Data Science Altmetrics

Information Science Roles in the Emerging Field of Data Science

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201615_s_005_w2aab2b8c32b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>The current development of patient safety reporting systems is criticized for loss of information and low data quality due to the lack of a uniformed domain knowledge base and text processing functionality. To improve patient safety reporting, the present paper suggests an ontological representation of patient safety knowledge.</p></sec><sec id="j_jdis.201615_s_006_w2aab2b8c32b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>We propose a framework for constructing an ontological knowledge base of patient safety. The present paper describes our design, implementation, and evaluation of the ontology at its initial stage.</p></sec><sec id="j_jdis.201615_s_007_w2aab2b8c32b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>We describe the design and initial outcomes of the ontology implementation. The evaluation results demonstrate the clinical validity of the ontology by a self-developed survey measurement.</p></sec><sec id="j_jdis.201615_s_008_w2aab2b8c32b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>The proposed ontology was developed and evaluated using a small number of information sources. Presently, US data are used, but they are not essential for the ultimate structure of the ontology.</p></sec><sec id="j_jdis.201615_s_009_w2aab2b8c32b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>The goal of improving patient safety can be aided through investigating patient safety reports and providing actionable knowledge to clinical practitioners. As such, constructing a domain specific ontology for patient safety reports serves as a cornerstone in information collection and text mining methods.</p></sec><sec id="j_jdis.201615_s_010_w2aab2b8c32b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>The use of ontologies provides abstracted representation of semantic information and enables a wealth of applications in a reporting system. Therefore, constructing such a knowledge base is recognized as a high priority in health care.</p></sec></abstract>

Knowledge Representation in Patient Safety Reporting: An Ontological Approach

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201614_s_007_w2aab2b8b3b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>In order to explain and predict the adoption of personal cloud storage, this study explores the critical factors involved in the adoption of personal cloud storage and empirically validates their relationships to a user’s intentions.</p></sec><sec id="j_jdis.201614_s_008_w2aab2b8b3b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>Based on technology acceptance model (TAM), network externality, trust, and an interview survey, this study proposes a personal cloud storage adoption model. We conducted an empirical analysis by structural equation modeling based on survey data obtained with a questionnaire.</p></sec><sec id="j_jdis.201614_s_009_w2aab2b8b3b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>Among the adoption factors we identified, network externality has the salient influence on a user’s adoption intention, followed by perceived usefulness, individual innovation, perceived trust, perceived ease of use, and subjective norms. Cloud storage characteristics are the most important indirect factors, followed by awareness to personal cloud storage and perceived risk. However, although perceived risk is regarded as an important factor by other cloud computing researchers, we found that it has no significant influence. Also, subjective norms have no significant influence on perceived usefulness. This indicates that users are rational when they choose whether to adopt personal cloud storage.</p></sec><sec id="j_jdis.201614_s_010_w2aab2b8b3b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>This study ignores time and cost factors that might affect a user’s intention to adopt personal cloud storage.</p></sec><sec id="j_jdis.201614_s_011_w2aab2b8b3b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>Our findings might be helpful in designing and developing personal cloud storage products, and helpful to regulators crafting policies.</p></sec><sec id="j_jdis.201614_s_012_w2aab2b8b3b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>This study is one of the first research efforts that discuss Chinese users’ personal cloud storage adoption, which should help to further the understanding of personal cloud adoption behavior among Chinese users.</p></sec></abstract>

Critical Factors for Personal Cloud Storage Adoption in China

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201613_s_006_w2aab2b8b5b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>This paper is an investigation of the effectiveness of the method of clustering biomedical journals through mining the content similarity of journal articles.</p></sec><sec id="j_jdis.201613_s_007_w2aab2b8b5b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>3,265 journals in PubMed are analyzed based on article content similarity and Web usage, respectively. Comparisons of the two analysis approaches and a citation-based approach are given.</p></sec><sec id="j_jdis.201613_s_008_w2aab2b8b5b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>Our results suggest that article content similarity is useful for clustering biomedical journals, and the content-similarity-based journal clustering method is more robust and less subject to human factors compared with the usage-based approach and the citation-based approach.</p></sec><sec id="j_jdis.201613_s_009_w2aab2b8b5b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>Our paper currently focuses on clustering journals in the biomedical domain because there are a large volume of freely available resources such as PubMed and MeSH in this field. Further investigation is needed to improve this approach to fit journals in other domains.</p></sec><sec id="j_jdis.201613_s_010_w2aab2b8b5b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>Our results show that it is feasible to catalog biomedical journals by mining the article content similarity. This work is also significant in serving practical needs in research portfolio analysis.</p></sec><sec id="j_jdis.201613_s_011_w2aab2b8b5b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>To the best of our knowledge, we are among the first to report on clustering journals in the biomedical field through mining the article content similarity. This method can be integrated with existing approaches to create a new paradigm for future studies of journal clustering.</p></sec></abstract>

National Center for Biotechnology Information

Mining Related Articles for Automatic Journal Cataloging

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201612_s_007_w2aab2b8c69b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>In the open science era, it is typical to share project-generated scientific data by depositing it in an open and accessible database. Moreover, scientific publications are preserved in a digital library archive. It is challenging to identify the data usage that is mentioned in literature and associate it with its source. Here, we investigated the data usage of a government-funded cancer genomics project, The Cancer Genome Atlas (TCGA), via a full-text literature analysis.</p></sec><sec id="j_jdis.201612_s_008_w2aab2b8c69b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>We focused on identifying articles using the TCGA dataset and constructing linkages between the articles and the specific TCGA dataset. First, we collected 5,372 TCGA-related articles from PubMed Central (PMC). Second, we constructed a benchmark set with 25 full-text articles that truly used the TCGA data in their studies, and we summarized the key features of the benchmark set. Third, the key features were applied to the remaining PMC full-text articles that were collected from PMC.</p></sec><sec id="j_jdis.201612_s_009_w2aab2b8c69b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>The amount of publications that use TCGA data has increased significantly since 2011, although the TCGA project was launched in 2005. Additionally, we found that the critical areas of focus in the studies that use the TCGA data were glioblastoma multiforme, lung cancer, and breast cancer; meanwhile, data from the RNA-sequencing (RNA-seq) platform is the most preferable for use.</p></sec><sec id="j_jdis.201612_s_010_w2aab2b8c69b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>The current workflow to identify articles that truly used TCGA data is labor-intensive. An automatic method is expected to improve the performance.</p></sec><sec id="j_jdis.201612_s_011_w2aab2b8c69b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>This study will help cancer genomics researchers determine the latest advancements in cancer molecular therapy, and it will promote data sharing and data-intensive scientific discovery.</p></sec><sec id="j_jdis.201612_s_012_w2aab2b8c69b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>Few studies have been conducted to investigate data usage by government-funded projects/programs since their launch. In this preliminary study, we extracted articles that use TCGA data from PMC, and we created a link between the full-text articles and the source data.</p></sec></abstract>

Identifying Scientific Project-generated Data Citation from Full-text Articles: An Investigation of TCGA Data Citation

<abstract xmlns="http://www.w3.org/1999/xhtml"><sec id="j_jdis.201611_s_005_w2aab2b8c36b1b7b1aab1c15b1Aa"><h3>Purpose</h3><p>To comprehensively evaluate the overall performance of a group or an individual in both bibliometrics and patentometrics.</p></sec><sec id="j_jdis.201611_s_006_w2aab2b8c36b1b7b1aab1c15b2Aa"><h3>Design/methodology/approach</h3><p>Trace metrics were applied to the top 30 universities in the 2014 Academic Ranking of World Universities (ARWU)—computer sciences, the top 30 ESI highly cited papers in the computer sciences field in 2014, as well as the top 30 assignees and the top 30 most cited patents in the National Bureau of Economic Research (NBER) computer hardware and software category.</p></sec><sec id="j_jdis.201611_s_007_w2aab2b8c36b1b7b1aab1c15b3Aa"><h3>Findings</h3><p>We found that, by applying trace metrics, the research or marketing impact efficiency, at both group and individual levels, was clearly observed. Furthermore, trace metrics were more sensitive to the different publication-citation distributions than the average citation and <italic>h</italic>-index were.</p></sec><sec id="j_jdis.201611_s_008_w2aab2b8c36b1b7b1aab1c15b4Aa"><h3>Research limitations</h3><p>Trace metrics considered publications with zero citations as negative contributions. One should clarify how he/she evaluates a zero-citation paper or patent before applying trace metrics.</p></sec><sec id="j_jdis.201611_s_009_w2aab2b8c36b1b7b1aab1c15b5Aa"><h3>Practical implications</h3><p>Decision makers could regularly examine the performance of their university/company by applying trace metrics and adjust their policies accordingly.</p></sec><sec id="j_jdis.201611_s_010_w2aab2b8c36b1b7b1aab1c15b6Aa"><h3>Originality/value</h3><p>Trace metrics could be applied both in bibliometrics and patentometrics and provide a comprehensive view. Moreover, the high sensitivity and unique impact efficiency view provided by trace metrics can facilitate decision makers in examining and adjusting their policies.</p></sec></abstract>

Department of Library and Information Science

Department of Mechanical Engineering and Institute of Industrial Engineering

Comparative Study of Trace Metrics between Bibliometrics and Patentometrics

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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 1 (2016): Issue 2 (May 2016)

Perspective