# Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
## 1 Introduction
### The Solution: A Hybrid Metadata Framework  
Using the proposed hybrid metadata framework, [PERSON]'s thesis would be documented in the following way:  
* **Research Strategy:** Qualitative.
* **Data Collection Method:** Semi-structured interviews.
* **Population:** unemployed youth (ages 18-30).
* **Geographic Coverage:** Rural Greece.  
With this metadata in place,  
* A student researching qualitative methods could now **filter by methodology;**
* A policymaker looking for data on **rural youth unemployment** would find [PERSON]'s thesis in seconds;* The thesis becomes part of a discoverable, **structured academic network**. This hybrid framework we propose bridges the gap between simplicity and specificity--making empirical theses like [PERSON]'s **findable**, **reusable**, and **valued** in the broader research ecosystem. The main contributions of this paper are summarized as follows:
* We introduce a hybrid metadata framework that merges selected elements of Dublin Core and DDI, offering a user-friendly yet expressive structure for documenting empirical research in theses across methodological types.
* We address a well-documented gap in existing standards by proposing a model that is both granular and accessible, particularly for non-specialist users working with qualitative and mixed-methods research.
* We validate the framework through a structured analysis of actual student theses, which informed the refinement of metadata fields and ensured alignment with real documentation practices.
* We implement a web-based prototype using open-source technologies, enabling students and institutional staff to input structured metadata through an intuitive interface. Novelty and Methodological Contribution While the use of Dublin Core and DDI is not new individually, our methodology demonstrates originality in the following ways:  
* Conducting a structured, thesis-specific content analysis to empirically inform field selection.
* Designing a hybrid model optimized for non-specialist academic contexts.
* Operationalizing the framework as a working prototype validated by real users.
* Addressing a documented but unresolved institutional need for empirical thesis metadata documentation.  
Notably, the DDI (Data Documentation Initiative) was originally developed to support the documentation of empirical research studies, not student theses. This makes our work particularly innovative, as it applies DDI in a context for which it was not initially designed. By adapting DDI to the structure and needs of academic theses, we demonstrate its flexibility and extend its utility to educational and institutional research practices beyond its conventional domain.  
Thus, the contribution is methodological in its applied rigor and practical in delivering a scalable metadata model ready for institutional adoption.  
The metadata landscape is rapidly evolving, with standards like Schema.org and RDF enabling semantic web integration and AI-driven tools like NLP facilitating automated metadata extraction. While our proposed hybrid model leverages established standards (Dublin Core, DDI) to address immediate repository needs, it is designed with future extensibility in mind. By balancing simplicity, empirical specificity, and potential integration with emerging technologies, this framework aims to position student theses as discoverable assets in both traditional and semantic scholarly ecosystems.  
The remainder of this paper is organized as follows: Section 2 reviews related work on metadata standards and prior initiatives for documenting empirical research. Section 3 details the methodology used to design and validate the proposed model. Section 4 outlines the documentation requirements for various types of theses. Section 5 elaborates on the theoretical foundations of the selected standards. Section 6 presents the empirical validation process and final metadata model. Section 7 discusses key findings and their implications. Finally, Section 8 concludes with limitations and future directions.