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The purpose of this paper is to critically analyze the influence of the algorithm used on scholarly search engines (Garfield’s algorithm) and propose metrics to improve it so that science could be based on a more democratic way.

This paper used a snow-ball approach to collect data that allowed identifying the history and the logic behind the Garfield’s algorithm. It follows on excerpting the foundation of existing algorithm and databases of major scholarly search engine. It concluded proposing new metrics so as to surpass restraints and to democratize the scientific discourse.

This paper finds that the studied algorithm currently biases the scientific discourse toward a narrow perspective, while it should take into consideration several researchers’ characteristics. It proposes the substitution of the h-index by the number of times the scholar’s most cited work has been cited. Finally, it proposes that works in languages different than English should be included.

The broad comprehension of any phenomena should be based on multiple perspectives; therefore, the inclusion of diverse metrics will extend the scientific discourse.

The improvement of the existing algorithm will increase the chances of contact among different cultures, which stimulate rapid progress on the development of knowledge.

The value of this paper resides in demonstrating that the algorithm used in scholarly search engines biases the development of science. If updated as proposed here, science will be unbiased and bias aware.

This paper aims to establish a fundamental and comprehensive understanding of non-fungible tokens (NFTs) by identifying and structuring common characteristics within a taxonomy. NFTs are hyped and increasingly marketed as essential building blocks of the Metaverse. However, the dynamic evolution of the NFT space has posed challenges for those seeking to develop a deep and comprehensive understanding of NFTs, their features and their capabilities.

Utilizing common guidelines for the creation of taxonomies, the authors developed (over 3 iterations), a multi-layer taxonomy based on workshops and interviews with 11 academic and 15 industry experts. Through an evaluation of 25 NFTs, the authors demonstrate the usefulness of the taxonomy.

The taxonomy has 4 layers, 14 dimensions and 42 characteristics, which describe NFTs in terms of reference object, token properties, token distribution and realizable value.

The authors' framework is the first to systematically cover the emerging NFT phenomenon. This framework is concise yet extendible and presents many avenues for future research in a plethora of disciplines. The characteristics identified in the authors' taxonomy are useful for NFT- and Metaverse-related research in finance, marketing, law and information systems. Additionally, the taxonomy can serve as an information source for policymakers as they consider NFT regulation.