Data and information management最新文献

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.

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.

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.

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.

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.

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.