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Tacit Knowledge Transfer in Training and the Inherent Limitations of Using Quantitative Measures
Amy Rosellini, Suliman Hawamdeh
University of North Texas, USA
The greatest challenge for many organizations today is not the acquisition, organization and storage of information, but rather the ability to transform such information into useful knowledge as well as the application and measurement of it. This paper discusses effective knowledge transfer in a training environment and the inherent limitations in using only quantitative measures as a tool of knowledge transfer measurement. By examining the measurement tool of a major U.S.-based airline, this study identifies disparity in peer and observer behavior assessment to understand other important factors that impact the measurement of knowledge transfer.
Data to Knowledge in Action: A Longitudinal Analysis of GenBank Metadata
Jeff Hemsley, Jian Qin, Sarah E. Bratt
Syracuse University, USA
Studies typically use publication-based authorship data to study the relationships between collaboration networks and knowledge diffusion. However, collaboration in research often starts long before publication with data production efforts. In this project we ask how does collaboration in data production networks affect and contribute to knowledge diffusion, as represented by patents, another form of knowledge diffusion. We drew our data from the meta-data associated with genetic sequence records stored in the National Institutes of Health's GenBank database. After constructing networks for each year and aggregating summary statistics, regressions were used to test a number of hypotheses. Key among our findings is that data production team size is positively related to the number of patents each year. Also, when actors on average have more links, we tend to see more patents. Our study makes a contribution in the area of science of science by highlighting the important role of data production in the diffusion of knowledge as measured by patents.
9:30am - 9:40am ID: 281 / PS-12: 3 Short Papers Topics: Archives; Data Curation; and Preservation Keywords: knowledge engineering, provenance, knowledge representation, systems analysis, geological informatics
Adapting Research Process Models for the Design of Knowledge Engineering Applications
Donald A. Keefer, Karen M. Wickett
University of Illinois at Urbana-Champaign, USA
To design knowledge bases that effectively address the desired reasoning goals, knowledge engineering requires a detailed description of information flow throughout the targeted reasoning processes. Most existing technologies for workflow modeling do not provide sufficient detail for projects where cognitive reasoning and field- or lab-based data collection are important components. Research Process Modeling (RPM) was developed to support curation and data lifecycle needs, providing user-targeted documentation on processes, agents, and artifacts within research projects that include both computational and field- or lab-based processes. We demonstrate the use of RPM to support the design of a knowledge engineering application within 3-D geologic mapping, by documenting and describing information flow through a complex research project involving field-, computation-, and cognitive process-generated data. The results demonstrate necessary modifications to the Research Processing Modeling approach, and the value that RPM provides for describing information flow to support the design of complex knowledge engineering applications.
9:40am - 9:50am ID: 353 / PS-12: 4 Short Papers Topics: Human Computer Interaction (HCI) Keywords: Structure research data; human data interaction; user behaviors
Task-Based Human-Structured Research Data Interaction: A Discipline Independent Examination
Fanghui Xiao, Rongqian Ma, Daqing He
University of Pittsburgh, USA
With the development of open data movement, an increasing number of structured research datasets (SRD) are available online due to the successful data infrastructure and the strong demand of sharing data. Yet it lacks a thorough, systematic investigation of researcher-SRD interaction, which is important to understand users’ needs, challenges, and expectations. Our work extends from the current scholarship and proposes a task-based approach to examining how researchers interact with SRD, without considering individual disciplines. This study identifies two types of research tasks, the data-driven tasks and model-driven tasks, and also proposes a framework for researcher-SRD interaction. Our findings will contribute to the research field of human data behaviors.