Data for the paper: From Potential to Practice: Modelling AI Readiness and Scholarly Integration in African Research Systems

This repository contains the cleaned, harmonized analysis datasets supporting the empirical study of artificial intelligence (AI) readiness and realized scholarly AI integration across African research systems. The data integrates large-scale bibliometric metadata retrieved from the OpenAlex database (covering 43,578 cleaned, peer-reviewed AI-related publications affiliated with African institutions) with national policy metrics from the Oxford Insights Government AI Readiness Index (AIRI). The datasets capture longitudinal trends in publication intensity, time-normalized citation impact, topic diversity (Simpson Index), and intra-African collaboration networks. Methodological Notes: Bibliometric Source: OpenAlex API (Extraction date: November 2025). The corpus was rigorously filtered to include only peer-reviewed or editorially vetted outputs (journal articles, conference proceedings, and book chapters) from 2000 to 2025. Policy Source: Oxford Insights Government AI Readiness Index (AIRI) scores, strictly harmonized to a 100-point scale for the 2019–2024 econometric window. Counting Method: Country-level aggregations utilize a full-counting approach, whereby multi-country collaborative works are credited to all participating African research systems. File Descriptions: panel_with_readiness.csv: The primary country–year panel dataset (2019–2024) used for the Two-Way Fixed Effects (TWFE) panel regressions. It contains national AI readiness scores alongside the composite Scholarly AI Integration Score and its underlying pillars (log publication intensity, relative citation impact, and topic diversity) for 52 African nations. country_centrality.csv: The structural network metrics used for the intra-African collaboration analysis, containing eigenvector and betweenness centrality scores for each national research system. institution_flagship_highered_top20_2000_2024.csv: Aggregated institutional output and citation metrics detailing the extreme concentration of AI scholarship among Africa’s leading higher-education "flagships." ai_education_top15.csv: The specific thematic subset used to analyze the highly concentrated AI-in-education research domain. Usage: Researchers are encouraged to use these datasets to replicate the core fixed-effects models, recreate the median-based readiness–integration typology, or conduct comparative scientometric analyses.本仓库包含经过清洗与统一标准化处理的分析数据集，用于支撑非洲科研体系中人工智能（AI）就绪程度与已实现学术AI融合的实证研究。 本数据集整合了从OpenAlex数据库（覆盖43578条经清洗、同行评议的非洲机构附属AI相关学术文献）获取的大规模文献计量元数据，以及来自牛津洞察（Oxford Insights）政府AI就绪指数（AIRI）的国家政策指标。本数据集涵盖了文献出版强度、时间归一化引文影响力、主题多样性（Simpson Index，辛普森指数）以及非洲内部合作网络的纵向发展趋势。 方法学说明： 文献计量数据源：OpenAlex API（数据提取日期：2025年11月）。本数据集经过严格筛选，仅纳入2000年至2025年间经同行评议或编辑审核的成果（包括期刊论文、会议论文及图书章节）。 政策数据源：牛津洞察（Oxford Insights）政府AI就绪指数（AIRI）得分，针对2019-2024年的计量经济学分析窗口，已统一标准化为100分制。 计数方法：国家层面的聚合分析采用全计数法，即多国家合作成果会将贡献权重分配给所有参与合作的非洲科研体系。 文件说明： panel_with_readiness.csv：用于双向固定效应（Two-Way Fixed Effects, TWFE）面板回归的核心国家-年度面板数据集（2019-2024年），涵盖52个非洲国家的国家AI就绪得分、综合学术AI融合得分及其核心构成指标（对数出版强度、相对引文影响力与主题多样性）。 country_centrality.csv：用于非洲内部合作网络分析的结构网络指标数据集，包含各国家科研体系的特征向量中心性与介数中心性得分。 institution_flagship_highered_top20_2000_2024.csv：聚合的机构产出与引文指标数据集，详细展示了非洲顶尖高等教育“旗舰院校”在AI学术研究上的高度集中性。 ai_education_top15.csv：用于分析高度集中的教育领域AI研究主题的专属子数据集。 使用说明：鼓励研究人员使用本数据集复刻核心固定效应模型、重现基于中位数的就绪程度-融合程度分类体系，或开展比较科学计量学分析。