<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The emergence of “Big Data” has been a dramatic development in recent years. Alongside it, a lesser-known but equally important set of concepts and practices has also come into being—“Smart Data.” This paper shares the author’s understanding of <italic>what</italic>, <italic>why</italic>, <italic>how</italic>, <italic>who</italic>, <italic>where</italic>, and <italic>which data</italic> in relation to Smart Data and digital humanities. It concludes that, challenges and opportunities co-exist, but it is certain that Smart Data, the ability to achieve big insights from trusted, contextualized, relevant, cognitive, predictive, and consumable data at any scale, will continue to have extraordinary value in digital humanities.</p><p>The emergence of “Big Data” has been a dramatic development in recent years. Alongside it, a lesser-known but equally important set of concepts and practices has also come into being—“Smart Data.”</p></abstract>

Smart Data for Digital Humanities

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0002_s_007_w2aab2b8b7b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>First, to review the state-of-the-art in patent citation analysis, particularly characteristics of patent citations to scientific literature (scientific non-patent references, SNPRs). Second, to present a novel mapping approach to identify technology-relevant research based on the papers cited by and referring to the SNPRs.</p></sec><sec id="j_jdis-2017-0002_s_008_w2aab2b8b7b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>In the review part we discuss the context of SNPRs such as the time lags between scientific achievements and inventions. Also patent-to-patent citation is addressed particularly because this type of patent citation analysis is a major element in the assessment of the economic value of patents. We also review the research on the role of universities and researchers in technological development, with important issues such as universities as sources of technological knowledge and inventor-author relations. We conclude the review part of this paper with an overview of recent research on mapping and network analysis of the science and technology interface and of technological progress in interaction with science. In the second part we apply new techniques for the direct visualization of the cited and citing relations of SNPRs, the mapping of the landscape around SNPRs by bibliographic coupling and co-citation analysis, and the mapping of the conceptual environment of SNPRs by keyword co-occurrence analysis.</p></sec><sec id="j_jdis-2017-0002_s_009_w2aab2b8b7b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>We discuss several properties of SNPRs. Only a small minority of publications covered by the Web of Science or Scopus are cited by patents, about 3%–4%. However, for publications based on university-industry collaboration the number of SNPRs is considerably higher, around 15%. The proposed mapping methodology based on a “second order SNPR approach” enables a better assessment of the technological relevance of research.</p></sec><sec id="j_jdis-2017-0002_s_010_w2aab2b8b7b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>The main limitation is that a more advanced merging of patent and publication data, in particular unification of author and inventor names, in still a necessity.</p></sec><sec id="j_jdis-2017-0002_s_011_w2aab2b8b7b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>The proposed mapping methodology enables the creation of a database of technology-relevant papers (TRPs). In a bibliometric assessment the publications of research groups, research programs or institutes can be matched with the TRPs and thus the extent to which the work of groups, programs or institutes are relevant for technological development can be measured.</p></sec><sec id="j_jdis-2017-0002_s_012_w2aab2b8b7b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>The review part examines a wide range of findings in the research of patent citation analysis. The mapping approach to identify a broad range of technology-relevant papers is novel and offers new opportunities in research evaluation practices.</p></sec></abstract>

Patent Citations Analysis and Its Value in Research Evaluation: A Review and a New Approach to Map Technology-relevant Research

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0005_s_005_w2aab2b8b6b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>Research fronts build on recent work, but using times cited as a traditional indicator to detect research fronts will inevitably result in a certain time lag. This study attempts to explore the effects of usage count as a new indicator to detect research fronts in shortening the time lag of classic indicators in research fronts detection.</p></sec><sec id="j_jdis-2017-0005_s_006_w2aab2b8b6b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>An exploratory study was conducted where the new indicator “usage count” was compared to the traditional citation count, “times cited,” in detecting research fronts of the regenerative medicine domain. An initial topic search of the term “regenerative medicine” returned 10,553 records published between 2000 and 2015 in the Web of Science (WoS). We first ranked these records with usage count and times cited, respectively, and selected the top 2,000 records for each. We then performed a co-citation analysis in order to obtain the citing papers of the co-citation clusters as the research fronts. Finally, we compared the average publication year of the citing papers as well as the mean cited year of the co-citation clusters.</p></sec><sec id="j_jdis-2017-0005_s_007_w2aab2b8b6b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>The citing articles detected by usage count tend to be published more recently compared with times cited within the same research front. Moreover, research fronts detected by usage count tend to be within the last two years, which presents a higher immediacy and real-time feature compared to times cited. There is approximately a three-year time span among the mean cited years (known as “intellectual base”) of all clusters generated by usage count and this figure is about four years in the network of times cited. In comparison to times cited, usage count is a dynamic and instant indicator.</p></sec><sec id="j_jdis-2017-0005_s_008_w2aab2b8b6b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>We are trying to find the cutting-edge research fronts, but those generated based on co-citations may refer to the hot research fronts. The usage count of older highly cited papers was not taken into consideration, because the usage count indicator released by WoS only reflects usage logs after February 2013.</p></sec><sec id="j_jdis-2017-0005_s_009_w2aab2b8b6b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>The article provides a new perspective on using usage count as a new indicator to detect research fronts.</p></sec><sec id="j_jdis-2017-0005_s_010_w2aab2b8b6b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>Usage count can greatly shorten the time lag in research fronts detection, which would be a promising complementary indicator in detection of the latest research fronts.</p></sec></abstract>

Usage Count: A New Indicator to Detect Research Fronts

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0004_s_005_w2aab2b8c31b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>This paper aims to gain an insight into the disciplinary structure of nanoscience &amp; nanotechnology (N&amp;N): What is the disciplinary network of N&amp;N like? Which disciplines are being integrated into N&amp;N over time? For a specific discipline, how many other disciplines have direct or indirect connections with it? What are the distinct subgroups of N&amp;N at different evolutionary stages? Such critical issues are to be addressed in this paper.</p></sec><sec id="j_jdis-2017-0004_s_006_w2aab2b8c31b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>We map the disciplinary network structure of N&amp;N by employing the social network analysis tool, Netdraw, identifying which Web of Science Categories (WCs) mediate nbetweenness centrality in different stages of nano development. Cliques analysis embedded in the Ucinet program is applied to do the disciplinary cluster analysis in the study according to the path of “Network-Subgroup-Cliques,” and a tree diagram is selected as the visualizing type.</p></sec><sec id="j_jdis-2017-0004_s_007_w2aab2b8c31b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>The disciplinary network structure reveals the relationships among different disciplines in the N&amp;N developing process clearly, and it is easy for us to identify which disciplines are connected with the core “N&amp;N” directly or indirectly. The tree diagram showing N&amp;N related disciplines provides an interesting perspective on nano research and development (R&amp;D) structure.</p></sec><sec id="j_jdis-2017-0004_s_008_w2aab2b8c31b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>The matrices used to draw the N&amp;N disciplinary network are the original ones, and normalized matrix could be tried in future similar studies.</p></sec><sec id="j_jdis-2017-0004_s_009_w2aab2b8c31b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>Results in this paper can help us better understand the disciplinary structure of N&amp;N, and the dynamic evolution of N&amp;N related disciplines over time. The findings could benefit R&amp;D decision making. It can support policy makers from government agencies engaging in science and technology (S&amp;T) management or S&amp;T strategy planners to formulate efficient decisions according to a perspective of converging sciences and technologies.</p></sec><sec id="j_jdis-2017-0004_s_010_w2aab2b8c31b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>The novelty of this study lies in mapping the disciplinary network structure of N&amp;N clearly, identifying which WCs have a mediating effect in different developmental stages (especially analyzing clusters among disciplines related to N&amp;N, revealing close or distant relationships among distinct areas pertinent to N&amp;N).</p></sec></abstract>

Insight into the Disciplinary Structure of Nanoscience &amp; Nanotechnology

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2017-0003_s_005_w2aab2b8c42b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>(1) To test basic assumptions underlying frequency-weighted citation analysis: (a) Uni-citations correspond to citations that are nonessential to the citing papers; (b) The influence of a cited paper on the citing paper increases with the frequency with which it is cited in the citing paper. (2) To explore the degree to which citation location may be used to help identify nonessential citations.</p></sec><sec id="j_jdis-2017-0003_s_006_w2aab2b8c42b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>Each of the in-text citations in all research articles published in Issue 1 of the <italic>Journal of the Association for Information Science and Technology</italic> (<italic>JASIST</italic>) 2016 was manually classified into one of these five categories: Applied, Contrastive, Supportive, Reviewed, and Perfunctory. The distributions of citations at different in-text frequencies and in different locations in the text by these functions were analyzed.</p></sec><sec id="j_jdis-2017-0003_s_007_w2aab2b8c42b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>Filtering out nonessential citations before assigning weight is important for frequency-weighted citation analysis. For this purpose, removing citations by location is more effective than re-citation analysis that simply removes uni-citations. Removing all citation occurrences in the Background and Literature Review sections and uni-citations in the Introduction section appears to provide a good balance between filtration and error rates.</p></sec><sec id="j_jdis-2017-0003_s_008_w2aab2b8c42b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>This case study suffers from the limitation of scalability and generalizability. We took careful measures to reduce the impact of other limitations of the data collection approach used. Relying on the researcher’s judgment to attribute citation functions, this approach is unobtrusive but speculative, and can suffer from a low degree of confidence, thus creating reliability concerns.</p></sec><sec id="j_jdis-2017-0003_s_009_w2aab2b8c42b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>Weighted citation analysis promises to improve citation analysis for research evaluation, knowledge network analysis, knowledge representation, and information retrieval. The present study showed the importance of filtering out nonessential citations before assigning weight in a weighted citation analysis, which may be a significant step forward to realizing these promises.</p></sec><sec id="j_jdis-2017-0003_s_010_w2aab2b8c42b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>Weighted citation analysis has long been proposed as a theoretical solution to the problem of citation analysis that treats all citations equally, and has attracted increasing research interest in recent years. The present study showed, for the first time, the importance of filtering out nonessential citations in weighted citation analysis, pointing research in this area in a new direction.</p></sec></abstract>

Functions of Uni- and Multi-citations: Implications for Weighted Citation Analysis

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Journal of Data and Information Science

Journal of Data and Information Science (JDIS, formerly Chinese Journal of Library and Information Science), sponsored by the Chinese Academy of Sciences (CAS) and published quarterly by the National Science Library of CAS, is the first internationally published English-language academic journal in Library and Information Science and related fields from China.  The Journal of Data and Information Science (JDIS) focuses on data-based research oriented toward the exploration of scientific research and innovation. The main areas of interest are science of science, evidence-based policymaking, research evaluation, computational social science, and scientometrics/bibliometrics/altmetrics/ informetrics. Emphasis is given to research that focuses on data, analytics, and knowledge discovery, and supports decision making and science policy. This includes modeling, innovation, data security, media and communications, and social development. Topics may include studies of metadata or full content data, text or non-textural data, structured or non-structural data, domain-specific or cross-domain data, and dynamic or interactive data.  Specific topic areas may include (but are not limited to):    Knowledge organization  Knowledge discovery and data mining  Knowledge integration and fusion  Semantic Web  Science of science  Bibliometrics and scientometrics  Analytic and diagnostic informetrics  Competitive intelligence  Predictive analysis  Social network analysis and metrics  Semantic and interactively analytic retrieval  Evidence-based policy analysis  Intelligent knowledge production  Knowledge-driven workflow management and decision-making  Knowledge-driven collaboration and its management  Domain knowledge infrastructure with knowledge fusion and analytics  Training for data &amp; information scientists  Development of data and information services    JDIS publishes theoretical and empirical work. Systematic reviews are welcome and applied research in development of advanced methods, services, and best practices is also an important part. But simple application of established informetrics on a specific research field or country is out of the scope.Welcome to submit your papers to JDIS.  Why subscribe and read  JDIS is the first and only English journal from China in Library and Information Science and related fields. With an aim to disseminate the cutting-edge research in these fields, it is devoted to the study and application of the theories, methods, techniques, services, and infrastructural facilities using big data to support knowledge discovery for decision and policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. JDIS has gathered a big body of high profile experts across the world who contribute their research to the journal. The international authors account for around 62% in its first publication year (2016).  Why submit  JDIS is the first and only English journal from China in Library and Information Science and related fields. It owns a number of world front-line scholars as editorial board members or reviewers. The turnaround time on average for a manuscript from submission to final decision is less than two and a half months.  Archiving  Sciendo archives the contents of this journal in Portico- digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 

Journal of Data and Information Science

Volume 2 (2017): Issue 1 (February 2017)

Perspective

Smart Data for Digital Humanities

Expert Review

Patent Citations Analysis and Its Value in Research Evaluation: A Review and a New Approach to Map Technology-relevant Research

Research Paper

Usage Count: A New Indicator to Detect Research Fronts

Insight into the Disciplinary Structure of Nanoscience & Nanotechnology

Functions of Uni- and Multi-citations: Implications for Weighted Citation Analysis

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