scholarly journals Comparing Social Science and Computer Science Workflow Processes for Studying Group Interactions

2017 ◽  
Vol 48 (5) ◽  
pp. 568-590 ◽  
Author(s):  
Joseph A. Allen ◽  
Colin Fisher ◽  
Mohamed Chetouani ◽  
Ming Ming Chiu ◽  
Hatice Gunes ◽  
...  
Keyword(s):  
Social Science ◽  
Computer Science ◽  
Group Interactions

scholarly journals Caracterización de las publicaciones de España y Portugal sobre Open Science y análisis de su presencia en las redes sociales

2019 ◽  
Vol 42 (2) ◽  
pp. 235 ◽  
Author(s):  
Daniela De Filippo ◽  
Paulo Silva ◽  
María Manuel Borges
Keyword(s):  
Social Science ◽  
Computer Science ◽  
Open Data ◽  
Open Science ◽  
Redes Sociales ◽  
Acceso Abierto

Se analizan las publicaciones sobre Ciencia Abierta de España y Portugal en la base de datos SCOPUS. A través de indicadores bibliométricos y altmétricos se estudia la repercusión de la producción en redes sociales. Entre 2000 y 2016 se detectaron 1273 documentos sobre el tema en ambos países, concentrados especialmente en el último quinquenio. Destacan las publicaciones sobre Open data y las temáticas de mayor producción han sido Computer Science y Social Science. Un tercio de las publicaciones con DOI ha tenido repercusión en las redes sociales siendo Twitter el medio que concentra mayor número de menciones. Si bien una tercera parte de los documentos se publicó en acceso abierto, no se detectó relación entre este indicador y la presencia en redes sociales.

scholarly journals Financial industry under the background of digital industry: A Bibliometric Analysis of Related Research

2020 ◽  
Vol 214 ◽  
pp. 03010
Author(s):  
Chung-Lien Pan ◽  
Xianghui Chen ◽  
Mei Lin ◽  
Zhuocheng Cai ◽  
Xiaolin Wu
Keyword(s):  
Social Science ◽  
Computer Science ◽  
Digital Technology ◽  
Publishing Industry ◽  
Research Field ◽  
Future Research ◽  
Research Front ◽  
Financial Industry ◽  
Research Papers ◽  
The Impact

In recent years, the innovation and breakthrough of digital technology have brought great convenience to the economic development of various sectors and People’s daily life, especially in the field of financial services. To explore the impact of digital technology on the financial industry, this paper searched 285 papers based on Web of Science (WoS) and conducted a systematic scientific metrology and literature review, providing a research front for future research. According to the research papers published between 1984 and 2020, the analysis results of co-citation and co-cited by sources, disciplines, and keywords show that in recent years, the publishing industry in this field has developed rapidly in various countries, and the research field involves such disciplines as business economics, computer science, social science, and interdisciplinary application. According to the research papers published between 1984 and 2020, the analysis results of co-citation and co-cited by sources, disciplines, and keywords show that in recent years, the publishing industry in this field has developed rapidly in various countries, and the research field involves such disciplines as business, finance; economics; computer science; social science and interdisciplinary application. Besides, American, Chinese and British institutions are also good at hosting such interdisciplinary work. And different types of keywords present important interactions in the visualization: (a) digital-based innovation, (b) big data and regulation, (c) Internet finance and financial innovation, (d) financial inclusion, (e) digital finance and risk management, and (f) mobile payment.

scholarly journals GESTÃO DO CONHECIMENTO E GESTÃO DA INOVAÇÃO: UM ESTUDO BIBLIOMÉTRICO NA WEB OF SCIENCE (1945-2015)

2016 ◽  
pp. 113
Author(s):  
Paulo Fernando Marschner ◽  
Lucas Veiga Ávila ◽  
Analisa Tiburski Sommer
Keyword(s):  
Knowledge Management ◽  
Social Science ◽  
Computer Science ◽  
Operations Research ◽  
Information Science ◽  
Web Of Science ◽  
Innovation Management ◽  
Library Science ◽  
Research Management ◽  
Business Economics

Este estudo tem como objetivo analisar as características das publicações sobre Knowledge management (Gestão do conhecimento) e Innovation management (Gestão da inovação) na base de dados Web of Science, no período de 1945 a 2015. O trabalho descritivo e quantitativo, de natureza bibliométrica, busca levantar as características da produção acadêmica. Como principal resultado das 372 publicações analisadas constatou-se que os anos com maior publicação foram os de 2008 e 2015, em especial nas seguintes áreas temáticas: Business economics (Economia Empresarial), Operations research management science (Gestão de Operações), Engineering (Engenharias), Computer science (Ciência da Computação), Information science library science (Ciência da informação/biblioteconomia), Social science (Ciências Sociais). Os documentos são 66,6% proceedings paper, e o principal titulo é o International journal of technology management. Os países com maior número de produção são a China e os Estados Unidos, e o principal idioma é a língua inglesa.

scholarly journals 2553 Authors’ perceptions of the interdisciplinarity of their research

2018 ◽  
Vol 2 (S1) ◽  
pp. 5-5
Author(s):  
Christine M. Weston ◽  
Mia S. Terkowitz ◽  
Daniel E. Ford
Keyword(s):  
Public Health ◽  
Social Science ◽  
Computer Science ◽  
Basic Science ◽  
Academic Disciplines ◽  
Research Article ◽  
Study Population ◽  
Subjective Views ◽  
Major Discipline ◽  
The Way

OBJECTIVES/SPECIFIC AIMS: The objectives of this study were to compare different methods for determining the disciplines involved in a research article. We sought to address the following questions: To what extent does the number of disciplines reported by an article’s corresponding author agree with their description of the article as unidisciplinary or interdisciplinary? (Q1) and To what extent does the corresponding author’s description of the research as unidisciplinary or interdisciplinary agree with its classification as unidisciplinary or interdisciplinary based on the affiliation of its co-authors? (Q2). METHODS/STUDY POPULATION: Using Scopus, we randomly selected 100 articles from 2010 and 2015 from science teams that had at least 1 author affiliated with Johns Hopkins. Author affiliations were grouped into common academic disciplines: Basic Science, Medicine (and all clinical specialties), Public Health, Engineering, Social Science, Computer Science, Pharmacy, Nursing, and Other. Articles with more than 1 discipline were considered, interdisciplinary. We then sent an online Qualtrics survey to the corresponding author of each article and asked them to indicate (1) all of the disciplines that contributed to the research article at hand, and (2) to indicate whether they considered the research to be “unidisciplinary” or “interdisciplinary” based on definitions that we provided. RESULTS/ANTICIPATED RESULTS: For Q1, we asked corresponding authors to indicate the number of disciplines involved in their research and then to choose the definition that best described their research. Among 76 respondents, 42 indicated that their research consisted of 1 discipline, and 34 indicated that their research consisted of more than 1 discipline. Of the 42 respondents who indicated that their research consisted of one discipline, 21 (50%) respondents described their research as “unidisciplinary” and 21 (50%) described their research as “interdisciplinary.” However, of the 34 respondents who indicated that their research consisted of more than 1 discipline, all but 1 (97%) described their research as “interdisciplinary.” For Q2, we assigned a discipline to each co-author based on his/her affiliation and counted the number of disciplines involved. Among 76 respondents, of the 22 who described their research as “unidisciplinary,” 16 (73%) were categorized as “unidisciplinary” and 6 (27%) were categorized as “interdisciplinary,” using this method. Of the 54 respondents who described their research as “interdisciplinary,” 30 (56%) were categorized as “interdisciplinary” and 24 (44%) as “unidisciplinary.” DISCUSSION/SIGNIFICANCE OF IMPACT: Our results highlight that different methods for determining whether a given research article is interdisciplinary are likely to yield different results. Even when researchers indicate that their research is based within one major discipline, they may still consider it interdisciplinary. Likewise, classifying an article as either unidisciplinary or interdisciplinary based on the affiliations of its co-authors, may not be consistent with the way it is viewed by its authors. It is important to acknowledge that assessing the interdisciplinarity of research is complex and that objective and subjective views may differ.

A Methodology for Creating Ontologies for Engineering Design

2005 ◽  
Author(s):  
Saeema Ahmed ◽  
Sanghee Kim ◽  
Ken M. Wallace
Keyword(s):  
Social Science ◽  
Computer Science ◽  
Engineering Design ◽  
Empirical Research ◽  
Research Study ◽  
Design Knowledge ◽  
Test Case ◽  
Engineering Knowledge ◽  
Domain Models

This paper describes a methodology for developing ontologies for engineering design. The methodology combines a number of methods from social science and computer science, together with taxonomies developed in the field of engineering design. A case study is used throughout the paper focusing upon the use of an ontology for searching, indexing and retrieving of engineering knowledge. An ontology for indexing design knowledge can assist the users to formulate their queries when searching for engineering design knowledge. The root concepts of the ontology were elicited from engineering designers during an empirical research study. These formed individual taxonomies within the ontology and were validated through indexing a set of ninety-two documents. Relationships between concepts are extracted as the ontology is populated with instances. The identified root concepts were found to be complete and sufficient for the purpose of indexing. A thesaurus and an automatic classification are being developed as a result of this evaluation. The methodology employed during the test case is presented in this paper. There are six separate stages, which are presented together with the research methods employed for each stage and the evaluation of each stage. The main contribution of this research is the development of a methodology to allow researchers and industry to create ontologies for their particular purpose and to develop a thesaurus for the terms within the ontology. The methodology is based upon empirical research and hence, focuses upon understanding a user’s domain models as opposed to extracting an ontology from documentation.

scholarly journals Study Designs for Quantitative Social Science Research Using Social Media

2020 ◽  
Author(s):  
Leticia Bode ◽  
Pamela Davis-Kean ◽  
Lisa Singh ◽  
Tanya Berger-Wolf ◽  
Ceren Budak ◽  
...  
Keyword(s):  
Social Media ◽  
Social Science ◽  
Computer Science ◽  
Quantitative Research ◽  
Social Science Research ◽  
Basic Research ◽  
Science Research ◽  
Social Media Data ◽  
Computer Scientists ◽  
Media Data

Social media provides a rich amount of data on the everyday lives, opinions, thoughts, beliefs, and behaviors of individuals and organizations in near real-time. Leveraging these data effectively and responsibly should therefore improve our ability to understand political, psychological, economic, and sociological behaviors and opinions across time. This article is the first in a series of white papers that will provide a summary of the discussions derived from meetings of social scientists and computer scientists with the goal of creating consensus for how social and computer science could converge to answer important questions about complex human behaviors and dynamics using social media data. We present three basic research designs that are commonly used in social science and are applicable to research using social media data: qualitative observation, experiments, and surveys. We also discuss a fourth design that is primarily informed by computer science, non-designed data, but that can inform social science research. After a brief discussion of the general approach of these designs and their applicability for use with social media data, we discuss the challenges associated with their use with social media data and potential solutions for “convergence” of these methods for future quantitative research in the social sciences.

scholarly journals Special Issue on New Trends in Agent-Based Simulation

2011 ◽  
Vol 15 (2) ◽  
pp. 165-165
Author(s):  
Keiki Takadama ◽  
Kiyoshi Izumi
Keyword(s):  
Social Sciences ◽  
Social Science ◽  
Computer Science ◽  
Mutual Influence ◽  
Real Data ◽  
Special Issue ◽  
Learning Mechanisms ◽  
Agent Based Simulation ◽  
Agent Based ◽  
The Social

Agent-Based Simulation (ABS), an interdisciplinary area embracing both the computer science and the social science, has attracted much attention and aided the understanding of socially complex phenomena. A current important issue in this research area is how to improve ABS effectiveness and comprehension, which makes further mutual influence between the computer science and the social sciences indispensable - e.g., (1) agent modeling involving learning mechanisms in the computer science and (2) social dynamics analysis needed in the social science. Such integration of these two areas would help fulfill the great potential of ABS, first in solving complex engineering problems using agent-based technology and second in developing and testing new theories on socially complex systems. This special issue features ABS papers from both of these important areas exploring new trends in ABS. The 10 papers composing this special issue start with papers by Nobutada Fujii and Hiroyasu Inoue analyzing the relationship between the network structure and system dynamics. In these papers, an agent-based computational economics approach has been active in applying agent-based technologies to financial and economic systems. Papers by Biliana Alexandrova-Kabadjova, Isamu Okada, TomokoOhi, and Nariaki Nishino cover consumer and financial markets using agent-based models. They test economic theory and examine market phenomena for market design. Agent-based simulation is increasingly used in application fields in the social sciences. Papers by Kiyoshi Izumi, Hideki Fujii, Hiromitsu Hattori, and Shigeo Sagai propose solutions for actual social problems such as injury prevention, traffic, and electrical power. Models are created based on behavior data, and the integration of an agent-based model and real data is a hot topic in this area. As the beginning of these technical papers, this issue starts by a position paper to give an ABS overview for understanding important issues in ABS from an overall viewpoint and for understanding state-of-the-art ABS. The information presented is invaluable in helping readers grasp the important features of ABS.

scholarly journals New Frontiers in Analyzing Dynamic Group Interactions: Bridging Social and Computer Science

2017 ◽  
Vol 48 (5) ◽  
pp. 519-531 ◽  
Author(s):  
Nale Lehmann-Willenbrock ◽  
Hayley Hung ◽  
Joann Keyton
Keyword(s):  
Computer Science ◽  
Group Interactions ◽  
Dynamic Group

scholarly journals A Glossary for Research on Human Crowd Dynamics

2019 ◽  
Vol 4 ◽  
Author(s):  
Juliane Adrian ◽  
Nikolai Bode ◽  
Martyn Amos ◽  
Mitra Baratchi ◽  
Mira Beermann ◽  
...  
Keyword(s):  
Social Science ◽  
Computer Science ◽  
Mutual Understanding ◽  
Crowd Dynamics ◽  
Ongoing Process ◽  
Multidisciplinary Meeting ◽  
Engineering Mathematics ◽  
Starting Point ◽  
Formal Definitions ◽  
Human Crowd

This article presents a glossary of terms that are frequently used in research on human crowds. This topic is inherently multidisciplinary as it includes work in and across computer science, engineering, mathematics, physics, psychology and social science, for example. We do not view the glossary presented here as a collection of finalised and formal definitions. Instead, we suggest it is a snapshot of current views and the starting point of an ongoing process that we hope will be useful in providing some guidance on the use of terminology to develop a mutual understanding across disciplines. The glossary was developed collaboratively during a multidisciplinary meeting. We deliberately allow several definitions of terms, to reflect the confluence of disciplines in the field. This also reflects the fact not all contributors necessarily agree with all definitions in this glossary. 

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