112,894 research outputs found
Measuring scientific impact beyond academia:An assessment of existing impact metrics and proposed improvements
How does scientific research affect the world around us? Being able to answer this question is of great importance in order to appropriately channel efforts and resources in science.
The impact by scientists in academia is currently measured by citation based metrics such as h-index, i-index and citation counts. These academic metrics aim to represent the dissemination of knowledge among scientists rather than the impact of the research on the
wider world. In this work we are interested in measuring scientific impact beyond academia, on the economy, society, health and legislation (comprehensive impact). Indeed scientists are asked to demonstrate evidence of such comprehensive impact by authoring case studies in the context of the Research Excellence Framework (REF). We first investigate the extent to which existing citation based metrics can be indicative of comprehensive impact.
We have collected all recent REF impact case studies from 2014 and we have linked these to papers in citation networks that we constructed and derived from CiteSeerX, arXiv and PubMed Central using a number of text processing and information retrieval techniques. We
have demonstrated that existing citation-based metrics for impact measurement do not correlate well with REF impact results. We also consider metrics of online attention surrounding
scientific works, such as those provided by the Altmetric API. We argue that in order to be able to evaluate wider non-academic impact we need to mine information from a much wider set of resources, including social media posts, press releases, news articles and political
debates stemming from academic work. We also provide our data as a free and reusable collection for further analysis, including the PubMed citation network and the correspondence between REF case studies, grant applications and the academic literature
Zipf's law and log-normal distributions in measures of scientific output across fields and institutions: 40 years of Slovenia's research as an example
Slovenia's Current Research Information System (SICRIS) currently hosts
86,443 publications with citation data from 8,359 researchers working on the
whole plethora of social and natural sciences from 1970 till present. Using
these data, we show that the citation distributions derived from individual
publications have Zipfian properties in that they can be fitted by a power law
, with between 2.4 and 3.1 depending on the
institution and field of research. Distributions of indexes that quantify the
success of researchers rather than individual publications, on the other hand,
cannot be associated with a power law. We find that for Egghe's g-index and
Hirsch's h-index the log-normal form
applies best, with and depending moderately on the underlying set of
researchers. In special cases, particularly for institutions with a strongly
hierarchical constitution and research fields with high self-citation rates,
exponential distributions can be observed as well. Both indexes yield
distributions with equivalent statistical properties, which is a strong
indicator for their consistency and logical connectedness. At the same time,
differences in the assessment of citation histories of individual researchers
strengthen their importance for properly evaluating the quality and impact of
scientific output.Comment: 8 pages, 3 figures; accepted for publication in Journal of
Informetrics [supplementary material available at
http://www.matjazperc.com/sicris/stats.html
Software tools for conducting bibliometric analysis in science: An up-to-date review
Bibliometrics has become an essential tool for assessing and analyzing the output of scientists, cooperation between
universities, the effect of state-owned science funding on national research and development performance and educational
efficiency, among other applications. Therefore, professionals and scientists need a range of theoretical and practical
tools to measure experimental data. This review aims to provide an up-to-date review of the various tools available
for conducting bibliometric and scientometric analyses, including the sources of data acquisition, performance analysis
and visualization tools. The included tools were divided into three categories: general bibliometric and performance
analysis, science mapping analysis, and libraries; a description of all of them is provided. A comparative analysis of the
database sources support, pre-processing capabilities, analysis and visualization options were also provided in order to
facilitate its understanding. Although there are numerous bibliometric databases to obtain data for bibliometric and
scientometric analysis, they have been developed for a different purpose. The number of exportable records is between
500 and 50,000 and the coverage of the different science fields is unequal in each database. Concerning the analyzed
tools, Bibliometrix contains the more extensive set of techniques and suitable for practitioners through Biblioshiny.
VOSviewer has a fantastic visualization and is capable of loading and exporting information from many sources. SciMAT
is the tool with a powerful pre-processing and export capability. In views of the variability of features, the users need to
decide the desired analysis output and chose the option that better fits into their aims
A Review of Theory and Practice in Scientometrics
Scientometrics is the study of the quantitative aspects of the process of science as a communication system. It is centrally, but not only, concerned with the analysis of citations in the academic literature. In recent years it has come to play a major role in the measurement and evaluation of research performance. In this review we consider: the historical development of scientometrics, sources of citation data, citation metrics and the âlaws" of scientometrics, normalisation, journal impact factors and other journal metrics, visualising and mapping science, evaluation and policy, and future developments
A maximal clique based multiobjective evolutionary algorithm for overlapping community detection
Detecting community structure has become one im-portant technique for studying complex networks. Although many community detection algorithms have been proposed, most of them focus on separated communities, where each node can be-long to only one community. However, in many real-world net-works, communities are often overlapped with each other. De-veloping overlapping community detection algorithms thus be-comes necessary. Along this avenue, this paper proposes a maxi-mal clique based multiobjective evolutionary algorithm for over-lapping community detection. In this algorithm, a new represen-tation scheme based on the introduced maximal-clique graph is presented. Since the maximal-clique graph is defined by using a set of maximal cliques of original graph as nodes and two maximal cliques are allowed to share the same nodes of the original graph, overlap is an intrinsic property of the maximal-clique graph. Attributing to this property, the new representation scheme al-lows multiobjective evolutionary algorithms to handle the over-lapping community detection problem in a way similar to that of the separated community detection, such that the optimization problems are simplified. As a result, the proposed algorithm could detect overlapping community structure with higher partition accuracy and lower computational cost when compared with the existing ones. The experiments on both synthetic and real-world networks validate the effectiveness and efficiency of the proposed algorithm
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