Dataset Information

Ranking scientific publications: the effect of nonlinearity.

ABSTRACT: Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating resources from different nodes to further enhance the effect of important papers. The validation of our method is performed on the data of American Physical Society (APS) journals. The results indicate that the nonlinearity improves the performance of the PageRank algorithm in terms of ranking effectiveness, as well as robustness against malicious manipulations. Although the nonlinearity analysis is based on the PageRank algorithm, it can be easily extended to other iterative ranking algorithms and similar improvements are expected.

SUBMITTER: Yao L

PROVIDER: S-EPMC4200399 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Ranking scientific publications: the effect of nonlinearity.

Yao Liyang L Wei Tian T Zeng An A Fan Ying Y Di Zengru Z

Scientific reports 20141017

Ranking the significance of scientific publications is a long-standing challenge. The network-based analysis is a natural and common approach for evaluating the scientific credit of papers. Although the number of citations has been widely used as a metric to rank papers, recently some iterative processes such as the well-known PageRank algorithm have been applied to the citation networks to address this problem. In this paper, we introduce nonlinearity to the PageRank algorithm when aggregating ...[more]

PMID: 25322852

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Project description:ObjectivesRheumatology-related diseases remain a significant burden worldwide. However, little is known about the comparative status of rheumatology research between Mainland China (MC) and the world's leading countries. The aim of this study is to compare the quantity and quality of research output in the field of rheumatology that were written by researchers from MC, the USA, the UK, the Netherlands and France.MethodsBetween 2007 and 2017, all articles published in 30 rheumatology journals were identified via Science Citation Index Expanded database. The number of total and annual articles, article types (randomized controlled trials (RCTs), reviews, case reports, clinical trials and meta-analysis), impact factor (IF), citations, h-index and articles in the high-impact journals were collected for quantity and quality comparisons. The correlation of socioeconomic factors and annual publications was also analyzed.ResultsFrom 2007 to 2017, there were 53,439 articles published in rheumatology journals, of which researchers from the USA published 13,391 articles, followed by the UK, the Netherlands, France and MC with 6,179, 4,310, 4,066 and 2,898 articles, respectively. Publications from MC represented the ninth, but the number is growing rapidly. For total and average citations, MC still lags behind the other four countries in the study. Similar trends were observed in average IF, h-index and articles in the high-impact journals. In terms of article types, the USA occupies the dominant place, except for meta-analysis. The annual numbers of articles from MC and the USA were positively correlated with gross domestic product (p < 0.05).ConclusionsThe USA has played predominant role in rheumatology research for the last 11 years. The annual number of published articles from MC has increased notably from 2007 to 2017. Although MC has made progress in the number of published articles over the past decade, it still lags far behind the highly developed countries in most bibliometric indicators. Thus, the general quality of publications from MC needs further improvement.

Project description:In addition to its primary regulatory role, the Office of Hematology and Oncology Products at the U.S. Food and Drug Administration (FDA) is engaged in many forms of scientific authorship. During the period of 2010 to 2018, FDA oncology staff contributed to 356 publications in the scientific literature. Here, we collaborated with analysts in the Office of Program Planning, Analysis, and Evaluation at the National Institute of General Medical Sciences, National Institutes of Health (NIH), to present a series of analyses aimed at quantifying the characteristics and potential impact of these contributions, as well as characterizing the areas of work addressed. We found that FDA oncology papers are enriched for high-impact publications and have about two times the number of citations as an average NIH-funded paper. Further impact of the publications was measured based on the presence of 65 publications that were cited by guidelines and 12 publications cited by publicly listed clinical trials. The results seen here are promising in determining the impact of FDA oncology publication work but prompt further investigation into longer-term impacts, such as the influence of this work on other regulatory activities at FDA. IMPLICATIONS FOR PRACTICE: This article describes the first comprehensive study of scientific publications produced by U.S. Food and Drug Administration (FDA) oncology staff. The analysis illustrates that staff are highly engaged in publishing in the scientific literature in addition to completing regulatory review work. Publications are generally in clinical medicine, consistent with the large number of medical oncologists working at the Office of Hematology and Oncology Products (OHOP). OHOP publications generally focus either on communicating important regulatory work (approval summaries) or highlighting regulatory science issues to encourage dialogue with the scientific community (commentaries, reviews, and expert working papers). The analysis also suggests that several FDA oncology publications may influence clinical guidelines, but further work is needed to evaluate impact.

Dataset Information

Ranking scientific publications: the effect of nonlinearity.

Publications

Ranking scientific publications: the effect of nonlinearity.

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