The science of science practice requires the analysis of large and complex bibliometric data. Traditional data exporting from companies’ websites is not sufficient, so APIs are used to access a larger corpus. Therefore, this study aims not only to establish a taxonomy but also to offer a comparative analysis of 44 bibliographic APIs from various non-profit and commercial organizations, analyzing their characteristics and metadata with descriptive analysis, their possible bibliometric analyses, and the interoperability of the APIs across four different data categories: general, content, search, and query modes. The study found that Clarivate Analytics and Elsevier offer highly versatile APIs, while non-profit organizations, such as OpenCitations and OurResearch promote the Open Science philosophy. Most organizations offer free access to APIs for non-commercial purposes, but some have limitations on metadata retrieval. However, CrossRef, OpenCitations, or OpenAlex have no restrictions on the metadata retrieval. Co-author analysis using author names and bibliometric evaluation using citations are the types of analyses that can be done with the data provided by most APIs. DOI, PubMedID, and PMCID are the most versatile identifiers for extending metadata in the APIs. Semantic Scholar, Dimensions, ORCID, and Embase are the APIs that offer the most extensibility. Considering the obtained results, there is no single API that gathers all the information needed to perform any bibliometric analysis. Combining two or more APIs may be the most appropriate option to cover as much information as possible and enrich reports and analyses. This study contributes to advancing the understanding and use of APIs in research practice.

Information that people rely on is often uncertain and partially reliable. Zadeh introduced the concept of Z-numbers as a more adequate formal construct for describing uncertain and partially reliable information. Most existing applications of Z-numbers involve discrete ones due to the high complexity of calculating continuous ones. However, the continuous form is the most common form of information in the real world. Simplifying continuous Z-number calculations is significant for practical applications. There are two reasons for the complexity of continuous Z-number calculations: the use of normal distributions and the inconsistency between the meaning and definition of Z-numbers. In this paper, we extend the triangular distribution as the hidden probability density function of triangular Z-numbers. We add a new parameter to the triangular distribution to influence its convexity and concavity, and then expand the value's domain of the probability measure. Finally, we implement the basic operations of triangular Z-numbers based on the extended triangular distribution. The suggested method is illustrated with numerical examples, and we compare its computational complexity and the entropy (uncertainty) of the resulting Z-number to the traditional method. The comparison shows that our method has lower computational complexity, higher precision and lower uncertainty in the results.