<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2018-0005_s_005_w2aab3b7b5b1b6b1aab1c17b1Aa"><h3>Purpose</h3><p>This study aims to reveal the landscape and trends of graphene research in the world by using data from Chemical Abstracts Service (CAS).</p></sec><sec id="j_jdis-2018-0005_s_006_w2aab3b7b5b1b6b1aab1c17b2Aa"><h3>Design/methodology/approach</h3><p>Index data from CAS have been retrieved on 78,756 papers and 23,057 patents on graphene from 1985 to March 2016, and scientometric methods were used to analyze the growth and distribution of R&amp;D output, topic distribution and evolution, and distribution and evolution of substance properties and roles.</p></sec><sec id="j_jdis-2018-0005_s_007_w2aab3b7b5b1b6b1aab1c17b3Aa"><h3>Findings</h3><p>In recent years R&amp;D in graphene keeps in rapid growth, while China, South Korea and United States are the largest producers in research but China is relatively weak in patent applications in other countries. Research topics in graphene are continuously expanding from mechanical, material, and electrical properties to a diverse range of application areas such as batteries, capacitors, semiconductors, and sensors devices. The roles of emerging substances are increasing in Preparation and Biological Study. More techniques have been included to improve the preparation processes and applications of graphene in various fields.</p></sec><sec id="j_jdis-2018-0005_s_008_w2aab3b7b5b1b6b1aab1c17b4Aa"><h3>Research limitations</h3><p>Only data from CAS is used and some R&amp;D activities solely reported through other channels may be missed. Also more detailed analysis need to be done to reveal the impact of research on development or vice verse, development dynamics among the players, and impact of emerging terms or substance roles on research and technology development.</p></sec><sec id="j_jdis-2018-0005_s_009_w2aab3b7b5b1b6b1aab1c17b5Aa"><h3>Practical implications</h3><p>This will provide a valuable reference for scientists and developers, R&amp;D managers, R&amp;D policy makers, industrial and business investers to understand the landscape and trends of graphene research. Its methodologies can be applied to other fields or with data from other similar sources.</p></sec><sec id="j_jdis-2018-0005_s_010_w2aab3b7b5b1b6b1aab1c17b6Aa"><h3>Originality/value</h3><p>The integrative use of indexing data on papers and patents of CAS and the systematic exploration of the distribution trends in output, topics, substance roles are distinctive and insightful.</p></sec></abstract>

Trends Analysis of Graphene Research and Development

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2018-0004_s_007_w2aab3b7b4b1b6b1aab1c17b1Aa"><h3>Purpose</h3><p>This paper presents findings of a quasi-experimental assessment to gauge the research productivity and degree of interdisciplinarity of research center outputs. Of special interest, we share an enriched visualization of research co-authoring patterns.</p></sec><sec id="j_jdis-2018-0004_s_008_w2aab3b7b4b1b6b1aab1c17b2Aa"><h3>Design/methodology/approach</h3><p>We compile publications by 45 researchers in each of 1) the iUTAH project, which we consider here to be analogous to a “research center,” 2) CG1— a comparison group of participants in two other Utah environmental research centers, and 3) CG2—a comparison group of Utah university environmental researchers not associated with a research center. We draw bibliometric data from Web of Science and from Google Scholar. We gather publications for a period before iUTAH had been established (2010–2012) and a period after (2014–2016). We compare these research outputs in terms of publications and citations thereto. We also measure interdisciplinarity using Integration scoring and generate science overlay maps to locate the research publications across disciplines.</p></sec><sec id="j_jdis-2018-0004_s_009_w2aab3b7b4b1b6b1aab1c17b3Aa"><h3>Findings</h3><p>We find that participation in the iUTAH project appears to increase research outputs (publications in the After period) and increase research citation rates relative to the comparison group researchers (although CG1 research remains most cited, as it was in the Before period). Most notably, participation in iUTAH markedly increases co-authoring among researchers—in general; and for junior, as well as senior, faculty; for men and women: across organizations; and across disciplines.</p></sec><sec id="j_jdis-2018-0004_s_010_w2aab3b7b4b1b6b1aab1c17b4Aa"><h3>Research limitations</h3><p>The quasi-experimental design necessarily generates suggestive, not definitively causal, findings because of the imperfect controls.</p></sec><sec id="j_jdis-2018-0004_s_011_w2aab3b7b4b1b6b1aab1c17b5Aa"><h3>Practical implications</h3><p>This study demonstrates a viable approach for research assessment of a center or program for which random assignment of control groups is not possible. It illustrates use of bibliometric indicators to inform R&amp;D program management.</p></sec><sec id="j_jdis-2018-0004_s_012_w2aab3b7b4b1b6b1aab1c17b6Aa"><h3>Originality/value</h3><p>New visualizations of researcher collaboration provide compelling comparisons of the extent and nature of social networking among target cohorts.</p></sec></abstract>

Measuring and Visualizing Research Collaboration and Productivity

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2018-0003_s_007_w2aab3b7b3b1b6b1aab1c17b1Aa"><h3>Purpose</h3><p>In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for customers who are more likely to respond positively to targeted advertisements.</p></sec><sec id="j_jdis-2018-0003_s_008_w2aab3b7b3b1b6b1aab1c17b2Aa"><h3>Design/methodology/approach</h3><p>We worked with two different data sets to examine whether Twitter users’ gender, inferred from the first name of the account and the profile description, correlates with the privacy setting of the account. We also used a set of features including the inferred gender of Twitter users to develop classifiers that predict user privacy settings.</p></sec><sec id="j_jdis-2018-0003_s_009_w2aab3b7b3b1b6b1aab1c17b3Aa"><h3>Findings</h3><p>We found that the inferred gender of Twitter users correlates with the account’s privacy setting. Specifically, females tend to be more privacy concerned than males. Users whose gender cannot be inferred from their provided first names tend to be more privacy concerned. In addition, our classification performance suggests that inferred gender can be used as an indicator of the user’s privacy preference.</p></sec><sec id="j_jdis-2018-0003_s_010_w2aab3b7b3b1b6b1aab1c17b4Aa"><h3>Research limitations</h3><p>It is known that not all twitter accounts are real user accounts, and social bots tweet as well. A major limitation of our study is the lack of consideration of social bots in the data. In our study, this implies that at least some percentage of the undefined accounts, that is, accounts that had names non-existent in the name dictionary, are social bots. It will be interesting to explore the privacy setting of social bots in the Twitter space.</p></sec><sec id="j_jdis-2018-0003_s_011_w2aab3b7b3b1b6b1aab1c17b5Aa"><h3>Practical implications</h3><p>Companies are investing large amounts of money in business intelligence tools that allow them to know the preferences of their consumers. Due to the large number of consumers around the world, it is very difficult for companies to have direct communication with each customer to anticipate market changes. For this reason, the social network Twitter has gained relevance as one ideal tool for information extraction. On the other hand, users’ privacy preference needs to be considered when companies consider leveraging their publicly available data. This paper suggests that gender recognition of Twitter users, based on Twitter users’ provided first names and their profile descriptions, can be used to infer the users’ privacy preference.</p></sec><sec id="j_jdis-2018-0003_s_012_w2aab3b7b3b1b6b1aab1c17b6Aa"><h3>Originality/value</h3><p>This study explored a new way of inferring Twitter user’s gender, that is, to recognize the user’s gender based on the provided first name and the user’s profile description. The potential of this information for predicting the user’s privacy preference is explored.</p></sec></abstract>

Twitter Users’ Privacy Concerns: What do Their Accounts’ First Names Tell Us?

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2018-0002_s_007_w2aab3b7b2b1b6b1aab1c18b1Aa"><h3>Purpose</h3><p>One of the main indicators of scientific production is the number of papers published in scholarly journals. Turkey ranks 18th place in the world based on the number of scholarly publications. The objective of this paper is to find out if the monetary support program initiated in 1993 by the Turkish Scientific and Technological Research Council (TÜBİTAK) to incentivize researchers and increase the number, impact, and quality of international publications has been effective in doing so.</p></sec><sec id="j_jdis-2018-0002_s_008_w2aab3b7b2b1b6b1aab1c18b2Aa"><h3>Design/methodology/approach</h3><p>We analyzed some 390,000 publications with Turkish affiliations listed in the Web of Science (WoS) database between 1976 and 2015 along with about 157,000 supported ones between 1997 and 2015. We used the interrupted time series (ITS) analysis technique (also known as “quasi-experimental time series analysis” or “intervention analysis”) to test if TÜBİTAK’s support program helped increase the number of publications. We defined ARIMA (1,1,0) model for ITS data and observed the impact of TÜBİTAK’s support program in 1994, 1997, and 2003 (after one, four and 10 years of its start, respectively). The majority of publications (93%) were full papers (articles), which were used as the experimental group while other types of contributions functioned as the control group. We also carried out a multiple regression analysis.</p></sec><sec id="j_jdis-2018-0002_s_009_w2aab3b7b2b1b6b1aab1c18b3Aa"><h3>Findings</h3><p>TÜBİTAK’s support program has had negligible effect on the increase of the number of papers with Turkish affiliations. Yet, the number of other types of contributions continued to increase even though they were not well supported, suggesting that TÜBİTAK’s support program is probably not the main factor causing the increase in the number of papers with Turkish affiliations.</p></sec><sec id="j_jdis-2018-0002_s_010_w2aab3b7b2b1b6b1aab1c18b4Aa"><h3>Research limitations</h3><p>Interrupted time series analysis shows if the “intervention” has had any significant effect on the dependent variable but it does not explain what caused the increase in the number of papers if it was not the intervention. Moreover, except the “intervention”, other “event(s)” that might affect the time series data (e.g., increase in the number of research personnel over the years) should not occur during the period of analysis, a prerequisite that is beyond the control of the researcher.</p></sec><sec id="j_jdis-2018-0002_s_011_w2aab3b7b2b1b6b1aab1c18b5Aa"><h3>Practical implications</h3><p>TÜBİTAK’s “cash-for-publication” program did not seem to have direct impact on the increase of the number of papers published by Turkish authors, suggesting that small amounts of payments are not much of an incentive for authors to publish more. It might perhaps be a better strategy to concentrate limited resources on a few high impact projects rather than to disperse them to thousands of authors as “micropayments.”</p></sec><sec id="j_jdis-2018-0002_s_012_w2aab3b7b2b1b6b1aab1c18b6Aa"><h3>Originality/value</h3><p>Based on 25 years’ worth of payments data, this is perhaps one of the first large-scale studies showing that “cash-for-publication” policies or “piece rates” paid to researchers tend to have little or no effect on the increase of researchers’ productivity. The main finding of this paper has some implications for countries wherein publication subsidies are used as an incentive to increase the number and quality of papers published in international journals. They should be prepared to consider reviewing their existing support programs (based usually on bibliometric measures such as journal impact factors) and revising their reward policies.</p></sec></abstract>

Does Monetary Support Increase the Number of Scientific Papers? An Interrupted Time Series Analysis

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis-2018-0001_s_008_w2aab3b7b1b1b6b1aab1c18b1Aa"><h3>Purpose</h3><p>In this contribution we continue our investigations related to the activity index (<italic>AI</italic>) and its formal analogs. We try to replace the <italic>AI</italic> by an indicator which is better suited for policy applications.</p></sec><sec id="j_jdis-2018-0001_s_009_w2aab3b7b1b1b6b1aab1c18b2Aa"><h3>Design/methodology/approach</h3><p>We point out that fluctuations in the value of the <italic>AI</italic> for a given country and domain are never the result of that country’s policy with respect to that domain alone because there are exogenous factors at play. For this reason we introduce the <italic>F</italic>-measure. This <italic>F</italic>-measure is nothing but the harmonic mean of the country’s share in the world’s publication output in the given domain and the given domain’s share in the country’s publication output.</p></sec><sec id="j_jdis-2018-0001_s_010_w2aab3b7b1b1b6b1aab1c18b3Aa"><h3>Findings</h3><p>The <italic>F</italic>-measure does not suffer from the problems the <italic>AI</italic> does.</p></sec><sec id="j_jdis-2018-0001_s_011_w2aab3b7b1b1b6b1aab1c18b4Aa"><h3>Research limitations</h3><p>The indicator is not yet fully tested in real cases.</p></sec><sec id="j_jdis-2018-0001_s_012_w2aab3b7b1b1b6b1aab1c18b5Aa"><h3>R&amp;D policy management</h3><p>In policy considerations, the <italic>AI</italic> should better be replaced by the <italic>F</italic>-measure as this measure can better show the results of science policy measures (which the <italic>AI</italic> cannot as it depends on exogenous factors).</p></sec><sec id="j_jdis-2018-0001_s_013_w2aab3b7b1b1b6b1aab1c18b6Aa"><h3>Originality/value</h3><p>We provide an original solution for a problem that is not fully realized by policy makers.</p></sec></abstract>

The <italic>F</italic>-measure for Research Priority

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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 3 (2018): Issue 1 (February 2018)

Research Paper

Trends Analysis of Graphene Research and Development

Measuring and Visualizing Research Collaboration and Productivity

Twitter Users’ Privacy Concerns: What do Their Accounts’ First Names Tell Us?

Does Monetary Support Increase the Number of Scientific Papers? An Interrupted Time Series Analysis

The F-measure for Research Priority

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