Towards a Data Network for Integrated Social Science Research

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Towards a Data Network for Integrated Social
Science Research

• Micah Altman
• Harvard University
• Archival Director, Henry A. Murray Research
  Archive
• Associate Director, Harvard-MIT Data Center
• Senior Research Scientist, Institute for
  Quantitative Social Sciences
• E micah_altman_at_harvard.eduW http//maltman.hmdc
  .harvard.edu/

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This Talk

• Why Social Science Data?
• Integrating Data Access -- Dataverse Network
• Replicated Institutional Preservation the
  Data-PASS Alliance for the Social Sciences
• Social Science and Cyberinfrastructure

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Related Work

• Papers
• M. Altman and G. King. A Proposed Standard for
  the Scholarly Citation of Quantitative Data,
  D-Lib, 13, 3/4 (March/April). 2007.
• M. Altman, et. al, Data Preservation Alliance
  for the Social Sciences A Model for
  Collaboration Proceedings of DigCcurr07, Chapel
  Hill. April 2007.
• G. King, An Introduction to the Dataverse
  Network as an Infrastructure for Data Sharing,
  Sociological Methods and Research, 32, 2
  (November, 2007) 173199.
• M. Altman , "A Fingerprint Method for
  Verification of Scientific Data" in, Advances in
  Systems, Computing Sciences and Software
  Engineering, (Proceedings of the International
  Conference on Systems, Computing Sciences and
  Software Engineering 2007) , Springer Verlag.
  Forthcoming 2008.
• Collaborators Co-conspirators
• Margaret Adams, Ken Bollen, Cavan Capps, Jonathan
  Crabtree, Darrell Donakowski, Myron Gutmann, Gary
  King, Lois Timms-Ferrarra, Marc Maynard, Amy
  Pienta
• Research Support
• Thanks to the Library of Congress (PANDP03-1),
  the National Institutes of Aging (P01
  AG17625-01), the National Science Foundation
  (SES-0318275, IIS-9874747), the Harvard
  University Library, the Institute for
  Quantitative SocialScience, the Harvard-MIT Data
  Center, and the Murray Research Archive.

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What is Digital Social-Science Data?

• DIGITAL
• Optical DVD, CD
• Magnetic Tapes, Floppies
• Paper cards, tapes
• SOCIAL SCIENCE
• Social class, crime, social movements, culture,
  folklore, family
• Economic wealth, prosperity, labor, business,
  equity
• Psychology cognition, attitudes, stereotypes
• Politicsjustice, democracy, public policy,
  public administration, international conflic
• DATA
• Raw measurements
• Numeric tables
• Administrative records ( email)
• Video and audio interviews, transcripts ( blogs)

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Data Access is the Key To Science

• Science is not (only) about being scientific
• Scientific progress requires community
  Competition and collaboration in the pursuit of
  common goals
• Without access to the same materials no
  community exists data is the nucleus of
  collaboration.
• The value of an article that cant be replicated
  ?
• Scholarly articles are summaries, not the actual
  research results
• But Data access is spotty by field, finding the
  data is still hard
• Hard for journal editors to verify.If you find
  it, how do you know its the same?
• Replication projects showmost published
  articles in social science cannot be
  replicated data is necessary for replication
  and versication

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Data Access is the Key To Democracy

• Statistics state-istics
• The state tax authority counting people,
  estimating wealth
• Reformers use data to assess the performance of
  the state
• Science informs public policy continually
• In modern democracy the public needs a direct
  source of information

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Why is Infrastructure for Data Needed?

• Accessibility
• Most large data sets in public archives
• Most data in published articles not accessible,
  results not replicable without the original
  author
• Most data sets from federal grants not publicly
  available
• Problems even with professional archives
• Data in different archives have different
  identifiers
• Archives change identifiers, links
• Changes to data are made identifiers are reused
  or removed old data are lost
• Data sets are not like books
• Static data files (even if on the web)
  unreadable after a few years
• When storage methods change some data sets are
  lost others have altered content!
• Why not Single Centralized infrastructure ?
• Single point of failure
• Impossible when data are heterogeneous in format,
  origin, size, effort needed to collect or
  analyze, IRB access rules, etc.
• Data producers want credit, control, and
  visibility
• Requirements
• Recognition, for data producers, distributors,
  related publishers
• Rule-based Public Distribution

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The Dataverse Network

• An Open-Source, Federated, Web 2.0 Data Network
• Gateway to over 20000 social science studies
  (worlds largest catalog)
• Web Virtual Hosting 2.0 Service-- Over 100
  virtual archives
• Federated access to other networks
• Unified access to major U.S. research data
  archives, government data
• Open service endowed hosting
• Open source GPL-Affero-3

• Discovery Services
• Simple fielded search
• Virtual collection browsing
• Management
• Ingest
• Curation review
• Virtual Hosting and administration
• Metadata delivery
• Descriptive and structural
• Provenance (chain-of custody metadata)
• Human and OAI interfaces
• Preservation
• Standards based
• Reformatting
• Universal Numeric Fingerprints
• Enhanced Delivery
• Replication
• Layered analysis services

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DVN Screenshots
http//dvn.iq.harvard.edu/
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Some Dataverse Uses

• Future Researchers discovery linking forward
  citation verification analysis
• Journals, for replication
• Authors, for their own data
• Teachers, in depth analysis
• Sections of scholarly organizations, to organize
  existing data
• Granting agencies
• Research centers
• Archives
• Major Research Projects
• Academic departments, universities, centers,
  libraries

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Fixing Data Citations

• Citations are a traditional formal mechanism to
  link together intellectual works
• Citations glue together Regulations,
  Publications, and Evidence
• But, lack of rules for citing numeric data
• No consistency in practice
• No fixed rules for copyeditors
• Sometimes in the list of references sometimes a
  casual mention in the text
• Sometimes the archive is noted
• Sometimes a version number exists
• Sometimes the version number is listed (if it
  exists)
• Archive numbers are sometimes given, if they
  exist
• Sometimes the author is noted
• Date of creation is sometimes given
• URLs often given, rarely persist
• Dates of access protect the researcher, do not
  help find the data
• The data may not be available publicly
• The data may no longer exist

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A New Citation Standard for Numeric Data
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DataPASS
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Future Replication as Institutional Insurance
Data-PASS Syndicated Storage Project

• External Causes of Preservation Failure
• Third party attacks
• Institutional funding
• Change in legal regimes
• Quis custodiet ipsos custodes?
• Unintentional curatorial modification
• Loss of institutional knowledge skills
• Intentional removal
• Change in institutional mission

• Schema drivencapture inter-archival
  preservation commitments
• Asymmetric resource commitments proportional
  to holdings
• Versioned versioned data and citations
• Integration LOCKSS DVN technology, archival
  workflows

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Future Social Science Data Deluge

• Collective holdings of all U.S. numeric social
  science data in all major data archives,
  government repositories estimated 10s of TB
• Ambient data increasingly becoming subject of
  social science research.
• Data deluge annually (2002 annual)
• Web (surface) 167 TB
• Radio 3,500 TB
• Television 69,000 TB
• Web (deep) 92000 TB
• Email (originals) 441,000 TB
• Telephone 18,000,000 TB

Or, what are you thinking?
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Whats Next New Social Science Examples

• From Social Science Research Computing
  Environment Project
• Assess need for high performance computing among
  social scientists at Harvard
• Prototype interfaces to make grid computing
  usable by social scientists
• Examples
• Harvesting and analysis of blogs for virtual
  political opinion surveys
• Continuous collection of CSPAN, real-time subject
  coding, continuous dissemination
• Cell phone data stream collection movement,
  proximity to others, social network analysis
• Participative goals-based redistricting
• Agent-based models of emerging institutions
• FMRI analyses of reaction to political and social
  scenarios
• Modal Features
• Analyses emerge through exploration and
  interactions
• Data collection from non-experimental, non
  instrumental, sources
• Increasing scale of data
• Compute limited
• Data confidentiality
• High-level analysis tools
• Remote collaboration is part of projects

Meta-features of social science messy data
an abundance of plausible models
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Social Science Research Infrastructure Challenges

• Social science challenges
• Few definitive answers
• Complex conceptual primitives
• Complex theories of behavior
• Reliance on observational data
• Specification uncertainty
• Changing evidence base (blogs, video,
  continuously recorded behavioral data)
• Some trends
• Compute-intensive inferential statistics
• Specification searches
• Sensitivity analyses
• Curse of dimensionality
• Data explosion
• Changing evidence base
• Agent-based models
• Important Gaps
• No tool covers entire scholarly research
  lifecycle
• Most not yet immature
• Poor integration across most tools

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For More Information
Dataverse Network Project http//TheData.Org Da
ta-PASS Alliance http//www.icpsr.umich.edu/DAT
APASS/ Contact me http//maltman.hmdc.harvard.e
du/