An updated h-index measures both the primary and total scientific output of a researcher

Bucur, Octavian; Almasan, Alexandru; Zubarev, Roman A.; Friedman, Mark; Nicolson, Garth L.; Sumazin, Pavel; Leabu, Mircea; Nikolajczyk, Barbara S.; Avram, Dorina; Kunej, Tanja; Calin, George A.; Godwin, Andrew K.; Adami, Hans‐Olov; Zaphiropoulos, Peter G.; Richardson, Des R.; Schmitt‐Ulms, Gerold; Westerblad, Håkan; Keniry, Megan; Grau, Georges E.; Carbonetto, Salvatore; Stan, Radu V; Popa‐Wagner, Aurel; Kasumov, Takhar; Baron, Beverly W.; Galardy, Paul J.; Yang, Feng; Data, Dipak; Fadare, Oluwole; Yeo, Kt Jerry; Gabreanu, G; Andrei, Ștefan; Soare, Georgiana R; Nelson, Mark A.; Liehn, Elisa A.

doi:10.15190/d.2015.42

Cited by 12 publications

(8 citation statements)

References 6 publications

(16 reference statements)

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“…Hirsh (2010) also proposes a more restrictive version called -index, normalized to domain or age. Other variants are designed with application-specific goals (Bucur et al 2015). All-in-all, h -index based measures are measures to analyze the productivity of researchers, but do not allow for the in-depth analysis of production, in opposition to main path analysis approaches.…”

Section: Related Workmentioning

confidence: 99%

Multiplex flows in citation networks

2017

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Knowledge is created and transmitted through generations, and innovation is often seen as a process generated from collective intelligence. There is rising interest in studying how innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherits. A citation network can be seen as a perfect example of one generative process leading to innovation. However, the impact and influence of scientific publication are always difficult to capture and measure. We offer a new take on investigating how the knowledge circulates and is transmitted, inspired by the notion of “stream of knowledge”. We propose to look at this question under the lens of flows in directed acyclic graphs (DAGs). In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. From this point on, we can take a finer look at the diffusion of knowledge through the lens of a multiplex network. In this network, each citation of a specific work constitutes one layer of interaction. Within our framework, we design three measures of multiplex flows in DAGs, namely the aggregated, sum and selective flow, to better understand how citations are influenced. We conduct our experiments with the arXiv HEP-Th dataset, and find insights through the visualization of these multiplex networks.

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Section: Related Workmentioning

confidence: 99%

Multiplex flows in citation networks

2017

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“…14 Practice for child physiotherapy, Hamburg, Germany. 15 London Children's Hospital, London, UK, 16 NIHR Great Ormond Street Hospital Biomedical Research Centre, London, UK. 17 Royal Children's Hospital, Florey and Murdoch Children's Research Institutes, Melbourne, Australia…”

Section: Declarationsmentioning

confidence: 99%

Guidelines on the Diagnosis, Clinical Assessments, Treatment and Management for CLN2 Disease Patients

Mole

Schulz

Badoe

et al. 2020

Preprint

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Background: CLN2 disease (Neuronal Ceroid Lipofuscinosis Type 2), or Late-Infantile Neuronal Ceroid Lipofuscinosis (LINCL), is an ultra-rare, neurodegenerative lysosomal storage disease, caused by an enzyme deficiency of tripeptidyl peptidase 1 (TPP1). Lack of disease awareness and the non-specificity of presenting symptoms often leads to delayed diagnosis. These guidelines provide robust evidence-based, expert-agreed recommendations on the risks/benefits of disease-modifying treatments and the medical interventions used to manage this condition. Methods: An expert mapping tool process was developed ranking multidisciplinary professionals, with knowledge of CLN2 disease, diagnostic or management experience of CLN2 disease, or family support professionals. Individuals were sequentially approached to identify two chairs, ensuring that the process was transparent and unbiased. A systematic literature review of published evidence using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidance was independently and simultaneously conducted to develop key statements based upon the strength of the publications. Clinical care statements formed the basis of an international modified Delphi consensus determination process using the virtual meeting (Within3) online platform which requested experts to agree or disagree with any changes. Statements reaching the consensus mark became the guiding statements within this manuscript, which were subsequently assessed against the Appraisal of Guidelines for Research and Evaluation (AGREEII) criteria.Results: Twenty-one international experts from 7 different specialities, including a patient advocate, were identified. Fifty-three guideline statements were developed covering 13 domains: General Description and Statements, Diagnostics, Clinical Recommendations and Management, Assessments, Interventions and Treatment, Additional Care Considerations, Social Care Considerations, Pain Management, Epilepsy / Seizures, Nutritional Care Interventions, Respiratory Health, Sleep and Rest, and End of Life Care. Consensus was reached after a single round of voting, with one exception which was revised, and agreed by 100% of the SC and achieved 80% consensus in the second voting round. The overall AGREE II assessment score obtained for the development of the guidelines was 5.7 (where 1 represents the lowest quality, and 7 represents the highest quality). Conclusion: This program provides robust evidence- and consensus-driven guidelines that can be used by all healthcare professionals involved in the management of patients with CLN2 disease. This addresses the clinical need to complement other information available.

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“…Hirsch [20] proposes a more restrictive version calledh-index, normalized to domain or age. Other variants could be mentioned (such as Bucur et al [6]), but each is designed with specific goals. All-in-all, h-index based measures are measures to analyze the productivity of researchers, but do not allow for the in-depth analysis of production, in contrary to main path analysis approaches.…”

Section: Related Workmentioning

confidence: 99%

Flows of Knowledge in Citation Networks

Renoust

Claver

Baffier

2016

Complex Networks &Amp; Their Applications V

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Knowledge is created and transmitted through generation. Innovation is often seen as a generative process from collective intelligence, but how does innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherit? A citation network can be seen as a perfect example of a generative process leading to innovation. Inspired by the notion of "stream of knowledge", we propose to look at the question of production of knowledge under the lens of DAGs. Although many works look for the evaluation of publications, we propose to look for production of knowledge within a framework for analyzing DAGs. In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. We propose an efficient dynamic algorithm for integration with modern graph databases, conducting our experiment with the Arxiv HEP-TH dataset. Our results validate the use of DAG flows for citation flows and show evidence of the relevance of the h-index.

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An updated h-index measures both the primary and total scientific output of a researcher

Cited by 12 publications

References 6 publications

Multiplex flows in citation networks

Multiplex flows in citation networks

Guidelines on the Diagnosis, Clinical Assessments, Treatment and Management for CLN2 Disease Patients

Flows of Knowledge in Citation Networks

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