Identifying Gaps in Subject Indexing of Theses and Dissertations in the Ganj Database and Ways to Improve It

This study aims to analyze the thematic gaps present in the theses and dissertations available in the Iranian Scientific Information Database (Ganj) and propose solutions for organizing these information resources effectively. This qualitative research utilized a comparative method to identify thematic gaps in the indexing of theses and dissertations based on their core meanings. The statistical population included 1,112,531 theses and dissertations in the Iranian Scientific Information Database (Ganj). A random-stratified sampling method was employed, with a sample size of 1,500 documents for analysis. Data were collected using a checklist containing 21 components related to subject area evaluation and analysis. Interviews with 4 information organization experts at IranDoc were conducted to strengthen the data obtained. The validity of the data collection tool was assessed using the face validity method with input from 5 academic experts in information science. The reliability of the checklist was determined using Cronbach's alpha statistical method, yielding a value of 0.872. The analysis revealed several thematic organization gaps, including issues related to subject sub-branches, depth of indexing, subject labels, concept matching, preference for subjects over academic fields, development of thematic classification, up-to-date classification, classification structure, time management, and natural language processing. The study highlighted shortcomings in the indexing structure and utilization of keywords. The relationship between subject classification structure levels significantly impacts indexing depth. Challenges such as concept-subject matching and accurate identification of content were identified. Recognizing relationships between topics and interdisciplinary sources aids in resource organization. Developing and expanding subject classifications to incorporate more keywords is crucial, as new topics emerge with scientific advancements. The structure of subject classification plays a key role in assigning appropriate keywords. The results showed that developing subtopics within the main subject enhances information organization and fosters stronger keyword relationships and conceptual alignment. Indexing depth at subject levels is vital, and hierarchical vocabulary systems support subject classification development. Proper subject labeling improves thesis retrieval. Identifying information content accurately poses challenges in subject organization. Keyword-based retrieval and awareness of the challenges of subject processing are crucial in this context. Matching subject areas to scientific progress is essential, and scientific classification based on the relationships between keywords can enhance the effectiveness of retrieval.