Data from: Do Open Science Advocates Walk the Talk? Evidence from Positive Position Papers

Replication Data for: Do Open Science Advocates Walk the Talk? Evidence from Positive Position Papers Overview This repository contains the replication dataset, source code, and intermediate results for the research article titled "Do Open Science Advocates Walk the Talk? Evidence from Positive Position Papers", submitted to Royal Society Open Science. Abstract of the Study This study examines the persistent attitude–behavior gap within the open science movement, investigating structural factors constraining scholars from translating advocacy into open access (OA) practices. Analyzing 2,470 Positive Position Papers on Open Science (PPPos) indexed in the Web of Science (2014–2023), this study compares the OA publishing patterns of PPPos across national contexts, academic disciplines, and funding environments using global benchmarks. The results reveal a strong but incomplete alignment between positive attitudes toward OA and actual engagement. The cumulative All OA (72.4%) and Immediate OA (54.5%) rates for PPPos are substantially higher than global averages (49.8% and 32.8%, respectively), demonstrating the effect of advocacy. However, this progress remains uneven and the attitude–behavior gap has yet to be completely bridged. Further analysis shows that this gap is shaped by national policies, disciplinary cultures, and research funding mechanisms. By integrating the theory of planned behavior with large-scale bibliometric evidence, this study offers a nuanced understanding of how institutional and cultural structures influence the translation of intentions into open scholarly practices. The findings provide theoretical insights into the dynamics of scholarly publishing behavior and practical guidance for designing equitable, policy-driven strategies to promote a more inclusive and sustainable open knowledge ecosystem. Data Description The dataset documents the complete research workflow, consisting of the following files: dataset_raw_abstracts.xlsx: The initial raw dataset retrieved from the Web of Science Core Collection (n=2,539). sentiment_analysis.py: The Python script used to perform sentiment classification using TextBlob. sentiment_analysis_results.xlsx: Intermediate output containing sentiment scores, used to screen and exclude non-positive papers. Dataset_Open_Science_Advocates.xlsx (Master Dataset): The final, curated dataset of 2,470 Positive Position Papers (PPPos). This file contains multiple sheets (Sheet 1-8) providing the detailed screening process, variable definitions, and aggregated data for Global, National, Disciplinary, and Funding comparisons. Methodology The study employs a "Human-in-the-Loop" methodology, combining computational sentiment analysis with manual verification to quantify the attitude-behavior gap in Open Science adoption. Usage For detailed variable definitions and replication instructions, please refer to the README.md file included in this repository.