ArticleScientometrics2023
COVID-19 and the scientific publishing system: growth, open access and scientific fields.
Article in Scientometrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 39 citations in OpenAlex.
- Substance use among young people in sub-Saharan Africa: a systematic review and meta-analysis.Frontiers in psychiatry · 2024Pooled it
- The long COVID research literature.Frontiers in research metrics and analytics · 2023Pooled it
- Consistency in North Korean Medical Academic Research Topics During the COVID-19 Pandemic: A Text Mining Analysis.Journal of Korean medical science · 2026Article
- Paying peer reviewers: benefits, risks, and challenges.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Academic visibility, trends, and emerging patterns of Peruvian university professors of health sciences with the highest number of theses: A scientometric analysis.Journal of education and health promotion · 2026Article
- Creating Our Professional Evidence-Base: A Journey, Not a Destination.Journal of medical radiation sciences · 2025Article
- AI-driven scientometric analysis with recency insights: lower urinary tract symptoms in older and frail populations.World journal of urology · 2025Article
- Trends in Dehydration in Older People: Identifying Landmark Scientific Contributions.Nutrients · 2025Review
- Decoding trends in mRNA vaccine research: A comprehensive bibliometric study.Human vaccines & immunotherapeutics · 2024Article
- Articles' retraction during the pandemic: COVID-19 is not the pacemaker.Annals of translational medicine · 2024Article
- Biosocial Technical Systems: An Emerging Approach to Analyse Responses to Novel Diseases.Advances in experimental medicine and biology · 2024Article
- Energy efficient ventilation and indoor air quality in the context of COVID-19 - A systematic review.Renewable & sustainable energy reviews · 2023Review
- Policy options and practical recommendations for determining priorities in public health research agendas in peripheral countries: insights from a collaborative work initiative in Argentina during the COVID-19 pandemic.Frontiers in medicine · 2023Article
- Tracking Openness and Topic Evolution of COVID-19 Publications January 2020-March 2021: Comprehensive Bibliometric and Topic Modeling Analysis.Journal of medical Internet research · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We model the growth of scientific literature related to COVID-19 and forecast the expected growth from 1 June 2021. Considering the significant scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the Autoregressive Integrated Moving Average (ARIMA) and exponential smoothing models using the Dimensions database. This source has the particularity of including in the metadata information on the date in which papers were indexed. We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by domain-specific repository (SSRN and MedRxiv) and by several research fields. We conclude by discussing our findings. Supplementary Information: The online version contains supplementary material available at 10.1007/s11192-022-04536-x.
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.