Using Co-authorship Networks to Map and Analyse Global Neglected Tropical Disease Research with an Affiliation to Germany
Creators: name: Bender, Max Ernst
name: Edwards, Suzanne
name: Philipsborn, Peter Von
name: Steinbeis, Fridolin
name: Keil, Thomas
name: Tinnemann, Peter
Publisher: name: Charité - Universitätsmedizin Berlin
Year: 2016
Subjects: subject: 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit

description: Background Research on Neglected Tropical Diseases (NTDs) has increased in recent decades, and significant need-gaps in diagnostic and treatment tools remain. Analysing bibliometric data from published research is a powerful method for revealing research efforts, partnerships and expertise. We aim to identify and map NTD research networks in Germany and their partners abroad to enable an informed and transparent evaluation of German contributions to NTD research. Methodology/Principal Findings A SCOPUS database search for articles with German author affiliations that were published between 2002 and 2012 was conducted for kinetoplastid and helminth diseases. Open-access tools were used for data cleaning and scientometrics (OpenRefine), geocoding (OpenStreetMaps) and to create (Table2Net), visualise and analyse co-authorship networks (Gephi). From 26,833 publications from around the world that addressed 11 diseases, we identified 1,187 (4.4%) with at least one German author affiliation, and we processed 972 publications for the five most published- about diseases. Of those, we extracted 4,007 individual authors and 863 research institutions to construct co-author networks. The majority of co- authors outside Germany were from high-income countries and Brazil. Collaborations with partners on the African continent remain scattered. NTD research within Germany was distributed among 220 research institutions. We identified strong performers on an individual level by using classic parameters (number of publications, h-index) and social network analysis parameters (betweenness centrality). The research network characteristics varied strongly between diseases. Conclusions/Significance The share of NTD publications with German affiliations is approximately half of its share in other fields of medical research. This finding underlines the need to identify barriers and expand Germany’s otherwise strong research activities towards NTDs. A geospatial analysis of research collaborations with partners abroad can support decisions to strengthen research capacity, particularly in low- and middle-income countries, which were less involved in collaborations than high-income countries. Identifying knowledge hubs within individual researcher networks complements traditional scientometric indicators that are used to identify opportunities for collaboration. Using free tools to analyse research processes and output could facilitate data-driven health policies. Our findings contribute to the prioritisation of efforts in German NTD research at a time of impending local and global policy decisions. Author Summary Neglected tropical disease research has changed considerably in recent decades, and the German government is committed to addressing its past neglect of NTD research. Our aim was to use an innovative social network analysis of bibliometric data to map neglected tropical disease research networks that are inside of and affiliated with Germany, thereby enabling data-driven health policy decision-making. We created and analysed co-author networks from publications in the SCOPUS database, with a focus on five diseases. We found that Germany's share of global publication output for NTDs is approximately half that of other medical research fields. Furthermore, we identified institutions with prominent NTD research within Germany and strong research collaborations between German institutions and partners abroad, mostly in other high-income countries. This allowed an assessment of strong collaborations for further development, e.g., for research capacity strengthening in low-income-countries, but also for identifying missed opportunities for collaboration within the network. Through co-authorship network analysis of individual researcher networks, we identified strong performers by using classic bibliometric parameters, and we identified academic talent by social network analysis parameters on an individual level.