{
    "v1_Abstract": "Citation metrics and h indices differ using different bibliometric databases. We compiled the number of publications, number of citations, h index and year since the first publication from 340 soil researchers from all over the world. On average, Google Scholar has the highest h index, number of publications and citations per researcher, and the Web of Science the lowest. The number of papers in Google Scholar is on average 2.3 times higher and the number of citations is 1.9 times higher compared to the data in the Web of Science. Scopus metrics are slightly higher than that of the Web of Science. The h index in Google Scholar is on average 1.4 times larger than Web of Science, and the h index in Scopus is on average 1.1 times larger than Web of Science. Over time, the metrics increase in all three databases but fastest in Google Scholar. The h index of an individual soil scientist is about 0.7 times the number of years since the first publication. There is a large difference between the number of citations, number of publications and the h index using the three databases. From this analysis it can be concluded that the choice of the database affects widely used citation and evaluation metrics but that bibliometric transfer functions exist to relate the metrics from these three databases. We also investigated the relationship between journal\u2019s impact factor and Google Scholar\u2019s h5-index. The h5-index is a better measure of a journal\u2019s citation than the 2 or 5 year window impact factor.",
    "v1_col_introduction": "introduction  : Scientific impact measures are increasingly being used for academic promotions, grant evaluations and evaluation of job vacancy candidates. They are also being used for the evaluations of university departments and research centres. Traditionally, the impact factor of a journal has been used \u2013 a metric developed by Garfield (1955) whereby the citations and number of papers published over a given period are divided. For most journals it shows considerable inter-annual fluctuation and it provides no information on individual papers nor individual authors. Since 2005, the h index has been used as an index for quantifying the scientific productivity of scientists based on their publication record (Hirsch, 2005). It is a personal index and provides information on the number of publications of an author and the number of citations: A scholar with an index of h has published h papers with at least h citations each. The h index can also be calculated for journals, departments, universities or countries.\nThe three widely used bibliometric databases for analysis and evaluations of\ncitations and the h index are Web of Science (Thomson Reuters), Scopus (Elsevier), and Google Scholar. Some papers have compared citations between these three databases. Although Google Scholar and Scopus seem to provide higher numbers of citations (Falagas et al., 2008), there is mixed information on the h index. For example, Bar-Ilan (2008) compared the h index for 47 highly-cited Israeli researchers across the three databases and concluded that the results from Google Scholar are considerably different from Web of Science and Scopus. Mingers and Lipitakis (2010) looked at 4,600 publications from three UK Business Schools, and found that Web of Science poorly covers the management discipline compared to Google Scholar. De Groote and Raszewski (2012) examined 31 faculty members from nursing faculty in the Midwestern USA, and concluded that more than one database should be used to calculate the h index. They further recommended that since the h index rankings differ among databases, comparisons between researchers should be done only within a specified database.\nThe difference between the three databases has been fairly well established and\nthe three databases will calculate different citations and h indices. As far as we know, the relationships between the three databases have not been investigated and derived. The aims of this paper are therefore: (i) to compare citations and h index across the three databases, (ii) to derive transfer functions to convert metrics from one database to\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\nPeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013)\nR ev ie w in g M an\nus cr ip t\nthe others, and (iii) to compare impact factors for journals and the h index. Hereto we have compared the data from 340 researchers and 31 journals. Since we are all three soil scientists, we have used only soil researchers and journals in this study.\nSoil science is a study of soil as a natural phenomenon and resource (Brevik and\nHartemink, 2010). It is a relatively small discipline, in terms of number of researchers, number of papers per annum, and citations. The IUSS (International Union of Soil Sciences) database lists about 50,000 soil scientists worldwide, but only a fraction of these are in research and actively publish, with a guestimate of 5,000 to 10,000 publishing researchers. The \u201csoil\u201d topic has lower number of papers and citations when compared to other subjects of natural resources such as \u201cair\u201d and \u201cwater\u201d (Minasny et al., 2007). The number of published papers in 2011 according to Scopus with \u201csoil\u201d in the abstract and keywords is 39,504, with a rate of increase of about 2,000 papers per year. In comparison the number of papers in 2011 on \u201cair\u201d is 1.4 times larger and the number of papers on \u201cwater\u201d is 3.5 times larger. The h index ratios for water, air, and soil (for the papers published in 2011) are 1.7, 1.3, and 1.0. Nevertheless soil is becoming more important with strong links to global issues of food security, biodiversity, land use change, and climate change (McBratney et al., 2014). While this study only used soil researchers the bibliometric results are illustrative to other agricultural, environmental, earth science and biology disciplines, and to small scientific disciplines in general.",
    "v2_Abstract": "Citation metrics and h indices differ using different bibliometric databases. We compiled the number of publications, number of citations, h index and year since first publication for 340 soil researchers from all over the world. On average, Google Scholar has the highest h index, number of publications and citations per researcher, and the Web of Science the lowest. The number of papers in Google Scholar is on average 2.3 times larger and the number of citations is 1.9 times larger compared with data in the Web of Science. Scopus metrics are slightly larger than that of the Web of Science. Over time, the metrics increase in all three databases but fastest in Google Scholar. The h index of an individual soil scientist is about 0.7 times the number of years since the first publication. About 10% of the h index is caused by self-citation but that may be higher for younger authors. There is a large difference between the number of citations, number of publications and the h index using the three different databases. We also compared journal impact factor and the h5-index from Google Scholar in 31 soil science journals. The h5-index is a better measure of a journal\u2019s citation than the 2or 5-year window impact factor. From this analysis it can be concluded that the choice of the database affects widely used citation and evaluation metrics but that pedobibliometric transfer functions exist to relate the metrics from these three databases.",
    "v2_col_introduction": "introduction  : Scientific impact measures are increasingly being used for academic promotions, grant evaluations and evaluation of job vacancy candidates. They are also being used for the evaluations of university departments and research centres. Traditionally, the impact factor of a journal was being used \u2013 a metric developed by Garfield (1955) whereby the citations and number of papers published over a given period (usually 2 years) are divided. For most journals it shows considerable inter-annual fluctuation and it provides no information on individual papers nor individual authors. Since 2005, the h index has been used as an index for quantifying the scientific productivity of scientists based on their publication record (Hirsch, 2005). It is a personal index and provides information on the number of publications of an author and the number of citations: A scholar with an index of h has published h papers with at least h citations each. The h index can also be calculated for journals, departments, universities or countries.\nThe three widely used bibliometric databases for analysis and evaluations of\ncitations and the h index are Web of Science (Thomson Reuters), Scopus (Elsevier), and Google Scholar. Some papers have compared citations between these three databases. Although Google Scholar and Scopus seem to provided higher number of citations (Falagas et al., 2008), there is mixed information on the h index. For example, Bar-Ilan (2008) compared the h index for 47 highly-cited Israeli researchers across the three databases and concluded that the results from Google Scholar are considerably different from Web of Science and Scopus. Mingers and Lipitakis (2010) looked at 4,600 publications from three UK Business Schools, and found that Web of Science poorly covers the management discipline compared to Google Scholar. De Groote and Raszewski (2012) examined 31 faculty members from nursing faculty in the mid-west USA, and concluded that more than one databases should be used to calculate the h index. They further recommended that since the h index rankings differ among databases, comparisons between researchers should be done only within a specified database.\nThe difference between the three databases has been fairly well established and\nthe three databases will calculate different citations and h indices. As far as we know, the relationships between the three databases have not been investigated and derived. The aims of this paper are therefore: (i) to compare citations and h index across the\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\nPeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013)\nR ev ie w in g M an\nus cr ip t\nthree databases, (ii) to derive transfer functions to convert metrics from one database to the others, and (iii) to compare impact factors for journals and the h index. Hereto we have compared the data from 340 researchers and 31 journals. Since we are all three soil scientists, we have used only soil researchers and journals in this study.\nData and Methods\nGoogle Scholar (GS) is a bibliographic database freely available from Google. It was introduced in 2004 and contains scholarly works across many disciplines and sources, including theses, books, reports, abstracts, peer-reviewed and non-reviewed articles, and web pages that are deemed scholarly. Google Scholar lists these automatically from its search engine activities (Harzing and van der Wal, 2009; Vine, 2006). An individual Google Scholar page was featured in 2012, where a researcher can create a webpage, with fields of interest. Google Scholar automatically searches and populates the individual\u2019s publications, calculates and displays the individual's total number of citations, h index, and i10 index. Scopus, or SciVerse Scopus, is a bibliographic database from Elsevier which contains abstracts and citations for academic journal articles, conference papers, and book chapters. Inclusion in the database is through the Scopus Content Selection and Advisory Board. Although its record goes back as early as 1823, its citations is reliable after 1995. The Web of Science is a bibliographic database from Thompson Reuters which only contained abstracts and citations for articles listed in the Web of Science indexed journals since 1900 (Harzing and van der Wal, 2009).\nData from researchers with the following areas of interest in: \u201csoil science\u201d, \u201csoil\u201d,\n\u201cpedology\u201d, \u201csoil physics\u201d, \u201csoil biology\u201d, \u201csoil chemistry\u201d, \u201csoil fertility\u201d, \u201csoil erosion\u201d, \u201csoil ecology\u201d, and \u201csoil carbon\u201d were retrieved from the Google Scholar\u2019s author page. The same researcher was located in Scopus and the Web of Science. In Scopus, the \u2018Author Identifier\u2019 tool was used to locate the researcher. In the Web of Science, the author\u2019s surname and first name\u2019s initial was used, together with \u201csoil\u201d in the search subject. When the name and publication were inconsistent across all three databases, the researcher was not included in our analysis. At the end, we collected data from 340 researchers and this included: number of total citations, h index, number of papers, and year of the first publication. These data were obtained for each researcher and from each of the three databases. The publications and citations are until June 2013.\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\nPeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013)\nR ev ie w in g M an\nus cr ip t",
    "v1_text": "results and discussion : Number of papers, citations and h index Table 1 shows the statistics of h index, number of publications, number of citations, and year of the first paper for 340 soil researchers in the three databases. Our data encompass a wide range of researchers from early-career to well-established and highly-cited researchers. The database is much larger and more diverse than previous studies where a small and focussed group of researchers was used to compare citation metrics between the databases (e.g. Franceschet, 2010; Meho and Rogers 2008; Patel et al., 2013). The median number of papers for the 340 soil researchers ranged from 23 (Web of Science) to 79 (Google Scholar) with Scopus having intermediate values. The number of citations is also highest in Google scholar, with a median of 866 citations per author whereas it is 291 in the Web of Science. The h index and its annual increase are lowest 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t in the Web of Science. This pattern holds for all of the metrics presented here: Google Scholar has the highest numbers and the Web of Science the lowest whereas the Scopus numbers are in between. Part of this may be the different types of publications included and also the periods of time covered by the 3 databases are slightly different. A simple linear regression without intercept was performed between the citation indices of the three databases (Table 2). Google Scholar has on average 2.3 times more articles and 1.9 times more citations than the Web of Science. The Scopus database (all years) has 1.1 times more papers than the Web of Science but a similar number of citations compared to the Web of Science. Since the citations are more correct and complete after 1995, a revision was made to the relationship for post 1995 authors; it shows that Scopus has about 1.2 times more citations than the Web of Science. The 20% higher citations are consistent with the findings by Falagas et al. (2008) in the field of medicine. Similarly, for articles in medical journals, Kulkarni et al. (2009) found that Google Scholar and Scopus retrieved more citations compared to Web of Science (1.22 and 1.20 times respectively). <Table 1. Somewhere here> <Table 2. Somewhere here> The relationship between number of papers and citations is scattered, especially for the number of papers (Fig. 1), however the relationships between h index values across the 3 databases appear to be quite linear. The h index in Google Scholar is on average 1.4 times larger than Web of Science, and the h index in Scopus (post 1995 authors) is on average 1.1 times larger than Web of Science. However for pre-1995 authors, their Scopus h index is similar and sometimes can be smaller when compared to Web of Science. While Google Scholar contains more grey literature (informally published written material) and its citations may contain errors (Harzing and van der Wal, 2009), the h index appears to be quite robust and comparable with Web of Science and Scopus. This is due to the fact that h index does not vary greatly if the number of articles increases (e.g. book chapters or unrefereed articles). In addition, extra citations do not have a large effect on the h index, as once a paper has reached h citations additional citations to that paper do not affect its value (Franceschini et al., 2013; Courtault and Hayek, 2008). 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t <Fig. 1. Somewhere here> Our results are different from the study by Franceschet (2010) who evaluated 13 computer scientists from his university\u2019s department and found that on average Google Scholar had five times more papers, eight times more citations and a three-fold larger h index. Our results from 340 soil researchers are more in line with De Groote and Raszewski (2012) who looked at 30 researchers from nursing and found that the h index from Google Scholar is 1.3 times larger than the Web of Science, and Scopus is 1.1 times larger than the h index in the Web of Science. Similar results were obtained by Meho and Rogers (2008) who evaluated 22 human-computer interaction researchers from the UK and found that the h index in Google Scholar is on average 1.6 times higher than Web of Science. Patel et al. (2013) compared publications and citations for 195 Nobel laureates in Physiology and Medicine using the three databases. They found no concordance between the three databases when considering the number of publications and citations count per laureate. However, the h index was the most reliably calculated bibliometric index across the three databases. We calculated the Spearman\u2019s rank correlation (\u03c1) of the h index of the 340 researchers from the three databases. The three databases show excellent correlation for the h index, with WoS and GS as having the largest concordance. Unexpectedly, the rank correlation in terms of no. citations and no. papers (Table 3) also indicates that the three databases are comparable. This implies that the ranking of individuals within a database is comparable with the other databases. Our correlation is also much higher compared to the 13 computer scientists studied by Franceschet (2010) who only obtained \u03c1 = 0.65. We used a much larger dataset, and the GS data came from the page that was created by the researcher, thus the listed papers and citations are assumed to be more complete. <Table 3. Somewhere here> a comparison between 5 year impact factor (if) and google scholar h5-index for 31 soil : science journals in 2012. PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Figure 6 (a) Relationship between cites and 5 year Impact factor (IF), and (b) relationship between cites and Google Scholar h5-index for soil science journals in 2012. Cites is the number of citations in 2012 for papers that were published in 2007 \u2013 2011. PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Table 1(on next page) Descriptive statistics of publication indices from Google Scholar, Scopus and Web of Science database for 340 soil researchers PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Table 1. Descriptive statistics of publication indices from Google Scholar, Scopus and Web of Science database for 340 soil researchers Number of citations Number of papers First year of publication h index m (rate of increase per year) conclusions : From this analysis the following can be concluded: - There is a large difference between the number of citations, number of publications and the h index using the three different databases. - On average, Google Scholar gives the largest number of publications, largest number of citations and the highest h index. The Web of Science gives the lowest averages. - There are solid relationships between the h indices in these three databases. - The h5-index has a correlation with the five year impact factor, however it is more robust and less affected by citation manipulation. It should be considered as an alternative to the journal\u2019s impact factor. This analysis has shown that the choice of the database affects the assessment of scientific impact for academic promotions, grant evaluations, job vacancy candidates or the evaluations of university departments and research centres. It is recommended that we should quote these bibliometric indices for all three databases as they reflect different types of publications. Web of Science uses mostly refereed journal articles, Scopus includes conference proceedings and book chapters, whereas Google Scholar includes other publications (including software). The established relationships between the databases (Table 2) can be used as bibliometric transfer functions by anyone interested in relating databases. We are not aware of whether these functions have been established for other scientific disciplines but assume they will be similar. As a test, we applied our function relating the h index of WoS and GS to the 30 nursing faculty data of DeGroote and Raszewski (2012) and the function gives a good prediction with a Spearman rank correlation of 0.852. We envisage that these functions would work better for science than socio-economical disciplines. However, this needs to be investigated. Although we focussed on the relatively small discipline of soil science, the reported researchers has a lot of cross-over with other disciplines, in particular earth science, agricultural science, biogeochemistry and ecology. Many researchers in ecology and microbiology work with soil as a medium, while they do not necessary study soil as a natural body in classical pedology. Their contributions also elevated the citations as 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t compared to pure soil research. The trend of h index for soil researchers appears to be in between the water and biochemistry disciplines (McCarty and Jawitz, 2013). However, soil science publication rate (average 2.5 papers per year per researcher) is lower compared to water and biochemistry (average of 3.1 and 3.8 papers per year, respectively). 335 336 337 338 339 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t the h index of soil researchers : In an earlier paper (Minasny et al., 2007) we investigated the relationship between the h index of 228 soil researchers and found that the index was 0.7 times the number of years since the first publication (which we called scientific age, or t). That means if a 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t researcher has been publishing for 10 years his/her h index should be about 7. We calculated this index using the Web of Science database, and now we repeated this using analysis to the 340 soil researchers in this study (which are different from the previous list) (Fig. 2). Although the data are scattered the relationship holds: h index = 0.73 x scientific age (R2 = 0.72). The Web of Science database shows that the average rate of h index increase over time (m) is 0.7, with the lowest value of 0.06 and highest value of 2.9 (Table 1). The average m value for Scopus is 0.7 and for Google Scholar it is 0.8 (Table 1). McCarty and Jawitz (2013) evaluated the linear relationship between scientific age and h index for 4 disciplines and found the following mean m values of 0.83, 0.47, 0.43, and 0.36 for biochemistry, water, economics, and anthropology, respectively. Thus the trend of soil science is in between water and biochemistry. For selected researchers, we tried to calculate the distribution of m (h index divided by the number of years since first publication) as a function of sub-disciplines in soil science. Table 5 shows the distribution of m for WoS and GS according to 6 subdisciplines. It shows that h index varies between sub-diciplines, for WoS, soil biology, biogeochemistry and ecology have the highest m values (median of 0.8). This is followed by soil physics, soil fertility and management, soil geography and pedometrics, chemistry and lastly pedology (average m = 0.5). The order in Google Scholar is slightly different, but it is consistent in that soil biology has the highest m value and pedology is the lowest. Therefore within soil science, the sub-disciplines also vary in terms of h index. The citation ratios are:- Soil biology, ecology and biogeochemistry, Soil management and fertility, Soil geography & pedometrics, Soil physics, Soil chemistry and mineralogy, Pedology 1: 0.9: 0.8: 0.8: 0.8: 0.6; respectively. Although the number of citations for researchers across the three databases can be quite different, the relationship between the number of citations and the h index is quite consistent across the three databases (Fig. 3): h index = \u00bd n\u00bd (R2 = 0.95) 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t This relationship follows the function postulated by Hirsch (2005) where number of citations is about 3 to 5 times h2, and it appears the h index follows an absorption-type relationship (Warrick, 2003), increasing rapidly at low numbers of citations with the rate decreasing with increasing number of citations. <Fig. 2. Somewhere here> <Fig. 3. Somewhere here> Table 4 shows the relationship of the average number of papers and citations per year for the 340 soil researchers. This can be interpreted as: on average, a soil researcher produces 5 articles per year, 2 articles in international refereed journals, 1 in a conference proceedings, and 2 other unrefereed publications. The researcher receives 65 citations per year from journal articles, an additional 13 citations from conference proceedings and another 44 citations from other publications. <Table 4. Somewhere here> <Table 5. Somewhere here> 1 times larger than web of science. over time, the metrics increase in all three databases : but fastest in Google Scholar. The h index of an individual soil scientist is about 0.7 times the number of years since the first publication. There is a large difference between the number of citations, number of publications and the h index using the three databases. From this analysis it can be concluded that the choice of the database affects widely used citation and evaluation metrics but that bibliometric transfer functions exist to relate the metrics from these three databases. We also investigated the relationship between journal\u2019s impact factor and Google Scholar\u2019s h5-index. The h5-index is a better measure of a journal\u2019s citation than the 2 or 5 year window impact factor. PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Citations and the h index of soil researchers and journals in the Web of Science, data and methods : Google Scholar (GS) is a bibliographic database freely available from Google. It was introduced in 2004 and contains scholarly works across many disciplines and sources, including theses, books, reports, abstracts, peer-reviewed and non-reviewed articles, and web pages that are deemed scholarly. Google Scholar lists these automatically from its search engine activities (Harzing and van der Wal, 2009; Vine, 2006). An individual Google Scholar page was featured in 2012, where a researcher can create a webpage, with fields of interest. Google Scholar automatically searches and populates the individual\u2019s publications, calculates and displays the individual's total number of citations, h index, and i10 index. Scopus, or SciVerse Scopus, is a bibliographic database from Elsevier which contains abstracts and citations for academic journal 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t articles, conference papers, and book chapters. Inclusion in the database is through the Scopus Content Selection and Advisory Board. Although its record goes back as early as 1823, its citations are reliable after 1995. The Web of Science is a bibliographic database from Thompson Reuters which only contained abstracts and citations for articles listed in the Web of Science indexed journals since 1900 (Harzing and van der Wal, 2009). Data from researchers who listed their areas of interest as: \u201csoil science\u201d, \u201csoil\u201d, \u201cpedology\u201d, \u201csoil physics\u201d, \u201csoil biology\u201d, \u201csoil chemistry\u201d, \u201csoil fertility\u201d, \u201csoil erosion\u201d, \u201csoil ecology\u201d, and \u201csoil carbon\u201d were retrieved from the Google Scholar author pages. The same researchers were located in Scopus and the Web of Science. In Scopus, the \u2018Author Identifier\u2019 tool was used to locate the researcher. In the Web of Science, the author\u2019s surname and first name\u2019s initial was used, together with \u201csoil\u201d in the search subject. When the name and publication record were inconsistent across all three databases, the researcher was not included in our analysis. At the end, we collected data from 340 researchers and this included: number of total citations, h index, number of papers, and year of the first publication. These data were obtained for each researcher and from each of the three databases. The publications and citations are until June 2013. 340 soil researchers from all over the world. on average, google scholar has the highest h : index, number of publications and citations per researcher, and the Web of Science the lowest. The number of papers in Google Scholar is on average 2.3 times higher and the number of citations is 1.9 times higher compared to the data in the Web of Science. Scopus metrics are slightly higher than that of the Web of Science. The h index in Google Scholar is on average 1.4 times larger than Web of Science, and the h index in Scopus is on average PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Figure 2 Relationship between the scientific age (t) of 340 soil researchers and the h index (Web of Science data). PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Figure 3 Relationship between the number of citations and the h index of 340 soil researchers from 3 databases Black dots are data from Web of Science, green squares are from Scopus, and blue triangles are from Google Scholar. PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Figure 4 (a) Relationship between the h index with and without self-citation, (b) relationship between the scientific age of 340 soil researchers and percentage self-citation based on Scopus data. PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Figure 5 scopus, and google scholar : Budiman Minasny 1 *, Alfred E. Hartemink 2 , Alex. McBratney 1, Ho-Jun Jang1 1 The University of Sydney, Department of Environmental Sciences, Faculty of Agriculture & Environment, NSW 2006, Australia. 2 University of Wisconsin \u2013 Madison, Department of Soil Science, FD Hole Soils Lab, 1525 Observatory Drive. Madison, WI, 53706, USA. ( * corresponding author) E-mail budiman.minasny@sydney.edu.au 1 2 3 4 5 6 7 8 9 10 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t self-citations : A way to boost the h index is by self-citation. Hyland (2003) found that self-citation is 12% of all references in biology, engineering and physics, compared to 4% in sociology, philosophy, linguistics, or marketing. For soil science journals, we found a mean of 12% self-citations but it differs between the sub-disciplines (Minasny et al., 2010). High rates of self-citation were accompanied by high journal impact factor ranking; China and the USA had the highest rates of self-citation whereas Egypt, Algeria, Ukraine, and Indonesia have low levels of self-citations in soil science (Minasny et al., 2010). So high rates of self-citation may influence the h index and the Scopus database allows calculation of the h index with and without self-citation. Self-citation here is the so-called diachronous kind (Lawani, 1982), which is self-citation from the citations received by the author. The other type is called synchronous which is more difficult to 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t calculate, i.e. author\u2019s self-referencing relative to the total number of references cited in a paper. The relationship for the 340 soil researchers is consistent and the average h index without self-citation is about 12% lower (Fig. 4): h index without self-citation = 0.88 \u00d7 h index (R2 = 0.97). We found a weak relationship between percentage of self-citation and scientific age (t) (Fig. 4). It suggests that some younger authors appear to have high rates of self citation as their works were not known widely and their citations mainly come from themselves, as the researchers mature their papers are more widely known and more external citations were gained thus a lower percentage of self-citations: Percent self-citation = 42 - 5 t0.5 (R2 = 0.18). <Fig. 4. Somewhere here> journal citations : We retrieved 31 Soil Science journal impact factors (IF) and other metrics from the 2012 Thompson Reuters Journal Citation Reports (JCR, released in June 2013). Google Scholar also has measures of journal\u2019s metric, the h5-index, which is the h index for articles published in that journal for the last five years. The list of journals for the soil science discipline in Google Scholar is slightly different from the Thompson Reuters Journal Citation Reports (JCR), and therefore we used the journals listed in JCR as the basis for comparison. We searched for the h5-index for the journals in Google Scholar metrics for 2012 (released July 2013). Table 6 shows that Google Scholar h5-index has a better correlation with the five year IF (impact factor) than the two year IF, and Figure 5 shows the comparison between GS h5-index and the five year IF. While the h5-index and five year IF have a high rank correlation (\u03c1 = 0.90), the ranking is different for different journals. The journals \u2018Soil Biology and Biochemistry\u2019 and \u2018Plant and Soil\u2019 both consistently ranked no. 1 and 2 in JCR and GS while other journals appear to be slightly different in their ranking (1 to 3 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t places difference). The top two journals are able to maintain a large number of citations relative to the number of papers they publish. <Table 6. Somewhere here> <Fig. 5. Somewhere here> There are 4 journals that are ranked much higher (>= 4 difference in rank) in Google Scholar compared to the IF: \u2018Soil Science Society of America Journal\u2019, \u2018Journal of Plant Nutrition and Soil Science\u2019, \u2018Pedosphere\u2019, and \u2018Revista Brasileira de Ci\u00eancia do Solo\u2019. All these journals are published by national soil science societies (USA, Germany, China and Brazil). In the case of \u2018Revista Brasileira de Ci\u00eancia do Solo\u2019 which ranked 12 in GS and 25 in JCR, Google scholar includes more citations from non-English articles. Contrarily, there are four journals that are ranked much lower (<= 4 difference in rank) in Google Scholar: \u2018European Journal of Soil Science\u2019, \u2018Soil Use and Management\u2019, \u2018Journal of Soil and Water Conservation\u2019, and \u2018Soil Science\u2019. The Thompson Reuters Journal Citation Reports suffers from a miscalculation, for example \u2018Australian Journal of Soil Research\u2019 was reported to have a 2 year IF of 3.443. This is a miscalculation, as the journal changed its name to \u2018Soil Research\u2019 in 2011, and the IF calculation for Australian Journal of Soil Research only accounts for papers published until 2010. \u2018Soil Research\u2019 was again listed as a separate journal in JCR. We have recalculated the actual impact factor for this journal in our analysis. While there is a positive correlation between cites (citations in 2012 to papers published from the previous 5 years) and IF, we can see that there are 2 trends (Fig. 6a). For journals that published <700 papers between 2007 and 2011 (or on average less than 140 papers per year) IF tends to increase rapidly with increasing citations (1.2 increase in IF per 1000 citations). For the other 7 journals that published more than 700 papers, the slope is half as much (0.6 IF increase per 1000 citations). So there is a drawback for journals that publish more papers. Meanwhile the h5-index is mostly controlled by number of citations following an absorption relationship (Fig. 6b). Although the citations come from WoS, the h5-index still holds the square-root relationship supporting its robustness. Table 6 also shows that the GS h5-index is more correlated to the Eigenfactor metric compared to IF. The Eigenfactor metric is based on the Google PageRank 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t algorithm which calculated the \u201cinfluence\u201d of the journal based on the citations weighted by the \u201cquality\u201d of the journal (Bergstrom, 2007). A citation from a highly cited journal will have a higher weighting than a lower cited journal. We showed that this metric is less vulnerable to self-citation than the impact factor (Minasny et al., 2010). Interestingly, Google Scholar does not apply its PageRank for citations. Vanclay (2006) and Cortault and Hayek (2008) established that the h index is robust and is relatively unaffected by grey literature and errors in citations such as in Google Scholar. Most of the errors (and distortions) in citation databases are found in the \u2018long tails\u2019 of the citation distribution and they tend not to affect the h index much. For the journals considered here, we calculated the ratio between h5-index and number of papers, and it shows that only 1- 9% (median 5%) of the total papers that contributed to h5-index. In other words, less than 10% of the cited papers are influencing the h5-index. We also demonstrated that the h5-index keeps its relationship even when using WoS citations. Harzing and van der Wal (2009) recommended the use of the GS h index for Management and International Business journals. We also concur that the h5-index is a better measure of a journal\u2019s citation performance than the impact factor as it is more robust and less affected by citation manipulation. It is now acknowledged that there are ways of manipulating impact factor, which include self-citation, and editorials that listed references to previously published articles (Falagas et al., 2008). The h index is less sensitive to the increase in number of citations, while individual highly cited papers can artificially increase the impact factor. In addition, it only considers the top influential h papers in the journal, thus it does not penalise a journal for publishing a larger number of papers. Although h index can also be manipulated by self-citation, in order to increase the h index considerably, a journal has to be more tactical by increasing a significant number of citations to certain papers. <Fig. 6. Somewhere here> 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t google scholar : Minimum 16 3 1953 1 0.09 25th Quantile 266 32 1985 26 0.56 Median 866 79 1993 15 0.85 75th Quantile 2596 146 2001 26 1.18 Maximum 49447 1159 2011 115 3.67 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Variable By Variable Spearman \u03c1 Prob > |\u03c1| WoS h index GS h index 0.939 <.0001 Scopus h index GS h index 0.931 <.0001 WoS h index Scopus h index 0.922 <.0001 WoS no. citations GS no. citations 0.939 <.0001 Scopus no. citations GS no. citations 0.955 <.0001 WoS no. citations Scopus no. citations 0.945 <.0001 WoS no. papers GS no. papers 0.840 <.0001 Scopus no. papers GS no. papers 0.896 <.0001 WoS no. papers Scopus no. papers 0.905 <.0001 2 3 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Table 4(on next page) Comparison of the h index over time using the data from 340 soil researchers in Google scopus : Minimum 1 1 1955 1 0.03 25th Quantile 116 14 1989 5 0.45 Median 469 34 1996 11 0.71 75th Quantile 1361 65 2004 19 1.00 Maximum 28693 423 2011 70 2.87 Authors who started to publish after 1995 Scopus h index = 1.11 WoS h index 0.02 0.954 2 3 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Table 3(on next page) Spearman\u2019s rank correlation coefficient ( ) of the \u03c1 h index of the 340 researchers using web of science : Minimum 1 1 1957 1 0.06 25th Quantile 76 10 1991 5 0.41 Median 291 23 1998 10 0.67 75th Quantile 945 48 2004 17 1.00 Maximum 32837 424 2011 96 2.87 1 2 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Table 2(on next page) Comparison of publication indices from Google Scholar (GS), Scopus and Web of science (wos) : PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Standard error of estimates R2 gs no. papers = 2.33 wos no. papers 0.06 0.797 : scholar (gs), scopus and web of science (wos) : PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Table 4. Comparison of the h index over time using the data from 340 soil researchers in Google Scholar (GS), Scopus and Web of Science (WoS) Standard error of estimates R2 GS h index = 0.84 \u00d7 year 0.02 0.745 WoS h index = 0.73 \u00d7 year 0.02 0.717 Scopus h index = 0.73 \u00d7 year 0.02 0.759 GS no. papers = 5.5 \u00d7 year 0.23 0.620 WoS no. papers = 2.5 \u00d7 year 0.12 0.567 Scopus no. papers = 3.0 \u00d7 year 0.12 0.656 GS no. citations = 122 \u00d7 year 9.1 0.344 WoS no. citations = 65 \u00d7 year 5.8 0.269 Scopus no.citations = 78 \u00d7 year 5.8 0.346 1 2 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Table 5(on next page) The distribution of m (h index divided by the number of years since first publication) according to sub-disciplines in soil science using the data from Google Scholar, Scopus and Web of Science, n is the number of samples, Q25 and Q75 refers t PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t n Min Q25 Median Q75 Max Google Scholar Pedology 25 0.09 0.37 0.56 0.83 1.67 Soil chemistry 42 0.09 0.45 0.78 1.16 2.00 Soil physics 67 0.21 0.56 0.79 1.00 2.55 Soil geography & pedometrics 28 0.16 0.58 0.87 1.02 1.93 Soil management & fertility 42 0.28 0.69 0.98 1.30 2.12 Soil biology, ecology, biogeochemistry 88 0.23 0.78 1.01 1.65 3.67 Web of Science Pedology 25 0.08 0.32 0.46 0.78 1.67 Soil chemistry 42 0.06 0.32 0.63 1.00 1.67 Soil geography & pedometrics 67 0.11 0.50 0.64 0.86 1.50 Soil management & fertility 28 0.15 0.47 0.67 1.00 1.63 Soil physics 42 0.17 0.46 0.72 1.00 2.14 Soil biology, ecology, biogeochemistry 88 0.14 0.63 0.83 1.33 2.87 2 3 4 5 6 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Table 6(on next page) Spearman\u2019s rank correlation coefficient ( ) of the Google Scholar h5-index and impact \u03c1 factor (IF), no. papers, citations, and Eigenfactor metrics from Journal Citation Reports for 31 soil science journals. Cites is the number of citations in 2012 for pape PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t Table 6. Spearman\u2019s rank correlation coefficient (\u03c1) of the Google Scholar h5-index and impact factor (IF), no. papers, citations, and Eigenfactor metrics from Journal Citation Reports for 31 soil science journals. Cites is the number of citations in 2012 for papers that were published in 2007 \u2013 2011. variable by variable spearman \u03c1 prob > |\u03c1| : h5-index Cites (5 years) 0.972 <.0001 h5-index Eigenfactor 0.970 <.0001 h5-index 5 year IF 0.903 <.0001 h5-index 2 year IF 0.870 <.0001 h5-index No. papers (5 years) 0.721 <.0001 2 3 4 5 6 PeerJ reviewing PDF | (v2013:09:794:1:1:NEW 1 Oct 2013) R ev ie w in g M an us cr ip t",
    "v2_text": "results and discussion : Number of papers, citations and h index Table 1 shows the statistics of h index, number of publications, number of citations, and year of the first paper from 340 soil researchers in the three databases. Our data encompass a wide range of researchers from early-career to well-established and highly-cited researchers. The database is much larger and more diverse than previous studies where a small and focussed group of researchers was used to compare citation metrics between the databases (e.g. Franceschet, 2010; Meho and Rogers 2008; Patel et al., 2013). The median number of papers for the 340 soil researchers ranged from 23 (Web of Science) to 79 (Google Scholar) with Scopus having intermediate values. The number of citation is also highest in Google scholar and the median is 866 citations per author whereas it is 291 in the Web of Science. The h index and its annual increase are lowest in the Web of Science. This pattern holds for all of the metrics presented here: Google Scholar has the highest numbers and the Web of Science the lowest whereas the Scopus numbers are in between. Part of this may be the effect of different types of publications being included and also the periods are slightly different. A simple linear regression without intercept was performed between the citation indices of the three databases (Table 2). Google Scholar has on average 2.3 times more articles and 1.9 times more citations than the Web of Science. The Scopus database (all years) has 1.1 times more papers than the Web of Science but a similar number of citations compared to the Web of Science. Since the citations are complete and more correct after 1995 and revising the relationship for post 1995 authors, it shows that Scopus has about 1.2 times more citations than the Web of Science. The 20% extra citations are consistent with the finding by Falagas et al. (2008) in the field of medicine. Similarly, for articles in medical journals, Kulkarni et al. (2009) found that Google Scholar and Scopus retrieved more citations compared to Web of Science (1.22 and 1.20 times respectively). 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t <Table 1. Somewhere here> <Table 2. Somewhere here> The relationship of number of papers and citations is scattered, especially for the number of papers (Fig. 1), however the relationships between h index values across the 3 databases appear to be quite linear. The h index in Google Scholar is on average 1.4 times larger than Web of Science, and the h index in Scopus (post 1995 authors) is on average 1.1 times higher than Web of Science. However for pre-1995 authors, their Scopus h index is similar and can be smaller when compared to Web of Science. While Google Scholar contains more grey literature (informally published written material) and its citations may contain errors (Harzing and van der Wal, 2009), the h index appears to be quite robust and comparable with Web of Science and Scopus. This is due to the fact that h index does not vary greatly if the number of articles increases (e.g. book chapters or unrefereed articles). In addition, extra citations will not affect much of the h index, as once a paper has reached h citations the additional citations to that paper does not affect its value (Franceschini et al., 2013; Courtault and Hayek, 2008). <Fig. 1. Somewhere here> Our results are different from the study from Franceschet (2010) who evaluated 13 computer scientists from his university\u2019s department. Franceschet (2010) found a much higher number when comparing Google Scholar to Web of Science indicators: on average Google Scholar has five times more papers, eight times more citations and a three times higher h index. Our results of 340 soil reearchers are more in line with De Groote and Raszewski (2012) who looked at 30 researchers from nursing and found that for the h index Google Scholar is 1.3 times higher than the Web of Science, and Scopus is 1.1 higher than the h index in the Web of Science. Similar results were obtained by Meho and Rogers (2008) who evaluated 22 human-computer interaction researchers from UK and found that the h index in Google Scholar is on average 1.6 times higher than Web of Science. Patel et al. (2013) compared publications and citations for 195 Nobel laureates in Physiology and Medicine using the three databases. They found no concordance between the three databases when considering the number of publications 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t and citations count per laureate. However, the h index was the most reliably calculated bibliometric across the three databases. We calculated the Spearman\u2019s rank correlation (\u03c1) of the h index of the 340 researchers from the three databases. The three databases show excellent correlation for the h index, with WoS and GS as having the largest concordance. Unexpectedly, the rank correlation in terms of no. citations and no. papers (Table 3) also indicates that the three databases are comparable. This implies that the ranking of individuals within a database is comparable with the other other databases. Our correlation is also much higher compared to the 13 computer scientists studied by Franceschet (2010) who only obtained \u03c1 = 0.65. We used a much larger dataset, and the GS data came from the page that was created by the researcher, thus the listed papers and citations are assumed to be more complete. <Table 3. Somewhere here> a comparison between 5 year impact factor (if) and google scholar h5-index for 31 soil : science journals in 2012. PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t Figure 6 The relationship between cites and 5 year Impact factor (IF) and Google Scholar h5index for soil science journals in 2012. Cites is the number of citations in 2012 for papers that were published in 2007 \u2013 2011. PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t Table 1(on next page) Tables 1-6 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t Table 1. Descriptive statistics of publication indices from Google Scholar, Scopus and Web of Science database for 340 soil researchers. Number of citations Number of papers First year of publication h index m (rate of increase per year) conclusions : From this analysis the following can be concluded: - There is a large difference between the number of citations, number of publications and the h index using the three different databases. - On average, Google Scholar gives the largest number of publications, largest number of citation and the highest h index. The Web of Science gives the lowest averages. - There are solid relationships of the h index between these three databases. - The h5-index has a high correlation with the impact factor, however it is more robust and less affected by citation manipulation. It should be considered as an alternative to the journal\u2019s impact factor. This analysis has shown that the choice of the database affects for assessing scientific impact for academic promotions, grant evaluations, job vacancy candidates or the evaluations of university departments and research centres. It is recommended that we should quote these bibliometric indices for all three databases as they reflect different types of publications. Web of Science uses refereed journal articles, Scopus includes conference proceedings and book chapters, whereas Google Scholar include other 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t publications (including software). The established relationships between the databases (Table 2) can be used as pedobibliometric transfer functions by anyone interested relating databases. We are not aware whether these functions have been established for other scientific disciplines but assume they will be similar. As a test, we applied our function relating h index of WoS and GS to the 30 nursing faculty data of DeGroote and Raszewski (2012) and the function gives a good prediction with a Spearman rank correlation of 0.852. the h index of soil researchers : In an earlier paper (Minasny et al., 2007) we investigated the relationship between the h index of 228 soil researchers and found that the index was about 0.7 times the number of years since the first publication (which we called scientific age, or t). That means if a researcher has been publishing since 10 years his/her h index should be about 7. We had calculated this index using the Web of Science database, and now we have repeated this using the 340 soil researchers (which are different from the previous list) (Fig. 2). Although the data are scattered the relationship holds: h index = 0.7 x scientific age (i.e. first year of publication) (R2 = 0.72). The Web of Science database shows that the average rate of h index increase over time (m) is 0.7, with the lowest value of 0.06 and highest value of 2.9 (Table 4). The average m value for Scopus is 0.7 and for Google Scholar it is 0.8 (Table 1). 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t For selected researchers, we tried to calculate the distribution of m (h index divided by the number of years since first publication) as a function of sub-disciplines in soil science. Table 5 shows the distribution of m for WoS and GS according to 6 subdisciplines. It shows that h index varies between sub-diciplines, for WoS, soil biology, biogeochemistry and ecology having the highest m values (median of 0.8). This is followed by soil physics, soil fertility and management, soil geography and pedometrics, chemistry and lastly pedology (average m = 0.5). The order in Google Scholar is slightly different, but it is consistent that soil biology has the highest m value and pedology is the lowest. Therefore within soil science, the sub-discipline also varies in terms of h index. The citation ratios are:- Soil biology, ecology and biogeochemistry, Soil management and fertility, Soil geography & pedometrics, Soil physics, Soil chemistry and mineralogy, Pedology 1: 0.9: 0.8: 0.8: 0.8: 0.6; respectively. Although the number of citations across the three databases can be quite different, the relationship between the number of citations and the h index is quite consistent across the three databases (Fig. 3): h index = \u00bd n\u00bd (R2 = 0.95) This relationship follows the function postulated by Hirsch (2005) where number of citations is about 3 to 5 times h2, and it appears the h index follows an absorption-type relationship (Warrick, 2003), increasing rapidly at low number of citations and the rate decreases with increasing number of citations. <Fig. 2. Somewhere here> <Fig. 3. Somewhere here> Table 4 shows the relationship of the average number of papers and citations per year for the 340 soil researchers. This can be interpreted as: on average, a soil researcher produces 5 articles per year, 2 articles in a journal, 1 in a conference proceeding, and 2 other unrefereed publications. The researcher receives 65 citations from journal articles, additional 13 citations from conference proceedings and another 44 citations from other publications. 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t <Table 4. Somewhere here> <Table 5. Somewhere here> journal citations : We retrieved 31 Soil Science journal impact factors (IF) and other metrics from the 2012 Thompson Reuters Journal Citation Reports (JCR, released in June 2013). Google Scholar also has measures of journal\u2019s metric, h5-index, which is the h index for articles published in that journal for the last five years. The list of journals for soil science discipline in Google Scholar is slightly different from the Journal Citation Reports, and therefore we used the journals listed in Thompson Reuters Journal Citation Reports as the basis for comparison. We searched for the h5-index for the journals in Google Scholar metric for 2012 (released July 2013). Table 5 shows that Google Scholar h5-index has a better correlation with the five year IF (impact factor) than the two year IF, whereas Figure 5 shows the comparison between GS h5-index and the five year IF. While the h5-index and five year IF has a high rank correlation (\u03c1 = 0.90), the ranking is different for different journals. The journals \u2018Soil Biology and Biochemistry\u2019 and \u2018Plant and Soil\u2019 both consistently ranked no. 1 and 2 in JCR and GS while other journals appear to be slightly different in the ranking (1 to 3 places difference). The 2 journals are able to maintain high number of citations relative to the number of papers they published. <Table 6. Somewhere here> <Fig. 5. Somewhere here> There are 4 journals that are ranked much higher (>= 4 difference in rank) in Google Scholar compared to the IF: \u2018Soil Science Society of America Journal\u2019, \u2018Journal of Plant Nutrition and Soil Science\u2019, \u2018Pedosphere\u2019, and \u2018Revista Brasileira de Ci\u00eancia do Solo\u2019. All these are journals are published by national soil science societies (USA, Germany, China and Brazil). In the case of \u2018Revista Brasileira de Ci\u00eancia do Solo\u2019 which ranked 12 in GS and ranked 25 in JCR, Google scholar includes more citations from non-English articles. Contrarily, there are four journals that are ranked much lower (<= 4 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t difference in rank) in Google Scholar: \u2018European Journal of Soil Science\u2019, \u2018Soil Use and Management\u2019, \u2018Journal of Soil and Water Conservation\u2019, and \u2018Soil Science\u2019. The Thompson Reuters Journal Citation Reports suffers from a miscalculation, for example \u2018Australian Journal of Soil Research\u2019 was reported to have a 2 year IF of 3.443. This is a miscalculation, as the journal changed its name to \u2018Soil Research\u2019 in 2010, and the IF calculation only accounts for papers published in 2010. \u2018Soil Research\u2019 was again listed as a separate journal in JCR. We have recalculated the actual impact factor for this journal in our analysis. While there is a positive correlation between citations (citations in 2012 to papers published from the previous 5 years) and IF, we can see that there are 2 trends (Fig. 6a). For journals that published <700 papers between 2007 and 2011 (or on average less than 140 papers per year) IF tends to increase much rapidly with increasing citations (1.2 increase in IF per 1000 citation). For the other 7 journals that published more than 700 papers, the slope is half (0.6 IF increase per 1000 citation). So there is a drawback for journals to publish more papers. Meanwhile h5-index is mostly controlled by no. citations following an absorption relationship (Fig. 6b). Although the citations come from WoS, h5-index still holds the square-root relationship supporting its robustness. Table 5 also shows that GS h5-index is more correlated to the Eigenfactor metric compared to IF. The Eigenfactor metric is based on the Google PageRank algorithm which calculated the \u201cinfluence\u201d of the journal based on the citations weighted by the \u201cquality\u201d of the journal (Bergstrom, 2007). Citation from a highly cited journal will have a higher weighting than a lower cited journal. We showed that this metric is less vulnerable to self-citation as compared to impact factor (Minasny et al., 2010). Interestingly, Google Scholar does not apply its PageRank for citations. Vanclay (2006) and Cortault and Hayek (2008) established that the h index is robust and is relatively unaffected by grey literature and errors in citations such as in Google Scholar. Most of the errors (and distortions) in citation databases are found in the \u2018long tails\u2019 of the citation distribution and they tend not to affect the h index much. For the journals considered here, we calculated the ratio between h5-index and number of papers, and it shows that only 1- 9% (median 5%) of the total papers that contributed to h5-index. And we also demonstrated that h5-index keeps its relationship even when using WoS citations. 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t Harzing and van der Wal (2009) recommended the use of GS h index for Management and International Business journals. We also concur that the h5-index is a better measure of a journal\u2019s citation performance when compared to impact factor as it is more robust and less affected by citation manipulation. It is now acknowledged that there are ways of manipulating impact factor, which include self-citation, and editorials that listed references to previously published articles (Falagas and Alexiou, 2008). The h index is less sensitive to the increase in number of citations, while individual highly cited papers can artificially increases the impact factor. Although h index can also be manipulated by self-citation, in order to increase the h index considerably, a journal has to be more tactical by increasing a significant number of citations to certain papers. In addition, it only considers the top influential h papers in the journal, thus it does not penalise a journal for publishing a larger number of papers. <Fig. 6. Somewhere here> science, scopus, and google scholar : Budiman Minasny 1 *, Alfred E. Hartemink 2 , Alex. McBratney 1, Ho-Jun Jang1 1 The University of Sydney, Department of Environmental Sciences, Faculty of Agriculture & Environment, NSW 2006, Australia. 2 University of Wisconsin \u2013 Madison, Department of Soil Science, FD Hole Soils Lab, 1525 Observatory Drive. Madison, 53706 WI, USA. ( * corresponding author) E-mail budiman.minasny@sydney.edu.au 1 2 3 4 5 6 7 8 9 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t self-citations : A way to boost the h index is by self citation. Hyland (2003) found that self-citation is 12% of all references in biology, engineering and physics, compared to 4% in sociology, philosophy, linguistics, or marketing. For all soil science journals, we found a mean of 12% self-citations but it differs between the subdisciplines (Minasny et al., 2010). High rates of self-citation were accompanied by high journal impact factor ranking; China and the USA had the highest rates of self-citation whereas Egypt, Algeria, Ukraine, and Indonesia have low levels of self-citations in soil science (Minasny et al., 2010). So high rates of self-citation may influence the h index and the Scopus database allows calculation of the h index with and without self-citation. Self-citation here is the so-called diachronous (Lawani, 1982) kind, which is self-citation from the citations received by the author. The other type is called synchronous which is more difficult to calculate, i.e. author\u2019s self-referencing relative to the total number of references quoted in a paper. The relationship for the 340 soil researchers is consistent and the average h index without self-citation is about 12% lower (Fig. 4): h index without self-citation = 0.88 \u00d7 h index (R2 = 0.97) We found a weak relationship between percentage self-citation and scientific age (t) which suggests that older and more established authors cite less of their own work (Fig. 4). It also suggests that some younger authors have very high rates of self citation as their works were not known widely, as the researcher matures the papers are more widely known and more citations were gained. Percent self-citation = 42 - 4.8 \u00d7 \u221at (R2 = 0.18) 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t <Fig. 4. Somewhere here> google scholar : Minimum 16 3 1953 1 0.09 25th Quantile 266 32 1985 26 0.56 Median 866 79 1993 15 0.85 75th Quantile 2596 146 2001 26 1.18 Maximum 49447 1159 2011 115 3.67 scopus : Minimum 1 1 1955 1 0.03 25th Quantile 116 14 1989 5 0.45 Median 469 34 1996 11 0.71 75th Quantile 1361 65 2004 19 1.00 Maximum 28693 423 2011 70 2.87 web of science : Minimum 1 1 1957 1 0.06 25th Quantile 76 10 1991 5 0.41 Median 291 23 1998 10 0.67 75th Quantile 945 48 2004 17 1.00 Maximum 32837 424 2011 96 2.87 1 2 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t Standard error of estimates R2 GS no. papers = 2.33 WoS no. papers 0.06 0.797 GS no. citations = 1.87 WoS no. citations 0.05 0.809 GS h index = 1.44 WoS h index 0.02 0.956 Scopus no. papers = 1.09 WoS no. papers 0.02 0.902 Scopus no. citations = 1.03 WoS no. citations 0.02 0.867 Scopus h index = 0.99 WoS h index 0.01 0.936 Authors who started to publish after 1995 Scopus no. papers = 1.11 WoS no. papers 0.03 0.900 Scopus no. citations = 1.17 WoS no. citations 0.02 0.949 Scopus h index = 1.11 WoS h index 0.02 0.954 3 4 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t Variable By Variable Spearman \u03c1 Prob > |\u03c1| WoS h index GS h index 0.939 <.0001 Scopus h index GS h index 0.931 <.0001 WoS h index Scopus h index 0.922 <.0001 WoS no. citations GS no. citations 0.939 <.0001 Scopus no. citations GS no. citations 0.955 <.0001 WoS no. citations Scopus no. citations 0.945 <.0001 WoS no. papers GS no. papers 0.840 <.0001 Scopus no. papers GS no. papers 0.896 <.0001 WoS no. papers Scopus no. papers 0.905 <.0001 5 6 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t Table 4. Comparison of the h index over time using the data from 340 soil researchers in Google Scholar (GS), Scopus and Web of Science (WoS) Standard error of estimates R2 GS h index = 0.84 \u00d7 year 0.02 0.745 WoS h index = 0.73 \u00d7 year 0.02 0.717 Scopus h index = 0.73 \u00d7 year 0.02 0.759 GS no. papers = 5.5 \u00d7 year 0.23 0.620 WoS no. papers = 2.5 \u00d7 year 0.12 0.567 Scopus no. papers = 3.0 \u00d7 year 0.12 0.656 GS no. citations = 122 \u00d7 year 9.1 0.344 WoS no. citations = 65 \u00d7 year 5.8 0.269 Scopus no.citations = 78 \u00d7 year 5.8 0.346 7 8 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t n Min Q25 Median Q75 Max Google Scholar Pedology 25 0.09 0.37 0.56 0.83 1.67 Soil chemistry 42 0.09 0.45 0.78 1.16 2.00 Soil physics 67 0.21 0.56 0.79 1.00 2.55 Soil geography & pedometrics 28 0.16 0.58 0.87 1.02 1.93 Soil management & fertility 42 0.28 0.69 0.98 1.30 2.12 Soil biology, ecology, biogeochemistry 88 0.23 0.78 1.01 1.65 3.67 Web of Science Pedology 25 0.08 0.32 0.46 0.78 1.67 Soil chemistry 42 0.06 0.32 0.63 1.00 1.67 Soil geography & pedometrics 67 0.11 0.50 0.64 0.86 1.50 Soil management & fertility 28 0.15 0.47 0.67 1.00 1.63 Soil physics 42 0.17 0.46 0.72 1.00 2.14 Soil biology, ecology, biogeochemistry 88 0.14 0.63 0.83 1.33 2.87 9 10 11 12 13 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t Table 6. Spearman\u2019s rank correlation coefficient (\u03c1) of the Google Scholar h5-index and impact factor (IF), no. papers, citations, and Eigenfactor metrics from Journal Citation Reports for 31 soil science journals. Cites is the number of citations in 2012 for papers that were published in 2007 \u2013 2011. Variable By Variable Spearman \u03c1 Prob > |\u03c1| h5-index Cites (5 years) 0.972 <.0001 h5-index Eigenfactor 0.970 <.0001 h5-index 5 year IF 0.903 <.0001 h5-index 2 year IF 0.870 <.0001 h5-index No. papers (5 years) 0.721 <.0001 14 15 16 17 PeerJ reviewing PDF | (v2013:09:794:0:1:NEW 9 Sep 2013) R ev ie w in g M an us cr ip t",
    "url": "https://peerj.com/articles/184/reviews/",
    "review_1": "Pei-Yuan Qian \u00b7 Oct 1, 2013 \u00b7 Academic Editor\nACCEPT\nI am very happy to know that you have addressed most issues raised by the reviewers and I am happy to accept this ms for publication. Thank you for choosing PeerJ as outlet of your research findings.",
    "review_2": "Pei-Yuan Qian \u00b7 Aug 18, 2013 \u00b7 Academic Editor\nMINOR REVISIONS\nThe issues raised by both reviewers need to be carefully addressed before this ms can be accepted for publication. Please revise your ms carefully and submit your revised version, together with a detailed response to reviewers' comments in a format of point-to-point.",
    "review_3": "Matthew Cole \u00b7 Aug 15, 2013\nBasic reporting\nOverall\n\nThis paper considers whether microplastics are ingested by barnacles that reside of floating debris in the North Pacific gyre. There are few studies of in-situ organisms ingesting microplastics so the data presented is a particularly important addition to the literature. The introduction is clear and well-informed, the methods and results clear and concise, and the discussion comprehensive. Overall I consider this a well-informed and highly interesting paper that I would be happy to recommend for publication.\nExperimental design\nMethodology\n\nLine 85-86 \u2013 Dissection procedure could do with a little more detail (e.g. how was intestine removed? Was it cut open longitudinally? Was observation for microplastic debris systematic? Was the intestine washed through or plastics picked off? What was the magnification used?). Further, were any protocols included to mitigate contamination of samples (i.e. airborne or introduced via scalpel or clothing for example)? If not, perhaps a sentence to justify why this was not required in this study?\n\nLine 92 \u2013 Does (N=30,518) refer to number of tows (as positioning of text suggests), or number of plastic particles collected?\nValidity of the findings\nResults\n\nFigure 1 \u2013 did you consider comparing the sites used with number of microplastic particles found in the seawater trawls?\n\nLine 117-119 \u2013 Looking at Figure 2b this correlation is not particularly evident; instead of looking at a correlation, I could see a histogram working much better here (similar to used in Fig2a). If you have data from the seawater trawls, would you be able to compare number of particles ingested Vs number of particles found in trawls around location the barnacle was sampled from?\n\nLine125-127 \u2013 What types of plastic dominated the seawater trawls? (i.e. were there lots of fibres or nurdles present in the seawater that the barnacles were not eating?)\n\n\n\nDiscussion\n\nIn method (Line 103) Raman laser melted some plastics. There is no follow-up on alternate method that might have been used instead?\nAdditional comments\nGeneral Comments\n\nWhile most readers will be familiar with what a barnacle looks like, \u201ccapitulum\u201d is a very specific term that will not be well known to the majority of readers. Perhaps a small figure with an image of one of your barnacle specimens with some basic annotations would be appropriate? Would also be interesting to see a photo of some of the plastics recovered.\nCite this review as\nCole M (2013) Peer Review #1 of \"Gooseneck barnacles (Lepas spp.) ingest microplastic debris in the North Pacific Subtropical Gyre (v0.1)\". PeerJ https://doi.org/10.7287/peerj.184v0.1/reviews/1",
    "review_4": "Daniel Rittschof \u00b7 Aug 1, 2013\nBasic reporting\nfine\nExperimental design\nwell explained\nValidity of the findings\nvalid for over half the particles.\nAdditional comments\nThis is a well done offering. There are several things to consider with this paper:\nHow long do particles stay in a barnacle gut? Are you really measuring just a snapshot of the percentabge of barnacles that ate particles in the last x hours? Is ingested the best word to use, or would a phrase like found in their guts be more appropriate. It is clear that fish eat Lepas, it is very likely that there is considerable predation on Lepas in the open ocean. Do you believe for example that oceanic Lepas die of old age? Ms could be improved by addressing these issues and at least commenting on alternative methods that might be used to determine what kinds of plastic the colored particles that melt are made out of. Have the macroplastics been characterized? How might they compare.\nCite this review as\nRittschof D (2013) Peer Review #2 of \"Gooseneck barnacles (Lepas spp.) ingest microplastic debris in the North Pacific Subtropical Gyre (v0.1)\". PeerJ https://doi.org/10.7287/peerj.184v0.1/reviews/2",
    "pdf_1": "https://peerj.com/articles/184v0.2/submission",
    "pdf_2": "https://peerj.com/articles/184v0.1/submission",
    "all_reviews": "Review 1: Pei-Yuan Qian \u00b7 Oct 1, 2013 \u00b7 Academic Editor\nACCEPT\nI am very happy to know that you have addressed most issues raised by the reviewers and I am happy to accept this ms for publication. Thank you for choosing PeerJ as outlet of your research findings.\nReview 2: Pei-Yuan Qian \u00b7 Aug 18, 2013 \u00b7 Academic Editor\nMINOR REVISIONS\nThe issues raised by both reviewers need to be carefully addressed before this ms can be accepted for publication. Please revise your ms carefully and submit your revised version, together with a detailed response to reviewers' comments in a format of point-to-point.\nReview 3: Matthew Cole \u00b7 Aug 15, 2013\nBasic reporting\nOverall\n\nThis paper considers whether microplastics are ingested by barnacles that reside of floating debris in the North Pacific gyre. There are few studies of in-situ organisms ingesting microplastics so the data presented is a particularly important addition to the literature. The introduction is clear and well-informed, the methods and results clear and concise, and the discussion comprehensive. Overall I consider this a well-informed and highly interesting paper that I would be happy to recommend for publication.\nExperimental design\nMethodology\n\nLine 85-86 \u2013 Dissection procedure could do with a little more detail (e.g. how was intestine removed? Was it cut open longitudinally? Was observation for microplastic debris systematic? Was the intestine washed through or plastics picked off? What was the magnification used?). Further, were any protocols included to mitigate contamination of samples (i.e. airborne or introduced via scalpel or clothing for example)? If not, perhaps a sentence to justify why this was not required in this study?\n\nLine 92 \u2013 Does (N=30,518) refer to number of tows (as positioning of text suggests), or number of plastic particles collected?\nValidity of the findings\nResults\n\nFigure 1 \u2013 did you consider comparing the sites used with number of microplastic particles found in the seawater trawls?\n\nLine 117-119 \u2013 Looking at Figure 2b this correlation is not particularly evident; instead of looking at a correlation, I could see a histogram working much better here (similar to used in Fig2a). If you have data from the seawater trawls, would you be able to compare number of particles ingested Vs number of particles found in trawls around location the barnacle was sampled from?\n\nLine125-127 \u2013 What types of plastic dominated the seawater trawls? (i.e. were there lots of fibres or nurdles present in the seawater that the barnacles were not eating?)\n\n\n\nDiscussion\n\nIn method (Line 103) Raman laser melted some plastics. There is no follow-up on alternate method that might have been used instead?\nAdditional comments\nGeneral Comments\n\nWhile most readers will be familiar with what a barnacle looks like, \u201ccapitulum\u201d is a very specific term that will not be well known to the majority of readers. Perhaps a small figure with an image of one of your barnacle specimens with some basic annotations would be appropriate? Would also be interesting to see a photo of some of the plastics recovered.\nCite this review as\nCole M (2013) Peer Review #1 of \"Gooseneck barnacles (Lepas spp.) ingest microplastic debris in the North Pacific Subtropical Gyre (v0.1)\". PeerJ https://doi.org/10.7287/peerj.184v0.1/reviews/1\nReview 4: Daniel Rittschof \u00b7 Aug 1, 2013\nBasic reporting\nfine\nExperimental design\nwell explained\nValidity of the findings\nvalid for over half the particles.\nAdditional comments\nThis is a well done offering. There are several things to consider with this paper:\nHow long do particles stay in a barnacle gut? Are you really measuring just a snapshot of the percentabge of barnacles that ate particles in the last x hours? Is ingested the best word to use, or would a phrase like found in their guts be more appropriate. It is clear that fish eat Lepas, it is very likely that there is considerable predation on Lepas in the open ocean. Do you believe for example that oceanic Lepas die of old age? Ms could be improved by addressing these issues and at least commenting on alternative methods that might be used to determine what kinds of plastic the colored particles that melt are made out of. Have the macroplastics been characterized? How might they compare.\nCite this review as\nRittschof D (2013) Peer Review #2 of \"Gooseneck barnacles (Lepas spp.) ingest microplastic debris in the North Pacific Subtropical Gyre (v0.1)\". PeerJ https://doi.org/10.7287/peerj.184v0.1/reviews/2\nReview 5: nan\nReview 6: \n",
    "LLM_extracted_review": "['Review 1: The reviewer is happy with the revisions made and accepts the manuscript for publication.\\n\\nReview 2: The manuscript requires careful revision to address issues raised by both reviewers before it can be accepted for publication.\\n\\nReview 3: \\n- Basic reporting: The paper is a significant addition to the literature on microplastics and barnacles.\\n- Experimental design: More detail is needed on the dissection procedure and contamination protocols. Clarification is needed on the number referenced (N=30,518).\\n- Validity of findings: Suggestions for improving data presentation and comparisons with seawater trawls are provided. Questions about the types of plastics found in seawater are raised.\\n- Discussion: There is a lack of follow-up on alternative methods for analyzing plastics that melted during Raman laser analysis. A suggestion is made to include a figure for better understanding of barnacle anatomy.\\n\\nReview 4: \\n- Basic reporting: The reporting is fine.\\n- Experimental design: The methodology is well explained.\\n- Validity of findings: Validity is confirmed for over half the particles.\\n- Additional comments: Questions are raised about the duration particles remain in barnacle guts and the appropriateness of terminology. Suggestions for addressing predation and characterizing macroplastics are made.']"
}