2026-10-14T04:06:49Z https://www.scidb.cn/oai

10.57760/sciencedb.38956 2026-06-16T09:25:05Z

2026-06-16 Coding_Template_Reliability_64 doi:10.57760/sciencedb.38956 en Dataset DescriptionBackground and PurposeThis dataset was developed within a comparative research project investigating how the concept of family is represented in graduate-level social science dissertations produced in Türkiye and Germany between 2019 and 2024. The study aimed to identify thematic trends, methodological preferences, disciplinary distributions, and emerging research topics associated with family-related scholarship in both countries.The dataset was generated through a systematic collection of publicly accessible dissertation metadata from two national repositories: the Turkish Council of Higher Education National Thesis Center (YÖK National Thesis Center) and the German National Library (Deutsche Nationalbibliothek). Only dissertations containing family-related keywords in their titles, abstracts, or keywords were included.Data Generation ProcessThe data collection process consisted of the following stages:Retrieval of dissertation metadata from the source repositories.Screening and eligibility assessment according to predefined inclusion criteria.Extraction of bibliographic metadata.Data cleaning and standardisation.Development of thematic dictionaries and coding categories.Manual validation and consistency checking.Final coding and dataset construction.The coding framework was developed iteratively and validated through multiple rounds of review. Detailed coding rules, category definitions, inclusion criteria, and validation procedures are documented in the accompanying codebook.Dataset ContentsThe repository contains two complementary files:File 1: DatasetThis file contains the structured dissertation metadata and coding results used for analysis.Typical variables include:Thesis_ID: Unique identifier assigned to each dissertation.Country: Türkiye or Germany.Year: Year of publication.Degree_Type: Master's or Doctoral dissertation.Discipline: Academic field or disciplinary classification.Title: Original dissertation title.Keywords: Author-provided keywords where available.Theme_Codes: Assigned thematic categories.Methodology_Code: Research methodology classification.Digitalization: Presence/absence of digitalization-related topics.Pandemic: Presence/absence of pandemic-related topics.Family_School: Presence/absence of family-school collaboration themes.Additional thematic indicators used in the study.File 2: CodebookThe codebook documents:Variable definitions.Coding categories.Thematic dictionaries.Inclusion and exclusion criteria.Coding decision rules.Validation procedures.Examples of category assignment.File Naming ConventionThe repository follows the naming convention:Dataset_Family_Dissertations_TR_DE_2019_2024.xlsxCodebook_Family_Dissertations_TR_DE_2019_2024.pdfFile names indicate the content, geographical scope, and temporal coverage of the data.Research ValueThe dataset provides a unique cross-national resource for researchers interested in:Family studies.Educational research.Comparative social sciences.Dissertation and thesis research.Bibliometric analysis.Thematic trend analysis.Research policy and agenda-setting studies.The data enable replication of the published analyses and support secondary analyses of thematic developments in family-related academic research across two national contexts.Reuse NotesAll metadata were obtained from publicly accessible repositories. The dataset contains no personal or sensitive information beyond publicly available bibliographic records. Users are encouraged to consult the accompanying codebook before conducting secondary analyses to ensure correct interpretation of variables and coding categories. validate the reliability of this AI-assisted coding environment and rule out algorithmic hallucinations or category drifts; AI-assisted coding; rule out algorithmic hallucinations; category drifts Bora BAŞARAN PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank