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Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders

Author

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  • J. Homolak

    (University of Zagreb School of Medicine)

  • I. Kodvanj

    (University of Zagreb School of Medicine)

  • D. Virag

    (University of Zagreb School of Medicine)

Abstract

The Pandemic of COVID-19, an infectious disease caused by SARS-CoV-2 motivated the scientific community to work together in order to gather, organize, process and distribute data on the novel biomedical hazard. Here, we analyzed how the scientific community responded to this challenge by quantifying distribution and availability patterns of the academic information related to COVID-19. The aim of this study was to assess the quality of the information flow and scientific collaboration, two factors we believe to be critical for finding new solutions for the ongoing pandemic. The RISmed R package, and a custom Python script were used to fetch metadata on articles indexed in PubMed and published on Rxiv preprint server. Scopus was manually searched and the metadata was exported in BibTex file. Publication rate and publication status, affiliation and author count per article, and submission-to-publication time were analysed in R. Biblioshiny application was used to create a world collaboration map. Preliminary data suggest that COVID-19 pandemic resulted in generation of a large amount of scientific data, and demonstrates potential problems regarding the information velocity, availability, and scientific collaboration in the early stages of the pandemic. More specifically, the results indicate precarious overload of the standard publication systems, significant problems with data availability and apparent deficient collaboration. In conclusion, we believe the scientific community could have used the data more efficiently in order to create proper foundations for finding new solutions for the COVID-19 pandemic. Moreover, we believe we can learn from this on the go and adopt open science principles and a more mindful approach to COVID-19-related data to accelerate the discovery of more efficient solutions. We take this opportunity to invite our colleagues to contribute to this global scientific collaboration by publishing their findings with maximal transparency.

Suggested Citation

  • J. Homolak & I. Kodvanj & D. Virag, 2020. "Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2687-2701, September.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:3:d:10.1007_s11192-020-03587-2
    DOI: 10.1007/s11192-020-03587-2
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    References listed on IDEAS

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    1. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
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    Cited by:

    1. Liwei Zhang & Liang Ma, 2023. "Is open science a double-edged sword?: data sharing and the changing citation pattern of Chinese economics articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2803-2818, May.
    2. Shohreh SeyyedHosseini & Reza BasirianJahromi, 2021. "COVID-19 pandemic in the Middle East countries: coronavirus-seeking behavior versus coronavirus-related publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7503-7523, September.
    3. E. Sachini & K. Sioumalas-Christodoulou & C. Chrysomallidis & G. Siganos & N. Bouras & N. Karampekios, 2021. "COVID-19 enabled co-authoring networks: a country-case analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5225-5244, June.
    4. Hafiz Suliman Munawar & Hina Inam & Fahim Ullah & Siddra Qayyum & Abbas Z. Kouzani & M. A. Parvez Mahmud, 2021. "Towards Smart Healthcare: UAV-Based Optimized Path Planning for Delivering COVID-19 Self-Testing Kits Using Cutting Edge Technologies," Sustainability, MDPI, vol. 13(18), pages 1-21, September.
    5. Ivan Kodvanj & Jan Homolak & Davor Virag & Vladimir Trkulja, 2022. "Publishing of COVID-19 preprints in peer-reviewed journals, preprinting trends, public discussion and quality issues," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1339-1352, March.
    6. Milad Haghani & Pegah Varamini, 2021. "Temporal evolution, most influential studies and sleeping beauties of the coronavirus literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7005-7050, August.
    7. Mohammad Ashraful Mobin & Masnun Mahi & M. Kabir Hassan & Marzia Habib & Shabiha Akter & Tahmina Hassan, 2023. "An analysis of COVID-19 and WHO global research roadmap: knowledge mapping and future research agenda," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 35-56, March.
    8. Shima Moradi & Sajedeh Abdi, 2021. "Pandemic publication: correction and erratum in COVID-19 publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1849-1857, February.
    9. Aleksander Aristovnik & Dejan Ravšelj & Lan Umek, 2020. "A Bibliometric Analysis of COVID-19 across Science and Social Science Research Landscape," Sustainability, MDPI, vol. 12(21), pages 1-30, November.
    10. Josip Strcic & Antonia Civljak & Terezija Glozinic & Rafael Leite Pacheco & Tonci Brkovic & Livia Puljak, 2022. "Open data and data sharing in articles about COVID-19 published in preprint servers medRxiv and bioRxiv," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2791-2802, May.
    11. Mona Farouk Ali, 2022. "Between panic and motivation: did the first wave of COVID-19 affect scientific publishing in Mediterranean countries?," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(6), pages 3083-3115, June.
    12. Constantin Bürgi & Klaus Wohlrabe, 2022. "The influence of Covid-19 on publications in economics: bibliometric evidence from five working paper series," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5175-5189, September.
    13. Shir Aviv-Reuven & Ariel Rosenfeld, 2021. "Publication patterns’ changes due to the COVID-19 pandemic: a longitudinal and short-term scientometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6761-6784, August.
    14. Guillaume Cabanac & Theodora Oikonomidi & Isabelle Boutron, 2021. "Day-to-day discovery of preprint–publication links," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5285-5304, June.
    15. Zhao, Yi & Liu, Lifan & Zhang, Chengzhi, 2022. "Is coronavirus-related research becoming more interdisciplinary? A perspective of co-occurrence analysis and diversity measure of scientific articles," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    16. Leonardo B. Furstenau & Bruna Rabaioli & Michele Kremer Sott & Danielli Cossul & Mariluza Sott Bender & Eduardo Moreno Júdice De Mattos Farina & Fabiano Novaes Barcellos Filho & Priscilla Paola Severo, 2021. "A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020," IJERPH, MDPI, vol. 18(3), pages 1-24, January.
    17. Meijun Liu & Yi Bu & Chongyan Chen & Jian Xu & Daifeng Li & Yan Leng & Richard B. Freeman & Eric T. Meyer & Wonjin Yoon & Mujeen Sung & Minbyul Jeong & Jinhyuk Lee & Jaewoo Kang & Chao Min & Min Song , 2022. "Pandemics are catalysts of scientific novelty: Evidence from COVID‐19," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1065-1078, August.
    18. Gabriela F. Nane & Nicolas Robinson-Garcia & François Schalkwyk & Daniel Torres-Salinas, 2023. "COVID-19 and the scientific publishing system: growth, open access and scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 345-362, January.
    19. Török, Ádám & Konka, Boglárka & Nagy, Andrea Magda, 2023. "A koronavírus-járvány a közgazdasági szakirodalomban. Egy új határterület tudománymetriai elemzése [The coronavirus pandemic in the economics literature. The scientometric analysis of a new discipl," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(3), pages 284-304.
    20. Liwei Zhang & Liang Ma, 2021. "Does open data boost journal impact: evidence from Chinese economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3393-3419, April.

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