A research intelligence approach to assess the research impact of the Dutch university medical centres.

BACKGROUND: The way in which research impact is evaluated and assessed has long been under debate. In recent years the focus is moving away from the use of numerical indicators, towards an emphasis on narratives. The Dutch university medical centres (UMCs) have a long-standing tradition of using bibliometric indicators. Because of the declining interest in indicators alone, this study was designed to repurpose bibliometrics to answer specific strategic questions. In this article we discuss the strategic and policy-based questions, the methodology we used in uncovering relevant information and conclusions we draw from the analyses we performed. The aim of this article is to inform a broader audience about the potential applications of bibliometric information to support a new form of research intelligence. METHODS: In this study we used a curated set of publications from the UMCs. We performed different bibliometric analyses and used bibliometric visualization tools to shed light on research focus, open science practices, collaboration, societal impact and scientific impact. RESULTS: The analyses allowed us to visualize and contextualize the research focus of the UMCs as a whole, but also to show specific focus areas of each UMC. The UMCs are active in the full spectrum of biomedical research, and at the same time are very complementary to each other. Furthermore, we were able to show the development of open access of UMC publications over time, to support the national mission. Visualizing collaboration is a powerful way of showing both the international orientation and the regional and national engine function of UMCs in research. We were able to assess societal impact by looking at the different channels in which publications find their way to societally relevant sources such as news media, policy documents and guidelines. Finally, we assessed scientific impact and put this into an international perspective. CONCLUSIONS: Research intelligence is able to transform bibliometric information by interpretation and annotation into highly relevant insights that can be used for several different strategic purposes and for research impact assessment in general.

From first report to clinical trials: a bibliometric overview and visualization of the development of Angelman syndrome research.

Angelman syndrome is a rare neurodevelopmental disorder caused by mutations affecting the chromosomal 15q11-13 region, either by contiguous gene deletions, imprinting defects, uniparental disomy, or mutations in the UBE3A gene itself. Phenotypic abnormalities are driven primarily, but not exclusively (especially in 15q11-13 deletion cases) by loss of expression of the maternally inherited UBE3A gene expression. The disorder was first described in 1965 by the English pediatrician Harry Angelman. Since that first description of three children with Angelman syndrome, there has been extensive research into the genetic, molecular and phenotypic aspects of the disorder. In the last decade, this has resulted in over 100 publications per year. Collectively, this research has led the field to a pivotal point in which restoring UBE3A function by genetic therapies is currently explored in several clinical trials. In this study, we employed a bibliometric approach to review and visualize the development of Angelman syndrome research over the last 50 years. We look into different parameters shaping the progress of the Angelman syndrome research field, including source of funding, publishing journals and international collaborations between research groups. Using a network approach, we map the focus of the research field and how that shifted over time. This overview helps understand the shift of research focus in the field and can provide a comprehensive handbook of Angelman syndrome research development.

A bibliometric overview of craniosynostosis research development.

This article reviews the development of research in the field of craniosynostosis from a bibliometric standpoint. Craniosynostosis is a malformation occurring during the early development of the skull, when one or more of the sutures close too early, causing problems with normal brain and skull growth. Research in this field has developed from early clinical case descriptions, to genetic discoveries responsible for the occurring malformations and onwards to developing sophisticated surgical treatment. In this article we describe these developments, zoom in on publication trends and characteristics and visualize developing networks and topic shifts in this research field.

Three-Dimensional Stereophotogrammetry in the Evaluation of Craniosynostosis: Current and Potential Use Cases.

ABSTRACT: Three-dimensional (3D) stereophotogrammetry is a novel imaging technique that has gained popularity in the medical field as a reliable, non-invasive, and radiation-free imaging modality. It uses optical sensors to acquire multiple 2D images from different angles which are reconstructed into a 3D digital model of the subject's surface. The technique proved to be especially useful in craniofacial applications, where it serves as a tool to overcome the limitations imposed by conventional imaging modalities and subjective evaluation methods. The capability to acquire high-dimensional data in a quick and safe manner and archive them for retrospective longitudinal analyses, provides the field with a methodology to increase the understanding of the morphological development of the cranium, its growth patterns and the effect of different treatments over time.This review describes the role of 3D stereophotogrammetry in the evaluation of craniosynostosis, including reliability studies, current and potential clinical use cases, and practical challenges. Finally, developments within the research field are analyzed by means of bibliometric networks, depicting prominent research topics, authors, and institutions, to stimulate new ideas and collaborations in the field of craniofacial 3D stereophotogrammetry.We anticipate that utilization of this modality's full potential requires a global effort in terms of collaborations, data sharing, standardization, and harmonization. Such developments can facilitate larger studies and novel deep learning methods that can aid in reaching an objective consensus regarding the most effective treatments for patients with craniosynostosis and other craniofacial anomalies, and to increase our understanding of these complex dysmorphologies and associated phenotypes.