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Visualizing Trends in Scientific Literature

Visualizing Trends in Scientific Literature. Steven Morris Mark Richardson Department of Electrical and Computer Engineering Oklahoma State University. Simplified Process for Generating a Tech Forecast. Reduced Database. Data sources. Acquisition/Feature Extraction.

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Visualizing Trends in Scientific Literature

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  1. Visualizing Trends in Scientific Literature Steven Morris Mark Richardson Department of Electrical and Computer Engineering Oklahoma State University

  2. Simplified Process for Generating a Tech Forecast Reduced Database Data sources Acquisition/Feature Extraction Tech Monitoring Workstation Summarized, visualized information Query Instructions Arbitration Client Problem Definition Facilitation Team Web Based Facilitation System Forecast Forecast Expert Panel

  3. Software Tools for Tech Forecasting • Gather and merge data • journal abstracts, patents, funding records, etc • Establish connections between data items • citations, common index terms, word co-occurrence • Interactively visualize data connections • enabling patents, critical technologies, indirect relations, etc • Interactively visualize technology trends • what’s fading, what’s coming in, what technology’s being borrowed from where.

  4. OSU Database Information Visualization and Analysis Software (DIVA) • Automated construction of patent datasets from key patent lists • Generation of 2D maps and timelines using SOM neural network • Map visualization • dotmap, landscape, contour • Link visualization • similarity threshold, by cluster, precedent, dependent • Visualization of time trends • Cluster reports

  5. Visualization Similarity Function Mapping Data Sources Project Database Clustering Indicators Reports CREATING AND EXPLORING A DOCUMENT DATABASE DOCUMENT SOURCES • US PATENTS • similarity function based on citations • ABSTRACTS • similarity function based on co-occurrence of terms • SCIENCE CITATION INDEX • similarity based on co-citation

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  7. DIVA

  8. GENERATING TERMS FROM FREE TEXT

  9. DIVA – Display of Cluster Links WELL CEMENTS SET RETARDATIONAGENTS POLYMER DRILLING MUDS

  10. DIVA – Display of Patents Date

  11. Silicon On Insulator Integrated Circuits 1986-1989 1994-1998 11

  12. Summarizing Document Cluster Data • Index terms or word occurrence frequency • helps label each cluster’s technical area • Author frequency table • identifies technology experts • Institution frequency table • identifies ‘centers of excellence’ • Graph of cluster document count vs. time • gives information about growth or decline of a specific technology • Table of links by cluster • gives information on which technologies are closely related • Graph of inter-cluster links vs time • gives information on cross-pollination of technology

  13. ‘Index Term’ frequency helps label clusters ‘Author Frequency’ identifies experts ‘Corporate Source’ frequency identifies centers of excellence

  14. ‘Emulsions/Water Pollution’ Cluster Graphs Flat volume of articles over time Increasing links to other emulsion technologies over time Emulsions – Water Pollution Links to ‘Hydration’ cluster peaks in 1998 Links to other clusters flat over time Question: Is there or was there an impending breakthrough in water pollution based on hydration? 14

  15. CHEMICAL SENSORSDocument Sources • CHEMICAL ABSTRACTS • PATENTS • SCIENCE CITATION INDEX

  16. Chemical SensorsApplications • Nerve agent, biological agent detection • Food spoilage detector • Drug screening • Genome imprinting • Synthetic antibodies • Molecular Imprinting

  17. CHEMICAL SENSORS – PUBLICATION RATE DOCUMENTS PER YEAR YEAR

  18. Similarity = 2*word co-occurence + 1*section co-occurence + 1*subsection co-occurence

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  22. Key Learnings – Chem Abstracts • Exponential rise in activity in last 10 years • Biological sensors most active • Large jumps in activity in 1992, 1995 • Large amount of Japanese, Swedish activity • Most active authors (experts) • Most active institutions (centers of excellence)

  23. PATENTS

  24. TIMELINE OF PATENTS

  25. Large spike in this group in 1992

  26. TOTAL PATENTS ISSUED PER YEAR Spike in 1992

  27. MOST CITED PATENTS 28

  28. MOST FREQUENT ASSIGNEES

  29. Key Learnings - Patents • Time trends in patents • what caused spike in 1992? • Most active institutions • Most active inventors

  30. SCIENCE CITATION INDEX

  31. title citation freq Count Author Noncovalent Molecular Imprinting - Antibody-Like Molecular Recognition in Polymeric Network Materials 2 Sellergren-B SCHWEITZ-L-1997-ANAL-CHEM-V69-P1179 8 Molecularly Imprinted Polymers - Useful Materials for Analytical-Chemistry 2 Mayes-AG Mosbach-K MULDOON-MT-1997-ANAL-CHEM-V69-P803 8 A Highly Selective Solid-Phase Extraction Sorbent for Preconcentration of Sameridine Made by Molecular Imprinting 2 Andersson-LI Paprica-A Arvidsson-T Uniform-Sized Molecularly Imprinted Polymer Material for Propranolol - Recognition of Propranolol and Its Metabolites 1 Haginaka-J Sakai-Y Narimatsu-S Selective Recognition of 2,4-Dichlorophenoxyacetic Acid Using a Molecularly Imprinted Polymer 1 Nomura-Y Muguruma-H Yano-K Kugimiya-A Mcniven-S Ikebukuro-K Karube-I Plastic Antibodies - Developments and Applications 1 Haupt-K Mosbach-K Molecularly Imprinted Polymeric Adsorbents for by-Product Removal 1 Ye-L Ramstrom-O Mosbach-K Molecular Imprinting Through Van-der-Waals Interactions - Fluorescence Detection of PAHs in Water 1 Dickert-FL Besenbock-H Tortschanoff-M Capillary Electrochromatography with Molecular Imprint-Based Selectivity for Enantiomer Separation of Local-Anesthetics 1 Schweitz-L Andersson-LI Nilsson-S Bubble Fractionation of Enantiomers from Solution Using Molecularly Imprinted Polymers as Collectors 1 Armstrong-DW Schneiderheinze-JM Hwang-YS Sellergren-B Analysis of Nicotine and Its Oxidation-Products in Nicotine Chewing Gum by a Molecularly Imprinted Solid-Phase Extraction 1 Zander-A Findlay-P Renner-T Sellergren-B Swietlow-A Affinity Capillary-Electrophoresis - Important Application Areas and Some Recent Developments 1 Heegaard-NHH Nilsson-S Guzman-NA 3 Approaches to Enantiomer Separation of Beta-Adrenergic Antagonists by Capillary Electrochromatography 1 Nilsson-S Schweitz-L Petersson-M TWO RECENT DOCUMENTS CITED 8 OR MORE TIMES, INDICATING IMPORTANCE Citing Documents

  32. citation author Count freq MOSBACH-K-1994-TRENDS-BIOCHEM-SCI-V19-P9 WULFF-G 275 116 VLATAKIS-G-1993-NATURE-V361-P645 SELLERGREN-B 208 77 ANDERSSON-LI WULFF-G-1995-ANGEW-CHEM-INT-EDIT-V34-P1812 157 66 KEMPE-M SHEA-KJ-1994-TRENDS-POLYM-SCI-V2-P166 140 60 FISCHER-L-1991-J-AM-CHEM-SOC-V113-P9358 MOSBACH-K 138 44 SELLERGREN-B-1988-J-AM-CHEM-SOC-V110-P5853 SHEA-KJ 113 41 MATSUI-J KEMPE-M-1994-J-CHROMATOGR-A-V664-P276 89 40 VLATAKIS-G ANDERSSON-LI-1995-P-NATL-ACAD-SCI-USA-V92-P4788 77 37 KRIZ-D KRIZ-D-1995-ANAL-CHEM-V67-P2142 74 34 RAMSTROM-O MATSUI-J-1993-ANAL-CHEM-V65-P2223 64 31 SELLERGREN-B-1993-J-CHROMATOGR-V635-P31 FISCHER-L 44 30 ANDERSSON-LI-1990-J-CHROMATOGR-V516-P313 NICHOLLS-IA 36 29 RAMSTROM-O-1994-TETRAHEDRON-ASYMMETR-V5-P649 OSHANNESSY-DJ 35 27 MAYES-AG RAMSTROM-O-1993-J-ORG-CHEM-V58-P7562 34 26 PILETSKY-SA BEACH-JV-1994-J-AM-CHEM-SOC-V116-P379 31 26 WHITCOMBE-MJ-1995-J-AM-CHEM-SOC-V117-P7105 HOSOYA-K 26 26 BEACH-JV 26 WHITCOMBE-MJ 26 DHAL-PK 24 MULDOON-MT 23 ROBINSON-DK 23 MORIHARA-K 23 MOST CITED AUTHORS MOST CITED DOCUMENTS

  33. Key Learning - ISI • Most cited authors • Most cited papers • Important recent trends

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