January 29, 2021 Dataset Open Access

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

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{
  "description": "<p>Submitted to&nbsp;The ISSI 2021 Conference.&nbsp;The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI &ndash; the International Society for Informetrics and Scientometrics (<a href=\"http://www.issi-society.org/\">http://www.issi-society.org/</a>).&nbsp;</p>\n\n<p>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.&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.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Universidad de Granada", 
      "@type": "Person", 
      "name": "Torres-Salinas, Daniel"
    }, 
    {
      "affiliation": "Universidad de Granada", 
      "@type": "Person", 
      "name": "Robinson-Garc\u00eda, Nicol\u00e1s"
    }, 
    {
      "affiliation": "Stellenbosch University", 
      "@type": "Person", 
      "name": "van Schalkwyk, Fran\u00e7ois"
    }, 
    {
      "affiliation": "TU Delft", 
      "@type": "Person", 
      "name": "Nane, Gabriela F."
    }, 
    {
      "affiliation": "Universidad de Granada", 
      "@type": "Person", 
      "name": "Castillo-Valdivieso, Pedro"
    }
  ], 
  "url": "https://zenodo.org/record/4478251", 
  "datePublished": "2021-01-29", 
  "keywords": [
    "forescast", 
    "covid", 
    "covid19", 
    "bibliometrics", 
    "dimensions", 
    "Growth"
  ], 
  "@context": "https://schema.org/", 
  "distribution": [
    {
      "contentUrl": "https://zenodo.org/api/files/34f5f919-e6b1-4eae-81d4-f2ccf3d232d2/Open data2 - The growth of COVID-19 scientific literature.xlsx", 
      "encodingFormat": "xlsx", 
      "@type": "DataDownload"
    }
  ], 
  "identifier": "https://doi.org/10.5281/zenodo.4478251", 
  "@id": "https://doi.org/10.5281/zenodo.4478251", 
  "@type": "Dataset", 
  "name": "The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings"
}