Journal of Informetrics最新文献

Patent references to science provide a paper trail of knowledge flow from science to innovation, and have attracted a lot of attention in recent years. However, we understand little about the differences between two types of patents references: front-page vs. in-text. While both types of references are becoming more accessible, we still lack a systematic understanding on how results are sensitive to which type of references are being analyzed in science and innovation studies. Using a dataset of 33,337 USPTO biotech utility patents, their 860,879 in-text and 637,570 front-page references to Web of Science journal articles, we found a remarkable low overlap between these two types of references. We also found that in-text references are more basic and have more scientific citations than front-page references. The difference in interdisciplinarity and novelty is small when comparing at the reference level and insignificant when comparing at the patent level. We analyze the association between patent value (as measured by patent citations and market value) and characteristics of referenced sciences. Results are substantially different between in-text and front-page references. In addition, in-text referenced papers have a higher chance of being listed on the front-page of the same patent when they are moderately basic, less interdisciplinary, less novel, and have more scientific citations.

While clinical citations have been widely used as the preeminent measure of the clinical impact of biomedical paper, there has been a scarcity of in-depth studies exploring their temporal and structural characteristics, as well as its influence on the clinical translation. To fill this gap, we categorized biomedical papers and their citations into four groups from the translational science perspective: basic, clinical, mixed, and human-related. Subsequently, we constructed an edge-labeled citation network and four clinical translation networks. Our analysis encompassed 114,342 papers in the field of Alzheimer's Disease, accompanied by 5,161,626 citations, of which 2.77 % were clinical citations, 18.77 % basic citations, 41.85 % mixed citations, and 36.61 % human-related citations. First, utilizing time- and structure-randomized networks, we conducted a quantitative analysis of clinical citations' incidence patterns, impact assortativity, temporal occurrence patterns, and temporal co-location patterns throughout the lifecycles of biomedical research. Second, in comparison to control groups, we evaluated the short- and long-term impacts of different types of citations on the academic influence and clinical translation of biomedical research. Our findings reveal that clinical citations effectively bolster the academic influence of biomedical papers, and this positive effect appears to amplify over time. Conversely, while basic, mixed, and human-related citations may initially aid in the clinical translation of biomedical research, over 70 % of them exhibit an inhibitory effect on clinical translation in the long run. These findings afford us a deep and specific understanding of how clinical citations operate within the context of biomedical papers, thereby serving as a crucial guide for effectively promoting the clinical translation of biomedical research.

Current studies on technology mining and analysis often focus on patent similarity, with relatively limited research on patent complementarity. Specifically, the hierarchical relationships among patents are seldom used and a standardized complementary patents dataset has not been established. In addition, it is necessary to utilize both network structure features and text content features of patents, and find the most suitable representation learning method for them. Finally, the relationships among different dimensions of feature representations are complex, making it essential to learn the contributions of each dimension considering complex interactions. Therefore, this paper first constructs a complementary patents dataset using hierarchical relationships contained in IPC numbers. Secondly, we design three types of embedding methods for patent semantic representation, including network embedding, text embedding and fusion embedding. Thirdly, we propose a deep learning framework enhanced by the CBAM (Convolutional Block Attention Module) to deal with the complex interactions between different dimensions of patent representation. The result shows that the proposed method CompGCN combined with ESimCSE_Attention performs best for complementary patent identification and the F1 score reaches 95.76 %. In addition, HeGAN and ESimCSE_Attention are the most suitable embedding methods for network structure and text content respectively. These results not only validate the effectiveness of the proposed approach, but also provide helpful and useful suggestions for method selection and complex relationships mining.

This study presents a thorough examination of gendered scholarly contributions and impact from 2003 to 2023, encompassing details on 212,631,585 authorships indexed in Scopus. The analysis unveils promising advancements towards gender equity, demonstrating an increase in contributions from both genders, which indicates the trend towards a progressive and inclusive environment. These findings challenge an initial perception of male prolificacy. The positive trends extend to female-led research teams, highlighting a correlation between gender balance and leadership. This evolving landscape is reflected in the convergence of male and female authorship participation over time. A decline in citable papers suggests a narrowing of the productivity gap, which challenges gender disparities in impact metrics and emphasizes the multifaceted nature of scholarly excellence across genders. Our data and gender classification method also enables us to look into the country level in order to characterize gender distribution locally. Contrary to conventional assumptions, developing countries are exhibiting a pronounced evolution in female authorship rates. In summary, the study underscores the positive trends towards gender equity, advocating for sustained efforts to promote diversity and foster nuanced understanding in academia.

In the procedure of exploring science-technology linkages, non-patent literature (NPL) in patents, particularly scientific NPL, is considered to signal the relatedness between the developed technology and the cited science. However, many prior art search tools may not be powered with the cross-collection recommendation technique, or have limited cross-collection recommendation capabilities. In this paper, we present an approach to recommend scientific NPL for a focal patent on the basis of heterogeneous information network. This study views this cross-collection recommendation problem as a link prediction problem on the basis of meta-path counting approach. Extensive experiments on DrugBank dataset in the pharmaceutical field indicate that our approach is feasible and effective. This work provides a novel perspective on scientific NPL recommendation for a focal patent and opens up further possibilities for the linkages between science and technology. Nevertheless, more experiments in other fields are required to verify the recommended effects of the approach proposed in this study.

The degree distribution, referred to as the delta-sequence of a network is studied. Using the non-normalized Lorenz curve, we apply a generalized form of the classical majorization partial order.

Next, we introduce a new class of small worlds, namely those based on the degrees of nodes in a network. Similar to a previous study, small worlds are defined as sequences of networks with certain limiting properties. We distinguish between three types of small worlds: those based on the highest degree, those based on the average degree, and those based on the median degree. We show that these new classes of small worlds are different from those introduced previously based on the diameter of the network or the average and median distance between nodes. However, there exist sequences of networks that qualify as small worlds in both senses of the word, with stars being an example. Our approach enables the comparison of two networks with an equal number of nodes in terms of their “small-worldliness”.

Finally, we introduced neighboring arrays based on the degrees of the zeroth and first-order neighbors.

The assessment of problems identified by peer researchers during peer review is difficult because the content of these reports is typically confidential. The current study sought to construct and apply a glossary for the identification of methodological and statistical concepts mentioned in peer review reports. Three assessors created a list of 1,036 different terms in 19 categories. The glossary was tested on the confidential PEERE database, a sample of 496,928 peer review reports from various scientific disciplines. The most frequently mentioned terms were related to data presentation (found in 40.3 % of the reports) and parametric descriptive statistics (33.3 %). Review reports suggesting a rejection were more likely to mention methodological issues, whereas statistical issues were raised more frequently in review reports recommending revisions. Across disciplines, methodological issues were more frequently mentioned in social sciences (64.1 %), while health and medical sciences were more predictive for the identification of statistical issues (40.1 %). Female reviewers identified more statistical issues compared to male reviewers. These results indicate that the glossary could be used as an additional tool for the assessment of the content of peer review reports and for understanding what help authors may need in writing research articles.

Science, technology, and innovation are becoming increasingly collaborative, prompting concerted efforts to understand and measure the factors influencing these collaborations. This study aims to explore the driving factors and underlying mechanisms of collaboration dynamics based on patent data. Multilayer longitudinal networks are constructed to scrutinize interactions among organizations as well as the embedding of their knowledge elements in the network fabric. We then analyze the structures and characteristics of collaboration and knowledge networks from global and local perspectives, in which process topological indicators and graphlets are used to feature each organization's collaborative patterns and knowledge stock. Knowledge elements are extracted to present the core concepts of patents, overcoming the limitations of predefined categorizations, such as IPC, when representing technological content and context. By performing a longitudinal analysis using a stochastic actor-oriented model, we integrate network structures, node characteristics, and different dimensions of proximity to model collaboration dynamics and reveal the driving factors behind them. An empirical study in the field of lithography finds that organizations with a larger number of partners or a higher number of annular graphlets in their collaboration networks are less likely to collaborate with others. If an assignee has a more extensive range of knowledge elements and demonstrates a higher capability for knowledge combination, or if its local knowledge network exhibits weaker connectivity, its propensity to seek new collaborators increases. Both cognitive and organizational proximity play important roles in fostering collaboration.

The relationship between disruption and delayed recognition is a critical research topic, yet the connection between the degree of disruption and delayed acknowledgment remains unclear. This study investigates the extent of recognition delay for disruptive papers using the SciSciNet dataset. We conducted a quantitative analysis based on this extensive dataset to examine the relationship between the Disruption Index and the Sleeping Beauty Index, revealing that highly disruptive papers often face a latency period before gaining acknowledgment, with significant variations across disciplines and over time. Our analysis of team dynamics indicates that larger teams, the presence of high-impact authors, fixed teams, and hierarchically structured teams can significantly reduce this delay. These findings provide insights into optimizing team strategies and understanding the complexities of academic recognition. They offer valuable implications for researchers and policymakers aiming to foster and accelerate the acknowledgment of groundbreaking scientific contributions.

A systematic understanding of the interaction between science and technology is beneficial for innovation policies aimed at improving the utilization of science to advance technological development. Traditional approaches primarily focus on direct citation-based linkages, often overlooking the complex, evolving nature of the interaction between scientific and technological knowledge (S&T knowledge interaction). To address this issue, we proposed a novel methodological framework utilizing co-citations between patents and papers, offering a more comprehensive insight into the S&T knowledge interaction. First, we measured the linkage between scientific and technological knowledge based on co-citations between patents and papers. Then, we identified interaction communities and analyzed their evolution. This method not only captures the potential linkages between patents and papers, but also reveals consolidated interactions and rapid changes in S&T knowledge interaction. The results highlight distinct phases in the evolution of S&T knowledge interaction, which are instrumental for understanding how S&T knowledge interaction evolve, especially in rapidly advancing fields like genetic engineering. The insights gained are crucial for academics and practitioners in anticipating future trends and navigating the evolving landscape of science and technology.