Dataset Open Access

The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings

Torres-Salinas, Daniel; Robinson-García, Nicolás; van Schalkwyk, François; Nane, Gabriela F.; Castillo-Valdivieso, Pedro


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    <dct:title>The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings</dct:title>
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    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2021</dct:issued>
    <dcat:keyword>forescast</dcat:keyword>
    <dcat:keyword>covid</dcat:keyword>
    <dcat:keyword>covid19</dcat:keyword>
    <dcat:keyword>bibliometrics</dcat:keyword>
    <dcat:keyword>dimensions</dcat:keyword>
    <dcat:keyword>Growth</dcat:keyword>
    <dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2021-01-29</dct:issued>
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    <dct:description>&lt;p&gt;Submitted to&amp;nbsp;The ISSI 2021 Conference.&amp;nbsp;The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI &amp;ndash; the International Society for Informetrics and Scientometrics (&lt;a href="http://www.issi-society.org/"&gt;http://www.issi-society.org/&lt;/a&gt;).&amp;nbsp;&lt;/p&gt; &lt;p&gt;We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount 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 ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed.&amp;nbsp; We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings.&lt;/p&gt;</dct:description>
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