PathOS Impact of Artefact Reuse in COVID-19 Publications Case Study Research Data, Code, and Analysis Results

This repository contains the complete dataset, analysis scripts, and results for the Impact of Artefact Reuse in COVID-19 Publications case study. Overview This study investigates whether observable open science behaviors, specifically creating research artifacts that are subsequently reused by others, are associated with measurable downstream impact in COVID-19 research. The analysis employs a regression-based approach using a filtered sample of 115,467 COVID-19 papers that created at least one dataset or software artifact and were cited at least once, ensuring all publications had potential for visibility and reuse. Reusability is operationalized through reuse-artifact citances: citations where other papers explicitly reference and reuse datasets or software created by the original publication. This provides empirical evidence that artifacts were not only shared but also found useful and actionable in practice. Repository Structure covid/ ├── README.md ├── complete_collection_df_fix.parquet ├── complete_collection_df_fix.xlsx ├── covid_create_collection.py ├── covid_calculate_indicators.py ├── covid_find_paper_affiliations.py ├── covid_find_paper_openaireids.py ├── covid_indicators_create_data_for_vis.py └── results/ ├── *.xlsx ├── *.txt ├── *.parquet ├── tables/ │ ├── 01_executive_summary.xlsx │ ├── 02_impact_by_artifact_type.xlsx │ └── ... ├── visualizations/ │ ├── 01_sample_overview.png │ └── ... └── final_visualization_data_figures/ ├── figures/ └── data/ Data Description Main Dataset (complete_collection_df_fix.parquet / .xlsx) The dataset includes COVID-19 research papers that created at least one research artifact and were cited at least once. Key variable groups: Paper identifiers: id, year, citationcount, authorcount Artifact creation: named_datasets_created, unnamed_datasets_created, named_software_created, unnamed_software_created, total_artifacts Treatment variable: has_reuse_artifact_citance, reuse_artifact_inbound Outcome variables: clinical trial/guideline citations (influential & non-influential), patent_citations, science_industry_collaboration Control variables: fwci, interdisciplinarity_macro, interdisciplinarity_meso, science_industry_collaboration Open access variables: isopenaccess_oaire, green, bronze, hybrid, gold, diamond Scripts and Methodology Core Analysis Scripts covid_create_collection.py – Data integration, indicator calculation, dataset creation covid_calculate_indicators.py – Regression analysis, interaction effects, statistical outputs covid_find_paper_affiliations.py – Affiliation and collaboration analysis covid_find_paper_openaireids.py – OpenAIRE ID linkage covid_indicators_create_data_for_vis.py – Visualization data and publication figures External Data Sources To fully reproduce the collection, large-scale data sources are required (not included here due to size/licensing): Semantic Scholar Academic Graph OpenAIRE Graph PubMed (clinical trial & guideline classification) PATSTAT (patent citations) ROR (Research Organization Registry) CORD-19 dataset SciNoBo Toolkit (for interdisciplinarity, FWCI, citance, and artifact analysis) The final processed dataset is provided, with all indicators and outcomes pre-computed. Key Findings COVID-19 papers with artifact reuse evidence show greater downstream impact: More citations from clinical trial studies More citations from clinical practice guidelines Higher patent citations (innovation impact) Increased science-industry collaboration Results and Outputs Executive summary: results/tables/01_executive_summary.xlsx Regression results: results/regression_output_*.txt Interaction effects: results/tables/16-19_interaction_*.xlsx Visualizations: results/visualizations/ and results/final_visualization_data_figures/ Usage Instructions For statistical analysis: Load dataset (complete_collection_df_fix.parquet / .xlsx) Review summary statistics (01_executive_summary.xlsx) Inspect regression results (regression_results_summary_covid.xlsx) Explore interaction tables (16–19) Use provided visualizations For replication: Configure all PATH_TO_* variables in scripts Install dependencies (pandas, statsmodels, matplotlib, seaborn) Run covid_calculate_indicators.py Generate visualizations via covid_indicators_create_data_for_vis.py For extension: Apply methods to other domains Add new outcome variables Modify treatment definitions or timeframes Adapt regression framework to bibliometric studies Sample Sizes and Coverage Total COVID-19 papers analyzed: 115,467 Time period: Publications through 2021 (avoiding recent citation bias) Coverage: Global (Semantic Scholar + OpenAIRE) Quality Assurance Multiple data validation steps Robustness tests with interaction analyses Documented and reproducible workflows Best-practice statistical methods (controls, CIs, effect sizes)本仓库包含针对「新冠出版物中研究工件复用的影响」案例研究的完整数据集、分析脚本与研究结果。 ## 概述 本研究旨在探究可观测的开放科学行为——具体而言即创建研究工件（research artifact）并被他人后续复用——是否与新冠研究中可量化的下游影响存在关联。 本分析采用基于回归的研究方法，筛选出115467篇至少创建过一个数据集或软件工件、且至少被引用过一次的新冠研究论文作为样本，确保所有纳入分析的出版物均具备可见性与复用潜力。 复用性通过「工件复用引用实例（reuse-artifact citance）」进行操作化定义：即其他论文明确引用并复用原出版物所创建的数据集或软件的引用实例。该定义可提供经验证据，证明研究工件不仅得到了共享，还在实际应用中被证明具有实用性与可操作性。 ## 仓库结构 covid/ ├── README.md ├── complete_collection_df_fix.parquet ├── complete_collection_df_fix.xlsx ├── covid_create_collection.py ├── covid_calculate_indicators.py ├── covid_find_paper_affiliations.py ├── covid_find_paper_openaireids.py ├── covid_indicators_create_data_for_vis.py └── results/ ├── *.xlsx ├── *.txt ├── *.parquet ├── tables/ │ ├── 01_executive_summary.xlsx │ ├── 02_impact_by_artifact_type.xlsx │ └── ... ├── visualizations/ │ ├── 01_sample_overview.png │ └── ... └── final_visualization_data_figures/ ├── figures/ └── data/ ## 数据说明 ### 主数据集（complete_collection_df_fix.parquet / .xlsx） 本数据集包含至少创建过一个研究工件且至少被引用过一次的新冠研究论文。 核心变量组： 1. 论文标识符类：id、year、citationcount、authorcount 2. 研究工件创建类：named_datasets_created、unnamed_datasets_created、named_software_created、unnamed_software_created、total_artifacts 3. 处理变量类：has_reuse_artifact_citance、reuse_artifact_inbound 4. 结果变量类：临床试验/指南引用（高影响力与非高影响力）、patent_citations、science_industry_collaboration 5. 控制变量类：领域加权引用影响（Field-Weighted Citation Impact, FWCI）、interdisciplinarity_macro、interdisciplinarity_meso、science_industry_collaboration 6. 开放获取变量类：isopenaccess_oaire、green、bronze、hybrid、gold、diamond ## 脚本与研究方法 ### 核心分析脚本 - covid_create_collection.py：数据整合、指标计算与数据集创建 - covid_calculate_indicators.py：回归分析、交互效应分析与统计结果输出 - covid_find_paper_affiliations.py：机构与合作关系分析 - covid_find_paper_openaireids.py：OpenAIRE标识符关联 - covid_indicators_create_data_for_vis.py：可视化数据与可发表图表生成 ## 外部数据源 若要完整复现数据集，需使用以下大规模数据源（因体积与授权限制，未随本仓库提供）： 1. Semantic Scholar学术图谱（Semantic Scholar Academic Graph） 2. OpenAIRE图谱（OpenAIRE Graph） 3. PubMed（临床试验与指南分类） 4. PATSTAT（专利引用数据） 5. 研究机构注册表（Research Organization Registry, ROR） 6. CORD-19数据集（CORD-19 dataset） 7. SciNoBo工具包（用于跨学科性、FWCI、引用实例与工件分析） 本仓库已提供最终处理后的数据集，所有指标与结果均已预先计算完成。 ## 核心研究发现 存在工件复用证据的新冠研究论文具有更显著的下游影响： 1. 临床试验研究产生的引用量更高 2. 临床实践指南产生的引用量更高 3. 专利引用量更高（体现创新影响） 4. 科研-产业合作关系更为频繁 ## 结果与产出 - 执行摘要：results/tables/01_executive_summary.xlsx - 回归结果：results/regression_output_*.txt - 交互效应分析结果：results/tables/16-19_interaction_*.xlsx - 可视化图表：results/visualizations/ 与 results/final_visualization_data_figures/ ## 使用说明 ### 统计分析流程 1. 加载数据集（complete_collection_df_fix.parquet / .xlsx） 2. 查看汇总统计结果（01_executive_summary.xlsx） 3. 检视回归分析结果（regression_results_summary_covid.xlsx） 4. 探索交互效应分析表格（16–19号表格） 5. 使用提供的可视化图表 ### 复现研究流程 1. 配置脚本中所有PATH_TO_*变量 2. 安装依赖包（pandas、statsmodels、matplotlib、seaborn） 3. 运行covid_calculate_indicators.py 4. 通过covid_indicators_create_data_for_vis.py生成可视化图表 ### 拓展研究流程 1. 将本研究方法应用于其他研究领域 2. 添加新的结果变量 3. 修改处理变量定义或时间范围 4. 调整回归框架以适配文献计量学研究 ## 样本量与覆盖范围 - 纳入分析的新冠研究论文总数：115467篇 - 时间范围：2021年及之前发表的论文（规避近期引用偏差） - 覆盖范围：全球（基于Semantic Scholar与OpenAIRE数据） ## 质量保障 - 实施了多轮数据验证步骤 - 通过交互效应分析开展稳健性检验 - 工作流可记录、可复现 - 采用符合最佳实践的统计方法（含控制变量、置信区间与效应量计算）