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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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  <dc:creator>Torres-Salinas, Daniel</dc:creator>
  <dc:creator>Robinson-García, Nicolás</dc:creator>
  <dc:creator>van Schalkwyk, François</dc:creator>
  <dc:creator>Nane, Gabriela F.</dc:creator>
  <dc:creator>Castillo-Valdivieso, Pedro</dc:creator>
  <dc:date>2021-01-29</dc:date>
  <dc:description>Submitted to The ISSI 2021 Conference. The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI – the International Society for Informetrics and Scientometrics (http://www.issi-society.org/). 

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.  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.</dc:description>
  <dc:identifier>https://zenodo.org/record/4478251</dc:identifier>
  <dc:identifier>10.5281/zenodo.4478251</dc:identifier>
  <dc:identifier>oai:zenodo.org:4478251</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.4478250</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>forescast</dc:subject>
  <dc:subject>covid</dc:subject>
  <dc:subject>covid19</dc:subject>
  <dc:subject>bibliometrics</dc:subject>
  <dc:subject>dimensions</dc:subject>
  <dc:subject>Growth</dc:subject>
  <dc:title>The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
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