- Détails du magazine
- Première publication
- 30 Mar 2017
- Période de publication
- 4 fois par an
- Accès libre
Pages: 1 - 18
Due to the recent explosion of big data, our society has been rapidly going through digital transformation and entering a new world with numerous eye-opening developments. These new trends impact the society and future jobs, and thus student careers. At the heart of this digital transformation is data science, the discipline that makes sense of big data. With many rapidly emerging digital challenges ahead of us, this article discusses perspectives on iSchools’ opportunities and suggestions in data science education. We argue that iSchools should empower their students with “information computing” disciplines, which we define as the ability to solve problems and create values, information, and knowledge using tools in application domains. As specific approaches to enforcing information computing disciplines in data science education, we suggest the three foci of user-based, tool-based, and application-based. These three foci will serve to differentiate the data science education of iSchools from that of computer science or business schools. We present a layered Data Science Education Framework (DSEF) with building blocks that include the three pillars of data science (people, technology, and data), computational thinking, data-driven paradigms, and data science lifecycles. Data science courses built on the top of this framework should thus be executed with user-based, tool-based, and application-based approaches. This framework will help our students think about data science problems from the big picture perspective and foster appropriate problem-solving skills in conjunction with broad perspectives of data science lifecycles. We hope the DSEF discussed in this article will help fellow iSchools in their design of new data science curricula.
- Big data
- Data science
- Information computing
- The fourth Industrial Revolution
- Computational thinking
- Data-driven paradigm
- Data science lifecycle
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Big Metadata, Smart Metadata, and Metadata Capital: Toward Greater Synergy Between Data Science and Metadata
Pages: 19 - 36
The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research. This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science.
This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts
The “utilitarian nature” and “historical and traditional views” of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part of a metadata
There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore.
The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem.
Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.
- Metadata research
- Data science
- Big metadata
- Smart metadata
- Metadata capital
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Pages: 68 - 111
The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps.
We use the combined set of 2015
Findings appear in the form of visuals throughout the manuscript. In
As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science.
Visualization of science overlay maps is now more accurate and true to the 2015
The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent
- Science overlay maps
- Science visualization
- Interdisciplinary research
- Research policy
- Research management
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Pages: 49 - 67
This study is a user evaluation on the usability of the Mogao Cave Panorama Digital Library (DL), aiming to measure its effectiveness from the users’ perspective and to propose suggestions for improvement.
Usability tests were conducted based on a framework of evaluation criteria and a set of information seeking tasks designed for the Dunhuang cultural heritage, and interviews were conducted for soliciting in-depth opinions from participants.
The results of the usability tests indicate that the DL was more efficient in supporting simple information seeking tasks than those of higher-complexity levels. Statistical tests reveal that there were correlations among dimensions of usability criteria and user effectiveness measures. Moreover, interview discourses exposed specific usability issues of the DL.
This research is based on a relatively small sample size, resulting in a limited representativeness of user diversity. A larger sample size is needed for a systematic cross group comparison.
This study evaluated the usability of the Mogao Cave Panorama DL and proposed suggestions for its improvement for better experience. The results also provide a reference to other cultural heritage DLs with panorama functions.
This study is one of the first evaluating cultural heritage DLs from the perspective of user experience. It provides methodological references for relevant studies: the evaluation framework, the designed information seeking tasks, and the interview questions can be adopted or adapted in evaluating other visually centric DLs of cultural heritage.
- Mogao Caves
- Cultural heritage
- Panorama digital library
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Pages: 37 - 48
In this paper, we combined the method of co-word analysis and alluvial diagram to detect hot topics and illustrate their dynamics.
Articles in the field of scientometrics were chosen as research cases in this study. A time-sliced co-word network was generated and then clustered. Afterwards, we generated an alluvial diagram to show dynamic changes of hot topics, including their merges and splits over time.
After analyzing the dynamic changes in the field of scientometrics from 2011 to 2015, we found that two clusters being merged did not mean that the old topics had disappeared and a totally new one had emerged. The topics were possibly still active the following year, but the newer topics had drawn more attention. The changes of hot topics reflected the shift in researchers’ interests. Research topics in scientometrics were constantly subdivided and re-merged. For example, a cluster involving “industry” was divided into several topics as research progressed.
When examining longer time periods, we encounter the problem of dealing with bigger data sets. Analyzing data year by year would be tedious, but if we combine, e.g. two years into one time slice, important details would be missed.
This method can be applied to any research field to illustrate the dynamics of hot topics. It can indicate the promising directions for researchers and provide guidance to decision makers.
The use of alluvial diagrams is a distinctive and meaningful approach to detecting hot topics and especially to illustrating their dynamics.
- Alluvial diagram
- Hot topics
- Timeline approach
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Pages: 112 - 112