# Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
## 8 Conclusions  
This paper presents a context-driven, hybrid metadata framework designed to improve the discoverability and structured documentation of empirical research embedded in academic theses. Rather than proposing an abstract theoretical model, the contribution of this work lies in its applied design: combining selected elements from Dublin Core and the Data Documentation Initiative (DDI) into a practical, student-friendly schema grounded in real thesis documentation patterns.  
Through a structured methodology involving content analysis of student theses, iterative field selection, and prototype development, we have demonstrated that it is possible to bridge the gap between simplicity and empirical specificity in metadata frameworks. The resulting 33-field model addresses the limitations of existing standards--Dublin Core'sgenerality and DDI's complexity--by offering a usable structure tailored for qualitative, quantitative, and mixed-method theses authored by students.  
Moreover, informal validation with students, faculty, and repository administrators has shown that the system is usable, adaptable, and aligned with real institutional needs. Participants affirmed the framework's clarity and value while providing suggestions to improve metadata completeness, field clarity, and user experience.  
Importantly, this work contributes not by inventing a new metadata theory, but by operationalizing a scalable, educationally grounded solution for a documented gap in institutional repositories: the underrepresentation and inconsistent documentation of empirical student research, particularly in the social sciences.  
Future Work  
Future development will focus on extending the prototype's functionality, including the following:  
* Integrating the framework with institutional repository platforms (e.g., DSpace, EPrints) to enhance adoption and scalability.
* Mapping the 33-field schema to Schema.org vocabularies (e.g., ScholarlyArticle, Dataset) to improve thesis visibility on web search engines, potentially through a JSON-LD export feature alongside the existing XML output.
* Exploring RDF serialization of the metadata schema to enable linked data integration, allowing theses to be linked to related resources (e.g., datasets, publications, or ORCID profiles) for cross-repository queries and semantic search.
* Developing an NLP module (or use LLMs) to extract metadata fields (e.g., methodology, sample size) from thesis PDFs, reducing manual entry for students, with pilot testing using tools like GROBID or spaCy to validate the approach for legacy theses.
* Supporting multilingual metadata entry.
* Expanding the framework to cover student-authored empirical papers and conference submissions beyond theses.
* Conducting broader user testing across diverse academic departments and institutional contexts to assess usability, adoption, and scalability.  
This system is intended to evolve in alignment with institutional practice and user feedback, contributing to the broader goal of making the gray literature more visible, reusable, and methodologically transparent within scholarly ecosystems.  
Conceptualization, G.V. and G.T.; methodology, G.V.; software, G.T.; validation, N.P., S.C. and T.N.; formal analysis, G.V.; investigation, G.T.; resources, N.P.; data curation, T.N.; writing--original draft preparation, S.C.; writing--review and editing, G.V.; visualization, G.T.; supervision, N.P.; project administration, G.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.