196 research outputs found
Emergence d’une spécialité scientifique dans l’espace - La réparation de l’ADN
International audienceIn the study of science, the specialty is seen as the ideal level of analysis to understand the genesis and development of scientific communities. This article uses bibliometric data to analyze the emergence of DNA repair by testing a hybrid method to identify the specialty’s appearance in geographical space by focusing on the geographical trajectories of the pioneers in this field. We try to identify the professional mobility of researchers using these bibliometric data, and if possible to highlight the structural networks of places during the emergence stage of the specialty. These networks determine places as much as they are built by individual trajectories. In this way, we try to make a place for the geography of science in the field of social studies of science.Dans l’étude des sciences, la spécialité est perçue comme le niveau d’analyse idéal pour comprendre la genèse et le développement des collectifs scientifiques. Cet article utilise des données bibliométriques pour analyser l’émergence de la Réparation de l’ADN en expérimentant une méthode mixte pour repérer son apparition dans l’espace géographique. En nous concentrant sur les trajectoires géographiques de pionniers dans cedomaine, nous tâchons de repérer leur mobilité professionnelle à l’aide de données bibliométriques dans la perspective de mettre en évidence les réseaux de lieux structurants dans la phase d’émergence de la spécialité. Ces réseaux de lieux déterminent autant qu’ils sont construits par les trajectoires individuelles. Nous essayons ainsi de faire une place à la géographie des sciences dans le domaine des études sociales des sciences
Neurotrauma clinicians' perspectives on the contextual challenges associated with long-term follow-up following traumatic brain injury in low-income and middle-income countries: a qualitative study protocol.
INTRODUCTION: Traumatic brain injury (TBI) is a global public health concern; however, low/middle-income countries (LMICs) face the greatest burden. The WHO recognises the significant differences between patient outcomes following injuries in high-income countries versus those in LMICs. Outcome data are not reliably recorded in LMICs and despite improved injury surveillance data, data on disability and long-term functional outcomes remain poorly recorded. Therefore, the full picture of outcome post-TBI in LMICs is largely unknown. METHODS AND ANALYSIS: This is a cross-sectional pragmatic qualitative study using individual semistructured interviews with clinicians who have experience of neurotrauma in LMICs. The aim of this study is to understand the contextual challenges associated with long-term follow-up of patients following TBI in LMICs. For the purpose of the study, we define 'long-term' as any data collected following discharge from hospital. We aim to conduct individual semistructured interviews with 24-48 neurosurgeons, beginning February 2020. Interviews will be recorded and transcribed verbatim. A reflexive thematic analysis will be conducted supported by NVivo software. ETHICS AND DISSEMINATION: The University of Cambridge Psychology Research Ethics Committee approved this study in February 2020. Ethical issues within this study include consent, confidentiality and anonymity, and data protection. Participants will provide informed consent and their contributions will be kept confidential. Participants will be free to withdraw at any time without penalty; however, their interview data can only be withdrawn up to 1 week after data collection. Findings generated from the study will be shared with relevant stakeholders such as the World Federation of Neurosurgical Societies and disseminated in conference presentations and journal publications
Characterizing Interdisciplinarity of Researchers and Research Topics Using Web Search Engines
Researchers' networks have been subject to active modeling and analysis.
Earlier literature mostly focused on citation or co-authorship networks
reconstructed from annotated scientific publication databases, which have
several limitations. Recently, general-purpose web search engines have also
been utilized to collect information about social networks. Here we
reconstructed, using web search engines, a network representing the relatedness
of researchers to their peers as well as to various research topics.
Relatedness between researchers and research topics was characterized by
visibility boost-increase of a researcher's visibility by focusing on a
particular topic. It was observed that researchers who had high visibility
boosts by the same research topic tended to be close to each other in their
network. We calculated correlations between visibility boosts by research
topics and researchers' interdisciplinarity at individual level (diversity of
topics related to the researcher) and at social level (his/her centrality in
the researchers' network). We found that visibility boosts by certain research
topics were positively correlated with researchers' individual-level
interdisciplinarity despite their negative correlations with the general
popularity of researchers. It was also found that visibility boosts by
network-related topics had positive correlations with researchers' social-level
interdisciplinarity. Research topics' correlations with researchers'
individual- and social-level interdisciplinarities were found to be nearly
independent from each other. These findings suggest that the notion of
"interdisciplinarity" of a researcher should be understood as a
multi-dimensional concept that should be evaluated using multiple assessment
means.Comment: 20 pages, 7 figures. Accepted for publication in PLoS On
Who is the best player ever? A complex network analysis of the history of professional tennis
We consider all matches played by professional tennis players between 1968
and 2010, and, on the basis of this data set, construct a directed and weighted
network of contacts. The resulting graph shows complex features, typical of
many real networked systems studied in literature. We develop a diffusion
algorithm and apply it to the tennis contact network in order to rank
professional players. Jimmy Connors is identified as the best player of the
history of tennis according to our ranking procedure. We perform a complete
analysis by determining the best players on specific playing surfaces as well
as the best ones in each of the years covered by the data set. The results of
our technique are compared to those of two other well established methods. In
general, we observe that our ranking method performs better: it has a higher
predictive power and does not require the arbitrary introduction of external
criteria for the correct assessment of the quality of players. The present work
provides a novel evidence of the utility of tools and methods of network theory
in real applications.Comment: 10 pages, 4 figures, 4 table
A reverse engineering approach to the suppression of citation biases reveals universal properties of citation distributions
The large amount of information contained in bibliographic databases has
recently boosted the use of citations, and other indicators based on citation
numbers, as tools for the quantitative assessment of scientific research.
Citations counts are often interpreted as proxies for the scientific influence
of papers, journals, scholars, and institutions. However, a rigorous and
scientifically grounded methodology for a correct use of citation counts is
still missing. In particular, cross-disciplinary comparisons in terms of raw
citation counts systematically favors scientific disciplines with higher
citation and publication rates. Here we perform an exhaustive study of the
citation patterns of millions of papers, and derive a simple transformation of
citation counts able to suppress the disproportionate citation counts among
scientific domains. We find that the transformation is well described by a
power-law function, and that the parameter values of the transformation are
typical features of each scientific discipline. Universal properties of
citation patterns descend therefore from the fact that citation distributions
for papers in a specific field are all part of the same family of univariate
distributions.Comment: 9 pages, 6 figures. Supporting information files available at
http://filrad.homelinux.or
Network Archaeology: Uncovering Ancient Networks from Present-day Interactions
Often questions arise about old or extinct networks. What proteins interacted
in a long-extinct ancestor species of yeast? Who were the central players in
the Last.fm social network 3 years ago? Our ability to answer such questions
has been limited by the unavailability of past versions of networks. To
overcome these limitations, we propose several algorithms for reconstructing a
network's history of growth given only the network as it exists today and a
generative model by which the network is believed to have evolved. Our
likelihood-based method finds a probable previous state of the network by
reversing the forward growth model. This approach retains node identities so
that the history of individual nodes can be tracked. We apply these algorithms
to uncover older, non-extant biological and social networks believed to have
grown via several models, including duplication-mutation with complementarity,
forest fire, and preferential attachment. Through experiments on both synthetic
and real-world data, we find that our algorithms can estimate node arrival
times, identify anchor nodes from which new nodes copy links, and can reveal
significant features of networks that have long since disappeared.Comment: 16 pages, 10 figure
Identifying Diffusion Patterns of Research Articles on Twitter: A Case Study of Online Engagement with Open Access Articles
The growing presence of research shared on social media, coupled with the increase in freely available research, invites us to ask whether scientific articles shared on platforms like Twitter diffuse beyond the academic community. We explore a new method for answering this question by identifying 11 articles from two open access biology journals that were shared on Twitter at least 50 times and by analyzing the follower network of users who tweeted each article. We find that diffusion patterns of scientific articles can take very different forms, even when the number of times they are tweeted is similar. Our small case study suggests that most articles are shared within single-connected communities with limited diffusion to the public. The proposed approach and indicators can serve those interested in the public understanding of science, science communication, or research evaluation to identify when research diffuses beyond insular communities.
 
Characterizing and modeling citation dynamics
Citation distributions are crucial for the analysis and modeling of the
activity of scientists. We investigated bibliometric data of papers published
in journals of the American Physical Society, searching for the type of
function which best describes the observed citation distributions. We used the
goodness of fit with Kolmogorov-Smirnov statistics for three classes of
functions: log-normal, simple power law and shifted power law. The shifted
power law turns out to be the most reliable hypothesis for all citation
networks we derived, which correspond to different time spans. We find that
citation dynamics is characterized by bursts, usually occurring within a few
years since publication of a paper, and the burst size spans several orders of
magnitude. We also investigated the microscopic mechanisms for the evolution of
citation networks, by proposing a linear preferential attachment with time
dependent initial attractiveness. The model successfully reproduces the
empirical citation distributions and accounts for the presence of citation
bursts as well.Comment: 8 pages, 5 figure
- …