ArticleFrontiers in research metrics and analytics2020
Tracking and Mining the COVID-19 Research Literature.
Article in Frontiers in research metrics and analytics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The long COVID research literature.Frontiers in research metrics and analytics · 2023Pooled it
- Coronavirus research topics, tracking twenty years of research.Scientific data · 2025Article
- Tech mining: a revisit and navigation.Frontiers in research metrics and analytics · 2024Review
- Responsible models and indicators: challenges from artificial intelligence.Frontiers in research metrics and analytics · 2023Article
- Expediting knowledge acquisition by a web framework for Knowledge Graph Exploration and Visualization (KGEV): case studies on COVID-19 and Human Phenotype Ontology.BMC medical informatics and decision making · 2022Article
- The role of blogs and news sites in science communication during the COVID-19 pandemic.Frontiers in research metrics and analytics · 2022Review
- Visibility, collaboration and impact of the Cuban scientific output on COVID-19 in Scopus.Heliyon · 2021Article
- Breaking community boundary: Comparing academic and social communication preferences regarding global pandemics.Journal of informetrics · 2021Article
- Impact of Coronavirus Outbreaks on Science and Society: Insights from Temporal Bibliometry of SARS and COVID-19.Entropy (Basel, Switzerland) · 2021Article
- Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics.Frontiers in research metrics and analytics · 2021Article
- Topic evolution, disruption and resilience in early COVID-19 research.Scientometrics · 2021Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The unprecedented, explosive growth of the COVID-19 domain presents challenges to researchers to keep up with research knowledge within the domain. This article profiles this research to help make that knowledge more accessible via overviews and novel categorizations. We provide websites offering means for researchers to probe more deeply to address specific questions. We further probe and reassemble COVID-19 topical content to address research issues concerning topical evolution and emphases on tactical vs. strategic approaches to mitigate this pandemic and reduce future viral threats. Data suggest that heightened attention to strategic, immunological factors is warranted. Connecting with and transferring in research knowledge from outside the COVID-19 domain demand a viable COVID-19 knowledge model. This study provides complementary topical categorizations to facilitate such modeling to inform future Literature-Based Discovery endeavors.
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.