Large-scale analysis of micro-level citation patterns reveals nuanced selection criteria

Paradigm Shifts in Vascular Surgery: Analysis of the Top 100 Innovative and Disruptive Academic Publications

BACKGROUND

Disruption score (DS) is a novel bibliometric created to identify research that shifts paradigms, which may be overlooked by citation count (CC). We analyzed the most disruptive, compared to the most cited, literature in vascular surgery, and hypothesized that DS and CC would not correlate.

METHODS

A PubMed search identified vascular surgery publications from 1954 to 2014. The publications were linked to the iCite NIH tool and DS algorithm to identify the top 100 studies by CC and DS, respectively. The publications were reviewed for study focus, design, and contribution, and subsequently compared.

RESULTS

A total of 56,640 publications were identified. The top 100 DS papers were frequently published in J Vasc Sur (43%) and Eur J Vasc Endovasc Surg (13%). The top 100 CC papers were frequently published in N Engl J Med (32%) and J Vasc Sur (20%). The most cited article is the fifth most disruptive; the most disruptive article is not in the top 100 cited papers. The DS papers had a higher mean DS than the CC papers (.17 vs .0001, P < .0001). The CC papers had a higher mean CC than the DS papers (866 vs 188, P < .0001). DS and CC are weakly correlated metrics (r = .22, P = .03).

DISCUSSION

DS was weakly correlated with CC and captured a unique subset of literature that created paradigm shifts in vascular surgery. DS should be utilized as an adjunct to CC to avoid overlooking impactful research and influential researchers, and to measure true academic productivity.

Top 100 cited articles in the thromboangiitis obliterans: a bibliometric analysis and visualized study

OBJECTIVE

Thromboangiitis obliterans (TAO) is one of the most common types of peripheral arterial disease (PAD). This study aimed to explore the characteristics of the top 100 most cited articles in the TAO.

METHODS

A bibliometric analysis based on the Web of Science (WOS) database was performed. Literature was retrieved and ranked by the citations. Listed below are the top 100 citations, including original articles, reviews, full-length proceeding papers, and case reports that were included for analysis. The type of literature, research areas, and languages were recorded. The trends of citations including the total citations, an analysis of publication and citation numbers were conducted each year. We analyzed citations from highly cited countries, authors, institutions, and journals. Research hotspots were gathered by a visualized analysis of author keywords.

RESULTS

Most of the highly cited literature was original articles. A rising trend was observed in the number of citations per year. The peaks in the number of highly cited articles appeared in the year 1998 and 2006. The majority of the articles focused on the cardiovascular system and surgery. Journal of Vascular Surgery published most of the highly cited articles. The USA and Japan contributed nearly half the number of highly cited articles. Mayo Clinic and Nagoya University were highly cited institutions. Shionoya S and Olin JW were both the author with the largest number of citations and the most highly cited author in the reference. Articles that were highly cited most often addressed the following topics: "vasculitis", "autoimmune disease", and "critical limb ischemia". Keywords that were mostly used in recent years were "stem cell therapy", "progenitor therapy", and "immunoadsorption". The detection of bursts of author keywords showed the following: "permeability", "differentiation", and "critical limb ischemia" are recent keywords that have burst.

CONCLUSIONS

In this study, the highly cited contributors in the field of TAO research were identified. Most cited articles in the top 100 focused on the cardiovascular system and surgery. Treatment and pathophysiology including stem cell therapy, progenitor therapy, genetics, autoimmunity, and inflammation are the hotspots of TAO.

Data, measurement and empirical methods in the science of science

The advent of large-scale datasets that trace the workings of science has encouraged researchers from many different disciplinary backgrounds to turn scientific methods into science itself, cultivating a rapidly expanding 'science of science'. This Review considers this growing, multidisciplinary literature through the lens of data, measurement and empirical methods. We discuss the purposes, strengths and limitations of major empirical approaches, seeking to increase understanding of the field's diverse methodologies and expand researchers' toolkits. Overall, new empirical developments provide enormous capacity to test traditional beliefs and conceptual frameworks about science, discover factors associated with scientific productivity, predict scientific outcomes and design policies that facilitate scientific progress.

OpCitance: Citation contexts identified from the PubMed Central open access articles

OpCitance contains all the sentences from 2 million PubMed Central open-access (PMCOA) articles, with 137 million inline citations annotated (i.e., the "citation contexts"). Parsing out the references and citation contexts from the PMCOA XML files was non-trivial due to the diversity of referencing style. Only 0.5% citation contexts remain unidentified due to technical or human issues, e.g., references unmentioned by the authors in the text or improper XML nesting, which is more common among older articles (pre-2000). PubMed IDs (PMIDs) linked to inline citations in the XML files compared to citations harvested using the NCBI E-Utilities differed for 70.96% of the articles. Using an in-house citation matcher, called Patci, 6.84% of the referenced PMIDs were supplemented and corrected. OpCitance includes fewer total number of articles than the Semantic Scholar Open Research Corpus, but OpCitance has 160 thousand unique articles, a higher inline citation identification rate, and a more accurate reference mapping to PMIDs. We hope that OpCitance will facilitate citation context studies in particular and benefit text-mining research more broadly.

Automated meta-analysis of the event-related potential (ERP) literature

Event-related potentials (ERPs) are a common approach for investigating the neural basis of cognition and disease. There exists a vast and growing literature of ERP-related articles, the scale of which motivates the need for efficient and systematic meta-analytic approaches for characterizing this research. Here we present an automated text-mining approach as a form of meta-analysis to examine the relationships between ERP terms, cognitive domains and clinical disorders. We curated dictionaries of terms, collected articles of interest, and measured co-occurrence probabilities in published articles between ERP components and cognitive and disorder terms. Collectively, this literature dataset allows for creating data-driven profiles for each ERP, examining key associations of each component, and comparing the similarity across components, ultimately allowing for characterizing patterns and associations between topics and components. Additionally, by examining large literature collections, novel analyses can be done, such as examining how ERPs of different latencies relate to different cognitive associations. This openly available dataset and project can be used both as a pedagogical tool, and as a method of inquiry into the previously hidden structure of the existing literature. This project also motivates the need for consistency in naming, and for developing a clear ontology of electrophysiological components.

The characteristics of early-stage research into human genes are substantially different from subsequent research

Throughout the last 2 decades, several scholars observed that present day research into human genes rarely turns toward genes that had not already been extensively investigated in the past. Guided by hypotheses derived from studies of science and innovation, we present here a literature-wide data-driven meta-analysis to identify the specific scientific and organizational contexts that coincided with early-stage research into human genes throughout the past half century. We demonstrate that early-stage research into human genes differs in team size, citation impact, funding mechanisms, and publication outlet, but that generalized insights derived from studies of science and innovation only partially apply to early-stage research into human genes. Further, we demonstrate that, presently, genome biology accounts for most of the initial early-stage research, while subsequent early-stage research can engage other life sciences fields. We therefore anticipate that the specificity of our findings will enable scientists and policymakers to better promote early-stage research into human genes and increase overall innovation within the life sciences.