Journal of Informetrics最新文献

Scientific papers are the essential carrier for disseminating knowledge in the scientific communication system. It is believed that in addition to deepening the scientific attainments in one's research field, the writing and revision of the manuscripts are also very important. This paper tries to quantitatively describe the revision process and the relationship between revision and academic impact. We acquire the different manuscript versions of the published scientific papers from bioRxiv platform, analyze the characteristics of the time (away from publication) and location (sections) of paper revisions, and explore the correlation between revision and impact using multiple linear regression. We find that 75 % or more of revisions occur within one year before publication, and 40 % or more of revisions take place in the last 3 months, which illustrates to a certain extent the importance of expert review in revising (and improving) research papers. Further, we find that the revision degree of papers has a significant correlation with academic impact, and elaborate revision of introduction plays a major role in acquiring academic impact for a research paper. We believe our results can provide a basis for guiding authors to enhance the academic impact of papers through scientific writing, and provide clues for the development of journal publishers and preprint platforms from the perspective of paper quality control and influence improvement.

This study aims to understand whether and under what condition openness to international collaboration enhances knowledge creation. How the configuration of openness, domestic ties, and foreign ties of an institution affects its knowledge creation is examined in egocentric networks. Using fixed-effects panel data models, we find that a positive association exists between openness and knowledge creation, which is catalyzed by domestic tie strength and diminished by foreign tie strength, and both domestic and foreign tie strength have an inverted U-shaped effect on knowledge creation. The findings contribute to international collaboration and knowledge-based view literature by proposing that maintaining strong domestic ties and a wide range of relatively weak foreign ties simultaneously is an efficient way to leverage international collaboration to integrate domestic and foreign knowledge. It also enriches the strong tie theory by suggesting the strength of strong domestic ties could be reinforced by openness.

Negative results are a routine part of the scientific research journey, yet they often receive insufficient attention in scientific publications. In this study, we investigate the scientific impact of negative results by comparing the citations and citation context between negative and positive results. Specifically, we compared 159 negative result papers from three journals: Journal of Negative Results in BioMedicine, PLoS One, and BMC Research Notes, with 1,058 matched positive result papers authored by the same first and corresponding authors. The citation context was categorized according to three dimensions: citation aspect, citation purpose, and citation polarity. The first two were automatically provided by Citation Opinion Retrieval and Analysis (CORA), while citation polarity was manually annotated. Our analysis revealed several key findings. Firstly, negative results received 38.6 % fewer citations than positive results, even after controlling for bibliographic factors. Secondly, negative results were associated with a significantly higher proportion of negative citations when compared to positive results. Lastly, a higher proportion of negative results were negatively cited in the methods section.

We define Hirsch-type equations and bundles being common generalizations of the defining equations of e.g. Hirsch-bundles, g-bundles, and Kosmulski-bundles. In this way, common properties of all these bundles can be proved. The main result proves basic inequalities for these bundles. They form the basis for convergence results as well as for criteria for these bundles to be impact bundles.

From an informetrics perspective, we discuss the relationship between the Least Effort Principle and information cost minimisation, and discuss the implications for Zipf's law. We propose a mathematical proof of a theorem demonstrating that if one minimises the average cost per unit of information, the Least Effort Principle (LEP) will be verified regardless of the effort function chosen. We then show that Zipf's law can be demonstrated on the basis of the two models studied, namely Average Cost per Unit of Information and the Least Effort Principle (LEP).

In the relentless pursuit of scientific advancement, comprehending the profound impact and innovation nature inherent in funded research projects assumes paramount significance. To illuminate this matter, I delve into the realm of research supported by the National Institutes of Health (NIH) and the National Science Foundation (NSF). The evaluative framework encompasses a spectrum of metrics, including citations by papers, patents, and Tweets, as markers of research impact. Moreover, I embrace ex-ante innovation (Novelty) and ex-post innovation (Disruption) as dual indispensable yardsticks for evaluating the innovative nature of research projects. Novelty denotes the manifestation of atypical combinations of existing knowledge, while Disruption signifies the extent of paradigm-shifting potential and the ability to exert a disruptive influence on future research endeavors. First, the analysis reveals that funded research projects manifest a conspicuously heightened impact in comparison to their non-funded counterparts. Second, I uncover a noteworthy finding: funded research demonstrates significantly higher levels of ex-ante innovation (Novelty). However, in a surprising twist, the impact of funding on ex-post innovation (Disruption) appears to be faint. Additionally, I undertake a meticulous scrutiny of the robustness of the research findings by scrutinizing patterns across years and fields. Despite the uneven distribution of NIH and NSF funded research and inconspicuous heterogeneity across fields, the patterns of the impact and dual innovation of funded research are consistent across almost all fields.

Interdisciplinary research increasingly fuels innovation, and is a key input for future breakthroughs. Yet the timing of when interdisciplinary research achieves its highest citation impact remains unclear. Here, we use the time of a paper to reach its citation peak to quantify citation dynamics, and examine its relationship with paper interdisciplinarity. Using large scale publication datasets spanning over 37 years, our results suggest that interdisciplinary papers show significant delayed citation impact both at the individual paper level and collectively, as it takes longer for highly interdisciplinary papers to reach their citation peak as well as their half citations. Such relationships are nearly universal across various scientific disciplines and time periods. Furthermore, we study the underlying forces behind this delayed impact, finding that the effect goes beyond the Matthew effect (i.e., the rich-get-richer effect). Although team size and content conventionality are partly related to the citation delay, they cannot fully explain this effect. Overall, our results suggest that governments, research administrators, and funding agencies should be aware of this general feature of interdisciplinary science, which may have broad policy implications.

The semantic text similarity (STS) estimation between patents is a critical issue for the patent portfolio analysis. Current methods such as keywords, co-word analysis and even the Subject-Action-Object (SAO) algorithms, are not quite reasonable for the patent similarity calculation due to the lack of fine-grained semantic knowledge, “property-parameter” features and flexible “functional or non-functional” combinations. In the meanwhile, standardized similarity datasets are also unavailable. In this paper, we have proposed a new kind of functional semantic knowledge (Function-Object-Property, i.e., FOP) instead of SAO triples, which can contribute directly to enhance the patent similarity. Moreover, patent STS datasets, including the matching dataset and the ranking dataset, have firstly been processed and released as benchmarks for the comparative evaluation. Preliminary results have demonstrated that FOP-based methods are more appropriate in the STS tasks incorporated with IPC codes, weights’ assignments and patent pre-trained vectors. To be further, the deep interaction-based models with the averaged FOP embeddings are recommended to be one of the most optimal choices of effectively improving the semantic learning capability. Finally, a new patent similarity calculation framework is summarized and successfully applied in the patent retrieval, which highlight that the proposed methodology serves as a dominant power in diverse patented STS tasks.

Subject categorization of scientific publications, i.e., journals, book series or conference proceedings, has become a main concern in academia, as publication impact and ranking are considered a basic criterion to evaluate paper quality. Publishers usually propose their own categorization, but they often include only their own publications and their categories might not be coherent with other proposals. Also, due to the dynamic nature of science, new categories may frequently appear. As traditional mechanisms for categorization have been questioned by many authors, a new research line has emerged to improve the category assignment process. Approaches usually rely on assessing publication similarity in terms of topics, co-citation, editorial boards, and/or shared author profiles. In this work, we propose a novel procedure for scientific publication hierarchical categorization based on the repetition or absence of relevant descriptors in association rules among publications. The key idea is that publication categories can be automatically defined by strong associations of nuclear topics. Also, some very specific subcategories can be defined by exclusion from any set of rules. This process can be used to construct a data-driven hierarchy of scientific publication categories from scratch or to improve any existing categorization by discovering new fields. In this paper the proposed algorithm uses SJR descriptors all journals in the SCImago dataset and the three-level classification in the Scopus dataset (covering only 35 % of publications of the SCImago dataset) to discover new categories and assign every journal to the resulting enhanced hierarchy one. We have focused on the field of “Physical Sciences and Engineering”, using the SCImago and Scopus datasets from 2019 (30,883 scientific publications). Our procedure combines data engineering techniques with association rules and generates as a result potential new categories and outlier subcategories. To evaluate the suitability of our proposal, we have analyzed classification results based on the original category list and our extended two-level categorization via the Jensen–Shannon divergence and supervised machine-learning techniques. Results reveal the consistency and suitability of our categorization procedure.

Reviewers with a present bias focus on the here and now, placing more importance on immediate rewards than on future intentions and benefits. In this paper, we are going to address two related research questions: First, can a reviewer's motivation to work hard be increased by using a higher evaluation goal? Second, would a reviewer be more willing to accept a higher evaluation goal if the review process provided a large enough reward? Using a formal model, we predict that a reviewer with a present bias increases their motivation to work hard by setting a higher goal for the manuscript evaluation (relative to having no goal). For example, the reviewer is willing to control the quality of the manuscript, in addition to helping the profession and keeping themselves up to date. However, a reviewer with a severe present bias prefers to exert a low level of effort. In this situation, we find that monetary incentives can play an important role for reviewers. In their absence, a reviewer may not accept the evaluation goal that motivates them to work hard in the peer review process. In this paper, using a series of computational experiments, we discuss the behavior of a reviewer with a present bias and the role of goal intentions and additional instrumental gains.