bluuebunny/crossref_metadata_2025_split huggingface dataset:
Research Trends from a Decade (2011–2020) for Information Literacy in Higher Education: Content and Bibliometric Mapping Analysis
Authors: Chen Chao‐Chen , Wang Ning‐Chiao , Tu Yun‐Fang , Lin Hsin Ju 
Published: October 2021

AbstractNew terms and theoretical concepts in information literacy have emerged over the last decade, and these have led to revisions in the standards for information literacy. In order to determine whether information literacy research has reflected these trends, we collected SSCI literature for the 2011 to 2020 period related to information literacy in higher education (ILHE) and conducted analysis using bibliographic mapping and content analysis. Our research found that the volume of research on ILHE has increased in the last five years as compared to the five years before that, and that keywords related to literacy (such as “digital literacy” and “multiliteracies”) have been getting a great deal of discussion. After the Framework for Information Literacy for Higher Education (FILHE) was announced, curriculum design research based on the Information Literacy Competency Standards for Higher Education (ILCSHE) continued to outnumber that done based on the Framework.
Exploring the Perceived Attractiveness of Online Celebrities Who Sell Knowledge: A Self‐Branding Perspective
Authors: Chen Xiaoyu , Chua Alton Y. K. 
Published: October 2021

AbstractGiven the limited understanding of the attractiveness among online celebrities, this study explores the perceived attractiveness of “Knowledge Wanghong,” an emerging class of online celebrities who sell knowledge products in China. Drawing on the self‐branding perspective, which argues that individuals may construct and manage their distinctive online image deliberately through various information practices, as the theoretical lens, we attempt to answer two questions: (1) What are the antecedents of the perceived attractiveness of Knowledge Wanghong? (2) How do Knowledge Wanghong make themselves attractive to users? From semi‐structured interviews with 28 Knowledge Wanghong, we derive two findings. First, the antecedents of the perceived attractiveness include perceived professionalism, perceived familiarity, and perceived intimacy. Second, Knowledge Wanghong make themselves appealing to users in two ways: (1) they disclose personal and professional information to users; (2) they employ multiple approaches to interacting with users. This study sheds light on the perceived attractiveness of Knowledge Wanghong in terms of the antecedents and how it is achieved. Also, it provides a novel reference point for discussing the information practices of online celebrities in a global context.
Controlled Digital Lending
Authors: Currier Chad , Centivany Alissa 
Published: October 2021

AbstractLibraries and library consortia are adopting controlled digital lending (CDL) as a strategy, accelerated by the ongoing COVID‐19 pandemic, to facilitate equitable access to print collections. While advocates of CDL contend that digitize‐and‐lend practices reflect an incremental, technology‐assisted adjustment to traditional library circulation, lending, and resource‐sharing practices, opponents of CDL in the United States and Canada argue that the practice contravenes well‐established copyright protections. This paper discusses current controversies surrounding CDL, its potential promise and perils, and concludes that a reasonable, equitable, and forward‐looking application of copyright laws ought to insulate libraries and library consortia from exposure to liability for engaging in CDL.
Designing eHealth Tutorials with and for Older Adults
Authors: Davis Nathan , Shiroma Kristina , Xie Bo , Yeh Tom , Han Xu , De Main Atami 
Published: October 2021

AbstractOlder adults may be excluded from using digital health technologies due to limited eHealth literacy. Research is much needed to decrease disparities in eHealth literacy and increase the inclusiveness of such technologies. Integrating the preferences and expertise of older adults is key to age‐appropriate design of eHealth tutorials. This study explores how participatory design (PD) techniques can be adapted to include older adults in the design of an eHealth tutorial. We worked with 9 older adults (aged 64 and 82) as co‐designers and conducted PD sessions over 11 weeks in a senior center's computer lab. Using thematic analysis, we identified 7 themes around the design of eHealth tutorials for older adults. We also identified successes and challenges in PD with older adults, along with benefits of partnering with senior centers. Our findings have implications for both the design of eHealth tutorials for older adults and for PD with older adults.
Exploring the Effect of Rational Factors and Trust in Health Behavior Change
Authors: Deng Jiaqi , Li Shijuan , Hansen Preben 
Published: October 2021

AbstractThis study examines how rational factors and trust modify health behaviors and various stages of trust mediate the rational factors in behavior changing through four stages of it. By integrating factors of Theory of Planned Behavior (TPB) and various stages of trust, a health behavior change framework was proposed. Quantitative data on TPB, trust and behavior change were collected from Chinese youths through online survey. Structural Equation Model (SEM) was applied to analyze the data from 448 valid questionnaires to verify it. The findings show: Both TPB and trust constructs have significant impacts on modifying health behavior change; Trust mediates the effect of perceived behavioral control on health behavior change, while the stages of site content evaluation and longer‐term engagement of trust act as partial mediators; Relationships existed between TPB factors and trust stages. This study advances the understanding of health behavior change with regards to rational factors and trust stages and provides implications for stakeholders.
Not Quite ‘Ask a Librarian’: AI on the Nature, Value, and Future of LIS
Authors: Dinneen Jesse David , Bubinger Helen 
Published: October 2021

AbstractAI language models trained on Web data generate prose that reflects human knowledge and public sentiments, but can also contain novel insights and predictions. We asked the world's best language model, GPT‐3, fifteen difficult questions about the nature, value, and future of library and information science (LIS), topics that receive perennial attention from LIS scholars. We present highlights from its 45 different responses, which range from platitudes and caricatures to interesting perspectives and worrisome visions of the future, thus providing an LIS‐tailored demonstration of the current performance of AI language models. We also reflect on the viability of using AI to forecast or generate research ideas in this way today. Finally, we have shared the full response log online for readers to consider and evaluate for themselves.
Social Connections Matter: Online and Offline Civic Engagement among College Students
Authors: Feng Shihui , Li Mengqian , Erstad Ola 
Published: October 2021

AbstractThis research examines civic engagement from both online and offline perspectives using 371 samples collected from two universities in China. We aimed to explore the effects of college students' social connections on their online and offline civic engagement using partial least squares structural equation modeling (PLS‐SEM). We found that weak ties in college students' civic discussion networks play a significant role in affecting both online and offline civic engagement. Additionally, students' characteristics, such as academic year, leadership role, and party membership, are associated with offline civic engagement, but not with online civic engagement. Political efficacy was also found to be a significant factor affecting both online and offline civic engagement. This study examines the weak tie theory in the context of online and offline civic engagement, sheds light on underlying principles for engaging young adults in civic life in the digital era and advocates the importance of developing a blended approach for engaging college students in civic engagement in both online and offline settings.
What is the Thermal Conductivity of Copper? The Production of a Fact Through Scientific Forgetting
Authors: Hauser Elliott 
Published: October 2021

AbstractWhat is the thermal conductivity of copper? This straightforward question leads to a fascinating instance of the production of scientific facts through documentation practices. Ho, Powell and Liley's 1974 The Thermal Conductivity of the Elements: A Comprehensive Review is examined as an artifact of scientific reference data production, and its answer to the initial question is traced to modern‐day search engine results. A short history of the Center that produced the book and some initial research into its authors is provided.Kuhn's concepts of normal science and normic lexical structures are utilized to clarify the Comprehensive Review's functioning within the broader scientific fields in which it is utilized. Bowker's concepts of memory practices and the jussive Archive help identify the forgetting embedded in the production of reference data, producing what Star called global certainty. Far from impugning the internal validity of these scientific facts, this forgetting is shown to be licensed by scientific rigor.This paper presents a novel historically informed investigation of how documentation practices produce scientific facts, and connect these activities to modern‐day knowledge graph information retrieval. The theoretical analyses provided show how scientifically licensed forgetting is a key mechanism of fact production, what Hayles termed constrained constructivism.
Forensic Analysis of Memetic Image Propagation: Introducing the SMOC BRISQUEt Method
Authors: Hodges James A. , Chaiet Mitch , Gupta Praful 
Published: October 2021

AbstractThis paper introduces a mixed‐methods approach for forensically reconstructing the propagation of visual media via networked digital devices. The authors present case studies drawn from political misinformation around the January 6, 2021 riots at the U.S. Capitol. Using interpretive analysis, the authors identify traces of user interfaces that remain in images being shared about the riots. Using computational analysis, the authors evaluate compression levels in digital photographs of the events in question, thus identifying which instances of the image are closer to the source (as well as which images appear to be identical). By combining these two approaches, the authors argue that SMOC BRISQUEt refines our understanding of misinformation's memetic spread—helpful in curbing future abuses as well as in guiding the production of more effective cross‐platform spread when desired.
Measuring Quality of Wikipedia Articles by Feature Fusion‐based Stack Learning
Authors: Hou Jingrui , Li Jiangnan , Wang Ping 
Published: October 2021

AbstractOnline open‐source knowledge repository such as Wikipedia has become an increasingly important source for users to access knowledge. However, due to its large volume, it is challenging to evaluate Wikipedia article quality manually. To fill this gap, we propose a novel approach named “feature fusion‐based stack learning” to assess the quality of Wikipedia articles. Pre‐trained language models including BERT (Bidirectional Encoder Representations from Transformers) and ELMo (Embeddings from Language Models) are applied to extract semantic information in Wikipedia content. The feature fusion framework consisting of semantic and statistical features is built and fed into an out‐of‐sample (OOS) stacking model, which includes both machine learning and deep learning models. We compare the performance of proposed model with some existing models with different metrics extensively, and conduct ablation studies to prove the effectiveness of our framework and OOS stacking. Generally, the experiment shows that our method is much better than state‐of‐the‐art models.
The Reproducible Data Reuse (ReDaR) Framework to Capture and Assess Multiple Data Streams
Authors: Keefer Donald A. , Blake Catherine L. 
Published: October 2021

AbstractMuch of the literature in knowledge discovery from data (KDD) focuses on algorithms that are faster and more accurate at capturing patterns in a given data set. However, answering a research question is fundamentally connected with how well the data is aligned with the questions being asked. Thus, data selection is one of the most important steps to ensure that models produced from the KDD process are useful in practice. A lack of documentation about the data selection rationale and the transformations needed to semantically align the data streams prevents others from reproducing the research and obfuscates development of best practices in data integration. Our goal in this paper is to provide KDD practitioners with a framework that brings together theories in provenance, information quality, and contextual reasoning, to enable researchers to achieve a semantically aligned dataset with data selection, description, and documentation based on an application‐focused assessment.
From Information to Knowledge Creation in the Archive: Observing Humanities Researchers' Information Activities
Authors: Leigh Alexandra , Makri Stephann , Taylor Alex , Mulinder Alec , Hamdi Sarra 
Published: October 2021

AbstractAs primary sources, archival records are a unique information source at the very heart of humanities research. However, how humanities researchers move from information to knowledge creation by making meaning from archival records has not been the focus of previous empirical research. This is surprising, as creating new knowledge through (re)interpretation of records is a core motivation and outcome of humanities research; as representations of historical and social occurrences, archival records rely on researchers' interpretation of content, context, and structure to establish an ‘archival’ meaning of the record, before applying this meaning within their own work. Therefore, constructing knowledge from archival materials necessitates a dual process of knowledge creation to create novel insights from a hybrid interpretation of archival meaning and the researcher's own interests. This paper presents findings from a naturalistic empirical observation of 11 humanities researchers engaging in research at a national archive, centring on key information activities that facilitate knowledge creation from archival records: Scanning, Relating, Capturing and Organising. Through these activities, scholars integrate their research aims and objectives with archival meaning to generate new insights. Deeper understanding of the nature of knowledge creation in archives can benefit archivists, archive users and systems designers alike.
Is Interdisciplinary Collaboration Research More Disruptive Than Monodisciplinary Research?
Authors: Liu Xin , Yi Bu , Li Ming , Li Jiang 
Published: October 2021

AbstractAs an important pattern of scientific research, interdisciplinary collaboration is universally encouraged by science and technology policy makers. However, it remains a question whether interdisciplinary collaboration research is more disruptive than monodisciplinary research. To address this research question in this study, interdisciplinary collaboration is measured as whether the authors of a paper are from at least two disciplines, and the degree of “disruptive” is measured by the Disruption index proposed by Funk & Owen‐Smith (2017). By using articles published in six journals from 1978 to 2019 in the Microsoft Academic Graph (MAG) database, we constructed an OLS regression model with journal fixed effect and time fixed effect to analyze the influence of interdisciplinary collaboration on the Disruption values with different citation windows. The findings show that interdisciplinary collaboration research is less disruptive than monodisciplinary research.
Using Symptoms and Healthcare Encounters to Capture a Rare Disease: A Study of Clinical Notes of the Alpha‐Gal Meat Allergy
Authors: Ma Yuanye , Flaherty Mary Grace 
Published: October 2021

AbstractThis paper examines clinical notes to identify reported symptoms and investigate patient‐provider communication processes in alpha‐gal syndrome (AGS). Clinical notes appear to be a credible and stable source of research where the researcher can find information regarding both symptoms and environmental factors of AGS. Compilation of notes could be used for a checklist to aid in diagnosis. This study analyzed clinicians' notes in patient records retrieved from the Electronic Medical Record Search Engine (EMERSE). The most reported symptoms fell into four general categories: skin (42%), inflammation (17%), gastrointestinal (20%), and anaphylaxis (21%). Environmental triggers were also commonly reported. This in‐depth analysis of clinical notes of AGS can serve as a basis for future automation of rare disease analysis; moreover, it provides a basic understanding of the granularity of information that an electronic health record (EHR) may provide for rare disease identification.
Neural Correlates of Realisation of Satisfaction in a Successful Search Process
Authors: Paisalnan Sakrapee , Moshfeghi Yashar , Pollick Frank 
Published: October 2021

AbstractIn a search process, searchers review documents to gather information relevant to their information need (IN). During this process, searchers may experience the satisfaction of their IN, indicating they have gathered adequate relevant information to answer their need. This complex concept of satisfaction is the ultimate goal of search systems. Most studies in Information Retrieval (IR) have been attempted to understand how searchers' needs are satisfied based on behavioural observation. However, the psychophysiological manifestation during the moment of satisfaction still remains unclear. Here, we use functional Magnetic Resonance (fMRI) to investigate which brain regions are involved during the moment of satisfaction. Twenty‐six participants participated in the experiment, designed to represent a search process while being scanned. Our result shows the human brain regions involved during the moment of satisfaction. These findings provide an important step in unravelling the concept of satisfaction in a search process.
Toward Best Practices for Unstructured Descriptions of Research Data
Authors: Phillips Dan , Smit Michael 
Published: October 2021

AbstractAchieving the potential of widespread sharing of open research data requires that sharing data is straightforward, supported, and well‐understood; and that data is discoverable by researchers. Our literature review and environment scan suggest that while substantial effort is dedicated to structured descriptions of research data, unstructured fields are commonly available (title, description) yet poorly understood. There is no clear description of what information should be included, in what level of detail, and in what order. These human‐readable fields, routinely used in indexing and search features and reliably federated, are essential to the research data user experience. We propose a set of high‐level best practices for unstructured description of datasets, to serve as the essential starting point for more granular, discipline‐specific guidance. We based these practices on extensive review of literature on research article abstracts; archival practice; experience in supporting research data management; and grey literature on data documentation. They were iteratively refined based on comments received in a webinar series with researchers, data curators, data repository managers, and librarians in Canada. We demonstrate the need for information research to more closely examine these unstructured fields and provide a foundation for a more detailed conversation.
MetaFAIR: A Metadata Application Profile for Managing Research Data
Authors: Tompkins Vivian Teresa , Honick Brendan John , Polley Katherine Louise , Qin Jian 
Published: October 2021

AbstractThis paper reports on the development of a metadata application profile (AP), MetaFAIR, designed to support research data management (RDM) to make research data findable, accessible, interoperable, and reusable. The development of MetaFAIR followed a three‐step process that included learning about the characteristics of datasets from researchers to establish their context and requirements, as well as iterative design and testing with researchers' feedback. Guided by the FAIR principles (Findability, Accessibility, Interoperability, and Reusability), MetaFAIR focuses on accommodating description needs particular to computational social science datasets while seeking to provide general enough elements to describe data collections across many different domains. In this paper, MetaFAIR is placed in the context of historical and recent developments in the areas of RDM and application profile creation; following this contextualization, the paper describes the central considerations and challenges of the MetaFAIR development process and discusses its significance for future work in RDM.
A Mixed‐Method Usability Study on User Experience with Systematic Review Software
Authors: Wang Manhua , Sharmin Selina , Wang Mengqian , Yu Fei 
Published: October 2021

AbstractSystematic reviews are widely used in evidence‐based medicine. Conducting a systematic review requires intensive mental efforts, especially during the study screening process. This challenge has motivated the development of intelligent software. This study examined and compared the performance, workload, and user experience of two systematic review tools – Colandr with Artificial Intelligence (AI) features and Covidence without AI features by conducting a mixed‐method usability study. The results showed that reviewers had higher precision in citation screening using Colandr than using Covidence. However, the user experience with Colandr was not optimal due to problems in its user interface design. Therefore, we suggest that the design and development of AI‐enabled SR software emphasize the usability of the interface and apply user‐centered design principles.
COVID‐19 Apps and Privacy Protections from Users' Perspective
Authors: Wang Tian , Guo Lin , Bashir Masooda 
Published: October 2021

AbstractAs the spread of the novel coronavirus (COVID‐19) continues to be a global challenge, there have been numerous efforts and actions from both government and private organizations towards keeping their community members healthy and safe. One of the approaches is to use mobile apps to trace contacts and update the status of the infected individuals efficiently and conveniently so that the spread of COVID‐19 can be minimized and contained. While these apps could offer many advantages, it also raises serious privacy concerns for many users and hence possibly refusing to adopt it. In this study, we aim to understand the users' expectations on the privacy protections and the provisions under which they are willing to use COVID‐19 apps. We believe our study results can guide policymakers and app developers on the design, deployment, and acceptability of the COVID‐19 apps that can be widely adopted.