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Identifying factors that associate with more cited or higher quality research may be useful to improve science or to support research evaluation. This article reviews evidence for the existence of such factors in article text and metadata. It also reviews studies attempting to estimate article quality or predict long‐term citation counts using statistical regression or machine learning for journal articles or conference papers. Although the primary focus is on document‐level evidence, the related task of estimating the average quality scores of entire departments from bibliometric information is also considered. The review lists a huge range of factors that associate with higher quality or more cited research in some contexts (fields, years, journals) but the strength and direction of association often depends on the set of papers examined, with little systematic pattern and rarely any cause‐and‐effect evidence. The strongest patterns found include the near universal usefulness of journal citation rates, author numbers, reference properties, and international collaboration in predicting (or associating with) higher citation counts, and the greater usefulness of citation‐related information for predicting article quality in the medical, health and physical sciences than in engineering, social sciences, arts, and humanities. [ABSTRACT FROM AUTHOR]

Value co‐creation as a research topic has been a critical proposition of interest to a wide range of disciplines. In recent years, the field of cultural heritage has also developed a range of information practices to promote different forms of value co‐creation. While the body of literature has begun to accumulate, we still lack a comprehensive understanding of conceptual foundations and how value co‐creation is implemented in cultural heritage information practices. This paper conducted a hermeneutic literature review to understand and conceptualize the core aspects of value co‐creation in various cultural heritage information works. In particular, this paper provides a research mapping of the genres of co‐creation in cultural heritage information practices, the actors, and the roles played by the stakeholders. Further, this review provides an overview of the dominant value co‐creation models in the cultural heritage field. It distills the drivers of value co‐creation and the core value consequences that arise from a process perspective. Finally, this paper puts forward a thematic, theoretical, and methodological agenda for future work based on these findings. The review contributes to the literature by proposing an integrated conceptual framework that summarizes the core elements of value co‐creation in cultural heritage information practices. The framework also serves as a call for action, providing a general understanding of the conceptual foundation for future research. [ABSTRACT FROM AUTHOR]

Data culture/s as a research topic has begun to attract attention from a wide range of disciplines, albeit with inconsistent application of definitions, dimensions, and applications. This work builds on a call to investigate data culture/s within the information studies domain as a topic related to, but distinct from, information culture. The purpose of this study is to explore what is known about data culture/s in greater depth. We apply a retroductive approach to select and consider likely dimensions, inputs, and aspects of data culture/s in order to further map this construct to the literature, and thereby highlight gaps and opportunities to add to this body of knowledge. The initial candidate dimensions explored below include data‐related skills and attitudes, data sharing, data use/reuse, data ethics and governance, and a specific focus on Indigenous perspectives to provide insights on why and how a group may contest the emergent dominant discourse of data culture/s. Our conclusion highlights areas needing further research to fully define and examine the dimensions, inputs, and aspects of data culture/s, and calls for greater understanding and engagement with data culture/s from the information studies community. [ABSTRACT FROM AUTHOR]

Classifying papers according to the fields of knowledge is critical to clearly understand the dynamics of scientific (sub)fields, their leading questions, and trends. Most studies rely on journal categories defined by popular databases such as WoS or Scopus, but some experts find that those categories may not correctly map the existing subfields nor identify the subfield of a specific article. This study addresses the classification problem using data from each paper (Abstract, Title, Keywords, and the KeyWords Plus) and the help of experts to identify the existing subfields and journals exclusive of each subfield. These "exclusive journals" are critical to obtain, through a pattern detection procedure that uses machine learning techniques (from software NVivo), a list of the frequent terms that are specific to each subfield. With that list of terms and with the help of optimization procedures, we can identify to which subfield each paper most likely belongs. This study can contribute to support scientific policy‐makers, funding, and research institutions—via more accurate academic performance evaluations—, to support editors in their tasks to redefine the scopes of journals, and to support popular databases in their processes of refining categories. [ABSTRACT FROM AUTHOR]

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First‐time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first‐time collaboration increased after the outbreak of COVID‐19, and international collaboration witnessed a sudden decrease. During COVID‐19, papers with more first‐time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic. [ABSTRACT FROM AUTHOR]

Each section header of an article has its distinct communicative function. Citations from distinct sections may be different regarding citing motivation. In this paper, we grouped section headers with similar functions as a structural function and defined the distribution of citations from structural functions for a paper as its citation structure. We aim to explore the relationship between citation structure and the future impact of a publication and disclose the relative importance among citations from different structural functions. Specifically, we proposed two citation counting methods and a citation life cycle identification method, by which the regression data were built. Subsequently, we employed a ridge regression model to predict the future impact of the paper and analyzed the relative weights of regressors. Based on documents collected from the Association for Computational Linguistics Anthology website, our empirical experiments disclosed that functional structure features improve the prediction accuracy of citation count prediction and that there exist differences among citations from different structural functions. Specifically, at the early stage of citation lifetime, citations from Introduction and Method are particularly important for perceiving future impact of papers, and citations from Result and Conclusion are also vital. However, early accumulation of citations from the Background seems less important. [ABSTRACT FROM AUTHOR]

With the wide application of keyphrases in many Information Retrieval (IR) and Natural Language Processing (NLP) tasks, automatic keyphrase prediction has been emerging. However, these statistically important phrases are contributing increasingly less to the related tasks because the end‐to‐end learning mechanism enables models to learn the important semantic information of the text directly. Similarly, keyphrases are of little help for readers to quickly grasp the paper's main idea because the relationship between the keyphrase and the paper is not explicit to readers. Therefore, we propose to generate keyphrases with specific functions for readers to bridge the semantic gap between them and the information producers, and verify the effectiveness of the keyphrase function for assisting users' comprehension with a user experiment. A controllable keyphrase generation framework (the CKPG) that uses the keyphrase function as a control code to generate categorized keyphrases is proposed and implemented based on Transformer, BART, and T5, respectively. For the Computer Science domain, the Macro‐avgs of P@5, R@5, and F1@5 on the Paper with Code dataset are up to 0.680, 0.535, and 0.558, respectively. Our experimental results indicate the effectiveness of the CKPG models. [ABSTRACT FROM AUTHOR]

Main path analysis is a popular method for extracting the scientific backbone from the citation network of a research domain. Existing approaches ignored the semantic relationships between the citing and cited publications, resulting in several adverse issues, in terms of coherence of main paths and coverage of significant studies. This paper advocated the semantic main path network analysis approach to alleviate these issues based on citation function analysis. A wide variety of SciBERT‐based deep learning models were designed for identifying citation functions. Semantic citation networks were built by either including important citations, for example, extension, motivation, usage and similarity, or excluding incidental citations like background and future work. Semantic main path network was built by merging the top‐K main paths extracted from various time slices of semantic citation network. In addition, a three‐way framework was proposed for the quantitative evaluation of main path analysis results. Both qualitative and quantitative analysis on three research areas of computational linguistics demonstrated that, compared to semantics‐agnostic counterparts, different types of semantic main path networks provide complementary views of scientific knowledge flows. Combining them together, we obtained a more precise and comprehensive picture of domain evolution and uncover more coherent development pathways between scientific ideas. [ABSTRACT FROM AUTHOR]

Academic research often draws on multiple funding sources. This paper investigates whether complementarity or substitutability emerges when different types of funding are used. Scholars have examined this phenomenon at the university and scientist levels, but not at the publication level. This gap is significant since acknowledgement sections in scientific papers indicate publications are often supported by multiple funding sources. To address this gap, we examine the extent to which different funding types are jointly used in publications, and to what extent certain combinations of funding are associated with higher academic impact (citation count). We focus on three types of funding accessed by UK‐based researchers: national, international, and industry. The analysis builds on data extracted from all UK cancer‐related publications in 2011, thus providing a 10‐year citation window. Findings indicate that, although there is complementarity between national and international funding in terms of their co‐occurrence (where these are acknowledged in the same publication), when we evaluate funding complementarity in relation to academic impact (we employ the supermodularity framework), we found no evidence of such a relationship. Rather, our results suggest substitutability between national and international funding. We also observe substitutability between international and industry funding. [ABSTRACT FROM AUTHOR]

Surveillance, as a concept and social practice, is inextricably linked to information. It is, at its core, about information extraction and analysis conducted for some regulatory purpose. Yet, information science research only sporadically leverages surveillance studies scholarship, and we see a lack of sustained and focused attention to surveillance as an object of research within the domains of information behavior and social informatics. Surveillance, as a range of contextual and culturally based social practices defined by their connections to information seeking and use, should be framed as information practice—as that term is used within information behavior scholarship. Similarly, manifestations of surveillance in society are frequently perfect examples of information and communications technologies situated within everyday social and organizational structures—the very focus of social informatics research. The technological infrastructures and material artifacts of surveillance practice—surveillance technologies—can also be viewed as information tools. Framing surveillance as information practice and conceptualizing surveillance technologies as socially and contextually situated information tools can provide space for new avenues of research within the information sciences, especially within information disciplines that focus their attention on the social aspects of information and information technologies in society. [ABSTRACT FROM AUTHOR]

It is well known that, within the Latin production of written text, peculiar metric schemes were followed not only in poetic compositions, but also in many prose works. Such metric patterns were based on so‐called syllabic quantity, that is, on the length of the involved syllables, and there is substantial evidence suggesting that certain authors had a preference for certain metric patterns over others. In this research we investigate the possibility to employ syllabic quantity as a base for deriving rhythmic features for the task of computational authorship attribution of Latin prose texts. We test the impact of these features on the authorship attribution task when combined with other topic‐agnostic features. Our experiments, carried out on three different datasets using support vector machines (SVMs) show that rhythmic features based on syllabic quantity are beneficial in discriminating among Latin prose authors. [ABSTRACT FROM AUTHOR]

Since information science concerns the transmission of records, it concerns context. The transmission of documents ensures their arrival in new contexts. Documents and their copies are spread across times and places. The amount of labor required to discover and retrieve relevant documents is also formulated by context. Thus, any serious consideration of communication and of information technologies quickly leads to a concern with context, relevance, and labor. Information scientists have developed many theories of context, relevance, and labor but not a framework for organizing them and describing their relationship with one another. We propose the words context and relevance can be used to articulate a useful framework for considering the diversity of approaches to context and relevance in information science, as well as their relations with each other and with labor. [ABSTRACT FROM AUTHOR]

With the booming of information technology, considerable progress has been witnessed in information storage carriers, accompanied by soaring storage capacity. 3D codes as an emerging information storage carrier has attracted wide attention. However, it is challenging for conventional 3D codes to transform their encoded information, restricting their applications. Due to their rich color characteristics and various stimuli responsiveness, fluorescent materials can be utilized to construct stimuli‐responsive 3D code systems that can exhibit pattern changes upon specific external stimuli, thereby realizing information transformation. According to the type of stimuli responsiveness, several kinds of stimuli‐responsive 3D code systems are classified and summarized, including pH responsiveness, ion responsiveness, light responsiveness, electricity responsiveness, time responsiveness, some separate stimuli responsiveness, and multiple responsiveness. This review summarizes the recent 3D code systems, elaborates on how they can realize information transformation, analyzes their potential applications, and puts forward some perspectives on further development in this field. The authors anticipate this review can provide guidelines for the design of novel information materials and promote the development of information science and technology. [ABSTRACT FROM AUTHOR]

This article builds on the argument that feminist technoscience will advance information practice scholarship beyond its current limitations. These limitations reflect neoliberalism in the field of information science and include a reliance on extractive logics in theories and models, monological individualism, binaries constructed between people and technologies, and techno‐solutionism. Here, we address the question: what does it look like to apply technofeminism to the study of information practice at methodological and methods levels? We first outline our metatheoretical conception of feminist technoscience, which embraces intersectionality and assemblage theory in order to move past white and colonialist logics embedded in cyborg theory. We next offer design justice as a methodological framework and movement that provides a necessary overhaul of the neoliberal ways that information science approaches scholarship, particularly in terms of participatory research. We suggest that speculative futurities provide a promising method for advancing technofeminism in information practice research because they explicitly reject neoliberalism and its techno‐solutionist bent. Overall, a feminist technoscience of information practice offers directions for our field that are rooted in liberatory epistemologies. We emphasize that in order to achieve liberation, a major overhaul in how our discipline approaches arrangements of information, people, and technologies is sorely needed. [ABSTRACT FROM AUTHOR]

Multi‐object tracking (MOT), which uses the context information of image sequences to locate, maintain identities and generate trajectories of multiple targets in each frame, is key technology in the field of computer vision. To address the problems of occlusion and scale variation in low‐viewpoint MOT, OsaMOT is proposed here. First, according to the global occlusion state of each frame, OsaMOT proposes the adaptive anti‐occlusion feature to enhance the awareness and adaptability for occlusion. At the same time, OsaMOT uses the cascade screening mechanism to reduce the "virtual new target" phenomenon due to the dramatic change in target features caused by scale variation and occlusion. Finally, considering that the occluded templates will affect the tracking performance, OsaMOT proposes an adaptive anti‐noise template update mechanism according to the partial occlusion state of the target, which improves the purity of the template library and further enhances the applicability to occlusion. The experimental results show that OsaMOT can weaken the influence of scale variation, partial occlusion, short‐term full occlusion and long‐term full occlusion in the low‐viewpoint tracking scenes. Most evaluation indexes of OsaMOT under low‐viewpoint tracking scenario are superior to those of some typical algorithms proposed in recent years, and the tracking robustness is improved. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses several articles published within issue on topics related to changes in the field of information.