Aggregate data for the forthcoming PLOS ONE article "Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences" DOI: 10.1371/journal.pone.0185578

Aggregate data for the PLOS ONE article "Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences." DOI: 10.1371/journal.pone.0185578 Table 1. Explained and cumulative variance for each axis Table 2. Relative contributions of the factor to the element for disciplines (expressed as a percentage) Table 3. Number of papers indexed in WoS (all and with funding acknowledgements) and percentage of papers with funding acknowledgements, by discipline (2015) For the purposes of the analysis presented in Fig 1 and 2, the dataset was partitioned by discipline and a Correspondence Analysis was applied to these subsets and using a MATLAB program.Fig 1. Bidimensional Correspondence Analysis for acknowledgements patterns by discipline (plane 1-2) Fig 2. Bidimensional Correspondence Analysis for acknowledgements patterns by discipline (plane 3-4). Supporting Information:S1 Fig. Frequency distribution of noun phrases found in acknowledgementsS1 Table. Frequency of the 214 most frequent noun phrases, by disciplineS2 Table. Quality of representation of the rows (cumulative contribution for each NP)S3 Table. Quality of representation of the columns (cumulative contribution for each discipline)本聚合数据集对应PLOS ONE期刊刊载的论文《Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences.》，DOI：10.1371/journal.pone.0185578，包含以下内容： 表1：各主轴的解释方差与累积方差 表2：各学科要素对应的因子相对贡献（以百分比形式表示） 表3：按学科分类的Web of Science（WoS）收录论文总数、含资助致谢信息的论文数量，以及2015年含资助致谢的论文占比。 针对图1与图2的分析需求，本数据集已按学科进行分区，并通过MATLAB程序对各子集实施对应分析（Correspondence Analysis）。 图1：按学科分类的致谢模式二维对应分析（平面1-2） 图2：按学科分类的致谢模式二维对应分析（平面3-4）。 辅助补充材料： S1图：致谢部分出现的名词短语频率分布 S1表：按学科分类的214个高频名词短语出现频次 S2表：行项（各名词短语）的表征质量（单一名词短语累积贡献度） S3表：列项（各学科）的表征质量（各学科累积贡献度）