Data at work supporting sharing in science and engineering
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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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