Raw data for D1.1: Inventory of skills and competencies

Raw data for the manuscript entitled: European Agrifood and Forestry Education for a Sustainable Future - Gap Analysis from an Informatics Approach Abstract Purpose: To evaluate how well European agrifood and forestry Masters program websites use vocabulary associated with the NextFood Project ‘categories of skills’. Methodology: Web-scraping Python scripts were used to collect texts from European Masters programs websites, which were then analysed using statistical tools including Partial Least Squares Regression and contextual relation analysis. A total of fourteen countries, twenty-seven universities, 1303 European Masters programs, 3305 web-pages and almost two million words were studied using this approach. Findings: While agrifood and forestry Masters programs used vocabulary from the NextFood Project ‘categories of skills’ in most cases equal to or more often than non-agrifood and forestry Masters programs, we found evidence for the relative underuse of words associated with networking skills, with least use among agriculture-related Masters programs. Practical Implications: The informatic approach provides evidence that European agrifood and forestry Masters programs are for the most part following the educational paths for meeting future challenges as outlined by the NextFood Project, with the possible exception of networking skills. Theoretical Implications: This text-based, informatic approach complements the more targeted approaches taken by the NextFood Project in studying the skilling-pathways, which involved focus-group interviews, surveys of stakeholders, interviews of individuals with expert-knowledge and literature reviews. Originality: A text-based, web-scraping informatic approach has thus far been limited in the study of agrifood and forestry higher education, especially relative to recent advances made in the social sciences.本数据集对应题为《面向可持续未来的欧洲农业食品与林业教育——基于信息学方法的差距分析》的研究手稿。 摘要 研究目的：评估欧洲农业食品与林业类硕士项目官网对NextFood项目（NextFood Project）「技能类别」相关词汇的使用情况。 研究方法：本研究采用Python网络爬虫脚本采集欧洲硕士项目官网文本，随后借助偏最小二乘回归（Partial Least Squares Regression）、上下文关联分析等统计工具开展分析。本研究共覆盖14个国家、27所高校、1303个欧洲硕士项目、3305个网页，累计分析文本近200万字。 研究发现：总体而言，农业食品与林业类硕士项目对NextFood项目「技能类别」相关词汇的使用频率不低于甚至高于非农业食品与林业类硕士项目，但本研究发现，二者对人际网络技能相关词汇的使用存在相对不足，其中农业相关硕士项目的使用频率最低。 实践启示：本研究采用的信息学方法证实，欧洲农业食品与林业类硕士项目总体上遵循了NextFood项目提出的应对未来挑战的教育路径，仅在人际网络技能相关内容上可能存在短板。 理论价值：本研究采用的基于文本的信息学方法，可对NextFood项目在技能培养路径研究中采用的更具针对性的方法形成补充——后者的研究手段包括焦点小组访谈、利益相关方调研、专家访谈与文献综述。 研究创新性：截至目前，基于文本采集与网络爬虫的信息学方法在农业食品与林业高等教育研究中的应用仍较为有限，相较于社会科学领域近年取得的研究进展尤为如此。