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.     

How citation boosts promote scientific paradigm shifts and nobel prizes

Nobel Prizes are commonly seen to be among the most prestigious achievements of our times. Based on mining several million citations, we quantitatively analyze the processes driving paradigm shifts in science. We find that groundbreaking discoveries of Nobel Prize Laureates and other famous scientists are not only acknowledged by many citations of their landmark papers. Surprisingly, they also boost the citation rates of their previous publications. Given that innovations must outcompete the rich-gets-richer effect for scientific citations, it turns out that they can make their way only through citation cascades. A quantitative analysis reveals how and why they happen. Science appears to behave like a self-organized critical system, in which citation cascades of all sizes occur, from continuous scientific progress all the way up to scientific revolutions, which change the way we see our world. Measuring the "boosting effect" of landmark papers, our analysis reveals how new ideas and new players can make their way and finally triumph in a world dominated by established paradigms. The underlying "boost factor" is also useful to discover scientific breakthroughs and talents much earlier than through classical citation analysis, which by now has become a widespread method to measure scientific excellence, influencing scientific careers and the distribution of research funds. Our findings reveal patterns of collective social behavior, which are also interesting from an attention economics perspective. Understanding the origin of scientific authority may therefore ultimately help to explain how social influence comes about and why the value of goods depends so strongly on the attention they attract.     

An increasing citation black hole in ecology and evolution

Citations published in online supplementary material (OSM) are invisible to search engines used to calculate citation counts, potentially negatively impacting popular performance indices and journal rankings that rely on citation counts for quantification. To quantify the number of citations that are “lost” in OSM, we conducted a systematic survey of supplementary citation practices in four high‐ranking, society‐run journals from two geographical locations (Europe and North America). In 2012, 6% of all citations were only included in the OSM and were therefore not included in citation counts. We found a significant increase in the number of references invisible to citation counting services over the last two decades. A solution to this problem is urgently required and could include journal indexing of citations in OSM or the inclusion of all references in the main text.     

Flawed citation practices facilitate the unsubstantiated perception of a global trend toward increased jellyfish blooms

Speculation over a global rise in jellyfish populations has become widespread in the scientific literature, but until recently the purported ‘global increase’ had not been tested. Here we present a citation analysis of peer‐reviewed literature to track the evolution of the current perception of increases in jellyfish and identify key papers involved in its establishment. Trend statements and citation threads were reviewed and arranged in a citation network. Trend statements were assessed according their degree of affirmation and spatial scale, and the appropriateness of the citations used to support statements was assessed. Analyses showed that 48.9% of publications misinterpreted the conclusions of cited sources, with a bias towards claiming jellyfish populations are increasing, with a single review having the most influence on the network. Collectively, these disparities resulted in a network based on unsubstantiated statements and citation threads. As a community, we must ensure our statements about scientific findings in general are accurately substantiated and carefully communicated such that incorrect perceptions, as in the case of jellyfish blooms, do not develop in the absence of rigorous testing.     

A simple index for the high-citation tail of citation distribution to quantify research performance in countries and institutions

Background

Conventional scientometric predictors of research performance such as the number of papers, citations, and papers in the top 1% of highly cited papers cannot be validated in terms of the number of Nobel Prize achievements across countries and institutions. The purpose of this paper is to find a bibliometric indicator that correlates with the number of Nobel Prize achievements.

Methodology/Principal Findings

This study assumes that the high-citation tail of citation distribution holds most of the information about high scientific performance. Here I propose the x-index, which is calculated from the number of national articles in the top 1% and 0.1% of highly cited papers and has a subtractive term to discount highly cited papers that are not scientific breakthroughs. The x-index, the number of Nobel Prize achievements, and the number of national articles in Nature or Science are highly correlated. The high correlations among these independent parameters demonstrate that they are good measures of high scientific performance because scientific excellence is their only common characteristic. However, the x-index has superior features as compared to the other two parameters. Nobel Prize achievements are low frequency events and their number is an imprecise indicator, which in addition is zero in most institutions; the evaluation of research making use of the number of publications in prestigious journals is not advised.

Conclusion

The x-index is a simple and precise indicator for high research performance.     

Terrestrial ecologists ignore aquatic literature: Asymmetry in citation breadth in ecological publications and implications for generality and progress in ecology

The search for generality in ecology should include assessing the influence of studies done in one system on those done in other systems. Assuming generality is reflected in citation patterns, we analyzed frequencies of terrestrial, marine, and freshwater citations in papers categorized as terrestrial, marine and freshwater in high-impact “general” ecological journals. Citation frequencies were strikingly asymmetric. Aquatic researchers cited terrestrial papers ~ 10 times more often than the reverse, implying uneven cross-fertilization of information between aquatic and terrestrial ecologists. Comparisons between citation frequencies in the early 1980s and the early 2000s for two of the seven journals yielded similar results. Summing across all journals, 60% of all research papers (n = 5824) published in these journals in 2002–2006 were terrestrial vs. 9% freshwater and 8% marine. Since total numbers of terrestrial and aquatic ecologists are more similar than these proportions suggest, the representation of publications by habitat in “general” ecological journals appears disproportional and unrepresentative of the ecological science community at large. Such asymmetries are a concern because (1) aquatic and terrestrial systems can be tightly integrated, (2) pressure for across-system understanding to meet the challenge of climate change is increasing, (3) citation asymmetry implies barriers to among-system flow of understanding, thus (4) impeding scientific and societal progress. Changing this imbalance likely depends on a bottom-up approach originating from the ecological community, through pressure on societies, journals, editors and reviewers.     

Long-Distance Interdisciplinarity Leads to Higher Scientific Impact

Scholarly collaborations across disparate scientific disciplines are challenging. Collaborators are likely to have their offices in another building, attend different conferences, and publish in other venues; they might speak a different scientific language and value an alien scientific culture. This paper presents a detailed analysis of success and failure of interdisciplinary papers—as manifested in the citations they receive. For 9.2 million interdisciplinary research papers published between 2000 and 2012 we show that the majority (69.9%) of co-cited interdisciplinary pairs are “win-win” relationships, i.e., papers that cite them have higher citation impact and there are as few as 3.3% “lose-lose” relationships. Papers citing references from subdisciplines positioned far apart (in the conceptual space of the UCSD map of science) attract the highest relative citation counts. The findings support the assumption that interdisciplinary research is more successful and leads to results greater than the sum of its disciplinary parts.     

Is There a Relationship between Research Sponsorship and Publication Impact? An Analysis of Funding Acknowledgments in Nanotechnology Papers

This study analyzes funding acknowledgments in scientific papers to investigate relationships between research sponsorship and publication impacts. We identify acknowledgments to research sponsors for nanotechnology papers published in the Web of Science during a one-year sample period. We examine the citations accrued by these papers and the journal impact factors of their publication titles. The results show that publications from grant sponsored research exhibit higher impacts in terms of both journal ranking and citation counts than research that is not grant sponsored. We discuss the method and models used, and the insights provided by this approach as well as it limitations.     

What determines the citation frequency of ecological papers?

Citation frequencies of scientific articles are increasingly used for academic evaluation in various disciplines, including ecology. However, the factors affecting citation rates have not been extensively studied. Here, we examine the association between the citation frequency of ecological articles and various characteristics of journals, articles and authors. Our analysis shows that the annual citation rates of ecological papers are affected by the direction of the study outcome with respect to the hypothesis tested (supportive versus unsupportive evidence), by article length, by the number of authors, and by their country and university of affiliation. These results cast doubt on the validity of using citation counts as an objective and unbiased tool for academic evaluation in ecology.     

Three options for citation tracking: Google Scholar,Scopus and Web of Science

Background

Researchers turn to citation tracking to find the most influential articles for a particular topic and to see how often their own published papers are cited. For years researchers looking for this type of information had only one resource to consult: the Web of Science from Thomson Scientific. In 2004 two competitors emerged – Scopus from Elsevier and Google Scholar from Google. The research reported here uses citation analysis in an observational study examining these three databases; comparing citation counts for articles from two disciplines (oncology and condensed matter physics) and two years (1993 and 2003) to test the hypothesis that the different scholarly publication coverage provided by the three search tools will lead to different citation counts from each.

Methods

Eleven journal titles with varying impact factors were selected from each discipline (oncology and condensed matter physics) using the Journal Citation Reports (JCR). All articles published in the selected titles were retrieved for the years 1993 and 2003, and a stratified random sample of articles was chosen, resulting in four sets of articles. During the week of November 7–12, 2005, the citation counts for each research article were extracted from the three sources. The actual citing references for a subset of the articles published in 2003 were also gathered from each of the three sources.

Results

For oncology 1993 Web of Science returned the highest average number of citations, 45.3. Scopus returned the highest average number of citations (8.9) for oncology 2003. Web of Science returned the highest number of citations for condensed matter physics 1993 and 2003 (22.5 and 3.9 respectively). The data showed a significant difference in the mean citation rates between all pairs of resources except between Google Scholar and Scopus for condensed matter physics 2003. For articles published in 2003 Google Scholar returned the largest amount of unique citing material for oncology and Web of Science returned the most for condensed matter physics.

Conclusion

This study did not identify any one of these three resources as the answer to all citation tracking needs. Scopus showed strength in providing citing literature for current (2003) oncology articles, while Web of Science produced more citing material for 2003 and 1993 condensed matter physics, and 1993 oncology articles. All three tools returned some unique material. Our data indicate that the question of which tool provides the most complete set of citing literature may depend on the subject and publication year of a given article.

     

Questionable papers in citation databases as an issue for literature review

In recent years, the academic world has been faced with much academic misconduct. Examples involve plagiarizing papers, manipulating data, and launching predatory or hijacked journals. The literature exposing these activities is growing exponentially, and so is the presentation of criteria or guidelines for counteracting the problem. Most of the research is focused on predatory or hijacked journal detection and providing suitable warnings. Overlooked in all this is the fact that papers published in these journals are questionable, but nevertheless show up in standard citation databases. We need some way to flag them so future researchers will be aware of their questionable nature and prevent their use in literature review.     

《环境昆虫学报》2001-2010年载文被引统计分析

利用"中国学术期刊文献评价统计分析系统",对《环境昆虫学报》载文在2001～2010年所刊载论文的被引用情况进行统计分析。结果表明,在2001～2010年间《环境昆虫学报》10年共载文504篇,有343篇论文,被引率为68.06%;被引用1904次,篇均被引次数为5.55,单篇被引最高次数为40次;被引用5次以下的论文占63.57%;引证期刊262种,引用3次以下的期刊占74.81%。同时,对被引论文作者进行了系统分析,特别阐述了高被引论文的详细信息,从而提出从统计得出的高被引频次论文作者及研究热点着手,加强组稿,突出栏目特色,提高刊物对高质量论文的吸纳能力。     

Modeling the Citation Network by Network Cosmology

Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.     

Characterizing Social Media Metrics of Scholarly Papers: The Effect of Document Properties and Collaboration Patterns

A number of new metrics based on social media platforms—grouped under the term “altmetrics”—have recently been introduced as potential indicators of research impact. Despite their current popularity, there is a lack of information regarding the determinants of these metrics. Using publication and citation data from 1.3 million papers published in 2012 and covered in Thomson Reuters’ Web of Science as well as social media counts from Altmetric.com, this paper analyses the main patterns of five social media metrics as a function of document characteristics (i.e., discipline, document type, title length, number of pages and references) and collaborative practices and compares them to patterns known for citations. Results show that the presence of papers on social media is low, with 21.5% of papers receiving at least one tweet, 4.7% being shared on Facebook, 1.9% mentioned on blogs, 0.8% found on Google+ and 0.7% discussed in mainstream media. By contrast, 66.8% of papers have received at least one citation. Our findings show that both citations and social media metrics increase with the extent of collaboration and the length of the references list. On the other hand, while editorials and news items are seldom cited, it is these types of document that are the most popular on Twitter. Similarly, while longer papers typically attract more citations, an opposite trend is seen on social media platforms. Finally, contrary to what is observed for citations, it is papers in the Social Sciences and humanities that are the most often found on social media platforms. On the whole, these findings suggest that factors driving social media and citations are different. Therefore, social media metrics cannot actually be seen as alternatives to citations; at most, they may function as complements to other type of indicators.     

SP-index: The measure of the scientific production of researchers

Ability to assess how solidly one is participating in their research arena is a metric that is of interest to many persons in academia. Such assessment is not easily defined, and differences exist in terms of which metric is the most accurate. In reality, no single production metric exists that is easy to determine and acceptable by the entire scientific community. Here we propose the SP-index to quantify the scientific production of researchers, representing the product of the annual citation number by the accumulated impact factors of the journals whereby the papers appeared, divided by the annual number of published papers. This article discusses such a productivity measure and lends support for the development of unified citation metrics for use by all participating in science research or teaching.     

Multiple Citation Indicators and Their Composite across Scientific Disciplines

Many fields face an increasing prevalence of multi-authorship, and this poses challenges in assessing citation metrics. Here, we explore multiple citation indicators that address total impact (number of citations, Hirsch H index [H]), co-authorship adjustment (Schreiber Hm index [Hm]), and author order (total citations to papers as single; single or first; or single, first, or last author). We demonstrate the correlation patterns between these indicators across 84,116 scientists (those among the top 30,000 for impact in a single year [2013] in at least one of these indicators) and separately across 12 scientific fields. Correlation patterns vary across these 12 fields. In physics, total citations are highly negatively correlated with indicators of co-authorship adjustment and of author order, while in other sciences the negative correlation is seen only for total citation impact and citations to papers as single author. We propose a composite score that sums standardized values of these six log-transformed indicators. Of the 1,000 top-ranked scientists with the composite score, only 322 are in the top 1,000 based on total citations. Many Nobel laureates and other extremely influential scientists rank among the top-1,000 with the composite indicator, but would rank much lower based on total citations. Conversely, many of the top 1,000 authors on total citations have had no single/first/last-authored cited paper. More Nobel laureates of 2011–2015 are among the top authors when authors are ranked by the composite score than by total citations, H index, or Hm index; 40/47 of these laureates are among the top 30,000 by at least one of the six indicators. We also explore the sensitivity of indicators to self-citation and alphabetic ordering of authors in papers across different scientific fields. Multiple indicators and their composite may give a more comprehensive picture of impact, although no citation indicator, single or composite, can be expected to select all the best scientists.     

Genealogical Trees of Scientific Papers

Many results have been obtained when studying scientific papers citations databases in a network perspective. Articles can be ranked according to their current in-degree and their future popularity or citation counts can even be predicted. The dynamical properties of such networks and the observation of the time evolution of their nodes started more recently. This work adopts an evolutionary perspective and proposes an original algorithm for the construction of genealogical trees of scientific papers on the basis of their citation count evolution in time. The fitness of a paper now amounts to its in-degree growing trend and a “dying” paper will suddenly see this trend declining in time. It will give birth and be taken over by some of its most prevalent citing “offspring”. Practically, this might be used to trace the successive published milestones of a research field.     

The Citation Wake of Publications Detects Nobel Laureates' Papers

For several decades, a leading paradigm of how to quantitatively assess scientific research has been the analysis of the aggregated citation information in a set of scientific publications. Although the representation of this information as a citation network has already been coined in the 1960s, it needed the systematic indexing of scientific literature to allow for impact metrics that actually made use of this network as a whole, improving on the then prevailing metrics that were almost exclusively based on the number of direct citations. However, besides focusing on the assignment of credit, the paper citation network can also be studied in terms of the proliferation of scientific ideas. Here we introduce a simple measure based on the shortest-paths in the paper''s in-component or, simply speaking, on the shape and size of the wake of a paper within the citation network. Applied to a citation network containing Physical Review publications from more than a century, our approach is able to detect seminal articles which have introduced concepts of obvious importance to the further development of physics. We observe a large fraction of papers co-authored by Nobel Prize laureates in physics among the top-ranked publications.     

Predicting Scholars' Scientific Impact

We tested the underlying assumption that citation counts are reliable predictors of future success, analyzing complete citation data on the careers of scientists. Our results show that i) among all citation indicators, the annual citations at the time of prediction is the best predictor of future citations, ii) future citations of a scientist''s published papers can be predicted accurately ( for a 1-year prediction, ) but iii) future citations of future work are hardly predictable.