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  • The six PLOS journals (i.e., PLOS Biology, PLOS Medicine, PLOS Computational Biology, PLOS Genetics, PLOS Pathogens, and PLOS Neglected Tropical Diseases) were chosen as the focus of this study for various reasons: (1) They are open access with well-established peer review criteria; (2) Their impact factors are increasing over time; (3) According to the purpose of this study, they have clearly defined disciplines; (4) They are by the same publisher; (5) They are early adopters of the altmetrics method to provide article-level metrics. To pursue rigor, we adopted several strategies: (1) to exclude PLOS ONE because of subject heterogeneity; (2) to collect all articles published in a specific year for inclusion and comparison; (3) to normalize data to minimize potential effect of journal size; (4) to stratify data by percentile ranks to factor in popularity; (5) to compare datasets across many journals by the same publisher for interdisciplinary comparison. Data collection was carried out in May 2015. Articles were collected from the six PLOS journals. These journals, representing six disciplinary areas, are appropriate for cross-disciplinary comparisons. PLOS ONE was excluded because of its multidisciplinary nature. The publication year of collected articles was 2012 to allow citations to occur in Web of Science. The final datasets include 2,406 research articles. For each article, the following data were extracted from the PLOS website: title, author, date of publication, DOI number, and AAS. Citations were from Thomson’s Web of Science and the AAS were provided by Altmetric (https://www.altmetric.com/products/free-tools/bookmarklet/). The Altmetric bookmarklet was installed in the Firefox browser bookmarks toolbar. The following process was utilized for each paper: (1) open a PLOS paper, (2) click on the “Altmetric It” from the toolbar, and (3) get the scores from the popup altmetric donut. (The total altmetric score shows in the middle of the donut graphic.) Raw data were imported into SPSS 20.0. Each article has four indicators (Table 2): citations (CIT), Altmetric Attention Scores (AAS), normalized citations (NCIT), and normalized Altmetric Attention Scores (NAAS). To account for the considerable variation across journals (Table 3), the raw data were further normalized for comparison. The field-normalization approach of citations has been used in previous studies [37, 38]. The formulae for normalizing article’s scores in this study are as follows: NAAS=article'sAAS/meanAASofthejournalinwhichthearticleispublished(1) NCIT=article'sCIT/meanCITofthejournalinwhichthearticleispublished(2) Table data removed from full text. Table identifier and caption: 10.1371/journal.pone.0194962.t002 List of indicators. Table data removed from full text. Table identifier and caption: 10.1371/journal.pone.0194962.t003 Summary of research articles published in six PLOS journals in 2012. Further, articles are stratified by percentile ranking of NAAS or AAS scores: labeled as top 1%, top 10%, top 25%, or top 50% for comparison. Because this ranking puts tied articles into the same rank, the inclusion of the articles in the top n% is based on the nearest n%. For example, when a journal has more articles tied above the n% (e.g., 50%), the number of articles in the top na% is greater than the number for n% (e.g., 50%) of the total articles for a journal. If a journal has more tied articles below the n%, the number of articles in top na% is less than the number of n%. Thus, the n% is to find the nearest number ~n%. To quantify the relationships between AAS and citations, Spearman correlations were tested on all articles and articles with AAS. Further analysis compared the stratified datasets based on percentile ranks of AAS: top 50%, top 25%, top 10%, and top 1%. Comparisons across the six journals provided additional insights.
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