Frequency of positive words in grant applications

The data was gathered to reproduce the methodology and findings presented in Lerchenmueller et al. (2019) in proposal texts that were submitted to different funding schemes offered by the Swiss National Science Foundation. Data description The data files (as .xlsx) contains three sheets: Career funding schemes (excluding fellowships): 1802 proposals included Spark funding scheme: 612 proposals included Project funding scheme (Projects): 5736 proposals included Each Sheet includes a data matrix where each row is a specific grant proposal. The unit of analysis are grant proposals and the texts used are the title and abstracts. The data is used in a project available from github (https://github.com/snsf-data/positive_language). [Paper soon to be submitted] The first 25 columns give us the 25 positive words and their respective counts in each of the analysed texts of the grant proposals. The positiv words (here the column names are) used are the following: amazing, assuring, astonishing, bright, creative, encouraging, enormous, excellent, favorable/ favourable, groundbreaking, hopeful, innovative, inspiring, inventive, novel, phenomenal, prominent, promising, reassuring, remarkable, robust, spectacular, supportive, unique, and unprecedented. Those were first proposed by Vinkers et al. (2015). Additionnally, the following columns are present in the data: sum_pos: The sum of the number of positive words in the texts. text_length and text_length100: the text length (count of words), and text length divided by 100. ResponsibleApplicantGender: the gender of the corresponding applicant (m or f) ResponsibleApplicantAge: the age of the corresponding applicant at submission (continuous) NationalityIsoCode: the nationality of the corresponding applicant (CH or not CH) IsApproved and IsFundable: binary variable indicating funding success, or whether the project would have been fundable given the grade with unlimited funding. Decision and CallYear: year of the call deadline, and year the funding decision was taken. ResearchInstitutionType: the type of institution the corresponding applicant is affiliated to (Cantonal University, ETH Domain, Other) which_lang: the language the proposal was written in (all english) Text processing The text corpus used in this analysis was also used as the basis for additional analyses. It therefore underwent a thorough cleaning with the help of the R-packages {tm} and {stringr}. After the pre-processing steps, the number of times each of the postitive words (see above) occured in the title and abstracts of the respective proposal is computed using a simple keyword search. Pre-processing steps: Punctuation was removed. All non-standard alphanumeric characters were removed. All characters were converted to lowercase. Extra white spaces were removed. Internet formatting was removed: URLs, email addresses, twitter formatting (words starting with # and @). Common English contractions were converted to their non-contracted form ("it's" --> "it is"). English language stopwords were removed.本数据集旨在复现Lerchenmueller等人（2019）提出的研究方法与结论，所用文本来自提交给瑞士国家科学基金会（Swiss National Science Foundation）各类资助计划的提案。 ### 数据说明 本数据集以.xlsx格式存储，包含三个工作表： 1. **职业资助计划（不含奖学金）**：纳入1802份提案 2. **星火资助计划（Spark funding scheme）**：纳入612份提案 3. **项目资助计划（Projects）**：纳入5736份提案 每个工作表均为数据矩阵，每行代表一份独立的资助提案。本分析的单元为资助提案，所用文本为提案的标题与摘要。本数据集可用于GitHub开源项目（https://github.com/snsf-data/positive_language），相关研究论文即将提交。 数据集的前25列为25个积极词汇及其在每份提案文本中的出现频次，所用积极词汇（即列名）如下：amazing、assuring、astonishing、bright、creative、encouraging、enormous、excellent、favorable/favourable、groundbreaking、hopeful、innovative、inspiring、inventive、novel、phenomenal、prominent、promising、reassuring、remarkable、robust、spectacular、supportive、unique、unprecedented。上述词汇由Vinkers等人（2015）首次提出。 此外，数据集中还包含以下列： - `sum_pos`：文本中积极词汇的总出现次数 - `text_length`与`text_length100`：分别为文本词长（单词总数）与词长除以100后的结果 - `ResponsibleApplicantGender`：对应申请人的性别（m为男性，f为女性） - `ResponsibleApplicantAge`：申请人提交提案时的年龄（连续型变量） - `NationalityIsoCode`：申请人的国籍（CH为瑞士籍，其余为非瑞士籍） - `IsApproved`与`IsFundable`：二分类变量，分别表示提案是否获得资助，以及在不限定资助额度的前提下提案是否符合资助标准 - `Decision`与`CallYear`：分别为资助申请截止年份与资助决策作出年份 - `ResearchInstitutionType`：申请人所属机构类型（州立大学、瑞士联邦理工学院体系、其他） - `which_lang`：提案撰写语言（本数据集所有提案均为英语） ### 文本预处理 本分析所用的文本语料库同时作为其他额外分析的基础，因此借助R语言包`{tm}`与`{stringr}`完成了全面的文本清洗。预处理步骤如下： 1. 移除所有标点符号 2. 移除所有非标准字母数字字符 3. 将所有字符转换为小写格式 4. 移除多余空格 5. 移除网络格式内容：统一资源定位符（URL）、电子邮件地址、Twitter格式文本（以#和@开头的词汇） 6. 将英语缩略词转换为完整形式（例如"it's"转换为"it is"） 7. 移除英语通用停用词 经上述预处理步骤后，通过简单的关键词检索，即可计算每份提案的标题与摘要中各积极词汇的出现频次。