5; Non-L. N = 13,076. The total number of international papers (2+) was divided by the total number of papers with country data available to calculate international percent. doi:10.1371/journal.pone.0134164.tPLOS ONE | DOI:10.1371/journal.pone.0134164 July 31,4 /A Network Analysis of Nobel Prize WinnersTable 3. The Effect of the Prize on Laureates’ Authorship Patterns. Time Period Pre-Prize N = 9620 2 Years Post-Prize N = 259 Total Post-Prize N = 5091 Measures Mean Median St.Dev. Mean Median St.Dev. Mean Median St.Dev. * = P<0.05 by GLM with Poisson distribution logit doi:10.1371/journal.pone.0134164.t003 Number of Authors per Co-authored Paper 4.4* 4.0 5.0 6.2* 5.0 7.0 6.4* 5.0 10.0 Percent Sole Author 11.0* 0 31.0 17.0* 0 0.37 9.0* 0 29.authors may have the exact same name and/or last name and first initial. This results in conflation of what fpsyg.2014.00726 should be separate authors. Without author disambiguation many coauthor network measures are unreliable. We disambiguated the HIV-1 integrase inhibitor 2 dose records using various methods. For the descriptive bibliometric statistics in Table 1, records were disambiguated using the data mining software Vantage Point. For the remaining analyses a second set of methods were used. We applied the principles of Gurney et al. [21] by first calculating the similarity between authors in the co-occurrence of title words and coauthors, the only ML240 site metadata that occur in all articles. The second indicator concerns proximity in the dataset. Raw Web of Science data were parsed into a relational database using the SAINT toolkit [22]. Similar author names that were processed in each C.I. 75535 chemical information other’s vicinity are more likely to refer to the same person. These two indicators provided a heuristic for manual disambiguation based on similarities in spelling [21]. After disambiguation, the resulting records allowed us to create three different types of networks. The first analysis compared the entire BMS-214662 manufacturer Laureate coauthor network with the entire nonLaureate network. The second analysis compared only the primary author networks from both groups, i.e. the Laureates and the non-Laureates without coauthors. The third combined the two groups. The free network analysis software, Gephi, was used to analyze and visualize the networks, providing the basis of comparison between the Laureate and non-Laureate wcs.1183 networks.Authorship differences between the Laureates and non-LaureatesWe compared the publication patterns between the Laureates and non-Laureates using data aggregated at the author level, shown in Table 1. In terms of numbers of publications, the Nobel Laureates produced on average significantly fewer papers over the course of their careers than the non-Laureates supporting Garfield and Welljams-Dorof [6] and Hirsch [13]. Laureates also had fewer coauthors across their career. Further, the Nobel Laureates produced a significantly higher percentage of sole-authored papers than the non-Laureates both before and after winning the Nobel Prize (see also Table 3). The two groups exhibited roughly identical percentages of papers where they appear as the first author or last author. Next, the data were aggregated at the paper level to conduct descriptive statistical analyses for the two groups with results shown in Table 2. The data show that the Laureates accumulated many more citations per paper (times cited) than the non-Laureates, supporting Garfield Welljams-Dorof [6], even with matched h-indexes. The data show no meaningful differences between the groups in the avera.5; Non-L. N = 13,076. The total number of international papers (2+) was divided by the total number of papers with country data available to calculate international percent. doi:10.1371/journal.pone.0134164.tPLOS ONE | DOI:10.1371/journal.pone.0134164 July 31,4 /A Network Analysis of Nobel Prize WinnersTable 3. The Effect of the Prize on Laureates’ Authorship Patterns. Time Period Pre-Prize N = 9620 2 Years Post-Prize N = 259 Total Post-Prize N = 5091 Measures Mean Median St.Dev. Mean Median St.Dev. Mean Median St.Dev. * = P<0.05 by GLM with Poisson distribution logit doi:10.1371/journal.pone.0134164.t003 Number of Authors per Co-authored Paper 4.4* 4.0 5.0 6.2* 5.0 7.0 6.4* 5.0 10.0 Percent Sole Author 11.0* 0 31.0 17.0* 0 0.37 9.0* 0 29.authors may have the exact same name and/or last name and first initial. This results in conflation of what fpsyg.2014.00726 should be separate authors. Without author disambiguation many coauthor network measures are unreliable. We disambiguated the records using various methods. For the descriptive bibliometric statistics in Table 1, records were disambiguated using the data mining software Vantage Point. For the remaining analyses a second set of methods were used. We applied the principles of Gurney et al. [21] by first calculating the similarity between authors in the co-occurrence of title words and coauthors, the only metadata that occur in all articles. The second indicator concerns proximity in the dataset. Raw Web of Science data were parsed into a relational database using the SAINT toolkit [22]. Similar author names that were processed in each other’s vicinity are more likely to refer to the same person. These two indicators provided a heuristic for manual disambiguation based on similarities in spelling [21]. After disambiguation, the resulting records allowed us to create three different types of networks. The first analysis compared the entire Laureate coauthor network with the entire nonLaureate network. The second analysis compared only the primary author networks from both groups, i.e. the Laureates and the non-Laureates without coauthors. The third combined the two groups. The free network analysis software, Gephi, was used to analyze and visualize the networks, providing the basis of comparison between the Laureate and non-Laureate wcs.1183 networks.Authorship differences between the Laureates and non-LaureatesWe compared the publication patterns between the Laureates and non-Laureates using data aggregated at the author level, shown in Table 1. In terms of numbers of publications, the Nobel Laureates produced on average significantly fewer papers over the course of their careers than the non-Laureates supporting Garfield and Welljams-Dorof [6] and Hirsch [13]. Laureates also had fewer coauthors across their career. Further, the Nobel Laureates produced a significantly higher percentage of sole-authored papers than the non-Laureates both before and after winning the Nobel Prize (see also Table 3). The two groups exhibited roughly identical percentages of papers where they appear as the first author or last author. Next, the data were aggregated at the paper level to conduct descriptive statistical analyses for the two groups with results shown in Table 2. The data show that the Laureates accumulated many more citations per paper (times cited) than the non-Laureates, supporting Garfield Welljams-Dorof [6], even with matched h-indexes. The data show no meaningful differences between the groups in the avera.5; Non-L. N = 13,076. The total number of international papers (2+) was divided by the total number of papers with country data available to calculate international percent. doi:10.1371/journal.pone.0134164.tPLOS ONE | DOI:10.1371/journal.pone.0134164 July 31,4 /A Network Analysis of Nobel Prize WinnersTable 3. The Effect of the Prize on Laureates’ Authorship Patterns. Time Period Pre-Prize N = 9620 2 Years Post-Prize N = 259 Total Post-Prize N = 5091 Measures Mean Median St.Dev. Mean Median St.Dev. Mean Median St.Dev. * = P<0.05 by GLM with Poisson distribution logit doi:10.1371/journal.pone.0134164.t003 Number of Authors per Co-authored Paper 4.4* 4.0 5.0 6.2* 5.0 7.0 6.4* 5.0 10.0 Percent Sole Author 11.0* 0 31.0 17.0* 0 0.37 9.0* 0 29.authors may have the exact same name and/or last name and first initial. This results in conflation of what fpsyg.2014.00726 should be separate authors. Without author disambiguation many coauthor network measures are unreliable. We disambiguated the records using various methods. For the descriptive bibliometric statistics in Table 1, records were disambiguated using the data mining software Vantage Point. For the remaining analyses a second set of methods were used. We applied the principles of Gurney et al. [21] by first calculating the similarity between authors in the co-occurrence of title words and coauthors, the only metadata that occur in all articles. The second indicator concerns proximity in the dataset. Raw Web of Science data were parsed into a relational database using the SAINT toolkit [22]. Similar author names that were processed in each other’s vicinity are more likely to refer to the same person. These two indicators provided a heuristic for manual disambiguation based on similarities in spelling [21]. After disambiguation, the resulting records allowed us to create three different types of networks. The first analysis compared the entire Laureate coauthor network with the entire nonLaureate network. The second analysis compared only the primary author networks from both groups, i.e. the Laureates and the non-Laureates without coauthors. The third combined the two groups. The free network analysis software, Gephi, was used to analyze and visualize the networks, providing the basis of comparison between the Laureate and non-Laureate wcs.1183 networks.Authorship differences between the Laureates and non-LaureatesWe compared the publication patterns between the Laureates and non-Laureates using data aggregated at the author level, shown in Table 1. In terms of numbers of publications, the Nobel Laureates produced on average significantly fewer papers over the course of their careers than the non-Laureates supporting Garfield and Welljams-Dorof [6] and Hirsch [13]. Laureates also had fewer coauthors across their career. Further, the Nobel Laureates produced a significantly higher percentage of sole-authored papers than the non-Laureates both before and after winning the Nobel Prize (see also Table 3). The two groups exhibited roughly identical percentages of papers where they appear as the first author or last author. Next, the data were aggregated at the paper level to conduct descriptive statistical analyses for the two groups with results shown in Table 2. The data show that the Laureates accumulated many more citations per paper (times cited) than the non-Laureates, supporting Garfield Welljams-Dorof [6], even with matched h-indexes. The data show no meaningful differences between the groups in the avera.5; Non-L. N = 13,076. The total number of international papers (2+) was divided by the total number of papers with country data available to calculate international percent. doi:10.1371/journal.pone.0134164.tPLOS ONE | DOI:10.1371/journal.pone.0134164 July 31,4 /A Network Analysis of Nobel Prize WinnersTable 3. The Effect of the Prize on Laureates’ Authorship Patterns. Time Period Pre-Prize N = 9620 2 Years Post-Prize N = 259 Total Post-Prize N = 5091 Measures Mean Median St.Dev. Mean Median St.Dev. Mean Median St.Dev. * = P<0.05 by GLM with Poisson distribution logit doi:10.1371/journal.pone.0134164.t003 Number of Authors per Co-authored Paper 4.4* 4.0 5.0 6.2* 5.0 7.0 6.4* 5.0 10.0 Percent Sole Author 11.0* 0 31.0 17.0* 0 0.37 9.0* 0 29.authors may have the exact same name and/or last name and first initial. This results in conflation of what fpsyg.2014.00726 should be separate authors. Without author disambiguation many coauthor network measures are unreliable. We disambiguated the records using various methods. For the descriptive bibliometric statistics in Table 1, records were disambiguated using the data mining software Vantage Point. For the remaining analyses a second set of methods were used. We applied the principles of Gurney et al. [21] by first calculating the similarity between authors in the co-occurrence of title words and coauthors, the only metadata that occur in all articles. The second indicator concerns proximity in the dataset. Raw Web of Science data were parsed into a relational database using the SAINT toolkit [22]. Similar author names that were processed in each other’s vicinity are more likely to refer to the same person. These two indicators provided a heuristic for manual disambiguation based on similarities in spelling [21]. After disambiguation, the resulting records allowed us to create three different types of networks. The first analysis compared the entire Laureate coauthor network with the entire nonLaureate network. The second analysis compared only the primary author networks from both groups, i.e. the Laureates and the non-Laureates without coauthors. The third combined the two groups. The free network analysis software, Gephi, was used to analyze and visualize the networks, providing the basis of comparison between the Laureate and non-Laureate wcs.1183 networks.Authorship differences between the Laureates and non-LaureatesWe compared the publication patterns between the Laureates and non-Laureates using data aggregated at the author level, shown in Table 1. In terms of numbers of publications, the Nobel Laureates produced on average significantly fewer papers over the course of their careers than the non-Laureates supporting Garfield and Welljams-Dorof [6] and Hirsch [13]. Laureates also had fewer coauthors across their career. Further, the Nobel Laureates produced a significantly higher percentage of sole-authored papers than the non-Laureates both before and after winning the Nobel Prize (see also Table 3). The two groups exhibited roughly identical percentages of papers where they appear as the first author or last author. Next, the data were aggregated at the paper level to conduct descriptive statistical analyses for the two groups with results shown in Table 2. The data show that the Laureates accumulated many more citations per paper (times cited) than the non-Laureates, supporting Garfield Welljams-Dorof [6], even with matched h-indexes. The data show no meaningful differences between the groups in the avera.
