Scientometrics最新文献

Scientific uncertainty is an integral part of the research process and inherent to the construction of new knowledge. In this paper, we investigate the ways in which uncertainty is expressed in articles and propose a new interdisciplinary annotation framework to categorize sentences containing uncertainty expressions along five dimensions. We propose a method for the automatic annotation of sentences based on linguistic patterns for identifying the expressions of scientific uncertainty that have been derived from a corpus study. We processed a corpus of 5956 articles from 22 journals in three different discipline groups, which were annotated using our automatic annotation method. We evaluate our annotation method and study the distribution of uncertainty expressions across the different journals and categories. The results show a predominant concentration of the distribution of the scientific uncertainty expressions in the Results and Discussion section (71.4%), followed by 12.5% of expressions in the Background section, and the largest proportion of uncertainty expressions, approximately 70.3%, are formed as author(s) statements. Our research contributes methodological advances and insights into the diverse manifestations of scientific uncertainty across disciplinary domains and provides a basis for ongoing exploration and refinement of the understanding of scientific uncertainty communication.

Scientific experience is crucial for producing high-quality research, and the approach of acquisition can significantly impact its accumulation rate. We present a framework for scientific experience acquisition that outlines the dual dimensions of experience accumulation: self-accumulation and accumulation under senior expert guidance. To validate the framework, we conducted a case study using 2,957,700 papers from all 568 Chinese humanities and social science journals, taking into account the limitations of the international journal system. Our findings reveal that self-accumulation has been gradually declining, decreasing from 57.67% in 1980 to 4.55% in 2020. Conversely, accumulation under senior expert guidance has been steadily increasing, rising from 5.7% in 1980 to 28.69% in 2020. Furthermore, the proportion of the two approaches varies by discipline. Social sciences such as Psychology, Economics, and Management, which rely more on large teams and collaborative research, have a higher proportion of accumulation under senior expert guidance than humanities disciplines like Art, History, and Philosophy, which depend more on individual research. Finally, this research also offers a distinctive exploration of the question posed by the US National Science and Technology Council (2008): how and why do communities of innovation form and evolve.

Academic collaboration can break through the geographical limitations of scholars and promote academic output among scholars. Academic big data will provide an important data source for more comprehensive and accurate modeling scholars due to the coexistence environment of various academic entities. Based on academic big data, an end-to-end model HCSP was proposed for predicting collaborative relationships among scholars. HCSP models scholars from two aspects: content-based features and structure-based features, and calculate the similarity between scholars based on this to predict whether there will be academic collaboration between scholars. When learning the content-based features of scholars, HCSP utilizes LSTM and multi-head attention mechanism to extract the overall and recent research interests of scholars, to capture the diversity of scholars’ research interests. When learning the structure-based features of scholars, HCSP adopts a subgraph sampling strategy based on meta paths to model the structural features of scholar nodes in heterogeneous academic network. By integrating scholars’ content-based and structure-based features, HCSP calculates the similarity between scholars to determine whether there will be a collaborative relationship between them. The experimental results indicate that the HCSP model achieves better prediction performance compared to the baseline models. It can be seen that integrating scholars’ content-based and structure-based characteristics can indeed provide a richer and more effective modeling basis for predicting their academic collaborative relationships.

Understanding knowledge transfer patterns is essential in providing valuable insights for shaping innovations and supporting economic growth. Our study identifies the main contributors and patterns of knowledge transfer within the pharmacology field from 2000 to 2019 by analyzing citation linkage and collaborative information between sector categories, affiliated institutions, and biomedical entities in articles from the Web of Science database. Our main contribution is mapping the knowledge transfer flow and identifying the main contributors to knowledge transfer within the pharmacology domain. We manually categorized affiliated institutions into four sector categories to observe knowledge transfer patterns. Subsequently, we performed a citation linkage analysis at three levels: sector categories, institution names, and biomedical entities. The results show that academic institutions are the most significant contributors to knowledge transfer in the pharmacology field, followed by commercial and government institutions. Although the majority of knowledge transfers originated from academic institutions, our study uncovered notable transfers from commercial to academic sectors and from government to academic sectors. Through named entity analysis on diseases, drugs, and genes, we found that research in the pharmacology field predominantly concentrates on subjects pertaining to cancers, chronic diseases, and neurodegenerative disorders.

Does a patent examiner rely more on external sources of information for prior art searches as the examiner becomes more experienced? This question is relevant to the policy debate because studies confirm that the seniority of examiners is associated with higher patent allowance rate in the U.S. However, little is known to date about how examiners’ citation behavior, particularly search behavior, is related to their experience. This paper first describes how examiner experience is related to the rate of patent allowance and repeated use of prior citations by the same examiner. Next, this paper analyzes how examiner experience is related to the extent of receiving spillover at the USPTO and the JPO. This paper uses an empirical methodology to identify examination spillovers from the European Patent Office (EPO) search result to the United States Patent and Trademark Office (USPTO) and also to the Japan Patent Office (JPO) in the sense that patent citations for rejection of a patent application tend to be “adopted” at a later office after the EPO issues search reports. The results show that more experienced examiners exhibit greater convergence of patent citations at the USPTO and at the JPO with the search report outcome at the EPO, although the spillover effect also depends on international patent application routes, such as the Patent Cooperation Treaty (PCT).

Data sharing is a major tenet in the global challenge to improve the reproducibility of scientific findings. Current researcher attitudes toward data sharing and Open Science in general are still far from optimal. The practice of data sharing and how it should be managed remain unclear and inconsistent, with many researchers keen to receive from, but not give back to the community. The lack of a data sharing culture, systemic resistance, misconceptions on data ownership and the unjustified fear of being “scooped”, all concur to create an enormous barrier to the promotion of scientific research based on increased information quality, transparency and openness, and replicability of results. These factors are also compounded by the erroneous perception that the sharing of data compromises competitiveness. Here, we present a rigorous observational study based on 198 researchers in the biomedical areas to evaluate factors affecting perception and natural attitude to data sharing in the biomedical sciences.

Research is a global enterprise underpinned by the general belief that findings need to be true to be considered scientific. In the complex system of scientific validation, editorial boards (EBs) play a fundamental role in guiding journals’ review process, which has led many stakeholders of sciences to metaphorically picture them as the “gatekeepers of knowledge.” In an attempt to address the academic structure that governs sciences through editorial board interlocking (EBI, the cross-presence of EB members in different journals) and social network analysis, the aim of this study is threefold: first, to map the connection between fields of knowledge through EBI; second, to visualize and empirically test the distance between social and general sciences; and third, to uncover the institutional structure (i.e., universities) that governs these connections. Our findings, based on the dataset collected through the Open Editors initiative for the journals indexed in the JCR, revealed a substantial level of collaboration between all fields, as suggested by the connections between EBs. However, there is a statistically significant difference between the weight of the edges and the path lengths connecting the fields of natural sciences to the fields of social sciences (compared to the connections within), indicating the development of different research cultures and invisible colleges in these two research areas. The results also show that a central group of US institutions dominates most journal EBs, indirectly suggesting that US scientific norms and values still prevail in all fields of knowledge. Overall, our study suggests that scientific endeavor is highly networked through EBs.

In this study, employing the IEEE Xplore database as the data source, articles on different topics (keywords) and their usage data generated from January 2011 to December 2020 were collected and analyzed. The study examined the temporal relationships between these usage data and publication counts at the topic level via Granger causality analysis. The study found that almost 80% of the topics exhibit significant usage-publication interactions from a time-series perspective, with varying time lag lengths depending on the direction of the Granger causality results. Topics that present bidirectional Granger causality show longer time lag lengths than those exhibiting unidirectional causality. Additionally, the study found that the direction of the unidirectional Granger causality was influenced by the significance of a topic. Topics with a greater preference for article usage as the Granger cause of publication counts were deemed more important. The findings’ reliability was confirmed by varying the maximum lag period. This study provides strong support for using usage data to identify hot topics of research.

Researchers adopt keywords to signify the core content of papers, and the spatial distribution of these keywords within the paper can provide insight into researchers’ adoption behavior patterns. In this study, the primary purpose was to investigate how keyword adoption patterns affect academic papers’ perceived value. First, we collected 5,739 papers from the China National Knowledge Infrastructure (CNKI) to extract the first-level subtitles for statistically characterizing the functional structure of papers in the Library and Information Science (LIS) field. Second, we introduce a balance degree indicator to measure the keywords’ spatial distribution. Next, we identify researchers’ keyword adoption behavior patterns based on the keyword spatial distribution in the functional structure. Finally, we investigate the effect of keyword adoption behavior patterns on paper impact. The findings of our study reveal that: (1) In the Library and Information Science field, the balance degree values exhibit a normal distribution and are verified to be valid. (2) Depending on the keyword distribution across the four segments, the keyword adoption behaviors of researchers can be categorized into 24 distinct types. (3) The balance degree is positively correlated with both the citation and download count, and notably, the keyword spatial distribution of the Introduction and Results & Discussion sections have a significant effect on a paper’s impact. These findings have significant implications for keyword selection and the early prediction of a paper’s citation and download frequency.

Technology knowledge elements play an important role in technology innovation. However, there is still challenges about their extraction and evaluation. Traditional methods exhibit limitations in precisely linking key technologies with functions, and they usually focus on measuring the overall novelty of patent documents rather than individual technology details, leading to poor interpretability and practicality of research outcomes. In this work, we present a framework that extracts technology knowledge triples and evaluates the novelty of triples based on deep learning model. This framework first identifies key sentences that reflect innovation from patent claims and then extracts technology knowledge elements from these sentences. A novelty index is then designed to evaluate the novelty of these technology knowledge elements based on the probability of their occurrence and the similarity to existing knowledge. The experimental results demonstrate the effectiveness of the proposed method. The extracted technology knowledge elements can use to construct an innovation knowledge graph, which provides practical applications in engineering knowledge retrieval, design and innovation support.