2,306 research outputs found
F1000 recommendations as a new data source for research evaluation: A comparison with citations
F1000 is a post-publication peer review service for biological and medical
research. F1000 aims to recommend important publications in the biomedical
literature, and from this perspective F1000 could be an interesting tool for
research evaluation. By linking the complete database of F1000 recommendations
to the Web of Science bibliographic database, we are able to make a
comprehensive comparison between F1000 recommendations and citations. We find
that about 2% of the publications in the biomedical literature receive at least
one F1000 recommendation. Recommended publications on average receive 1.30
recommendations, and over 90% of the recommendations are given within half a
year after a publication has appeared. There turns out to be a clear
correlation between F1000 recommendations and citations. However, the
correlation is relatively weak, at least weaker than the correlation between
journal impact and citations. More research is needed to identify the main
reasons for differences between recommendations and citations in assessing the
impact of publications
Hacia los estudios de medios sociales de la ciencia: las métricas de los medios sociales, presente y futuro
During the last years a new research topic has rapidly emerged in the field of scientometrics. This new topic, popularly known as altmetrics, was first proposed in the Altmetrics manifesto (Priem et al., 2010). Since its proposal, altmetrics has been a concept of difficult definition (Haustein, Bowman & Costas, 2016), even being considered as “a good idea, but a bad name” (Rousseau & Ye, 2013). Altmetrics have been usually related to new metrics around scholarly objects captured through events recorded in online social media platforms (Haustein et al., 2016). However, the large diversity of sources and metrics that fall within the realm of altmetrics has made it hard to come up with a consensus of what can be considered as altmetrics (Haustein et al., 2016)
Towards the social media studies of science: social media metrics, present and future
In this paper we aim at providing a general reflection around the present and
future of social media metrics (or altmetrics) and how they could evolve into a
new discipline focused on the study of the relationships and interactions
between science and social media, in what could be seen as the social media
studies of science.Comment: Spanish version:
http://revistas.bnjm.cu/index.php/anales/article/view/417
What makes papers visible on social media? An analysis of various document characteristics
In this study we have investigated the relationship between different
document characteristics and the number of Mendeley readership counts, tweets,
Facebook posts, mentions in blogs and mainstream media for 1.3 million papers
published in journals covered by the Web of Science (WoS). It aims to
demonstrate that how factors affecting various social media-based indicators
differ from those influencing citations and which document types are more
popular across different platforms. Our results highlight the heterogeneous
nature of altmetrics, which encompasses different types of uses and user groups
engaging with research on social media.Comment: Presented at the 21th International Conference in Science &
Technology Indicators (STI), 13-16, September, 2016, Valencia, Spai
DataCite as a novel bibliometric source: Coverage, strengths and limitations
This paper explores the characteristics of DataCite to determine its
possibilities and potential as a new bibliometric data source to analyze the
scholarly production of open data. Open science and the increasing data sharing
requirements from governments, funding bodies, institutions and scientific
journals has led to a pressing demand for the development of data metrics. As a
very first step towards reliable data metrics, we need to better comprehend the
limitations and caveats of the information provided by sources of open data. In
this paper, we critically examine records downloaded from the DataCite's OAI
API and elaborate a series of recommendations regarding the use of this source
for bibliometric analyses of open data. We highlight issues related to metadata
incompleteness, lack of standardization, and ambiguous definitions of several
fields. Despite these limitations, we emphasize DataCite's value and potential
to become one of the main sources for data metrics development.Comment: Paper accepted for publication in Journal of Informetric
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