The top 100 papers

Noorden, Richard Van; Maher, Brendan A.; Nuzzo, Regina

doi:10.1038/514550a

Cited by 433 publications

(361 citation statements)

References 36 publications

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“…Obviously, a technical procedure of identifying and filtering out highly cited papers is not sufficient to claim that such papers are indeed breakthrough papers or that they are a proxy for some breakthrough research, even though some theory may suggest that extremely highly cited papers could be a sign of such breakthrough research (e.g., www.highlycited.com; van Noorden, Maher & Nuzzo, 2014). It is important to recognize that while we may postulate this in general, individual differences are to be expected.…”

Section: Multi-layered Case Study Evaluation Of the Three Citation-bamentioning

confidence: 99%

Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches

Schneider

Costas

2016

Asso for Info Science & Tech

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The article presents three advanced citation-based methods used to detect potential breakthrough papers among very highly cited papers. We approach the detection of such papers from three different perspectives in order to provide different typologies of breakthrough papers. In all three cases we use the classification of scientific publications developed at CWTS based on direct citation relationships. This classification establishes clusters of papers at three levels of aggregation.Papers are clustered based on their similar citation orientations and it is assumed that they are focused on similar research interests. We use the clustering as the context for detecting potential breakthrough papers. We utilize the Characteristics Scores and Scales (CSS) approach to partition citation distributions and implement a specific filtering algorithm to sort out potential highly-cited 'followers', papers not considered breakthroughs in themselves. After invoking thresholds and filtering, three methods are explored: A very exclusive one where only the highest cited paper in a micro-cluster is considered as a potential breakthrough paper (M1); as well as two conceptually different methods, one that detects potential breakthrough papers among the two percent highest cited papers according to CSS (M2a), and finally a more restrictive version where, in addition to the CSS two percent filter, knowledge diffusion is also taken in as an extra parameter (M2b). The advance citation-based methods are explored and evaluated using specifically validated publication sets linked to different Danish funding instruments including centres of excellence.

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Section: Multi-layered Case Study Evaluation Of the Three Citation-bamentioning

confidence: 99%

Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches

Schneider

Costas

2016

Asso for Info Science & Tech

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“…This success is also demonstrated by the fact that 12 out of the top-100 most cited papers in the entire scientific literature worldwide deal with quantum simulations using density-functional theory [1]. The availability of robust codes based on these accurate physical models, the sustained increase in high-performance computational (HPC) capacity, and the appearance of curated materials databases has paved the way for the emergence of the model of materials design and discovery via high-throughput computations (see Refs.…”

Section: Introductionmentioning

confidence: 92%

AiiDA: automated interactive infrastructure and database for computational science

Pizzi

Cepellotti

Sabatini

et al. 2016

Computational Materials Science

493

417

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Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.

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“…Nevertheless, scientific outputs and activities which linked to research investments are the most favorable criteria in assessing research impact, with a major share coming from published papers. Though millions of research papers are published yearly by journals and indexed in databases such as Web of Science and Scopus, a study in 2014 reported that over 43% of Web of Science papers have never received any citations [2]. On the other hand, the most cited work might not necessarily be the best piece produced by a researcher [3].…”

Section: Introductionmentioning

confidence: 99%

Publishing Research Support Documents in Open Access Platform to Improve Research Impact

Ebrahim¹,

Bong²

2017

Preprint

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Researchers, journals, and universities want to receive more citations for their scholarly publications. However, a paper citations depend on its quality, visibility and author's online profile. Research support documents (unpublished papers, white papers, project reports, datasets, software, posters, online resources and teaching materials) can be additional source for increasing the author's visibility. To enhance research visibility and impact, the full range of scholarly output should be available online on the open access platform. With open access platform, key research findings are made accessible immediately to the scientific community. Therefore, the publicly available research support documents may result in receiving more citations in addition to the published papers. In this paper we conducted a simplified three stages study on the benefits of research support documents publications on open access platform. We also elaborated on approaches of improving your research visibility and impact through these document.

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The top 100 papers

Cited by 433 publications

References 36 publications

Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches

Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches

AiiDA: automated interactive infrastructure and database for computational science

Publishing Research Support Documents in Open Access Platform to Improve Research Impact

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