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Disciplines, Documents, and Data: Convergence and Divergence in the Scholarly Information Infrastructure Christine L. Borgman Professor & Presidential Chair in Information Studies University of California, Los Angeles i-Conference Plenary Presentation 17 October 2006 These slides are available under Creative Commons Non-commercial Attribution License, Christine L. Borgman, 2006 http://creativecommons.org Scholarly Information Infrastructure  Cyberinfrastructure, e-Science, e-Social Science, eHumanities, …e-Research  Goal: enable new forms of scholarship that are      information-intensive data-intensive distributed collaborative multi-disciplinary  Means: use information technology to  improve access to scholarly information  manage the “data deluge”  leverage data as a form of scholarly capital http://www.fairholme.qld.edu.au/images/Image66.jpg 2 Driving Forces  Technology push  Distributed access to content and computing resources  Tools and services for data collection, mining  Collaboration pull  Virtual organizations  Share distributed resources  Open science, open scholarship  Open access  “Author self-archiving”  “Author pays” journals  Institutional repositories…  Open source software http://www.tzanis.org/tzanisblog/archives/images/push-pull-thumb.jpg 3 e-Science infrastructure: Layered Model Content Digital Libraries Applications Space User Interfaces & Tools Scientific DBs Information & knowledge layer Middleware services layer ITC Infrastructure Processors, memory, network 4 Slide courtesy of Stephen Griffin, NSF, and Norman Wiseman, JISC Content layer  Documents  Publications: books, journals, conference papers, ...  Semi-formal: technical reports, working papers, proposals…  Unpublished: websites, blogs, wikis…  Data     Observational Computational Experimental Records  Composite objects http://www.medscape.com/content/2004/00/46/81/468129/art-mgm468129.fig1.jpg 5 Value chain of information  Links  Cited/citing documents  Publications to data sources  Data to publications in which reported  Across boundaries     Repositories Publisher databases Disciplines Countries Image: http://www.indexgeo.com.au/tech/asdd/discover.gif 6 How do publications enter the value chain? Function Legitimization Authority, quality, priority, trustworthiness Print Peer review Digital Peer review Dissemination Awareness, diffusion, publicity Publisher Pre-print distribution Copy Mail Publisher Pre-print distribution Post on Web Deposit Access, preservation, curation Availability, discovery, retrieval, persistence Library Library Publisher Repository Homepage 7 Role of data in the value chain  Scholarly capital  Human capital  Instrumentation  Data  Leverage research investments  Replicate, verify research findings  Ask new questions with extant data  Computational biology, chemistry  Longitudinal and comparative social research  Mining large bodies of text  Collaborative research  Data creation  Data sharing, reuse 8 9 10 11 12 13 Roman Forum, Western End, ca. 400AD, copyright Regents of the University of California 14 What are data? Technical definition: A reinterpretable representation of information in a formalized manner suitable for communication, interpretation, or processing. Examples of data include a sequence of bits, a table of numbers, the characters on a page, the recording of sounds made by a person speaking, or a moon rock specimen Reference Model for an Open Archival Information System (2002). Socio-technical definition: “alleged evidence” (Buckland, 2006) Image: http://cdiac.ornl.gov/oceans/NAtl_map.jpg 15 How do data enter the value chain? Function Legitimization Authority, quality, priority, trustworthiness Reported in a publication Contributed to a data repository Peer review in context Quality of method Evidence for conclusions Verify, reanalyze? Peer review Quality of metadata, documentation “test drive” the data Author reputation Dissemination Awareness, diffusion, publicity Description in a publication Repository publisher Access, preservation, curation Availability, discovery, retrieval, persistence Request to author Author maintains own data Author directs requestor to data source Repository Immediate access Embargo period Curation responsibility 16 Disciplinary differences in the content layer  Sciences  Social sciences  Humanities Image: Christine L. Borgman, 2005 17 Citation distribution of scientific literature 18 Graphs by Jillian Wallis, UCLA Science Citation Index coverage 19 SCI and Scopus Coverage 20 Citation distribution of social scientific literature 21 Social Sciences Citation Index coverage 22 SSCI and Scopus coverage 23 Citation distribution of humanities literature 24 Arts & Humanities Citation Index coverage 25 A&HCI and Scopus coverage 26 Disciplinary Comparison of Online Access to Literature  Business models  Web of Knowledge: Long tail  Scopus: Hits  Sciences  Steepest curve: shortest time span of use  Online access goes furthest into the tail  Most advantaged of the disciplines  Humanities  Shallowest curve: longest time span of use  Online access goes least far into the tail  Least advantaged of the disciplines  Mass digitization projects will favor the humanities 27 Scientific data  Examples  Ecology: weather, ground water, sensor readings, historical record  Medicine: xrays  Chemistry: protein structures  Astronomy: spectral surveys  Biology: specimens  Physics: events, objects  Documentation: Lab and field notebooks, spreadsheets  Sources  Generate own data  Acquire from collaborators, other scientists  Data repository 28 Social scientific data  Examples          Opinion polls Surveys, interviews Mass media Laboratory Experiments Field experiments Demographic records Census records Voting records Economic indicators  Sources      Generate own data Acquire from other scholars Data repositories: Social Surveys Government records Corporate records http://www.census.gov/population/cen2000/map02.gif 29 Humanities data  Examples           Newspapers Photographs Letters Diaries Books Articles Birth, death, marriage records Church records Court records School and college yearbooks Maps…  Sources     Search libraries, archives, public records Acquire from other scholars Data repositories: Beazley, Arts & Humanities Data Service (UK) Corporate records, mass media  http://ecai.org/silkroad/cultures/index.html 30 Data sources by discipline Research data generated within the field High Non-research data from external sources Sciences Social sciences Humanities Humanities Social sciences Sciences Medium Low 31 Incentives to share  Tradition of “open science”  Collaboration  Reciprocity  Recognition  Coercion: Required by funding agency, journal, university Image source: www.buffaloworks.us/ images/sharing%20orangs.jpg 32 Contributing to the content layer  Publications  Publisher dissemination, library access  “Author self-archiving” (Harnad)  Institutional or disciplinary repositories  Post online: personal, lab, department site  Contribution rates  Physics (arXiv): very high  4,000 new submissions/month  15,000 to 38,000 connections/hour  Other fields: about 15%  PubMed Central: less than 4% 33 Christine L. Borgman, 1995 Contributing to the content layer  Data  Maintain locally, release upon request  Maintain locally, post publicly, provide links from publications  Contribute to data repository  Discipline: Protein Databank, Survey Research center…  Institutional: University  Contribution, sharing rates  Mandatory: high  Optional: low Image source: www.buffaloworks.us/ images/sharing%20orangs.jpg http://buckminster.physics.sunysb.edu/images/ac60poly.jpg 34 Data sharing in science  Physics: shared by collaborators, not openly published  Genomics: deposit expected  Seismology: two-year embargo  Chemistry: highly contentious intellectual property  Ecology: many small, local projects, local data Image source: http://www.bbc.co.uk/schools/gcsebitesize/img/ict04datastorage.gif 35 Incentives not to share  Rewards for publication, not for data management  Effort to document data  Competition, priority of claims  Intellectual property  control of own resources  access to resources controlled by others Image source: www.buildingsrus.co.uk/.../ target1.htm 36 Sciences: Incentives not to share  Rewards for publication, not for data management  Effort to document data  Competition, priority of claims  Local, personal use: minimal  Shared use: extensive, follow metadata standards  Control until publication  Control until finished mining data  Control of own resources  Intellectual property  Collaborative resources  Authority to release  Multiple jurisdictions  Concerns for misuse, misinterpretation  Concerns for “free riders”  Resale value of products - chemical, drug data http://nick.omp.net/graphics/tongue.jpg 37 Social sciences: Incentives not to share  Rewards for publication, not for data management  Effort to document data  Local, personal use: minimal  Shared use: extensive, follow metadata standards  Personal data must be de-identified  Control until publication  Control until finished mining data  Competition, priority of claims  Intellectual property      Control of own resources  Collaborative resources  Authority to release  Multiple jurisdictions Concerns for misuse, misinterpretation Concerns for “free riders” Resale value of products - low Confidential data that cannot be released  Use of resources controlled by others  Acquire permission to use resources  Permissions may not be transferable http:www.fci.org 38 Humanities: Incentives not to share  Rewards for publication, not for data management  Effort to document data  Competition, priority of claims  Intellectual property  Local, personal use: minimal  Shared use: extensive, follow metadata standards  Control until publication  Control until finished mining sources  Control of own resources  Collaborative resources  Authority to release  Multiple jurisdictions  Concerns for misuse, misinterpretation  Concerns for “free riders”  Resale value of products - art, cultural heritage  Use of resources controlled by others  Acquire permission to use resources  Permissions may not be transferable  Permissions may be prohibitively expensive or unavailable http://www.fortunecity.com/millennium/galaxyway/375/14months/talking.jpg 39 Research frontiers in information  Cyberinfrastructure/e-Research  Infrastructure design and development  Changing nature of scholarship  Interaction of social, technical, economic, policy factors  Information  Expansion of sources and resources  Computational uses of content  Information as evidence  Society  Differential access to resources  Differential uses of resources  Institutions and policy  Roles of libraries, archives, museums  Institutional information infrastructure  Policy implemented by information technology 40 Education frontiers in information  Information professionals     Librarians Archivists Information architects Data scientists … Social questions Technical questions Policy questions Subject domain expertise Collaborative expertise Disciplines Information institutions Universities Private enterprise, publishers Funding agencies Policy makers 41  Information scholars       Convene stakeholders       http://www.nelsonmullins.com/legal-practice-area/Practice_Insets/Intellectual-Property-Inter.jpg Thank you! http://www.eng.ox.ac.uk/ http://www.rhodesscholar.org/ http:www.cens.ucla.edu New York Times 42