DOI: 10.5281/zenodo.17046919
title: PathOS Impact of Artefact Reuse in COVID-19 Publications Case Study Research Data, Code, and Analysis Results

description:  

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)