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Data at Work: Supporting Sharing in Science and Engineering. ( Birnholtz & Bietz , 2003) Adam Worrall LIS 6269 Seminar in Information Science 3/30/2010. Data and data sharing. Information science needs “a better understanding of the use of data in practice” (p. 339)

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data at work supporting sharing in science and engineering

Data at Work: Supporting Sharing in Science and Engineering

(Birnholtz & Bietz, 2003)

Adam Worrall

LIS 6269 Seminar in Information Science

3/30/2010

data and data sharing
Data and data sharing
  • Information science needs “a better understanding of the use of data in practice” (p. 339)
  • Data fundamentally “different from documents”(p. 339)
  • Data sharing important (p. 339-340)
    • “Openness” of scientific process
      • Confirm findings, replicate results
      • Build on previous work
    • Large data sets require distributed collaboration
      • Collaboratories, e-science

LIS 6269 Seminar in Information Science

data sharing problems
Data sharing problems
  • Collaborating and sharing of data should be encouraged
    • But it “is not easy” to do so (p. 340)
  • Why?
  • Lack of willingness to share, trust others
    • Competition for “revenue” (p. 345)
    • Restrictions imposed by commercial interests
    • Trust of sources
    • Trust of others; will they use data well?(see also Van House, 2003)

LIS 6269 Seminar in Information Science

data sharing problems1
Data sharing problems
  • Reasons (continued)
    • Problems with finding shared data
      • Negotiate access
    • Difficulties interpreting and using shared data
      • How collected?
      • How analyzed?
      • What format?
      • Metadata
        • Format, encoding, controlled vocabularies, etc.
      • Data quality (see also Stvilia et al., 2008; Wand & Wang, 1996)
      • “Tacit” knowledge of data (p. 340)

LIS 6269 Seminar in Information Science

methodology
Methodology
  • Three disciplines
    • Earthquake engineering
    • HIV / AIDS research
    • Space physics
  • Observation and interviews of all three, surveys of earthquake engineers
  • Inductive, grounded approach
    • Claimed they made “no assumptions about the purpose of data” (p. 340)

LIS 6269 Seminar in Information Science

data dimensions
Data dimensions
  • Two dimensions identified (p. 341)
    • “news” vs. “confirmation”
      • Confirm existing or expected results
      • Something unexpected needing further exploration
      • Something not fitting expected / prevailing model
    • “streams” vs. “events”
      • Longitudinal vs. cross-sectional
      • Context for data may change
      • Rate of data different
  • Different disciplines, different data use

LIS 6269 Seminar in Information Science

data s role in scientific communities
Data’s role in scientific communities
  • Defines boundaries between communities
    • Experimental, deductive
      • More possessive of data
    • Theoretical, inductive
      • More interested in sharing data
      • More interested in using shared data
    • Increasing blurring of boundaries in some fields
  • Provides gateway into communities
    • Access to data, knowledge about data is “valuable resource” (p. 343)
    • Those who control data and knowledge, and access to it, act as “gatekeepers of the field” (p. 343)

LIS 6269 Seminar in Information Science

data s role in scientific communities1
Data’s role in scientific communities
  • Indicates status in community
    • Using one’s own data “seen as ‘better’” than using public data (p. 344)
      • “Analyzing somebody else’s data … arguably ‘counts’ for less” (p. 344)
    • Higher quality data means better reputation
      • For researchers, research groups, and institutions
  • Enables indoctrination into community
    • Students often work with collecting, managing data
    • Degree of sharing of responsibilities differs between fields, sometimes by seniority in field

LIS 6269 Seminar in Information Science

categories of data uses p 345
Categories of data uses (p. 345)
  • Identified with an eye to “revenue” from use
    • Benefits: reputation, publications, funding, etc.
  • “A scientist’s data set is her [or his] castle”
    • Researcher wants to and is able to use data to solve a particular problem or question
    • Will increase revenue
  • “With a little help from my friends”
    • Researcher wants to use data, but needs to collaborate with others in order to do so successfully
    • Data can be shared privately
      • Limited risk (but still some risk)
    • Will increase revenue

LIS 6269 Seminar in Information Science

categories of data uses p 3451
Categories of data uses (p. 345)
  • “One scientist’s junk is another one’s treasure”
    • Researcher has no interest in using the data for a particular problem, but others do have interest
    • Sharing data will slightly increase revenue
    • May not be worth risk of losing other revenues
  • “D’oh!”
    • Researcher has not thought of a use, but it would be relevant to them and help them with a problem or question
    • Sharing data could be embarrassing, decrease revenue

LIS 6269 Seminar in Information Science

categories of data use
Categories of data use
  • Researchers will be less willing to share data unless incentives high, risks low
  • Data sharing follows social networks
  • Provide facilities for communication around abstractions of data sets
    • Encourage sharing and collaboration (category 2)
      • Extend researcher’s social network
    • Reduce risks of embarrassment (category 4)
      • Preliminary abstractions allow questions / comments before they are embarrassing
    • Increase incentives and benefits (categories 2 & 3)
      • Beyond boundaries of researcher’s community

LIS 6269 Seminar in Information Science

recommendations and conclusions
Recommendations and conclusions
  • Efforts to support “social interaction around data abstractions and the data themselves” should be made (p. 346)
  • Metadata should be augmented through “the sharing of supplementary materials” (i.e. abstractions) (p. 346)
  • Consideration of the “social and scientific roles of data” and how to support them necessary in future research (p. 346)
  • Better understanding of data abstractions needed (p. 347)

LIS 6269 Seminar in Information Science

issues with study and article
Issues with study and article
  • Bias towards natural sciences
    • Social scientists may use, share data differently
  • Only 3 disciplines studied, others may differ further
  • Generally coherent, but some parts hard to follow
    • Indoctrination examples appeared similar, despite what authors termed “critical” distinction (p. 344)
    • Promised “three aspects of the way data are used” but only discussed two dimensions (p. 341)
  • Limitations only discussed briefly

LIS 6269 Seminar in Information Science

questions comments

Questions, comments?

LIS 6269 Seminar in Information Science

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