다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining - Concepts and Techniques.

비만아 부모의 돌봄 경험: q 방법론

Criticism and the Growth of Knowledge. Edited by I. Lakatos and A. Musgrave. Pp. viii, 282. £3·50, paperback £1. 1970. (Cambridge University Press.)

In the last two decades there have been studies claiming that science is becoming ever more interdisciplinary. However, the evidence has been anecdotal or partial. Here we investigate how the degree of interdisciplinarity has changed between 1975 and 2005 over six research domains. To do so, we compute well-established bibliometric indicators alongside a new index of interdisciplinarity (Integration score, aka Rao-Stirling diversity) and a science mapping visualization method. The results attest to notable changes in research practices over this 30 year period, namely major increases in number of cited disciplines and references per article (both show about 50% growth), and co-authors per article (about 75% growth). However, the new index of interdisciplinarity only shows a modest increase (mostly around 5% growth). Science maps hint that this is because the distribution of citations of an article remains mainly within neighboring disciplinary areas. These findings suggest that science is indeed becoming more interdisciplinary, but in small steps — drawing mainly from neighboring fields and only modestly increasing the connections to distant cognitive areas. The combination of metrics and overlay science maps provides general benchmarks for future studies of interdisciplinary research characteristics.

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Is science becoming more interdisciplinary? Measuring and mapping six research fields over time

OVERVIEW AND PRINCIPLES. Management, Technology, and the Future. Principles of Sociotechnical Change. Technology Planning. Forecasting. Managing the Forecasting Project. METHODS. General Methodological Issues. The Stimulation of Creativity. Monitoring. Basic Tools for Quantitative Trend Extrapolation. Extrapolating Technological Trends. Expert Opinion. Simulation. Scenarios. Economic Forecasting and Analysis. ASSESSMENT TO MANAGE TECHNOLOGICAL CHANGE. Impact Assessment: General Issues and the Identification of the Impacts of Technology. Analysis of the Impacts of Technologies. Benefit/Cost and Risk Analysis. Evaluation of Technologies and Their Impacts. Managing the Present from the Future. Appendices. Bibliography. Index.

Forecasting and Management of Technology

The ability to delineate the boundaries of an emerging technology is central to obtaining an understanding of the technology’s research paths and commercialization prospects. Nowhere is this more relevant than in the case of nanotechnology (hereafter identified as “nano”) given its current rapid growth and multidisciplinary nature. (Under the rubric of nanotechnology, we also include nanoscience and nanoengineering.) Past efforts have utilized several strategies, including simple term search for the prefix nano, complex lexical and citation-based approaches, and bootstrapping techniques. This research introduces a modularized Boolean approach to defining nanotechnology which has been applied to several research and patenting databases. We explain our approach to downloading and cleaning data, and report initial results. Comparisons of this approach with other nanotechnology search formulations are presented. Implications for search strategy development and profiling of the nanotechnology field are discussed.

Refining search terms for nanotechnology

List of Figures. Preface. Acknowledgments. Acronyms & Shorthands-Glossary. PART I. UNDERSTAND TECH MINING. Chapter 1. Technological Innovation and the Need for Tech Mining. 1.1 Why Innovation is Significant. 1.2 Innovation Processes. 1.3 Innovation Institutions and Their Interests. 1.4 Innovators and Their Interests. 1.5 Technological Innovation in an Information Age. 1.6 Information About Emerging Technologies. Chapter 1 Take-Home Messages. Chapter Resources. Chapter 2. How Tech Mining Works. 2.1 What is Tech Mining? 2.2 Why Do Tech Mining? 2.3 What Is Tech Mining's Ancestry? 2.4 How to Conduct the Tech Mining Process? 2.5 Who Does Tech Mining? 2.6 Where Is Tech Mining Most Needed? Chapter 2 Take-Home Messages. Chapter Resources. Chapter 3. What Tech Mining Can Do for You. 3.1 Tech Mining Basics. 3.2 Tech Mining Analyses. 3.3 Putting Tech Mining Information to Good Use. 3.4 Managing and Measuring Tech Mining. Chapter 3 Take-Home Messages. Chapter 4. Example Results: Fuel Cells Tech Mining. 4.1 Overview of Fuel Cells. 4.2 Tech Mining Analyses. 4.3 Tech Mining Results. 4.4 Tech Mining Information Processes. 4.5 Tech Mining Information Products. Chapter 4 Take-Home Messages. Chapter Resources. Chapter 5. What to Watch For in Tech Mining. 5.1 Better Basics. 5.2 Research Profiling and Other Perspectives on the Data. 5.3 More Informative Products. 5.4 Knowledge Discovery. 5.5 Knowledge Management. 5.6 New Tech Mining Markets. 5.7 Dangers. Chapter 5 Take-Home Messages. Chapter Resources. PART II. DOING TECH MINING. Chapter 6. Finding the Right Sources. 6.1 R&D Activity. 6.2 R&D Output Databases. 6.3 Determining the Best Sources. 6.4 Arranging Access to Databases. Chapter 6 Take-Home Messages. Chapter Resources. Chapter 7. Forming the Right Query. 7.1 An Iterative Process. 7.2 Queries Based on Substantive Terms. 7.3 Nominal Queries. 7.4 Tactics and Strategies for Query Design. 7.5 Changing the Query. Chapter 7 Take-Home Messages. Chapter 8. Getting the Data. 8.1 Accessing Databases. 8.2 Search and Retrieval from a Database. 8.3 What to Do, and Not to Do. Chapter 8 Take-Home Messages. Chapter 9. Basic Analyses. 9.1 In the Beginning. 9.2 What You Can Do with the Data. 9.3 Relations Among Documents and Terms Occurring in Their Information Fields. 9.4 Relationships. 9.5 Helpful Basic Analyses. Chapter 9 Take-Home Messages. Chapter 10. Advanced Analyses. 10.1 Why Perform Advanced Analyses? 10.2 Data Representation. 10.3 Analytical Families. Chapter 10 Take-Home Messages. Chapter Resources. Chapter 11. Trend Analyses. 11.1 Perspective. 11.2 An Example Time Series Description and Forecast. 11.3 Multiple Forecasts. 11.4 Research Fronts. 11.5 Novelty. Chapter 11 Take-Home Messages. Chapter Resources. Chapter 12. Patent Analyses. 12.1 Why patent Analyses? 12.2 Getting Started. 12.3 The 'What' and 'Why' of patent Analysis. 12.4 Tech Mining Patent Analysis Case Illustration: Fuel Cells. 12.5 Patent Citation Analysis. 12.6 For Whom? 12.7 TRIZ. 12.8 Reflections. Chapter 12 Take-Home Messages. Chapter Resources. Chapter 13. Generating and Presenting Innovation Indicators. 13.1 Expert Opinion in Tech Mining. 13.2 Innovation Indicators. 13.3 Information Representation and Packaging. 13.4 Examples of Putting Tech Mining Information Representation to Use. 13.5 Summing Up. Chapter Resources. Chapter 14. Managing the Tech Mining Process. 14.1 Tough Challenges. 14.2 Tech Mining Communities. 14.3 Process Management. 14.4 Enhancing the Prospects of Tech Mining Utilization. 14.5 Institutionalizing the Tech Mining Function. 14.6 The Learning Curve. Chapter 14 Take-Home Messages. Chapter 15. Measuring Tech Mining Results. 15.1 Why Measure? 15.2 What to Measure. 15.3 How to Measure. 15.4 Enabling Measurement. 15.5 Effective Measurement. 15.6 Using Measurements to Bolster Tech Mining. Chapter 15 Take-Home Messages. Chapter Resources. Chapter 16. Examples Process: Tech Mining on Fuel Cells. 16.1 Introduction. 16.2 First Step: Issue Identification. 16.3 Second Step: Selection of Information Sources. 16.4 Third Step: Search Refinement and Data Retrieval. 16.5 Fourth Step: Data Cleaning. 16.6 Fifth Step: Basic Analyses. 16.7 Sixth Step: Advanced Analyses. 16.8 Seventh Step: Representation. 16.9 Eight Step: Interpretation. 16.10 Ninth Step: Utilization. 16.11 What Can We Learn. Chapter 6 Take-Home Messages. Chapter Resources. Appendix A: Selected Publication and patent Databases. Appendix B: Text Mining Software. Appendix C: What You Can Do Without Tech Mining Software. Appendix D: Statistics and Distributions for Analyzing Text Entities. References. Index.

Alan L. Porter

Papers

Is science becoming more interdisciplinary? Measuring and mapping six research fields over time

Forecasting and Management of Technology

Refining search terms for nanotechnology

Tech Mining: Exploiting New Technologies for Competitive Advantage

Technology futures analysis: Toward integration of the field and new methods