An Overview of Covid-19 Dedicated Scientific Databases

Document Type : Review Article.


PhD in Knowledge and Information Sciences, Gonabad University of Medical Sciences, Gonabad, Iran.


The rapid and challenging spread of the COVID-19 virus disease has become a significant threat in all countries of the world, which has provoked immediate reactions from the scientific and medical society and led to scientific publications on various aspects of the disease.  Therefore, quick and easy access to these publications' results and sharing scientific data and findings to understand the disease control and create treatments and vaccines is one of the biggest ways to quickly and usefully transmit research results. The purpose of the present narrative review is to introduce the dedicated scientific databases of COVID-19 disease. For this purpose, an appropriate search was used to extract the studies conducted in the Google Scholar and PubMed databases. In the results, 16 databases of COVID-19 disease have been identified and introduced. Researchers are able to use these resources for their science and research purposes in accordance with the tools and capabilities available in the COVID-19 databases provided in this report.


1. Meskarpour Amiri M, Nasiri T, Mahdizadeh P. Subjects Clustering Analysis and Science Mapping on COVID-19 Researches in Scopus database. Journal Mil Med. 2020;22(6):663-9.
2. Kousha K, Thelwall M. COVID-19 publications: Database coverage, citations, readers, tweets, news, Facebook walls, Reddit posts. Quantitative Science Studies. 2020:1-28.  
3. Song P, Karako T. COVID-19: Real-time dissemination of scientific information to fight a public health emergency of international concern. Bioscience trends. 2020.
4. Ali MY, Bhatti R. COVID-19 (Coronavirus) Pandemic: Information Sources Channels for the Public Health Awareness. Asia-Pacific Journal of Public Health. 2020.
PMid:32429681 PMCid:PMC7240310  
5. Dastani M, Mokhtarzadeh M, Eydi M, Delshad A. Evaluating The Internet-Based Electronic Health Literacy Among Students of Gonabad University of Medical Sciences. Journal of Medical Education and Development. 2019;14(1).  
6. Shoaei MD, Dastani M. The Role of Social Media During the COVID-19 Crisis: a Narrative Review. Health Technology Assessment in Action. 2020;4(1).  
7. Paré G, Kitsiou S. Chapter 9 Methods for Literature Reviews. In: Lau F, Kuziemsky C, editors. Handbook of eHealth Evaluation: An Evidence-based Approach [Internet]. Victoria (BC): University of Victoria; 2017 Feb 27. Available from:  
8. Tworowski D, Gorohovski A, Mukherjee S, Carmi G, Levy E, Detroja R, et al. COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics. Nucleic acids research. 2020:1.
PMid:33166390 PMCid:PMC7778969  
9. Wang LL, Lo K, Chandrasekhar Y, Reas R, Yang J, Eide D, Funk K, Kinney R, Liu Z, Merrill W, Mooney P. CORD-19: The Covid-19 Open Research Dataset. ArXiv. 2020.  
10. Chen Q, Allot A, Lu Z. LitCovid: an open database of COVID-19 literature. Nucleic Acids Research. 2020.
PMid:33166392 PMCid:PMC7778958  
11. Chen Q, Allot A, Lu Z. Keep up with the latest coronavirus research. Natur. 2020;579(7798):193.
12. Trewartha A, Dagdelen J, Huo H, Cruse K, Wang Z, He T, et al. COVIDScholar: An automated COVID-19 research aggregation and analysis platform. arXiv preprint arXiv:201203891. 2020.  
13. Le Bras P, Gharavi A, Robb DA, Vidal AF, Padilla S, Chantler MJ. Visualising COVID-19 Research. arXiv preprint arXiv:200506380. 2020.  
14. Zhang E, Gupta N, Tang R, Han X, Pradeep R, Lu K, et al. Covidex: Neural ranking models and keyword search infrastructure for the covid-19 open research dataset. arXiv preprint arXiv:200707846. 2020.  
15. Verspoor K, Šuster S, Otmakhova Y, Mendis S, Zhai Z, Fang B, et al. COVID-SEE: Scientific Evidence Explorer for COVID-19 related research. arXiv preprint arXiv:200807880. 2020.  
16. Nye BE, Nenkova A, Marshall IJ, Wallace BC. Trialstreamer: Mapping and Browsing Medical Evidence in Real-Time. arXiv preprint arXiv:200510865. 2020.  
17. Hope T, Portenoy J, Vasan K, Borchardt J, Horvitz E, Weld D, et al. SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search. bioRxiv; 2020.  
18. Kricka LJ, Polevikov S, Park JY, Fortina P, Bernardini S, Satchkov D, et al. Artificial intelligence-powered search tools and resources in the fight against COVID-19. Ejifcc. 2020;31(2):106.  
19. Janiaud P, Axfors C, Saccilotto R, et al. : COVID-evidence: a living database of trials on interventions for COVID-19.2020. 10.17605/OSF.IO/GEHFX  
20. Janiaud P, Axfors C, Van't Hooft J, Saccilotto R, Agarwal A, Appenzeller-Herzog C, Contopoulos-Ioannidis DG, Danchev V, Dirnagl U, Ewald H, Gartlehner G. The worldwide clinical trial research response to the COVID-19 pandemic-the first 100 days. F1000Research. 2020;9.
PMid:33082937 PMCid:PMC7539080  
21. Rivière P, Ripoll P, Barnier J, Vuillemot R, Ferrand G, Cohen-Boulakia S, Ravaud P, Boutron I, Alawadhi S, Amer-Yahia S, Ávila C. Research response to coronavirus disease 2019 needed better coordination and collaboration: a living mapping of registered trials. Journal of Clinical Epidemiology.;130:107-16.
PMid:33096223 PMCid:PMC7575422  
22. Nath, Hemanta and Gary, Todd and Shepard-Smith, Andrew, Artificial Intelligence Tools and Models Used by the Scientific Community to Address the COVID-19 Pandemic (July 21, 2020). Available at SSRN: or