Using Co-authorship Networks to Map and Analyse Global Neglected Tropical Disease Research with an Affiliation to Germany
Identifieur interne : 000018 ( OpenAccess/Analysis ); précédent : 000017; suivant : 000019Using Co-authorship Networks to Map and Analyse Global Neglected Tropical Disease Research with an Affiliation to Germany
Auteurs : Max Ernst Bender [Allemagne] ; Suzanne Edwards [Allemagne] ; Peter Von Philipsborn [Allemagne] ; Fridolin Steinbeis [Allemagne] ; Thomas Keil [Allemagne] ; Peter Tinnemann [Allemagne]Source :
- PLoS Neglected Tropical Diseases [ 1935-2727 ] ; 2015.
Abstract
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.
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.
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.
Url:
DOI: 10.1371/journal.pntd.0004182
PubMed: 26719978
PubMed Central: 4703140
Affiliations:
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PMC:4703140Le document en format XML
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<series><title level="j">PLoS Neglected Tropical Diseases</title>
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<front><div type="abstract" xml:lang="en"><sec id="sec001"><title>Background</title>
<p>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.</p>
</sec>
<sec id="sec002"><title>Methodology/Principal Findings</title>
<p>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.</p>
</sec>
<sec id="sec003"><title>Conclusions/Significance</title>
<p>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.</p>
</sec>
</div>
</front>
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<author><name sortKey="Guzman, J" uniqKey="Guzman J">J Guzman</name>
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<author><name sortKey="Farrell, P" uniqKey="Farrell P">P Farrell</name>
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</analytic>
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<author><name sortKey="Pecoul, B" uniqKey="Pecoul B">B Pecoul</name>
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<author><name sortKey="Torvik, Vi" uniqKey="Torvik V">VI Torvik</name>
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</analytic>
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</analytic>
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<author><name sortKey="Serruya, Sj" uniqKey="Serruya S">SJ Serruya</name>
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<author><name sortKey="Jensen, P" uniqKey="Jensen P">P Jensen</name>
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</author>
<author><name sortKey="Heymann, S" uniqKey="Heymann S">S Heymann</name>
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<author><name sortKey="Jacomy, M" uniqKey="Jacomy M">M Jacomy</name>
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</analytic>
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</author>
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</analytic>
</biblStruct>
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</author>
<author><name sortKey="Peters, P" uniqKey="Peters P">P Peters</name>
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<biblStruct><analytic><author><name sortKey="Lancet, T" uniqKey="Lancet T">T Lancet</name>
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</biblStruct>
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<biblStruct><analytic><author><name sortKey="Bertsimas, D" uniqKey="Bertsimas D">D Bertsimas</name>
</author>
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</TEI>
<affiliations><list><country><li>Allemagne</li>
</country>
<region><li>Bavière</li>
<li>Berlin</li>
<li>District de Haute-Bavière</li>
</region>
<settlement><li>Berlin</li>
<li>Munich</li>
</settlement>
<orgName><li>Université technique de Munich</li>
</orgName>
</list>
<tree><country name="Allemagne"><region name="Berlin"><name sortKey="Bender, Max Ernst" sort="Bender, Max Ernst" uniqKey="Bender M" first="Max Ernst" last="Bender">Max Ernst Bender</name>
</region>
<name sortKey="Bender, Max Ernst" sort="Bender, Max Ernst" uniqKey="Bender M" first="Max Ernst" last="Bender">Max Ernst Bender</name>
<name sortKey="Edwards, Suzanne" sort="Edwards, Suzanne" uniqKey="Edwards S" first="Suzanne" last="Edwards">Suzanne Edwards</name>
<name sortKey="Keil, Thomas" sort="Keil, Thomas" uniqKey="Keil T" first="Thomas" last="Keil">Thomas Keil</name>
<name sortKey="Steinbeis, Fridolin" sort="Steinbeis, Fridolin" uniqKey="Steinbeis F" first="Fridolin" last="Steinbeis">Fridolin Steinbeis</name>
<name sortKey="Steinbeis, Fridolin" sort="Steinbeis, Fridolin" uniqKey="Steinbeis F" first="Fridolin" last="Steinbeis">Fridolin Steinbeis</name>
<name sortKey="Tinnemann, Peter" sort="Tinnemann, Peter" uniqKey="Tinnemann P" first="Peter" last="Tinnemann">Peter Tinnemann</name>
<name sortKey="Von Philipsborn, Peter" sort="Von Philipsborn, Peter" uniqKey="Von Philipsborn P" first="Peter" last="Von Philipsborn">Peter Von Philipsborn</name>
<name sortKey="Von Philipsborn, Peter" sort="Von Philipsborn, Peter" uniqKey="Von Philipsborn P" first="Peter" last="Von Philipsborn">Peter Von Philipsborn</name>
</country>
</tree>
</affiliations>
</record>
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