Dataset for paper "Understanding Registered Report Adoption in Software Engineering: Evidence from a Mixed-Method Survey"

Replication package for the study "Understanding Registered Report Adoption in Software Engineering: Evidence from a Mixed-Method Survey". This repository contains the complete replication package supporting the empirical study investigating Registered Report (RR) adoption within the Software Engineering research community. The package includes: Survey instrument (Google Forms questionnaire in PDF format), containing the informed consent form, participant characterization questions, quantitative items, and open-ended questions used throughout the study. Anonymized survey responses in Microsoft Excel format, including the quantitative responses and the anonymized qualitative comments used in the analyses. Qualitative coding workbook, documenting the complete thematic analysis conducted for the qualitative component of the study. The workbook includes: participant profiles (anonymized); source excerpts; meaning units; open codes; focused codes; analytical categories; codebook; audit trail linking raw responses to findings; mixed-method integration tables; evidence supporting the final analytical findings and recommendations. The study employed a convergent mixed-method design, integrating quantitative statistical analyses with qualitative thematic analysis to investigate Software Engineering researchers' familiarity with Registered Reports, perceptions of their scientific benefits and operational challenges, factors associated with adoption intention, and practical experiences reported by Registered Report authors and reviewers. All personal identifiers have been removed or anonymized before publication. Participant identifiers used in the qualitative coding (e.g., A1, RV1, AR1) are pseudonyms created solely to preserve analytical traceability and cannot be linked to individual respondents. This dataset is intended to support: reproducibility of the published analyses; inspection of the qualitative coding process; secondary analyses; educational purposes; future replication and extension studies on Registered Reports, Open Science, and empirical Software Engineering. If this dataset is used in academic work, please cite both this Zenodo record and the associated journal publication.