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<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)

本聚合数据集对应PLOS ONE期刊发表的论文《超越资助：生物医学、自然科学与社会科学中的致谢模式》，DOI：10.1371/journal.pone.0185578
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（以百分比计）
表3 2015年按学科划分的Web of Science（WoS）收录论文总数（含全部论文与含资助致谢论文）及含资助致谢论文占比
针对图1与图2所示的分析需求，本数据集按学科进行拆分，并通过MATLAB程序对各子集开展对应分析（Correspondence Analysis）
图1 按学科划分的致谢模式二维对应分析（平面1-2）
图2 按学科划分的致谢模式二维对应分析（平面3-4）
补充材料：
S1图 致谢文本中提取的名词短语频率分布
S1表 按学科划分的214个高频名词短语的出现频次
S2表 行变量的表征质量（各名词短语（NP）的累积贡献度）
S3表 列变量的表征质量（各学科的累积贡献度）