PurposePreprint has become an important vehicle for academic communications and discussions. However, in preprint, there is a lack of a sufficient quality control mechanism such as peer review, which is a proven quality assurance practice that is used in traditional academic publishing services. To address the problem leveraging on the power of this practice, the authors introduce into preprint a self-organizing peer review method by applying the concept of token economy and the blockchain technology.Design/methodology/approachSpecifically, this paper proposes an idea that applies the token economy concept to the design of the incentive and penalty mechanisms for peer reviewers in preprint to assure the qualities of its publications. Steemit has been studied to demonstrate the characteristics of the mechanisms.FindingsA token economy-enhanced framework for self-organizing peer review in preprint is also proposed. The resulting preprint system is an academic community-oriented, self-organizing and blockchain-based content publishing system that is designed to run on both permissioned and permissionless blockchains.Research limitations/implicationsFirst, since peer review is on a voluntary basis and not profits oriented, the “monetary” incentive and penalty mechanisms borrowed from Steemit may conflict with academic ethics. Second, the authors proposed to deploy the authors’ token economy on blockchain, but the current mainstream decentralized blockchain services are too few to warrant a foreseeable successful future for the authors’ application. In fact, as the flagship of blockchain 2.0, the Ethereum blockchain suffers from the problem of scalability, which leads to its applications' lower performances, longer response times and eventually more negative user experiences as time goes by. Finally, the authors’ proposed version of preprint has not been implemented, and hence, its practical effectiveness and acceptance by academia are yet to be evaluated.Practical implicationsIn this paper, the authors proposed a token economy-based framework for self-organizing peer review in preprint leveraging on blockchain technology. This framework encourages positive interactions between authors and reviewers, which helps to establish a healthy academic ecology that produces more contents with better qualities. Application of a solution based on the authors’ framework should impact the current academic communities by offering a new academic peer reviewing tool that has a built-in mechanism for self-behavior correction and quality assurance.Social implicationsThrough adaption, the framework can be applied to other domains as well. In such domains, a large amount of feedbacks from partakers are needed and there exists a tremendous amount of work to filter noises in feedbacks so as to ensure that as many the quality ones as possible are delivered for a variety of purposes. The authors’ framework essentially impacts almost all domains where there exists a need to collect and filter large amount of feedbacks, and using the authors’ framework-based solution is cost-saving, which can be seen as a major potential contribution of the research.Originality/valueThe incentive and penalty mechanisms encourage positive interactions between authors and reviewers, and it helps to establish a healthy academic ecology that produces high-volume contents with good qualities.
This paper explores the effect of publishing a data paper in the Open Access journal Data in Brief (DIB) on the citation counts of the related research paper. Using regression analysis, citation content analysis and a survey method, we investigate whether research papers with a related data paper have higher citation counts and the potential reasons.After controlling variables that correlate with the citation counts, research papers with a related data paper were found to have higher citation counts than those published in the same issue of the same journal. Next, we explored the causal relationship between the two variables by surveying the corresponding authors of 618 papers who shared datasets in DIB from 2014 to 2021. The results show that the authors acknowledge the benefits of sharing data in DIB, including citation increase and career reputation enhancement. We further explored how the data papers in DIB increase the citations of the related research papers by using citation content analysis. We found that scientists co-cite the data papers and their related research papers for the purpose of reusing the underlying data or portraying a better understanding of the underlying data and related research articles.
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