A Bibliometric Study on Swedish Neutron Users for the Period 2006–2020

Barriga, Hanna M. G.; Cárdenas, M. A. María Del Pilar Iguíniz; Hall, Stephen A.; Hellsing, Maja S.; Karlsson, Mattias; Pavan, Adriano F.; Peng, Ru Lin; Strandqvist, Nanny; Wolff, Max

doi:10.1080/10448632.2021.1999147

Cited by 1 publication

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“…Through this novel approach we are able to showcase the evolution and distribution of the community and the main foci of research in a quantitative manner. The outcome of this approach is shown to be consistent with the findings reported by others [3][4][5].…”

Section: Introductionsupporting

confidence: 90%

Rendering the European Neutron Research Landscape

Velichko,

Abele,

Barlow

et al. 2024

Preprint

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Neutrons, owing to their unique properties, serve as indispensable probes for investigating the structure and dynamics of materials across various length scales. The scientific community utilizing neutron research infrastructures encompasses a diverse range of disciplines, making it challenging to quantify its scientific and societal impact. To address this challenge, we apply Natural Language Processing (NLP) and machine learning techniques to analyze the scientific output of the European neutron science community. Leveraging open-source software toolkits, our method allows for the quantitative assessment of community evolution and research focus. Our analysis reveals consistent growth in the neutron community despite a reduction in sources, underscoring the enduring significance of neutron methods in scientific research. Furthermore, an increase in unique authors and an even distribution of publications across diverse scientific topics highlight the community's interdisciplinary nature and collaborative spirit. Beyond neutron scattering, our methodology holds promise for a broad range of scientific communities reliant on Large Research Infrastructures (LRIs), offering opportunities for collaboration, optimization of experimental approaches, and informed decision-making by governmental and funding bodies.

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Section: Introductionsupporting

confidence: 90%