Edgar Schiebel



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Edgar Schiebel

  • Edgar Schiebel


Introduction to the stepwise procedure

  • Introduction to the stepwise procedure

  • Offline Demonstration of the steps with the software BibTechMon



Collect a set of publications: Download meta data from Web of Science

  • Collect a set of publications: Download meta data from Web of Science

  • Not all publications are helpful: reduce the number of objects

    • Calculation of sum similarities (Jaccard index) of bibliographically coupled publications
    • Selection of a subset of publications with a threshold of the sum similarity (beyond the expected value)
  • Get a heat map of agglomerations of similar publications:

    • Calculation and visualization of a two dimensional map of the subset of bibliographically coupled publications with a spring model (use the second order similarity)
    • Filter and visualize the local density of the number of publications weighted with the similarity to draw agglomerations of publications with hot zone.
  • Collect the right set of representatives of an agglomeration of publications

    • Graphically assisted selection of documents in the center of a hot red zone
    • Selection of additional elements of the community by thresholds of similarity (Jaccard index, number of common references) (use the first order similarity)
  • Examine the goodness of the set of representatives: Visualization of the coupling elements (cited references - broad or narrow or spread knowledge base)

  • Name the subfields: Providing lists of TFIDF ranked keywords, read highest cited references, reviews as well as titles and abstracts of the publications with highest sum similarity

  • Comparison with cluster analysis:

    • Cluster analysis (Pearson, Ward or other similarities and linkage methods) of the reduced set of publications. Visualization with a circular dendrogram. Select clusters in different hierarchies
    • Visualization of selected clusters in the heat map
    • Comparison of the identified communities of both approaches (Concordance matrix)


  • Web of Science (WoS) database. We used a keyword based search strategy in the topic search feature of WoS. The time span was 1990 to 2014. The keyword based search focused on the explicit usage of terms formally derived from multiscale simulation and modelling (Set 1). In detail we got the following number of hits: multiscale simul* (1057 Publ.); multi scale simul* (361); multiscale model* (3664); multi scale model* (1554). The union of the hits delivered 6326 records.

  • Secondly we collected records on tribology research combined with MSSM and enriched the set with citing publications. We searched for publications in tribology research with the following search strings and hits: tribo* (33.755); lubric* (40.952); friction (131.023); wear* (105.615); rheo* (81.963) with a union of 326.066 records (Set 2). The intersection of Set 1 with Set 2 covered 249 publications (Set 3). To have a broader view on the application and development of multiscale techniques in tribology we enriched Set 3 with citing publications without self-citations and got additional 1872 records (Set 4). The final data set of 8145 publications was formed by the union of Set 1, Set 3 and Set 4. The data was downloaded the 20th Oct. 2014.



Calculation of sum similarities (Jaccard index) of bibliographically coupled publications (ResearchFronts_aij and ResearchFronts_SumSim)

  • Calculation of sum similarities (Jaccard index) of bibliographically coupled publications (ResearchFronts_aij and ResearchFronts_SumSim)



Selection of a subset of publications with a threshold for the sum similarity (beyond the expected value of 1.9)

  • Selection of a subset of publications with a threshold for the sum similarity (beyond the expected value of 1.9)

  • 2325 out of 8145 publications

  • :



Calculation and visualization of a two dimensional map of the subset of bibliographically coupled publications with a spring model (use the second order similarity), Filter and visualize the local density of the number of publications weighted with the similarity to draw agglomerations of publications with hot zone.

  • Calculation and visualization of a two dimensional map of the subset of bibliographically coupled publications with a spring model (use the second order similarity), Filter and visualize the local density of the number of publications weighted with the similarity to draw agglomerations of publications with hot zone.



















Useful for some 10k publications

  • Useful for some 10k publications

  • It is a procedure to identify research issues not large fields

  • Semiautomatic and uses bibliometrics as a toolbox

  • Different use of second order and first order similarities bt documents

  • Allows non objective decisions by the person or the team who works on the issues for the selection of publications

  • The purpose is to identify and monitor research issues in the scientific community for example for expert organizations







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