A Nonstandard Neutrosophic Off-Norm Model for Evaluating Research Data Management Service Capability in University Libraries

Abstract

University libraries offer many data-related services such as storing research files, 
organizing metadata, and helping users access and manage information. These services 
are important, but their quality is not always the same. Some services work better than 
expected, others do not meet basic needs, and some show mixed or unclear results. Most 
traditional methods cannot fully describe these situations, especially when the data is 
uncertain or the service performance is inconsistent. 
This paper presents a new model called the Nonstandard Neutrosophic Off-Norm Model 
(NSN-ONM) to evaluate how well data management services perform in university 
libraries. The model combines three advanced ideas. First, it uses N-norm and N-conorm 
operations to bring together different kinds of service data. Second, it uses the concept of 
Over, Under, and Off norms to describe when a service does better than expected, worse 
than expected, or acts in a confusing way. Third, it uses a special kind of logic called the 
Nonstandard Neutrosophic MoBiNad Set, which helps represent uncertain and unusual 
situations in service performance. The model gives each service a score that reflects its 
overall ability and behavior. We include full explanations, mathematical steps, and a case 
study based on real data from a university library. The results show that this model can 
help librarians understand which services are strong, which need improvement, and 
which may be acting in unpredictable ways. 

DOI: 10.5281/zenodo.15786710