Aggregate data for the forthcoming PLOS ONE article "Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences"

Aggregate data for the PLOS ONE article "Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences."<br>Table 1. Explained and cumulative variance for each axis<br>Table 2. Relative contributions of the factor to the element for disciplines (expressed as a percentage)<br>Table 3. Number of papers indexed in WoS (all and with funding acknowledgements) and percentage of papers with funding acknowledgements, by discipline (2015)<br>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)<br>Fig 2. Bidimensional Correspondence Analysis for acknowledgements patterns by discipline (plane 3-4).<br>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)

《公共科学图书馆·综合》（PLOS ONE）刊载的论文"超越资助：生物医学、自然科学与社会科学中的致谢模式"的汇总数据集。
表1 各轴的解释方差与累积方差
表2 各学科中因子对元素的相对贡献（以百分比表示）
表3 2015年按学科分类的Web of Science（WoS）收录论文总量、含资助致谢的论文量，以及含资助致谢的论文占比
针对图1与图2呈现的分析内容，本数据集按学科进行分区，并使用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双向对应分析（平面1-2）
图2 按学科分类的致谢模式双向对应分析（平面3-4）
补充材料：
S1图 致谢部分所提取名词短语的频次分布
S1表 按学科分类的214个高频名词短语的频次
S2表 行的表征质量（各名词短语的累积贡献）
S3表 列的表征质量（各学科的累积贡献）