What Researchers Want Cody Dunne Dept. of Computer Science and Human-Computer Interaction Lab, University of Maryland STM 3 rd Master Class November 7-9, 2011 Adelphi, MD, USA Links from this talk: bit.ly/stmwant 1

Researchers want to… 1.Find a specific paper 2.Explore a research area 3.Do retrospective analysis 4.Share their results 2

1. Find a specific paper Metadata or PDF? From memory (search) From reference list – DOI/URL – Search 3

2. Exploring a research area Foundations Emerging research topics State of the art/open problems Collaborations & relationships between Communities Field evolution Easily understandable surveys 4

User requirements Control over the paper collection – Choose custom subset via query, then iteratively drill down, filter, & refine Overview either as visualization or text statistics – Orient within subset Easy to understand metrics for identifying interesting papers – Ranking & filtering Create groups & annotate with findings – Organize discovery process – Share results 5

Action Science Explorer Bibliometric lexical link mining to create a citation network and citation context Network clustering and multi-document summarization to extract key points Potent network analysis and visualization tools 6

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Reference management & grouping 8

Citation network overview Communities, outliers, invalid data 9

Statistics & visualization Network statistics – Degree – Betweenness – Closeness – Pagerank Attributes – Year – Downloads – Citations – References 10

Field evolution 11

Citation context & summarization Citation context – Key contributions – Critical reception – Citations to subsequent/similar work Hyperlinked citations in text – See surrounding context of citation – View cited papers while reading Multi-document summarization – Citation context – Abstract – Full text 12

3. Retrospective analysis Automatic collection & processing of bibliometric data Easy access to visual analytic tools for finding clusters, trends, outliers Communities for sharing data, tools, & results 13

STICK Project Scientific, data-driven way to track innovations – Vs. current expert-based, time consuming approaches (e.g., Gartner’s Hype Cycle, tire track diagrams) Includes both concept and product forms – Study relationships between Study the innovation ecosystem – Organizations & people – Both those producing & using innovations stick.ischool.umd.edu 14

Case study: tree visualization Problem: Traditional 2D node-link diagrams of trees become too large Solutions: – Treemaps: Nested Rectangles – Cone Trees: 3D Interactive Animations – Hyperbolic Trees: Focus + Context Measures: – Papers, articles, patents, citations,… – Press releases, blog posts, tweets,… – Users, downloads, sales,… 15

Treemaps: nested rectangles 16

Smartmoney MarketMap Feb 27, 2007 smartmoney.com/marketmap 17

Cone trees: 3D interactive animations Robertson, G. G., Card, S. K., and Mackinlay, J. D., Information visualization using 3D interactive animation, Communications of the ACM, 36, 4 (1993), Robertson, G. G., Mackinlay, J. D., and Card, S. K., Cone trees: Animated 3D visualizations of hierarchical information, Proc. ACM SIGCHI Conference on Human Factors in Computing Systems, ACM Press, New York, (April 1991),

Hyperbolic trees: focus & context Lamping, J. and Rao, R., Laying out and visualizing large trees using a hyper-bolic space, Proc. 7th Annual ACM symposium on User Interface Software and Technology, ACM Press, New York (1994), Lamping, J., Rao, R., and Pirolli, P., A focus+context technique based on hy-perbolic geometry for visualizing large hierarchies, Proc. SIGCHI Conference on Human Factors in Computing Systems, ACM Press, New York (1995),

Case study: tree visualization impact TM=Treemaps CT=Cone Trees HT=Hyperbolic Trees Trade Press Articles Academic Papers Patents 20

Case study: tree visualization citations TM=Treemaps CT=Cone Trees HT=Hyperbolic Trees Academic Papers Patents 21

Case study: business intelligence Year Proquest News Co-occurrence of concepts with organizations Data Mining National Security Agency White House FBI AT&T American Civil Liberties Union Electronic Frontier Foundation Dept. of Homeland Security CIA 22

Business Intelligence Matrix showing Co- Occurrence of concepts and entities 23

Business Intelligence : (subset) 24

Business Intelligence : Data mining NSA CIA FBI White House Pentagon DOD DHS AT&T ACLU EFF Senate Judiciary Committee 25

Business Intelligence : Tech1 Google Yahoo Stanford Apple Tech2 IBM, Cognos Microsoft Oracle Finance NASDAQ NYSE SEC NCR MicroStrategy 26

Business Intelligence : Air Force Army Navy GSA UMD* 27

STICK Process News Dissertation Academic Patent Blogs Identify concepts Query data sources Processing Automatic entity recognition Crowd-sourced verification Co-occurrence networks Visualizing & analyzing Overall statistics Co-occurrence networks Network evolution Sharing results 28

4. Sharing results Easily usable metadata (BibTeX, EndNote, etc.) Collaborative authoring Online communities 29

Collaborative literature reviews Organized references Annotated PDFs 30

Shared data & analysis repositories stick.ischool.umd.edu/community 31

Researchers want to… 1.Find a specific paper 2.Explore a research area 3.Do retrospective analysis 4.Share their results 32

What Researchers Want Cody Dunne Dept. of Computer Science and Human-Computer Interaction Lab, University of Maryland This work has been partially supported by NSF grants IIS (ASE) and SBE (STICK) Links from this talk: bit.ly/stmwant 33