PRIORITIZING QUALITY INDICATORS OF RESEARCH DATA BASED ON MULTIPLE CRITERIA DECISION MAKING TECHNIQUES

Research in today's world is one of the most important ways to get out of a resource-based economy into a knowledge-based economy. Hence, research data produced and disseminated during the research play an important role in the growth of knowledge economy. In this study, data quality indicators were screened and their significance was determined in order to assess the quality of research data. The main criteria affecting the quality of the data were identified and categorized, including essential, inherent, accessibility, textual and display dimensions. Then, using the paired comparison and the fuzzy demilitarization technique based on fuzzy ANP, we determined the internal and external relationships of the factors and sub-factors. Intrinsic quality of data not only includes precision and objectivity, but also credibility and reputation. Accessibility includes two sub-dimensions of data access and security. Data quality should be considered in the context of the data used. This dimension includes the following dimensions of value added, relevance, timeliness, completeness and adequacy of data. The ability to display data about the presentation framework (succinct and stable presentation) and the meaning of data (interpretability and comprehensiveness). The results of the research show that accessibility, security and ease of perception are considered to be the most influential factors affecting the quality of research data. In this regard, the design of security layers in the research data registration system, the design of database fields to enhance user perceptions and the need to review the requirements for future development of this system are among the practical proposals for this study.