A Nonstandard Neutrosophic Off-Norm Model for Evaluating Research Data Management Service Capability in University Libraries - Tao Xu (Neutrosophic Sets and Systems)
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.