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All author names and all cited references in each article of the database were recorded.
We found that sixteen articles were not cited by any other article of the database and did not cite any other article.
We investigated possible causes of the fragmentation of the authorship network into collaborative groups by characterizing each article of the database according to: the type of drug or pesticide considered; the type of target organism considered; the modelling approach used; the first author's geographical location; and the first author's academic discipline (Table 1).
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Only 28 of the articles of the database did so.
Five of the 187 articles of the database (further classified as 'isolated articles'; see Figure 1) did not contain any list of references or did not share any reference with other articles of the database.
A total of 4,154 references were cited by the 187 articles of the database; 3,297 were cited only once and were not included in the analysis.
To circumvent the bias of common grouping resulting from shared biological references, we applied the clustering algorithm to the unipartite network: this network links two articles of the database if one of them cited the other.
The clustering into the groups C1 and C2 was statistically validated by the multiresponse permutation procedure: the citation dissimilarity between articles of the database was lower within than between groups C1 and C2 (A = 0.010, p<0.001, Table S3).
In total, 3,937 articles of the database search did not meet the inclusion criteria.
From March to August 2012, five biocurators added a total of 9631 articles to the database, of which 4254 (44%; including the 1252 articles described herein for the three journals TS, CBI and EHP, plus 3002 articles from the additional nine new journals) were derived exclusively from targeted journal curation.
For example a Scholar h-I = 81 in 2010 (h-I2010), for an author with a mean speed (h-speed) of 2.53, it was possible to extrapolate that for one article of the present database published in 1995 (h-Ibaseline) the h-I was at this baseline date: h-Ibaseline = h-I2010- h-I2010- h-I2010-1995)) = 81-2.53x(15) = 43.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com