{"title":"Research on linkage of science and technology in the library and information science field","authors":"Xiao Yang , Lingzi Feng , Junpeng Yuan","doi":"10.1016/j.dim.2023.100033","DOIUrl":"https://doi.org/10.1016/j.dim.2023.100033","url":null,"abstract":"<div><p>Scientific research serves as both the root and the driving force behind technological innovation. While the transformation of science to technology is more commonly observed in hard science disciplines, the application of knowledge from soft science disciplines to technological innovation has received less attention, so is the Library and Information Science (abbreviated as LIS). This study seeks to examine the role of LIS knowledge in bridging the link between science and technology, how the LIS knowledge contributes to patented inventions, and and discover the future trend of scientific and technological innovation in LIS. The study collected LIS papers cited by patents and the citing patents’ data to explore the relationship between LIS papers and their citing patents based on time, impact, and topic. Six hypotheses were developed to analyze the flow of knowledge between science and technology in LIS, providing insight into the characteristics of knowledge flow and transformation of this field into technology. The study found that the amount of LIS scientific knowledge transformed into technology applications has been increasing, and the average “paper-patent” transformation time lag per year has been getting longer, etc.. Additionally, the topics “Knowledge organization and strategic planning”, “Network service and reference consultation,” and “Library and public services” in the LIS have the potential to further contribute to the technology field.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"7 2","pages":"Article 100033"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49778466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaofei Zhang , Yixuan Liu , Ziru Qin , Zilin Ye , Fanbo Meng
{"title":"Understanding the role of social media usage and health self-efficacy in the processing of COVID-19 rumors: A SOR perspective","authors":"Xiaofei Zhang , Yixuan Liu , Ziru Qin , Zilin Ye , Fanbo Meng","doi":"10.1016/j.dim.2023.100043","DOIUrl":"10.1016/j.dim.2023.100043","url":null,"abstract":"<div><p>Apart from the direct health and behavioral influence of the COVID-19 pandemic itself, COVID-19 rumors as an infodemic enormously amplified public anxiety and cause serious outcomes. Although factors influencing such rumors propagation have been widely studied by previous studies, the role of spatial factors (e.g., proximity to the pandemic) on individuals’ response regarding COVID-19 rumors remain largely unexplored. Accordingly, this study, drawing on the stimulus-organism-response (SOR) framework, examined how proximity to the pandemic (stimulus) influences anxiety (organism), which in turn determines rumor beliefs and rumor outcomes (response). Further, the contingent role of social media usage and health self-efficacy were tested. The research model was tested using 1246 samples via an online survey during the COVID-19 pandemic in China. The results indicate that: (1)The proximity closer the public is to the pandemic, the higher their perceived anxiety; (2) Anxiety increases rumor beliefs, which is further positively associated rumor outcomes; (3) When the level of social media usage is high, the relationship between proximity to the pandemic and anxiety is strengthened; (4) When the level of health self-efficacy is high, the effect of anxiety on rumor beliefs is strengthened and the effect of rumor beliefs on rumor outcomes is also strengthened. This study provides a better understanding of the underlying mechanism of the propagation of COVID-19 rumors from a SOR perspective. Additionally, this paper is one of the first that proposes and empirically verifies the contingent role of social media usage and health self-efficacy on the SOR framework. The findings of study can assist the pandemic prevention department in to efficiently manage rumors with the aim of alleviating public anxiety and avoiding negative outcomes cause by rumors.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"7 2","pages":"Article 100043"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10229203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9629320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Data science and the information professions: Challenges and opportunities","authors":"Gillian Oliver","doi":"10.1016/j.dim.2023.100030","DOIUrl":"https://doi.org/10.1016/j.dim.2023.100030","url":null,"abstract":"<div><p>Information science and data science are closely related, but the relationships and synergies between them may not be sufficiently addressed in educational curricula for information professionals. Consequently, information professionals are at risk of being perceived as having little relevance in data-intensive settings and may fail to demonstrate the unique contribution that they can make in such environments. The knowledge and skills that information professionals can bring relate to the social, cultural, and ethical dimensions of data that are essential to recognise for successful data governance. If information professionals are not actively engaged with data initiatives, then they may be ceding their professional jurisdiction to other occupations.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"7 1","pages":"Article 100030"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49730492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An essay on the differences and linkages between data science and information science","authors":"Fred Y. Ye , Fei-Cheng Ma","doi":"10.1016/j.dim.2023.100032","DOIUrl":"https://doi.org/10.1016/j.dim.2023.100032","url":null,"abstract":"<div><p>When there are differences in research objects and methodology between data science and information science, there are also linkages between data science and information science, based on the DIKW hierarchy to the concept chain, namely data – information – knowledge – wisdom. While knowledge metrics provides a quantitative linkage of data – information – knowledge – wisdom, information is the logarithm of data and knowledge is the logarithm of information, on which the mechanism of Brookes’ basic equation of information science is revealed. We suggest to maintain similar principles of data science and information science, including the principle of order, the principle of correlation, the principle of reorganized transformation, the principle of scatter distribution, the principle of logarithmic perspective, and the principle of least effort. Also, we extend to discuss a few issues on knowledge science.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"7 1","pages":"Article 100032"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49730446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Information and data sciences: Context, units of analysis, meaning, and human impact","authors":"Gary Marchionini","doi":"10.1016/j.dim.2023.100031","DOIUrl":"https://doi.org/10.1016/j.dim.2023.100031","url":null,"abstract":"<div><p>Information Science has been evolving for almost a century and an allied field of study called data science is generating excitement and impact. This article provides a framework for advancing and distinguishing information science and data science. The terms ‘data’ and ‘information’ are compared with respect to the word ‘knowledge’, and the consequent areas of study and practice called data science and information science are then compared on factors such as degree of context, primary unit of interest, consideration of meaning, and attention to human impact.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"7 1","pages":"Article 100031"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49730472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A virtue ethical approach to the use of artificial intelligence","authors":"Michael J. Cuellar","doi":"10.1016/j.dim.2023.100037","DOIUrl":"https://doi.org/10.1016/j.dim.2023.100037","url":null,"abstract":"","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46922669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Seven ways to make a data science project fail","authors":"Robert J. Glushko","doi":"10.1016/j.dim.2023.100029","DOIUrl":"https://doi.org/10.1016/j.dim.2023.100029","url":null,"abstract":"<div><p>The rapid emergence of data science as a field has made it a rival or replacement for information science from an industry perspective. In particular, the “big data” meme in data science and a heavy reliance on “black box” technology emphasize the quantity of data used in a project and asks, “what data do we have” rather than “what data do we need to solve our business problems.” This perspective also undermines the perceived importance of domain expertise, user research, data semantics and provenance, and other considerations valued in information science. This article uses a composite (and somewhat caricatured) case study of a data science project and discusses seven ways in which it is destined to fail, and then explains how “good information science” would have prevented or ameliorated them. Data science and information science need to recognize that together they can accomplish more than they can accomplish separately.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"7 1","pages":"Article 100029"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49730491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The past, the present, and the future of information and data sciences: A pragmatic view","authors":"Chirag Shah","doi":"10.1016/j.dim.2023.100028","DOIUrl":"https://doi.org/10.1016/j.dim.2023.100028","url":null,"abstract":"<div><p>While data science and information science emerged as two separate disciplines with different roots, in the recent past, they have been getting integrated and intertwined in interesting and impactful ways. The traditional distinction between data and information does not easily explain the differences and overlaps between the two sciences named after them. If one claims, for instance, that information is ‘meaningful data’ then it is important to note that a main objective of data science is indeed to derive meaningful information out of data. Information science is not necessarily a superset or a higher level of data science. Both of these disciplines have earned their place in sciences through different pasts, paths, and possibilities. Keeping that in mind, they are discussed here while tracing their origins and understanding their positionalities in the current context. More than the past and the present, what becomes then important is where they are heading next. Several suggestions are provided to keep data science a meaningful offering within information science – as a uniqueness for the former with the strengths of the latter.</p></div>","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"7 1","pages":"Article 100028"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49730490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Introduction to the special issue on data science and information science","authors":"Feicheng Ma , Gary Marchionini","doi":"10.1016/j.dim.2023.100034","DOIUrl":"https://doi.org/10.1016/j.dim.2023.100034","url":null,"abstract":"","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"7 1","pages":"Article 100034"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49730465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research on linkage of science and technology in the library and information science field","authors":"X. Yang, Lingzi Feng, Junpeng Yuan","doi":"10.1016/j.dim.2023.100033","DOIUrl":"https://doi.org/10.1016/j.dim.2023.100033","url":null,"abstract":"","PeriodicalId":72769,"journal":{"name":"Data and information management","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54141834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}