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Publication date: January 2024
Source: The Journal of Academic Librarianship, Volume 50, Issue 1
Author(s): Youngok Choi, Emma Brodfuehrer Hastings
Publication date: January 2024
Source: The Journal of Academic Librarianship, Volume 50, Issue 1
Author(s): Yaw Owusu-Ansah, Perpetua Sekyiwa Dadzie
Publication date: January 2024
Source: The Journal of Academic Librarianship, Volume 50, Issue 1
Author(s): Anne Larrivee
Publication date: November 2023
Source: Journal of Informetrics, Volume 17, Issue 4
Author(s): Youngjin Seol, Seunghyun Lee, Cheolhan Kim, Janghyeok Yoon, Jaewoong Choi
Publication date: February 2024
Source: Journal of Informetrics, Volume 18, Issue 1
Author(s): Cristina Urdiales, Eduardo Guzmán
Publication date: November 2023
Source: Journal of Informetrics, Volume 17, Issue 4
Author(s): Kazuki Nakajima, Ruodan Liu, Kazuyuki Shudo, Naoki Masuda
Publication date: February 2024
Source: Journal of Informetrics, Volume 18, Issue 1
Author(s): Hao Teng, Nan Wang, Hongyu Zhao, Yingtong Hu, Haitao Jin
Public health surveillance based on data analytics plays a crucial role in detecting and responding to public health crises, such as infectious disease outbreaks. Previous information science research on the topic has focused on developing analytical algorithms and visualization tools. This study seeks to extend the research by investigating information practices in data analytics for public health surveillance. Through a case study of how data analytics was conducted for surveilling Influenza A and COVID-19 outbreaks, both exploration information practices (i.e., probing, synthesizing, exchanging) and exploitation information practices (i.e., scavenging, adapting, outreaching) were identified and detailed. These findings enrich our empirical understanding of how data analytics can be implemented to support public health surveillance.
Online opinion formation has received much scholarly attention since the mass proliferation of social networks. Inter alia, online opinions have been viewed as a new part of public deliberation. However, the pre-Internet era's vision on deliberation imposes extremely high demands on users as deliberators. We argue that opinion formation online neither pursues the goals nor follows the rules of institutionalized consensus-oriented round-table deliberative processes. Moreover, the growing academic evidence shows that opinion formation online is predominantly cumulative, not deliberative in nature. Thus, we introduce the concept of cumulative deliberation as an alternative and addition to classic institutional deliberation and argue that it describes opinion formation online more precisely. Importantly, it allows for two crucial additions to the deliberation theory, which are the use of systemic approaches to measuring and predicting public opinion and new normativity that sees a user as an initially neutral discussion unit. It also allows for healthier distinction between “natural” user communication and intentional counter-deliberative distortions in online communication, like computational propaganda or cyberbullying. We end up with suggesting a research agenda on cumulative deliberation.
Reviews have long been recognized as among the most important forms of scientific communication. The rapid growth of the primary literature has further increased the need for reviews to distill and interpret the literature. This review on Reviews and Reviewing: Approaches to Research Synthesis encompasses the evolution of the review literature, taxonomy of review literature, uses and users of reviews, the process of preparing reviews, assessment of review quality and impact, the impact of information technology on the preparation of reviews, and research opportunities for information science related to reviews and reviewing. In addition to providing a synthesis of prior research, this review seeks to identify gaps in the published research and to suggest possible future research directions.
While technology affords creation of digital collections, and promises access to all, the reality is that many cultural data collections exist in a precarious ecosystem, where erratic funding, fragmented support, and disconnected expertise threaten their continued existence. As a significant branch of the broader information ecosystem, cultural data collections range in size and scope, from national institutions to bespoke local collections supported by individuals. This exploratory, qualitative study engaged cultural data experts in Australia, Canada, and the United Kingdom to map the broad cultural data ecosystem and to identify opportunities for healthier growth. The development and maintenance of cultural data collections requires integration across the spheres of expertise of creators, curators, subject matter experts, information science, and computing and technology. The foundational structural elements of the ecosystem include funding, policies, access to existing data, community context, and technological infrastructure. The key elements of a healthy data ecosystem are clarity of purpose, user-focused design, sustainability, allied coproduction, and reciprocal interconnection. A healthier cultural data ecosystem means more collections and initiatives will have positive impacts for research, knowledge, and diverse communities, contributing positively to the broader information ecosystem and to society, at large.
Open Access (OA) publishing has progressed from an initial fringe idea to a still-growing, major component of modern academic communication. The proliferation of OA publishing presents a context to examine how new innovations and institutions develop. Based on analyses of 1,296,304 articles published in 83 OA journals, we analyze changes in the institutional status, gender, age, citedness, and geographical locations of authors over time. Generally, OA journals tended towards core-to-periphery diffusion patterns. Specifically, journal authors tended to decrease in high-status institutional affiliations, male and highly cited authors over time. Despite these general tendencies, there was substantial variation in the diffusion patterns of OA journals. Some journals exhibited no significant demographic changes, and a few exhibited periphery-to-core diffusion patterns. We find that although both highly and less-legitimate journals generally exhibit core-to-periphery diffusion patterns, there are still demographic differences between such journals. Institutional and cultural legitimacy—or lack thereof—affects the social and intellectual diffusion of new OA journals.
Open data as an integral part of the open science movement enhances the openness and sharing of scientific datasets. Nevertheless, the normative utilization of data journals, data papers, scientific datasets, and data citations necessitates further research. This study aims to investigate the citation practices associated with data papers and to explore the role of data papers in disseminating scientific datasets. Dataset accession numbers from NCBI databases were employed to analyze the prevalence of data citations for data papers from PubMed Central. A dataset citation practice identification rule was subsequently established. The findings indicate a consistent growth in the number of biomedical data journals published in recent years, with data papers gaining attention and recognition as both publications and data sources. Although the use of data papers as citation sources for data remains relatively rare, there has been a steady increase in data paper citations for data utilization through formal data citations. Furthermore, the increasing proportion of datasets reported in data papers that are employed for analytical purposes highlights the distinct value of data papers in facilitating the dissemination and reuse of datasets to support novel research.
Linking historical and contemporary geographic information in biodiversity data is a useful approach to approximate species population. However, one of the prominent factors that causes ambiguity in geographic information, and hinders the linking process, is the way sovereignty information is used. While historical biodiversity records often use sovereignties as proxies for geographic information about a species, contemporary records do not. This study proposes a conceptual model that incorporates sovereignty information in biodiversity data to foster the linkage between historical and contemporary geographical information. The model comprises two phases: the first phase relates tangible data sources and core components needed to construct historical sovereignty taxonomies; and the second phase is a process model to align historical sovereignty taxonomies with contemporary taxonomies in four phases. The output of the model presents all possible sovereignties that a geographic entity belongs to based on the degree of congruence between the historical and contemporary taxonomies. The contributions of this work are threefold: (1) making all possible ambiguities in historical geographic information explicit in biodiversity data; (2) bringing attention to the modeling choices that domain experts have to make when deciding which sovereignty a place name belongs to; and (3) extending and improving current geo-referencing practices.
Collaboration across disciplines is a critical form of scientific collaboration to solve complex problems and make innovative contributions. This study focuses on the association between multidisciplinary collaboration measured by coauthorship in publications and the disruption of publications measured by the Disruption (D) index. We used authors' affiliations as a proxy of the disciplines to which they belong and categorized an article into multidisciplinary collaboration or monodisciplinary collaboration. The D index quantifies the extent to which a study disrupts its predecessors. We selected 13 journals that publish articles in six disciplines from the Microsoft Academic Graph (MAG) database and then constructed regression models with fixed effects and estimated the relationship between the variables. The findings show that articles with monodisciplinary collaboration are more disruptive than those with multidisciplinary collaboration. Furthermore, we uncovered the mechanism of how monodisciplinary collaboration disrupts science more than multidisciplinary collaboration by exploring the references of the sampled publications.
Detecting science–technology hierarchical linkages is beneficial for understanding deep interactions between science and technology (S&T). Previous studies have mainly focused on linear linkages between S&T but ignored their structural linkages. In this paper, we propose a network coupling approach to inspect hierarchical interactions of S&T by integrating their knowledge linkages and structural linkages. S&T knowledge networks are first enhanced with bidirectional encoder representation from transformers (BERT) knowledge alignment, and then their hierarchical structures are identified based on K-core decomposition. Hierarchical coupling preferences and strengths of the S&T networks over time are further calculated based on similarities of coupling nodes' degree distribution and similarities of coupling edges' weight distribution. Extensive experimental results indicate that our approach is feasible and robust in identifying the coupling hierarchy with superior performance compared to other isomorphism and dissimilarity algorithms. Our research extends the mindset of S&T linkage measurement by identifying patterns and paths of the interaction of S&T hierarchical knowledge.
Improving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply–demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply-side and demand-side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease-related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy-making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.
This study analyses the coverage of seven free-access bibliographic databases (Crossref, Dimensions—non-subscription version, Google Scholar, Lens, Microsoft Academic, Scilit, and Semantic Scholar) to identify the potential reasons that might cause the exclusion of scholarly documents and how they could influence coverage. To do this, 116 k randomly selected bibliographic records from Crossref were used as a baseline. API endpoints and web scraping were used to query each database. The results show that coverage differences are mainly caused by the way each service builds their databases. While classic bibliographic databases ingest almost the exact same content from Crossref (Lens and Scilit miss 0.1% and 0.2% of the records, respectively), academic search engines present lower coverage (Google Scholar does not find: 9.8%, Semantic Scholar: 10%, and Microsoft Academic: 12%). Coverage differences are mainly attributed to external factors, such as web accessibility and robot exclusion policies (39.2%–46%), and internal requirements that exclude secondary content (6.5%–11.6%). In the case of Dimensions, the only classic bibliographic database with the lowest coverage (7.6%), internal selection criteria such as the indexation of full books instead of book chapters (65%) and the exclusion of secondary content (15%) are the main motives of missing publications.
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