenues en recherche sur les maladies du rein ailleurs dans le monde

Authorship, citations, acknowledgments and visibility in social media : symbolic capital in the multifaceted reward system of science

The reward system of science is undergoing significant changes, as traditional indicators compete with initiatives that offer novel means of disseminating and assessing scholarly impact. This paper considers a number of aspects of this reward system, including authorship, citations, acknowledgements, and the growing use of social media platforms by academics, with an eye towards identifying contemporary issues relating to scholarly communication practices, as understood through the perspectives of Bourdieu’s symbolic capital and Merton’s recognition paradigms. This paper posits that, while scientific capital remains the foundation upon which the reward system of science is built, this system is revealing itself to be more and more multifaceted, extremely complex, and facing increasing tension between its traditional means of evaluation and the potential of new indicators in the digital era. The paper presents an extended literature review, as well as recommendations for further considerations and empirical research. A better understanding of the perceptions of academics would be necessary to properly assess the effects of these new indicators on scholarly communication practices and the reward system of science

A small world of citations? The influence of collaboration networks on citation practices

This paper examines the proximity of authors to those they cite using degrees of separation in a co-author network, essentially using collaboration networks to expand on the notion of self-citations. While the proportion of direct self-citations (including co-authors of both citing and cited papers) is relatively constant in time and across specialties in the natural sciences (10% of citations) and the social sciences (20%), the same cannot be said for citations to authors who are members of the co-author network. Differences between fields and trends over time lie not only in the degree of co-authorship which defines the large-scale topology of the collaboration network, but also in the referencing practices within a given discipline, computed by defining a propensity to cite at a given distance within the collaboration network. Overall, there is little tendency to cite those nearby in the collaboration network, excluding direct self-citations. By analyzing these social references, we characterize the social capital of local collaboration networks in terms of the knowledge production within scientific fields. These results have implications for the long-standing debate over biases common to most types of citation analysis, and for understanding citation practices across scientific disciplines over the past 50 years. In addition, our findings have important practical implications for the availability of 'arm's length' expert reviewers of grant applications and manuscripts

Meta-Research: investigating disagreement in the scientific literature

Disagreement is essential to scientific progress but the extent of disagreement in science, its evolution over time, and the fields in which it happens remain poorly understood. Here we report the development of an approach based on cue phrases that can identify instances of disagreement in scientific articles. These instances are sentences in an article that cite other articles. Applying this approach to a collection of more than four million English-language articles published between 2000 and 2015 period, we determine the level of disagreement in five broad fields within the scientific literature (biomedical and health sciences; life and earth sciences; mathematics and computer science; physical sciences and engineering; and social sciences and humanities) and 817 meso-level fields. Overall, the level of disagreement is highest in the social sciences and humanities, and lowest in mathematics and computer science. However, there is considerable heterogeneity across the meso-level fields, revealing the importance of local disciplinary cultures and the epistemic characteristics of disagreement. Analysis at the level of individual articles reveals notable episodes of disagreement in science, and illustrates how methodological artifacts can confound analyses of scientific texts.Merit, Expertise and Measuremen

Meta-Research: investigating disagreement in the scientific literature

Disagreement is essential to scientific progress but the extent of disagreement in science, its evolution over time, and the fields in which it happens remain poorly understood. Here we report the development of an approach based on cue phrases that can identify instances of disagreement in scientific articles. These instances are sentences in an article that cite other articles. Applying this approach to a collection of more than four million English-language articles published between 2000 and 2015 period, we determine the level of disagreement in five broad fields within the scientific literature (biomedical and health sciences; life and earth sciences; mathematics and computer science; physical sciences and engineering; and social sciences and humanities) and 817 meso-level fields. Overall, the level of disagreement is highest in the social sciences and humanities, and lowest in mathematics and computer science. However, there is considerable heterogeneity across the meso-level fields, revealing the importance of local disciplinary cultures and the epistemic characteristics of disagreement. Analysis at the level of individual articles reveals notable episodes of disagreement in science, and illustrates how methodological artifacts can confound analyses of scientific texts.Merit, Expertise and Measuremen