The Science of Science (Sci2) Tool and Its Utility for Research ... - VW

The Science of Science (Sci 2 ) Tool andIts Utility for ResearchKaty Börner and the Cyberinfrastructure forNetwork Science CenterSchool of Library and Information ScienceIndiana University, Bloomington, INkaty@indiana.eduWith special thanks to Kevin W. Boyack, Micah Linnemeier,Russell J. Duhon, Patrick Phillips, Joseph Biberstine, Chintan TankNianli Ma, Angela M. Zoss, Hanning Guo, Mark A. Price,Scott WeingartNetworks and Complex Systems Talk Series, IUBDecember 7 th , 2009Overview1. Plug-and-Play Macroscopes2. Sci2 Tool Introduction and Demos3. Sample Science of Science Studies

The Changing Scientific LandscapeStar Scientist -> Research Teams: In former times, science was driven by key scientists. Today, science is drivenby effectively collaborating co-author teams often comprising expertise from multiple disciplines and severalgeospatial locations (Börner, Dall'Asta, Ke, & Vespignani, 2005; Shneiderman, 2008).Users -> Contributors: Web 2.0 technologies empower anybody to contribute to Wikipedia and to exchangeimages and videos via Fickr and YouTube. WikiSpecies, WikiProfessionals, or WikiProteins combine wiki andsemantic technology in support of real time community annotation of scientific datasets (Mons et al., 2008).Cross-disciplinary: The best tools frequently borrow and synergistically combine methods and techniques fromdifferent disciplines of science and empower interdisciplinary and/or international teams of researchers,practitioners, or educators to fine-tune and interpret results collectively.One Specimen -> Data Streams: Microscopes and telescopes were originally used to study one specimen at atime. Today, many researchers must make sense of massive streams of multiple types of data with differentformats, dynamics, and origin.Static Instrument -> Evolving Cyberinfrastructure (CI): The importance of hardware instruments that arerather static and expensive decreases relative to software infrastructures that are highly flexible andcontinuously evolving according to the needs of different sciences. Some of the most successful services andtools are decentralized increasing scalability and fault tolerance.Modularity: The design of software modules with well defined functionality that can be flexibly combined helpsreduce costs, makes it possible to have many contribute, and increases flexibility in tool development,augmentation, and customization.Standardization: Adoption of standards speeds up development as existing code can be leveraged. It helps poolresources, supports interoperability, but also eases the migration from research code to production code andhence the transfer of research results into industry applications and products.Open data and open code: Lets anybody check, improve, or repurpose code and eases the replication of scientificstudies.Microscopes, Telescopes, and MacrocopesJust as the microscope empowered our naked eyes to see cells, microbes, and virusesthereby advancing the progress of biology and medicine or the telescope opened ourminds to the immensity of the cosmos and has prepared mankind for the conquest ofspace, macroscopes promise to help us cope with another infinite: the infinitely complex.Macroscopes give us a ‘vision of the whole’ and help us ‘synthesize’. They let us detectpatterns, trends, outliers, and access details in the landscape of science. Instead of makingthings larger or smaller, macroscopes let us observe what is at once too great, too slow, ortoo complex for our eyes.

Desirable Features of Plug-and-Play MacroscopesDivision of Labor: Ideally, labor is divided in a way that the expertise and skills of computerscientists are utilized for the design of standardized, modular, easy to maintain and extend “corearchitecture”. Dataset and algorithm plugins, i.e., the “filling”, are initially provided by those thatcare and know most about the data and developed the algorithms: the domain experts.Ease of Use: As most plugin contributions and usage will come from non-computer scientists it mustbe possible to contribute, share, and use new plugins without writing one line of code. Wizarddrivenintegration of new algorithms and data sets by domain experts, sharing via email or onlinesites, deploying plugins by adding them to the ‘plugin’ directory, and running them via a Menudriven user interfaces (as used in Word processing systems or Web browsers) seems to work well.Plugin Content and Interfaces: Should a plugin represent one algorithm or an entire tool? Whatabout data converters needed to make the output of one algorithm compatible with the input ofthe next? Should those be part of the algorithm plugin or should they be packaged separately?Supported (Central) Data Models: Some tools use a central data model to which all algorithmsconform, e.g., Cytoscape, see Related Work section. Other tools support many internal datamodels and provide an extensive set of data converters, e.g., Network Workbench, see below.The former often speeds up execution and visual rendering while the latter eases the integrationof new algorithms. In addition, most tools support an extensive set of input and output formats.Core vs. Plugins: As will be shown, the “core architecture” and the “plugin filling” can beimplemented as sets of plugin bundles. Answers to questions such as: “Should the graphical userinterface (GUI), interface menu, scheduler, or data manager be part of the core or its filling?” willdepend on the type of tools and services to be delivered.Supported Platforms: If the software is to be used via Web interfaces then Web services need to beimplemented. If a majority of domain experts prefers a stand-alone tool running on a specificoperating system then a different deployment is necessary.Hubble telescope has cost a total of about $10billion over its 20 years of operation. Ed Weiler,HST space science chief, computed that this"...equates to about two cents per week perAmerican taxpayer over 20 years" – a true bargain.

IlluminatedDiagram DisplayW. Bradford Paley,Kevin W. Boyack,Richard Kalvans, andKaty Börner (2007)Mapping,Illuminating, andInteracting withScience.SIGGRAPH 2007,San Diego, CA.

Overview1. Plug-and-Play Macroscopes2. Sci2 Tool Introduction and Demos3. Sample Science of Science Studies

Open Data and Open CodeStudying Individual, Local, and Global Flows and Activity PatternsDesign comprehensive databases that capture relevant data andcyberinfrastructures that can be used to make sense of this data(stream).Science studies can be conducted at different levels:‣ local (individual),‣ meso (local, e.g., one institute, one funding agency), or‣ global level (all of science or world wide).Using‣ Statistical Analysis/Profiling‣ Temporal Analysis (When)‣ Geospatial Analysis (Where)‣ Topical Analysis (What)‣ Network Analysis (With Whom?)CI for a Science of Science StudiesScholarly Database: 23 million scholarly recordshttp://sdb.slis.indiana.eduInformation Visualization Cyberinfrastructurehttp://iv.slis.indiana.eduNetwork Workbench Tool + Community Wikihttp://nwb.slis.indiana.eduSci 2 Tool and Science of Science CI Portalhttp://sci.slis.indiana.eduEpidemics Cyberinfrastructurehttp://epic.slis.indiana.edu/18

Macroscope DesignNetwork Workbench ToolA large-scale networkanalysis, modeling andvisualization toolkit forbiomedical, social science andphysics research.Science of Science Toolfor science of science studiesat the individual, local, orglobal level for temporal,geospatial, semantic, ornetwork analysis and vis.Epidemics Research ToolAn open computationalinfrastructure for epidemicsresearch.http://nwb.slis.indiana.edu http://sci.slis.indiana.edu http://epic.slis.indiana.eduCIShell.org19Sci 2 Toolhttp://sci.slis.indiana.edu“Open Code for S&T Assessment”Branded OSGi/CIShell based tool with NWB pluginsand many new plugins.GUESS Network VisSci MapsHorizontal Time GraphsBörner, Katy, Huang, Weixia (Bonnie), Linnemeier, Micah, Duhon, Russell Jackson, Phillips, Patrick, Ma,Nianli, Zoss, Angela, Guo, Hanning & Price, Mark. (2009). Rete-Netzwerk-Red: Analyzing andVisualizing Scholarly Networks Using the Scholarly Database and the Network Workbench Tool.Proceedings of ISSI 2009: 12th International Conference on Scientometrics and Informetrics, Rio de Janeiro,Brazil, July 14-17 . Vol. 2, pp. 619-630.

Sci 2 ToolGeo MapsCircular HierarchyTopic Mapping: UCSD Science Map22

Sci 2 Tool: Supported Data FormatsPersonal Bibliographies‣ Bibtex (.bib)‣ Endnote Export Format (.enw)Data Providers‣ Web of Science by Thomson Scientific/Reuters (.isi)‣ Scopus by Elsevier (.scopus)‣ Google Scholar (access via Publish or Perish save as CSV, Bibtex,EndNote)‣ Awards Search by National Science Foundation (.nsf)Scholarly Database (all text files are saved as .csv)‣ Medline publications by National Library of Medicine‣ NIH funding awards by the National Institutes of Health(NIH)‣ NSF funding awards by the National Science Foundation (NSF)‣ U.S. patents by the United States Patent and Trademark Office(USPTO)‣ Medline papers – NIH FundingNetwork Formats‣ NWB (.nwb)‣ Pajek (.net)‣ GraphML (.xml or.graphml)‣ XGMML (.xml)Burst Analysis Format‣ Burst (.burst)Other Formats‣ CSV (.csv)‣ Edgelist (.edge)‣ Pajek (.mat)‣ TreeML (.xml)23Sci 2 Tool: AlgorithmsSee https://nwb.slis.indiana.edu/communityPreprocessingExtract Top N% RecordsExtract Top N RecordsNormalize TextSlice Table by Line---------------------------------------------Extract Top NodesExtract Nodes Above or Below ValueDelete Isolates---------------------------------------------Extract top EdgesExtract Edges Above or Below ValueRemove Self LoopsTrim by DegreeMST-Pathfinder Network ScalingFast Pathfinder Network Scaling---------------------------------------------Snowball Sampling (in nodes)Node SamplingEdge Sampling---------------------------------------------SymmetrizeDichotomizeMultipartite Joining---------------------------------------------Geocoder---------------------------------------------Extract ZIP CodeModelingRandom GraphWatts-StrogatzSmall WorldBarabási-Albert Scale-FreeTARLAnalysisNetwork Analysis Toolkit (NAT)Unweighted & UndirectedNode DegreeDegree Distribution---------------------------------------------K-Nearest Neighbor (Java)Watts-Strogatz Clustering CoefficientWatts Strogatz Clustering Coefficient over K---------------------------------------------DiameterAverage Shortest PathShortest Path DistributionNode Betweenness Centrality---------------------------------------------Weak Component ClusteringGlobal Connected Components---------------------------------------------Extract K-CoreAnnotate K-Coreness---------------------------------------------HITSWeighted & UndirectedClustering CoefficientNearest Neighbor DegreeStrength vs DegreeDegree & StrengthAverage Weight vs End-point DegreeStrength DistributionWeight DistributionRandomize Weights---------------------------------------------Blondel Community Detection---------------------------------------------HITSUnweighted & DirectedNode IndegreeNode OutdegreeIndegree DistributionOutdegree Distribution---------------------------------------------K-Nearest NeighborSingle Node in-Out Degree Correlations---------------------------------------------Dyad ReciprocityArc ReciprocityAdjacency Transitivity---------------------------------------------Weak Component ClusteringStrong Component Clustering---------------------------------------------24

Sci 2 Tool: Algorithms cont.See https://nwb.slis.indiana.edu/community--------------------------------Extract K-CoreAnnotate K-Coreness--------------------------------HITSPageRankWeighted & DirectedHITSWeighted PageRankTextualBurst DetectionVisualizationGnuPlotGUESSImage Viewer---------------------------------------------Radial Tree/Graph (prefuse alpha)Radial Tree/Graph with Annotation(prefuse beta)Tree View (prefuse beta)Tree Map (prefuse beta)Force Directed with Annotation(prefuse beta)Fruchterman-Reingold with Annotation(prefuse beta)---------------------------------------------DrL (VxOrd)Specified (prefuse beta)---------------------------------------------Horizontal Line GraphCircular HierarchyGeo Map (Circle Annotation Style)Geo Map (Colored-Region Annotation Style)ScientometricsRemove ISI Duplicate RecordsRemove Rows with Multitudinous FieldsDetect Duplicate NodesUpdate Network by Merging Nodes---------------------------------------------Extract Directed NetworkExtract Paper Citation NetworkExtract Author Paper Network---------------------------------------------Extract Co-Occurrence NetworkExtract Word Co-Occurrence NetworkExtract Co-Author NetworkExtract Reference Co-Occurrence(Bibliographic Coupling) Network---------------------------------------------Extract Document Co-Citation Network25NWB=Sci 2 Tool: Output Formats‣ NWB tool can be used for data conversion. Supported output formats comprise:‣ CSV (.csv)‣ NWB (.nwb)‣ Pajek (.net)‣ Pajek (.mat)‣ GraphML (.xml or .graphml)‣ XGMML (.xml)‣ GUESSSupports export of images intocommon image file formats.‣ Horizontal Bar Graphs‣ saves out raster and ps files.26

Sample Study – NSF Funding of STEMUsing NSF Awards Search:http://www.nsf.gov/awardsearchdownload relevant NSF awardsthat have “stem” and “education”in title, abstract, and awards.Active awards only.Number of awards: 1,340Total awarded amount to date:$1,347,802,833Retrieved on Oct 18, 2009Federal K-12 STEM Education Program Funding in 2006SOURCE: Department of Education, Report of the Academic Competitiveness Council, 2007Search for awards that have “stem” and “education”in title, abstract, and awards.Active awards only. Query run on 10/18/2009.

Top-10 Projects with highest Award Amount to Date1340 Funded ProjectsHorizontal Bar GraphsHorizontal Line Graph was selected.Input Parameters:Start Date: Start DateSize By: Awarded Amount to DateLabel: TitleEnd Date: Expiration DateText, e.g., titleArea size equals numericalvalue, e.g., award amount.Start dateEnd date

1,340 Funded ProjectsGeographic MapsGeocoder was selected.Input Parameters:Place Name Column: Organization StatePlace Type: STATE..........Geo Map (Circle Annotation Style) was selected.Input Parameters:Longitude: LongitudeSize Circles By: Awarded Amount to DateColor Circle Exteriors By: Awarded Amount to DateColor Circle Interiors By: None (no inner color)Exterior Color Scaling: LinearExterior Color Range: Green to RedInterior Color Range: Green to RedSize Scaling: LinearProjection: Albers Equal-Area ConicMap: US StatesAuthor Name: K. BornerInterior Color Scaling: LinearLatitude: LatitudeWhat Co-PI Networks Exist?Extract Directed Network was selected.Input Parameters:Source Column: Principal InvestigatorText Delimiter: |Target Column: Co-PI Name(s)..........Network Analysis Toolkit (NAT) was selected.Nodes: 3225Isolated nodes: 276Edges: 2265Average total degree: 1.4047Average in degree: 0.7023Average out degree: 0.7023..........Delete Isolates was selected...........Node Degree was selected...........Weak Component Clustering was selected.Number of top clusters: 10722 clusters found, generating graphsfor the top 10 clusters.Giant component has 39 nodesNext largest networks have 35, 17, 16 nodes

Co-PI Networks – Giant ComponentNodes = investigatorsSize and color coded by number of collaborators (degree)Directed edges from PI to Co-PIWhat Projects Fund Which PIs?Extract Directed Network was selected.Input Parameters:Source Column: TitleText Delimiter: |Target Column: Principal Investigator..........Network Analysis Toolkit (NAT) was selected.Nodes: 2478Isolated nodes: 0Edges: 1337Average total degree: 1.0791Average in degree: 0.5395Average out degree: 0.5395This graph is not weakly connected.There are 1144 weakly connected components. (0 isolates)The largest connected component consists of 14 nodes.Density (disregarding weights): 0.0002..........Node Indegree was selected...........Node Outdegree was selected...........GUESS

What Projects Fund Which PIs - ZoomWhat Programs at NSF are Co-Funding STEM?Extract Co-Occurrence Network was selected.Input Parameters:Text Delimiter: |Column Name: Program(s)..........Node Degree was selected...........Network Analysis Toolkit (NAT) was selected.Nodes: 226Isolated nodes: 71Edges: 483No self loops were discovered.Average degree: 4.2743Density (disregarding weights): 0.019..........GUESS..........Weak Component Clustering was selected.79 clusters found..........Network Analysis Toolkit (NAT) was selected.Nodes: 135Isolated nodes: 0Edges: 467No self loops were discovered.Average degree: 6.9185Density (disregarding weights): 0.0516..........GUESS

What Organizations are funded by what NSF Programs?Color and size coding bynumber of NSF programs thatfund these organizations.Institutions which are fundedby 10 or more programs arelabeled.OrganizationNSF ProgramWhat NSF Programs fund how many Organizations?Color and size coding bynumber of organizations thatare funded by these programs.NSF programs which fund 10or more organizations arelabeled.OrganizationNSF Program

http://sci.slis.indiana.eduOverview1. Plug-and-Play Macroscopes2. Sci2 Tool Introduction and Demos3. Sample Science of Science Studies

Type of Analysis vs. Level of AnalysisMicro/Individual(1-100 records)Meso/Local(101–10,000 records)Macro/Global(10,000 < records)StatisticalAnalysis/ProfilingIndividual person andtheir expertise profilesLarger labs, centers,universities, researchdomains, or statesAll of NSF, all of USA,all of science.Temporal Analysis(When)Funding portfolio ofone individualMapping topic burstsin 20-years of PNAS113 Years of PhysicsResearchGeospatial Analysis(Where)Career trajectory of oneindividualMapping a statesintellectual landscapePNAS PublicationsTopical Analysis(What)Base knowledge fromwhich one grant draws.Knowledge flows inChemistry researchVxOrd/Topic maps ofNIH fundingNetwork Analysis(With Whom?)NSF Co-PI network ofone individualCo-author networkNSF’s core competency43Type of Analysis vs. Level of AnalysisMicro/Individual(1-100 records)Meso/Local(101–10,000 records)Macro/Global(10,000 < records)StatisticalAnalysis/ProfilingIndividual person andtheir expertise profilesLarger labs, centers,universities, researchdomains, or statesAll of NSF, all of USA,all of science.Temporal Analysis(When)Funding portfolio ofone individualMapping topic burstsin 20-years of PNAS113 Years of PhysicsResearchGeospatial Analysis(Where)Career trajectory of oneindividualMapping a statesintellectual landscapePNAS publciationsTopical Analysis(What)Base knowledge fromwhich one grant draws.Knowledge flows inChemistry researchVxOrd/Topic maps ofNIH fundingNetwork Analysis(With Whom?)NSF Co-PI network ofone individualCo-author networkNIH’s core competency44

Mapping Indiana’s Intellectual SpaceIdentifyGeospatial/Network‣Analysis Pockets of innovation2001-2006, BioMed, IN Scope‣ Pathways from ideas to productsAcademic-Industry collaborations and knowledge diffusion‣ Interplay of industry and academia45Mapping Topic BurstsCo-word space ofthe top 50 highlyfrequent and burstywords used in thetop 10% mosthighly cited PNASpublications in1982-2001.Mane & Börner. (2004)PNAS, 101(Suppl. 1):5287-5290.Temporal/Topical Analysis1982-2001, US, PNAS (BioMed) ScopeTopic evolution and bursts46

Spatio-Temporal Information Production and Consumption of Major U.S.Research InstitutionsBörner, Katy, Penumarthy, Shashikant, Meiss, Mark and Ke, Weimao. (2006)Mapping the Diffusion of Scholarly Knowledge Among Major U.S. ResearchInstitutions. Scientometrics. 68(3), pp. 415-426.Research questions:1. Does space still matterin the Internet age?2. Does one still have tostudy and work at major researchinstitutions in order toTemporal/Geospatialhave access toAnalysishigh quality data and expertise1982-2001,andUS,to producePNAShigh(BioMed) Scopequality research?Citation impact and knowledge diffusion3. Does the Internet lead to more global citationpatterns, i.e., more citation links between papersproduced at geographically distant researchinstructions?Contributions:‣ Answer to Qs 1 + 2 is YES.‣ Answer to Qs 3 is NO.‣ Novel approach to analyzing the dual role ofinstitutions as information producers andconsumers and to study and visualize the diffusionof information among them.47Research Collaborations by the Chinese Academy of SciencesBy Weixia (Bonnie) Huang, Russell J. Duhon, Elisha F. Hardy, Katy Börner, Indiana University, USAGeospatial AnalysisWorld, Chinese Academy of ScienceCollaboration and knowledge diffusion via co-author networksThis map highlights the research co-authorship collaborations of the Chinese Academy ofSciences with locations in China and countries around the world. The large geographic mapshows the research collaborations of all CAS institutes. Each smaller geographic map shows theresearch collaborations by the CAS researchers in one province-level administrative division.Collaborations between CAS researchers are not included in the data. On each map, locationsare colored on a logarithmic scale by the number of collaborations from red to yellow. Thedarkest red is 3,395 collaborations by all of CAS with researchers in Beijing. Also, flow lines aredrawn from the location of focus to all locations collaborated with. The width of the flow line islinearly proportional to the number of collaborations with the locations it goes to, with thesmallest flow lines representing one collaboration and the largest representing differing amountson each geographic map.48

Individual Co-PI NetworkKe & Börner, (2006)Temporal/Network Analysis2001-2006, US, InfoVis ScopeEvolving project-PI networks49Mapping the Evolution of Co-Authorship NetworksKe, Visvanath & Börner, (2004) Won 1st price at the IEEE InfoVis Contest.50

Temporal/Network Analysis1986-2004, US, InfoVis ScopeEvolving co-author networks and author impact51Studying the Emerging Global Brain: Analyzing and Visualizing the Impact ofCo-Authorship TeamsBörner, Dall’Asta, Ke & Vespignani (2005) Complexity, 10(4):58-67.Research question:• Is science driven by prolific single expertsor by high-impact co-authorship teams?Contributions:• New approach to allocate citationalcredit.• Novel weighted graph representation.Temporal/Network Analysis• Visualization of the growth 1986-2004, of weighted US, InfoVis Scopeco-author network. Impact of co-author relations• Centrality measures to identify authorimpact.• Global statistical analysis of paperproduction and citations in correlationwith co-authorship team size over time.• Local, author-centered entropy measure.52

113 Years of Physical Reviewhttp://scimaps.org/dev/map_detail.php?map_id=171Bruce W. Herr II and Russell Duhon (Data Mining & Visualization), Elisha F. Hardy (Graphic Design), ShashikantPenumarthy (Data Preparation) and Katy Börner (Concept)Topical/Network Analysis1893-2006, World, Physics ScopeTopic evolution, knowledge diffusion, Nobel predictions53Topical Composition and Knowledge Flow Patterns in Chemistry Researchfor 1974 and 2004Kevin W. Boyack, Katy Börner, & Richard Klavans (2007)Temporal/Network Analysis1974-2004, US, NSF Chemistry Funding ScopeMapping the main structure, topic evolution, and knowledge diffusion54

Interactive Maps of Science – NIH FundingGoogle maps with charts and tablesTopical/Network Analysis2007, US, NIH Funding ScopeMapping the main structure color coded by funding institutehttp://scimaps.org/maps/nih/2007Mapping Transdisciplinary Tobacco Use ResearchCenters PublicationsCompare R01 investigator based funding with TTURCCenter awards in terms of number of publications andevolving co-author networks.Zoss & Börner, forthcoming.Temporal/Network Analysis1998-2009, US, Tobacco Research ScopeComparing co-author networks with different funding56

Reference MapperDuhon & Börner, forthcoming.Topical/Network Analysis2009, US, NSF FundingGrouping interdiciplinary funding proposals for review57DataThousands of fulltext,locationspecific,timestamped jobpostings fromNature Jobs andScience CareersRSS feeds. Theposts have beenparsed and storedin a relationalMySQL database.Jobs have beengeolocated on aGoogle map.Mapping S&T Job Market Data in Real Time – GeoMapAngela Zoss, Michael Conover

The UCSD Mapof Science usedhere is the productof a large study byresearchers at theUniversity ofCalifornia - SanDiego using 7.2million papers andover 16,000separate journals,proceedings, andseries fromThomson Scientificand Scopus overthe five year periodfrom 2001 to 2005.The UCSD Map ofScience used here isthe product of a largestudy by researchers atthe University ofCalifornia - San Diegousing 7.2 millionpapers and over16,000 separatejournals, proceedings,and series fromThomson Scientificand Scopus over thefive year period from2001 to 2005.Jobs were associatedwith nodes in the Mapof Science by way ofkeyword extraction.Mapping S&T Job Market Data in Real Time – SciMapAngela Zoss, Michael Conover

Geospatial and Topical/Network Analysis2008-2009, English speaking, Job RSS feedsSupport interactive search for job postings in geo and topic space.Mapping Science Exhibit – 10 Iterations in 10 yearshttp://scimaps.orgThe Power of Maps (2005) Science Maps for Economic Decision Makers (2008)The Power of Reference Systems (2006)Science Maps for Science Policy Makers (2009)Science Maps for Scholars (2010)Science Maps as Visual Interfaces to Digital Libraries (2011)Science Maps for Kids (2012)Science Forecasts (2013)The Power of Forecasts (2007) Telling Lies With Science Maps (2014)Exhibit has been shown in 72 venues on four continents. Currently at- NSF, 10th Floor, 4201 Wilson Boulevard, Arlington, VA- Wallenberg Hall, Stanford University, CA- Center of Advanced European Studies and Research, Bonn, Germany- Science Train, Germany.62

Debut of 5 th Iteration of Mapping Science Exhibit at MEDIA X was on May 18, 2009 at Wallenberg Hall,Stanford University, http://mediax.stanford.edu, http://scaleindependentthought.typepad.com/photos/scimaps63This is the only mockup in this slide show.Everything else is available today.

All papers, maps, cyberinfrastructures, talks, press are linkedfrom http://cns.slis.indiana.edu