ArticleScientometrics2021
Characterizing research leadership on geographically weighted collaboration network.
Article in Scientometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI applied to Saudi Arabia higher education: systematic literature review-SLR with PRISMA and VOSviewer.Frontiers in big data · 2026Pooled it
- Global trends, translational gaps, and emerging research directions inFrontiers in fungal biology · 2026Pooled it
- Responsible research evaluation: integrating quality, leadership, and integrity in national systems. The case of Peru.Frontiers in research metrics and analytics · 2026Article
- Early citation dynamics and predictors of time to first citation in Ecuadorian medical publications: a survival analysis of research visibility.Frontiers in research metrics and analytics · 2026Article
- Harnessing artificial intelligence of things for cardiac sensing: current advances and network-based perspectives.Frontiers in public health · 2025Article
- Internationalizing AI: evolution and impact of distance factors.Scientometrics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research collaborations, especially long-distance and international collaborations, have become increasingly prevalent worldwide. Recent studies highlighted the significant role of research leadership in collaborations. However, existing measures of the research leadership do not take into account the intensity of leadership in the co-authorship network. More importantly, the spatial features, which influence the collaboration patterns and research outcomes, have not been incorporated in measuring the research leadership. To fill the gap, we construct an institution-level weighted co-authorship network that integrates two types of weight on the edges: the intensity of collaborations and the spatial score (the geographical distance adjusted by the cross-linguistic-border nature). Based on this network, we propose a novel metric, namely the spatial research leadership rank, to identify the leading institutions while considering both the collaboration intensity and the spatial features. The leadership of an institution is measured by the following three criteria: (a) the institution frequently plays the corresponding rule in papers with other institutions; (b) the institution frequently plays the corresponding rule in longer distance and even cross-linguistic-border collaborations; (c) the participating institutions led by the institution have high leadership status themselves. Harnessing a dataset of 323,146 journal publications in pharmaceutical sciences during 2010-2018, we perform a comprehensive analysis of the geographical distribution and dynamic patterns of research leadership flows at the institution level. The results demonstrate that the SpatialLeaderRank outperforms baseline metrics in predicting the scholarly impact of institutions. And the result remains robust in the field of Information Science and Library Science. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11192-021-03943-w.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.