ArticleLearned publishing : journal of the Association of Learned and Professional Society Publishers2021
Evolution of scientific collaboration on COVID-19: A bibliometric analysis.
Article in Learned publishing : journal of the Association of Learned and Professional Society Publishers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- What we should learn from pandemic publishing.Nature human behaviour · 2024Article
- Türkiye's 3-Year Coronavirus Disease 2019 Publication Performance in the Shadow of the Pandemic: A Bibliometric Analysis.Thoracic research and practice · 2024Article
- Cooperation in the Time of COVID.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024Review
- A Bibliometric Analysis of Research Publications of the Bucharest University of Economic Studies in Time of Pandemics: Implications for Teachers' Professional Publishing Activity.International journal of environmental research and public health · 2022Article
- Promoting lab culture to enhance academic resilience during crises.Ecology and evolution · 2022Article
- Does It Matter to Establish a Strategic Partnership for COVID-19 Prevention and Control? The Perspective of Multiple Distances in Emerging Economies.Frontiers in public health · 2022Article
- Rapid Response in an Uncertain Environment: Study of COVID-19 Scientific Research Under the Parallel Model.Risk management and healthcare policy · 2022Article
- Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining.International journal of environmental research and public health · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper considers the pattens of international collaboration by analysing publications on COVID-19 published in the first 6 months of the pandemic. The data set comprised articles on COVID-19 indexed in the Web of Science Core Collection (WoS CC) downloaded four times between 1 April 2020 and 1 June 2020. The analysis of 5,827 documents revealed that 128 countries, 23,127 authors, and 6,349 institutes published on the pandemic. The data reveal that the three main publishing countries were the USA, China, and England with Italy closely following. Although publication was widely spread, most of the institutions with the highest volume of output were in China. Network analysis showed growth in international cooperation with an average degree of country/region cooperation rising to 23.06 by 1 June. There was also a clear core-periphery structure to international collaboration. Institutional collaboration was shown to be highly regionalized. The data reveal a high and growing incidence of international collaboration on the pandemic.
Indexed as
Identifiers
What OpenQuestion holds
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.