Methodology and About us

Description of the search and method applied

Our article «The emerging COVID-19 research: dynamic and regularly updated science maps and analyses» describes the background for our work, the methods, and how this web-resource can be used. Click here to read the preprint. This project is also registered at the Open Science Framework (OSF) platform. Click here to read about the project at OSF. For readers that want just a brief overview of the methods used, the text below provides that.

Search string:

All documents in the Scopus database and PubMed, published in 2020, with any of the following keywords:

  • COVID-19
  • SARS-CoV-2
  • severe acute respiratory syndrome coronavirus 2
  • 2019-nCoV
  • 2019 novel coronavirus

In Scopus, we searched for these terms in TITLE-ABS-KEY. In PubMed, we searched for these terms in Title/Abstract

Keyword co-occurrence analysis

The conceptual idea behind keyword co-occurrence analysis1 is that when a set of words occur in different documents, the concepts behind these words are likely closely related. By analyzing the content of a group of documents we can establish how closely related they are. From these results, we can build a conceptual network structure of the research field 2. In our corpus from PubMed, this includes both author-generated keywords and MeSH terms.

We used the keywords to construct a two-dimensional keyword-map, where the layout is based on a framework for mapping and clustering, in the VOSviewer software 3. Keywords were mapped so that keyword relatedness is associated with proximity on the map. The size of the nodes reflects keyword frequency, and the weight of connecting lines indicates in how many articles the keywords co-occur. The keywords are clustered using an approach akin to modularity-based clustering3: keywords that co-occur often are placed in the same cluster, signified by color. The network map gives an overview of the research field, also showing which topics are studied in conjunction with each other, and it can give an indication of there may be knowledge gaps in the research field.

Bibliometric coupling analysis

With the bibliometric coupling analysis we examine documents reference lists to identify shared references. The extent of overlap between reference lists is a measure of the strength of connection between documents4. A large overlap, when two documents share many references indicate a probability that the documents are on a related topic. Where there is little overlap, it suggests the documents are based on distinct literatures and likely cover different topics. We present a two-dimensional map, created using VOSviewer, where the layout is determined using a unified framework for clustering and mapping5. The articles are located so that the distance between the nodes represent their relatedness and are grouped in clusters, which indicates a shared theme. The size of the node indicates the number and strength of connections to other articles. The articles that do not have a reference list or that does not share any references with other articles, are not allocated to a cluster.

To identify important nodes in each network graph, we calculate two centrality measures. Weighted degree centrality (referred to as centrality) which is the sum of links a given node has to other nodes, taking the strength of the link into account. This measure indicates the importance of a node. In the co-occurrence network graph, some keywords connect the whole or large parts of the network and represent generally important terms, not specific to any one topic. We identify these as having a high bridging centrality, a metric for how often a node is on the shortest path between any other two nodes6.

The project is run by:

Njål Andersen, Msc.; BI Norwegian Business School
Ingunn Olea Lund, PhD.; Norwegian Institute of Public Health
Jørgen G. Bramness, PhD; Norwegian Institute of Public Health

References

  1. Callon M, Courtial J-P, Turner WA, Bauin S. From translations to problematic networks: An introduction to co-word analysis. Inf (International Soc Sci Counc. 1983;22(2):191-235.
  2. Börner K, Chen C, Boyack KW. Visualizing knowledge domains. Annu Rev Inf Sci Technol. 2003;37(1):179-255.
  3. Waltman L, van Eck NJ, Noyons ECM. A unified approach to mapping and clustering of bibliometric networks. J Informetr. 2010. doi:10.1016/j.joi.2010.07.002
  4. Kessler MM. Bibliographic coupling between scientific papers. Am Doc. 1963;14(1):10-25. doi:10.1002/asi.5090140103
  5. van Eck NJ, Waltman L. Text mining and visualization using VOSviewer. ISSI Newsl. 2011;7(3):50-54. doi:10.1371/journal.pone.0054847
  6. Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Icwsm. 2009;8(2009):361-362.

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