-
Zhang, Y.: ¬The impact of Internet-based electronic resources on formal scholarly communication in the area of library and information science : a citation analysis (1998)
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- Abstract
- Internet based electronic resources are growing dramatically but there have been no empirical studies evaluating the impact of e-sources, as a whole, on formal scholarly communication. reports results of an investigation into how much e-sources have been used in formal scholarly communication, using a case study in the area of Library and Information Science (LIS) during the period 1994 to 1996. 4 citation based indicators were used in the study of the impact measurement. Concludes that, compared with the impact of print sources, the impact of e-sources on formal scholarly communication in LIS is small, as measured by e-sources cited, and does not increase significantly by year even though there is observable growth of these impact across the years. It is found that periodical format is related to the rate of citing e-sources, articles are more likely to cite e-sources than are print priodical articles. However, once authors cite electronic resource, there is no significant difference in the number of references per article by periodical format or by year. Suggests that, at this stage, citing e-sources may depend on authors rather than the periodical format in which authors choose to publish
- Date
- 30. 1.1999 17:22:22
- Source
- Journal of information science. 24(1998) no.4, S.241-254
-
Zhang, Y.: Developing a holistic model for digital library evaluation (2010)
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- Abstract
- This article reports the author's recent research in developing a holistic model for various levels of digital library (DL) evaluation in which perceived important criteria from heterogeneous stakeholder groups are organized and presented. To develop such a model, the author applied a three-stage research approach: exploration, confirmation, and verification. During the exploration stage, a literature review was conducted followed by an interview, along with a card sorting technique, to collect important criteria perceived by DL experts. Then the criteria identified were used for developing an online survey during the confirmation stage. Survey respondents (431 in total) from 22 countries rated the importance of the criteria. A holistic DL evaluation model was constructed using statistical techniques. Eventually, the verification stage was devised to test the reliability of the model in the context of searching and evaluating an operational DL. The proposed model fills two lacunae in the DL domain: (a) the lack of a comprehensive and flexible framework to guide and benchmark evaluations, and (b) the uncertainty about what divergence exists among heterogeneous DL stakeholders, including general users.
- Source
- Journal of the American Society for Information Science and Technology. 61(2010) no.1, S.88-110
-
Zhang, Y.; Jansen, B.J.; Spink, A.: Identification of factors predicting clickthrough in Web searching using neural network analysis (2009)
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- Date
- 22. 3.2009 17:49:11
- Source
- Journal of the American Society for Information Science and Technology. 60(2009) no.3, S.557-570
-
Zhang, Y.; Liu, J.; Song, S.: ¬The design and evaluation of a nudge-based interface to facilitate consumers' evaluation of online health information credibility (2023)
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- Date
- 22. 6.2023 18:18:34
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.7, S.828-845
-
Zhang, Y.; Wu, M.; Zhang, G.; Lu, J.: Stepping beyond your comfort zone : diffusion-based network analytics for knowledge trajectory recommendation (2023)
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- Date
- 22. 6.2023 18:07:12
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.7, S.775-790
-
Shah, C.; Anderson, T.; Hagen, L.; Zhang, Y.: ¬An iSchool approach to data science : human-centered, socially responsible, and context-driven (2021)
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- Abstract
- The Information Schools, also referred to as iSchools, have a unique approach to data science with three distinct components: human-centeredness, socially responsible, and rooted in context. In this position paper, we highlight and expand on these components and show how they are integrated in various research and educational activities related to data science that are being carried out at iSchools. We argue that the iSchool way of doing data science is not only highly relevant to the current times, but also crucial in solving problems of tomorrow. Specifically, we accentuate the issues of developing insights and solutions that are not only data-driven, but also incorporate human values, including transparency, privacy, ethics, fairness, and equity. This approach to data science has meaningful implications on how we educate the students and train the next generation of scholars and policymakers. Here, we provide some of those design decisions, rooted in evidence-based research, along with our perspective on how data science is currently situated and how it should be advanced in iSchools.
- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.6, S.793-796
-
Xu, H.; Bu, Y.; Liu, M.; Zhang, C.; Sun, M.; Zhang, Y.; Meyer, E.; Salas, E.; Ding, Y.: Team power dynamics and team impact : new perspectives on scientific collaboration using career age as a proxy for team power (2022)
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- Abstract
- Power dynamics influence every aspect of scientific collaboration. Team power dynamics can be measured by team power level and team power hierarchy. Team power level is conceptualized as the average level of the possession of resources, expertise, or decision-making authorities of a team. Team power hierarchy represents the vertical differences of the possessions of resources in a team. In Science of Science, few studies have looked at scientific collaboration from the perspective of team power dynamics. This research examines how team power dynamics affect team impact to fill the research gap. In this research, all coauthors of one publication are treated as one team. Team power level and team power hierarchy of one team are measured by the mean and Gini index of career age of coauthors in this team. Team impact is quantified by citations of a paper authored by this team. By analyzing over 7.7 million teams from Science (e.g., Computer Science, Physics), Social Sciences (e.g., Sociology, Library & Information Science), and Arts & Humanities (e.g., Art), we find that flat team structure is associated with higher team impact, especially when teams have high team power level. These findings have been repeated in all five disciplines except Art, and are consistent in various types of teams from Computer Science including teams from industry or academia, teams with different gender groups, teams with geographical contrast, and teams with distinct size.
- Source
- Journal of the Association for Information Science and Technology. 73(2022) no.10, S.1489-1505
-
Zhang, M.; Zhang, Y.: Professional organizations in Twittersphere : an empirical study of U.S. library and information science professional organizations-related Tweets (2020)
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- Abstract
- Twitter is utilized by many, including professional businesses and organizations; however, there are very few studies on how other entities interact with these organizations in the Twittersphere. This article presents a study that investigates tweets related to 5 major library and information science (LIS) professional organizations in the United States. This study applies a systematic tweets analysis framework, including descriptive analytics, network analytics, and co-word analysis of hashtags. The findings shed light on user engagement with LIS professional organizations and the trending discussion topics on Twitter, which is valuable for enabling more successful social media use and greater influence.
- Source
- Journal of the Association for Information Science and Technology. 71(2020) no.4, S.491-496
-
Xie, B.; He, D.; Mercer, T.; Wang, Y.; Wu, D.; Fleischmann, K.R.; Zhang, Y.; Yoder, L.H.; Stephens, K.K.; Mackert, M.; Lee, M.K.: Global health crises are also information crises : a call to action (2020)
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- Abstract
- In this opinion paper, we argue that global health crises are also information crises. Using as an example the coronavirus disease 2019 (COVID-19) epidemic, we (a) examine challenges associated with what we term "global information crises"; (b) recommend changes needed for the field of information science to play a leading role in such crises; and (c) propose actionable items for short- and long-term research, education, and practice in information science.
- Source
- Journal of the Association for Information Science and Technology. 71(2020) no.12, S.1419-1423
-
Zhang, Y.; Wu, D.; Hagen, L.; Song, I.-Y.; Mostafa, J.; Oh, S.; Anderson, T.; Shah, C.; Bishop, B.W.; Hopfgartner, F.; Eckert, K.; Federer, L.; Saltz, J.S.: Data science curriculum in the iField (2023)
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- Abstract
- Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.
- Footnote
- Beitrag in einem Special issue on "Data Science in the iField".
- Source
- Journal of the Association for Information Science and Technology. 74(2023) no.6, S.641-662
-
Zhang, Y.; Zhang, G.; Zhu, D.; Lu, J.: Scientific evolutionary pathways : identifying and visualizing relationships for scientific topics (2017)
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- Abstract
- Whereas traditional science maps emphasize citation statistics and static relationships, this paper presents a term-based method to identify and visualize the evolutionary pathways of scientific topics in a series of time slices. First, we create a data preprocessing model for accurate term cleaning, consolidating, and clustering. Then we construct a simulated data streaming function and introduce a learning process to train a relationship identification function to adapt to changing environments in real time, where relationships of topic evolution, fusion, death, and novelty are identified. The main result of the method is a map of scientific evolutionary pathways. The visual routines provide a way to indicate the interactions among scientific subjects and a version in a series of time slices helps further illustrate such evolutionary pathways in detail. The detailed outline offers sufficient statistical information to delve into scientific topics and routines and then helps address meaningful insights with the assistance of expert knowledge. This empirical study focuses on scientific proposals granted by the United States National Science Foundation, and demonstrates the feasibility and reliability. Our method could be widely applied to a range of science, technology, and innovation policy research, and offer insight into the evolutionary pathways of scientific activities.
- Source
- Journal of the Association for Information Science and Technology. 68(2017) no.8, S.1925-1939
-
Tenopir, C.; Wang, P.; Zhang, Y.; Simmons, B.; Pollard, R.: Academic users' interactions with ScienceDirect in search tasks : affective and cognitive behaviors (2008)
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- Abstract
- This article presents part of phase 2 of a research project funded by the NSF-National Science Digital Library Project, which observed how academic users interact with the ScienceDirect information retrieval system for simulated class-related assignments. The ultimate goal of the project is twofold: (1) to find ways to improve science and engineering students' use of science e-journal systems; (2) to develop methods to measure user interaction behaviors. Process-tracing technique recorded participants' processes and interaction behaviors that are measurable; think-aloud protocol captured participants' affective and cognitive verbalizations; pre- and post-search questionnaires solicited demographic information, prior experience with the system, and comments. We explored possible relationships between affective feelings and cognitive behaviors. During search interactions both feelings and thoughts occurred frequently. Positive feelings were more common and were associated more often with thoughts about results. Negative feelings were associated more often with thoughts related to the system, search strategy, and task. Learning styles are also examined as a factor influencing behavior. Engineering graduate students with an assimilating learning style searched longer and paused less than those with a converging learning style. Further exploration of learning styles is suggested.
-
Chen, H.; Zhang, Y.; Houston, A.L.: Semantic indexing and searching using a Hopfield net (1998)
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- Abstract
- Presents a neural network approach to document semantic indexing. Reports results of a study to apply a Hopfield net algorithm to simulate human associative memory for concept exploration in the domain of computer science and engineering. The INSPEC database, consisting of 320.000 abstracts from leading periodical articles was used as the document test bed. Benchmark tests conformed that 3 parameters: maximum number of activated nodes; maximum allowable error; and maximum number of iterations; were useful in positively influencing network convergence behaviour without negatively impacting central processing unit performance. Another series of benchmark tests was performed to determine the effectiveness of various filtering techniques in reducing the negative impact of noisy input terms. Preliminary user tests conformed expectations that the Hopfield net is potentially useful as an associative memory technique to improve document recall and precision by solving discrepancies between indexer vocabularies and end user vocabularies
- Source
- Journal of information science. 24(1998) no.1, S.3-18
-
Zhang, Y.: Scholarly use of Internet-based electronic resources (2001)
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- Abstract
- By Internet resources Zhang means any electronic file accessible by any Internet protocol. Their usage is determined by an examination of the citations to such sources in a nine-year sample of four print and four electronic LIS journals, by a survey of editors of these journals, and by a survey of scholars with "in press" papers in these journals. Citations were gathered from Social Science Citation Index and manually classed as e-sources by the format used. All authors with "in press" papers were asked about their use and opinion of Internet sources and for any suggestions for improvement. Use of electronic sources is heavy and access is very high. Access and ability explain most usage while satisfaction was not significant. Citation of e-journals increases over the eight years. Authors report under citation of e-journals in favor of print equivalents. Traditional reasons are given for citing and not citing, but additional reasons are also present for e-journals.
- Source
- Journal of the American Society for Information Science and technology. 52(2001) no.8, S.628-654
-
Zhang, Y.: Using the Internet for survey research : a case study (2000)
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- Source
- Journal of the American Society for Information Science. 51(2000) no.1, S.57-68
-
Lu, C.; Zhang, Y.; Ahn, Y.-Y.; Ding, Y.; Zhang, C.; Ma, D.: Co-contributorship network and division of labor in individual scientific collaborations (2020)
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- Abstract
- Collaborations are pervasive in current science. Collaborations have been studied and encouraged in many disciplines. However, little is known about how a team really functions from the detailed division of labor within. In this research, we investigate the patterns of scientific collaboration and division of labor within individual scholarly articles by analyzing their co-contributorship networks. Co-contributorship networks are constructed by performing the one-mode projection of the author-task bipartite networks obtained from 138,787 articles published in PLoS journals. Given an article, we define 3 types of contributors: Specialists, Team-players, and Versatiles. Specialists are those who contribute to all their tasks alone; team-players are those who contribute to every task with other collaborators; and versatiles are those who do both. We find that team-players are the majority and they tend to contribute to the 5 most common tasks as expected, such as "data analysis" and "performing experiments." The specialists and versatiles are more prevalent than expected by our designed 2 null models. Versatiles tend to be senior authors associated with funding and supervision. Specialists are associated with 2 contrasting roles: the supervising role as team leaders or marginal and specialized contributors.
- Source
- Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1162-1178
-
Ku, Y.; Chiu, C.; Zhang, Y.; Chen, H.; Su, H.: Text mining self-disclosing health information for public health service (2014)
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- Source
- Journal of the Association for Information Science and Technology. 65(2014) no.5, S.928-947
-
Zhang, X.; Fang, Y.; He, W.; Zhang, Y.; Liu, X.: Epistemic motivation, task reflexivity, and knowledge contribution behavior on team wikis : a cross-level moderation model (2019)
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- Source
- Journal of the Association for Information Science and Technology. 70(2019) no.5, S.448-461
-
Zhang, Y.; Li, X.; Fan, W.: User adoption of physician's replies in an online health community : an empirical study (2020)
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- Source
- Journal of the Association for Information Science and Technology. 71(2020) no.10, S.1179-1191
-
Zhang, Y.; Ren, P.; Rijke, M. de: ¬A taxonomy, data set, and benchmark for detecting and classifying malevolent dialogue responses (2021)
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- Source
- Journal of the Association for Information Science and Technology. 72(2021) no.12, S.1477-1497
