Collaboration in an Open Data eScience: A Case Study of Sloan Digital Sky Survey

Current science and technology has produced more and more publically accessible scientific data. However, little is known about how the open data trend impacts a scientific community, specifically in terms of its collaboration behaviors. This paper aims to enhance our understanding of the dynamics of scientific collaboration in the open data eScience environment via a case study of co-author networks of an active and highly cited open data project, called Sloan Digital Sky Survey. We visualized the co-authoring networks and measured their properties over time at three levels: author, institution, and country levels. We compared these measurements to a random network model and also compared results across the three levels. The study found that 1) the collaboration networks of the SDSS community transformed from random networks to small-world networks; 2) the number of author-level collaboration instances has not changed much over time, while the number of collaboration instances at the other two levels has increased over time; 3) pairwise institutional collaboration become common in recent years. The open data trend may have both positive and negative impacts on scientific collaboration.Comment: iConference 201

Understanding the evolution of NSAID: a knowledge domain visualization approach to evidence-based medicine

The 9th International Conference on Information Visualization (IV 2005), (London, England, July 6-8, 2005), pp. 945-952. Retrieved 6/21/2006 from http://www.pages.drexel.edu/~cc345/papers/iv2005.pdf.Finding the most rigorous, updated, and well received clinical evidence is a crucial and challenging task in the practice of Evidence-Based Medicine (EBM). In this article, we describe a knowledge domain visualization-based quantitative approach that is designed to support the task of searching for highquality clinical evidence in the medical literature. We illustrate the use of this new approach with the knowledge domain of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs). A sample of the literature is visualized in a base map depicting structural and temporal properties of emerging themes and references made by such themes over time. In addition, the visualization highlights the rigorousness of a published clinical trial in terms of the type of study design retrieved dynamically from PubMed. The contribution of this approach is that it offers users an integrated search environment so that the rigorousness, recentness, and consensus of clinical evidence can be assessed with the support of visual exploration facilities

Research landscape and trends in corporate foresight

Corporate Foresight (СF) gains increasing research interest as an efficient decision-making tool in the face of growing market uncertainty. We carried out a bibliometric analysis of the CF literature published between 2001 and 2021. The results of bibliometric analysis propose in which journals researchers should publish their papers to obtain more citations, which to cite, which keywords to use, and which references to explore. This allows managers, researchers, and practitioners to gain in-depth knowledge of CF literature.info:eu-repo/semantics/publishedVersio

Herramienta informática para vigilancia tecnológica -vigtech-

El artículo presenta una herramienta de software que apoya la vigilancia teconológica. La herramienta permite encontrar relaciones cognitivas y sociales en un conjunto de documentos extraídos de una base referencial tal como SCOPUS. Específicamente, la herramienta soporta las actividades de obtención de información de documentos científicos, extracción de metadatos, cálculo de estadísticas descriptivas, análisis de redes sociales, análisis de redes de palabras claves y visualización. El artículo presenta una descripción de las bases conceptuales que fundamentaron el desarrollo de la herramienta, así como una descripción de su arquitectura y funcionalidad

Measuring the movement of a research paradigm

Proc. of SPIE-IS&T: Visualization and Data Analysis 2005, (San Jose, CA , January 17-18, 2005), SPIE, vol. 5669, pp. 63-76. Retrieved 6/21/2006 from http://www.pages.drexel.edu/~cc345/papers/vda2005.pdf.A research paradigm is a dynamical system of scientific works, including their perceived values by peer scientists, and governed by intrinsic intellectual values and associated citation endurance and decay. Identifying an emerging research paradigm and monitoring changes in an existing paradigm have been a challenging task due to the scale and complexity involved. In this article, we describe an exploratory data analysis method for identifying a research paradigm based on clustering scientific articles by their citation half life and betweenness centrality as well as citation frequencies. The Expectation Maximization algorithm is used to cluster articles based on these attributes. It is hypothesized that the resultant clusters correspond to dynamic groupings of articles manifested by a research paradigm. The method is tested with three example datasets: Social Network Analysis (1992-2004), Mass Extinction (1981-2004), and Terrorism (1989- 2004). All these subject domains have known emergent paradigms identified independently. The resultant clusters are interpreted and assessed with reference to clusters identified by co-citation links. The consistency and discrepancy between the EM clusters and the link-based co-citation clusters are also discussed

Mapping online hate: A scientometric analysis on research trends and hotspots in research on online hate

Internet and social media participation open doors to a plethora of positive opportunities for the general public. However, in addition to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the form of cyberbullying, bigotry, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of research, research hotspots, and influential stakeholders such as organizations and contributing regions. For this, we use scientometric techniques and collect research papers from the Web of Science core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and analyze the data using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute most to OHR, with Western countries accounting for most of the publications, funded by North American and European funding agencies. We also observed increased research activity post-2005, starting from more than 50 publications to more than 550 in 2018. This applies to a number of publications as well as citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified four main clusters of OHR: (1) Cyberbullying, (2) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (4) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated problems for individuals and society

Mapping online hate: A scientometric analysis on research trends and hotspots in research on online hate

Internet and social media participation open doors to a plethora of positive opportunities for the general public. However, in addition to these positive aspects, digital technology also provides an effective medium for spreading hateful content in the form of cyberbullying, bigotry, hateful ideologies, and harassment of individuals and groups. This research aims to investigate the growing body of online hate research (OHR) by mapping general research indices, prevalent themes of research, research hotspots, and influential stakeholders such as organizations and contributing regions. For this, we use scientometric techniques and collect research papers from the Web of Science core database published through March 2019. We apply a predefined search strategy to retrieve peer-reviewed OHR and analyze the data using CiteSpace software by identifying influential papers, themes of research, and collaborating institutions. Our results show that higher-income countries contribute most to OHR, with Western countries accounting for most of the publications, funded by North American and European funding agencies. We also observed increased research activity post-2005, starting from more than 50 publications to more than 550 in 2018. This applies to a number of publications as well as citations. The hotbeds of OHR focus on cyberbullying, social media platforms, co-morbid mental disorders, and profiling of aggressors and victims. Moreover, we identified four main clusters of OHR: (1) Cyberbullying, (2) Sexual solicitation and intimate partner violence, (3) Deep learning and automation, and (4) Extremist and online hate groups, which highlight the cross-disciplinary and multifaceted nature of OHR as a field of research. The research has implications for researchers and policymakers engaged in OHR and its associated problems for individuals and society.</p