The Experts below are selected from a list of 135858 Experts worldwide ranked by ideXlab platform
Elizabeth Yakel - One of the best experts on this subject based on the ideXlab platform.
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Social Scientists satisfaction with data reuse
Association for Information Science and Technology, 2016Co-Authors: Ixchel M Faniel, Adam Kriesberg, Elizabeth YakelAbstract:Much of the recent research on digital data repositories has focused on assessing either the trustworthiness of the repository or quantifying the frequency of data reuse. Satisfaction with the data reuse experience, however, has not been widely studied. Drawing from the information systems and information science literature, we developed a model to examine the relationship between data quality and data reusers' satisfaction. Based on a survey of 1,480 journal article authors who cited Inter-University Consortium for Political and Social Research ICPSR data in published papers from 2008-2012, we found several data quality attributes-completeness, accessibility, ease of operation, and credibility-had significant positive associations with data reusers' satisfaction. There was also a significant positive relationship between documentation quality and data reusers' satisfaction.
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trust in digital repositories
International Journal of Digital Curation, 2013Co-Authors: Elizabeth Yakel, Adam Kriesberg, Ixchel M Faniel, Ayoung YoonAbstract:ISO 16363:2012, Space Data and Information Transfer Systems - Audit and Certification of Trustworthy Digital Repositories (ISO TRAC), outlines actions a repository can take to be considered trustworthy, but research examining whether the repository’s designated community of users associates such actions with trustworthiness has been limited. Drawing from this ISO document and the management and information systems literatures, this paper discusses findings from interviews with 66 archaeologists and quantitative Social Scientists. We found similarities and differences across the disciplines and among the Social Scientists. Both disciplinary communities associated trust with a repository’s transparency. However, archaeologists mentioned guarantees of preservation and sustainability more frequently than the Social Scientists, who talked about institutional reputation. Repository processes were also linked to trust, with archaeologists more frequently citing metadata issues and Social Scientists discussing data selection and cleaning processes. Among the Social Scientists, novices mentioned the influence of colleagues on their trust in repositories almost twice as much as the experts. We discuss the implications our findings have for identifying trustworthy repositories and how they extend the models presented in the management and information systems literatures.
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data reuse and sensemaking among novice Social Scientists
Proceedings of the American Society for Information Science and Technology, 2012Co-Authors: Ixchel M Faniel, Adam Kriesberg, Elizabeth YakelAbstract:We know little about the data reuse practices of novice data users. Yet large scale data reuse over the long term depends in part on uptake from early career researchers. This paper examines 22 novice Social science researchers and how they make sense of Social science data. Novices are particularly interested in understanding how data: 1) are transformed from qualitative to quantitative data, 2) capture concepts not well-established in the literature, and 3) can be matched and merged across multiple datasets. We discuss how novice data users make sense of data in these three circumstances. We find that novices seek to understand the data producer's rationale for methodological procedures and measurement choices, which is broadly similar to researchers in other scientific communities. However we also find that they not only reflect on whether they can trust the data producers' decisions, but also seek guidance from members of their disciplinary community. Specifically, novice Social science researchers are heavily influenced by more experienced Social science researchers when it comes to discovering, evaluating, and justifying their reuse of other's data.
Florentin Smarandache - One of the best experts on this subject based on the ideXlab platform.
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super fuzzy matrices and super fuzzy models for Social Scientists
arXiv: General Mathematics, 2008Co-Authors: Florentin Smarandache, W Vasantha B Kandasamy, K. AmalAbstract:This book introduces the concept of fuzzy super matrices and operations on them. This book will be highly useful to Social Scientists who wish to work with multi-expert models. Super fuzzy models using Fuzzy Cognitive Maps, Fuzzy Relational Maps, Bidirectional Associative Memories and Fuzzy Associative Memories are defined here. The authors introduce 13 multi-expert models using the notion of fuzzy supermatrices. These models are described with illustrative examples. This book has three chapters. In the first chaper, the basic concepts about super matrices and fuzzy super matrices are recalled. Chapter two introduces the notion of fuzzy super matrices adn their properties. The final chapter introduces many super fuzzy multi expert models.
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elementary fuzzy matrix theory and fuzzy models for Social Scientists
2007Co-Authors: W Vasantha B Kandasamy, Florentin Smarandache, K KandasamyAbstract:This book aims to assist Social Scientists to analyze their problems using fuzzy models. The basic and essential fuzzy matrix theory is given. The book does not promise to give the complete properties of basic fuzzy theory or basic fuzzy matrices. Instead, the authors have only tried to give those essential basically needed to develop the fuzzy model. The authors do not present elaborate mathematical theories to work with fuzzy matrices; instead they have given only the needed properties by way of examples. The authors feel that the book should mainly help Social Scientists who are interested in finding out ways to emancipate the society. Everything is kept at the simplest level and even difficult definitions have been omitted. Another main feature of this book is the description of each fuzzy model using examples from real-world problems. Further, this book gives lots of references so that the interested reader can make use of them.
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elementary fuzzy matrix theory and fuzzy models for Social Scientists
arXiv: General Mathematics, 2007Co-Authors: Florentin Smarandache, W Vasantha B Kandasamy, K IlanthenralAbstract:This book gives the basic notions of fuzzy matrix theory and its applications to simple fuzzy models. The approach is non-traditional in order to attract many students to use this methodology in their research. The traditional approach of mathematicians has conditioned students of sociology in such a manner that they are averse to using mathematical tools. Six simple types of fuzzy models that make use of fuzzy matrices are given. These models are distinct because they are time-dependent and can even be used for statistical data. The Fuzzy Cognitive Maps models gives the hidden pattern. Fuzzy Relational Maps model not only gives the hidden pattern but also gives the inter-relations between two sets of disjoint attributes. The Bidirectional Associative Memories model analyzes data depending on the time-period, while the Fuzzy Associative Memories model can give the gradation of importance of each attribute. Finally, the Fuzzy Relational Equation model is capable of giving a solution closer to the predicted solution. All the models are illustrated through elaborate examples of particular Social problems.
Ixchel M Faniel - One of the best experts on this subject based on the ideXlab platform.
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Social Scientists satisfaction with data reuse
Association for Information Science and Technology, 2016Co-Authors: Ixchel M Faniel, Adam Kriesberg, Elizabeth YakelAbstract:Much of the recent research on digital data repositories has focused on assessing either the trustworthiness of the repository or quantifying the frequency of data reuse. Satisfaction with the data reuse experience, however, has not been widely studied. Drawing from the information systems and information science literature, we developed a model to examine the relationship between data quality and data reusers' satisfaction. Based on a survey of 1,480 journal article authors who cited Inter-University Consortium for Political and Social Research ICPSR data in published papers from 2008-2012, we found several data quality attributes-completeness, accessibility, ease of operation, and credibility-had significant positive associations with data reusers' satisfaction. There was also a significant positive relationship between documentation quality and data reusers' satisfaction.
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trust in digital repositories
International Journal of Digital Curation, 2013Co-Authors: Elizabeth Yakel, Adam Kriesberg, Ixchel M Faniel, Ayoung YoonAbstract:ISO 16363:2012, Space Data and Information Transfer Systems - Audit and Certification of Trustworthy Digital Repositories (ISO TRAC), outlines actions a repository can take to be considered trustworthy, but research examining whether the repository’s designated community of users associates such actions with trustworthiness has been limited. Drawing from this ISO document and the management and information systems literatures, this paper discusses findings from interviews with 66 archaeologists and quantitative Social Scientists. We found similarities and differences across the disciplines and among the Social Scientists. Both disciplinary communities associated trust with a repository’s transparency. However, archaeologists mentioned guarantees of preservation and sustainability more frequently than the Social Scientists, who talked about institutional reputation. Repository processes were also linked to trust, with archaeologists more frequently citing metadata issues and Social Scientists discussing data selection and cleaning processes. Among the Social Scientists, novices mentioned the influence of colleagues on their trust in repositories almost twice as much as the experts. We discuss the implications our findings have for identifying trustworthy repositories and how they extend the models presented in the management and information systems literatures.
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data reuse and sensemaking among novice Social Scientists
Proceedings of the American Society for Information Science and Technology, 2012Co-Authors: Ixchel M Faniel, Adam Kriesberg, Elizabeth YakelAbstract:We know little about the data reuse practices of novice data users. Yet large scale data reuse over the long term depends in part on uptake from early career researchers. This paper examines 22 novice Social science researchers and how they make sense of Social science data. Novices are particularly interested in understanding how data: 1) are transformed from qualitative to quantitative data, 2) capture concepts not well-established in the literature, and 3) can be matched and merged across multiple datasets. We discuss how novice data users make sense of data in these three circumstances. We find that novices seek to understand the data producer's rationale for methodological procedures and measurement choices, which is broadly similar to researchers in other scientific communities. However we also find that they not only reflect on whether they can trust the data producers' decisions, but also seek guidance from members of their disciplinary community. Specifically, novice Social science researchers are heavily influenced by more experienced Social science researchers when it comes to discovering, evaluating, and justifying their reuse of other's data.
Andrew P Davis - One of the best experts on this subject based on the ideXlab platform.
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Social Scientists testimony before congress in the united states between 1946 2016 trends from a new dataset
PLOS ONE, 2020Co-Authors: Thomas V Maher, Charles Seguin, Yongjun Zhang, Andrew P DavisAbstract:Congressional hearings are a venue in which Social Scientists present their views and analyses before lawmakers in the United States, however quantitative data on their representation has been lacking. We present new, publicly available, data on the rates at which anthropologists, economists, political Scientists, psychologists, and sociologists appeared before United States congressional hearings from 1946 through 2016. We show that Social Scientists were present at some 10,347 hearings and testified 15,506 times. Economists testify before the US Congress far more often than other Social Scientists, and constitute a larger proportion of the Social Scientists testifying in industry and government positions. We find that Social Scientists’ testimony is increasingly on behalf of think tanks; political Scientists, in particular, have gained much more representation through think tanks. Sociology, and psychology’s representation before Congress has declined considerably beginning in the 1980s. Anthropologists were the least represented. These findings show that academics are representing a more diverse set of organizations, but economists continue to be far more represented than other disciplines before the US Congress.
Youngseek Kim - One of the best experts on this subject based on the ideXlab platform.
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Social Scientists data reuse behaviors exploring the roles of attitudinal beliefs attitudes norms and data repositories
Library & Information Science Research, 2017Co-Authors: Ayoung Yoon, Youngseek KimAbstract:Abstract Many disciplines within the Social sciences have a dynamic culture of sharing and reusing data. Because Social science data differ from data in the hard sciences, it is necessary to explicitly examine Social science data reuse. This study explores the data reuse behaviors of Social Scientists in order to better understand both the factors that influence those Social Scientists' intentions to reuse data and the extent to which those factors influence actual data reuse. Using an integrated theoretical model developed from the theory of planned behavior (TPB) and the technology acceptance model (TAM), this study provides a broad explanation of the relationships among factors influencing Social Scientists' data reuse. A total of 292 survey responses were analyzed using structural equation modeling. Findings suggest that Social Scientists' data reuse intentions are directly influenced by the subjective norm of data reuse, attitudes toward data reuse, and perceived effort involved in data reuse. Attitude toward data reuse mediated Social Scientists' intentions to reuse data, leading to the indirect influence of the perceived usefulness and perceived concern of data reuse, as well as the indirect influence of the subjective norm of data reuse. Finally, the availability of a data repository indirectly influenced Social Scientists' intentions to reuse data by reducing the perceived effort involved.
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Social Scientists' data sharing behaviors
International Journal of Information Management, 2015Co-Authors: Youngseek Kim, Melissa AdlerAbstract:Propose data sharing model based on motivational, institutional, and resource factors.The research model was validated by the results of a survey of 361 Social Scientists.Career benefit and risk significantly affect Social Scientists' data sharing attitude.Attitude, effort, and norm significantly influence Social Scientists' data sharing.Funding agency and journal's pressures and data repository need to be more encouraged. The purpose of this study is to locate individual, institutional, and resource factors that influence data sharing behaviors among Social Scientists. Given the benefits to the Social science disciplines in the advancement of scholarship, and the recent data sharing policy changes of funding agencies, it is necessary to determine the factors that support and impede data sharing behaviors. A research model was developed and validated based on the results of a survey of 361 Social Scientists. The model is informed by theory of planned behavior and institutional theory to map underlying individual motivations, institutional pressures, and availability of resources facilitating Social Scientists' data sharing. It was found that Social Scientists' data sharing behaviors are mainly driven by personal motivations (i.e., perceived career benefit and risk, perceived effort, and attitude toward data sharing) and perceived normative pressure. Funding agencies' pressure, journals' pressure, and availability of data repository were not found to be significant factors in influencing Social Scientists' data sharing. This research suggests that personal motivations and norm of data sharing currently support Social Scientists' data sharing; however, institutional pressures by funding agencies and journals and data repository need to be further encouraged to better facilitate Social Scientists' data sharing behaviors.
