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


Citation Style Language JSON Export

{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.4478251", 
  "author": [
    {
      "family": "Torres-Salinas, Daniel"
    }, 
    {
      "family": "Robinson-Garc\u00eda, Nicol\u00e1s"
    }, 
    {
      "family": "van Schalkwyk, Fran\u00e7ois"
    }, 
    {
      "family": "Nane, Gabriela F."
    }, 
    {
      "family": "Castillo-Valdivieso, Pedro"
    }
  ], 
  "issued": {
    "date-parts": [
      [
        2021, 
        1, 
        29
      ]
    ]
  }, 
  "abstract": "<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>", 
  "title": "The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings", 
  "type": "dataset", 
  "id": "4478251"
}
142
13
views
downloads
All versions This version
Views 142142
Downloads 1313
Data volume 2.9 MB2.9 MB
Unique views 123123
Unique downloads 1313

Share

Cite as