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Rdap13 Dharma Akmon The Role of Value in Data Practices
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Rdap13 Dharma Akmon The Role of Value in Data Practices


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Dharma Akmon, University of Michigan School of Information …

Dharma Akmon, University of Michigan School of Information

The Role of Value in Data Practices
Research Data Access & Preservation Summit 2013
Baltimore, MD April 4, 2013 #rdap13

Published in: Technology

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    • 2. MOTIVATION• Increasing attention to data as a valuable product of science• Scientists’ actions throughout data life cycle impacts significantly on what is available for preservation and reuse
    • 3. PREVIOUS WORK• Scientists withhold or inadequately manage data because: • Documentation is labor intensive and unrewarded (Birnholtz & Bietz, 2003; Campbell et al., 2002; Louis, Jones, & Campbell, 2002) • They are more concerned with publications (Borgman, Wallis, Mayernik, & Pepe, 2007) • They fear data contributions will not be recognized (Louis et al., 2002)
    • 4. PREVIOUS WORK CONT.• Social scientists reported they’d be more likely to document and deposit data if they thought data "would be used and have a broader public benefit" (Hedstrom & Niu, 2008)• “Shareable” data are those that are expected to have the greatest potential for generating new results (Cragin, Palmer, Carlson, & Witt, 2010)• Less likely to share high value or hard-won data (Tucker, 2009; Borgman, Wallis, & Enyedy, 2007)
    • 5. RESEARCH QUESTIONHow do scientists conceive of the value of theirdata, and how is this reflected in their data practices?• What uses for data are salient to scientists?• What time spans do scientists use to think about datas value?• How do scientists create data that are valuable and what do they do to make data accessible over time?
    • 6. SITE & METHODS• 3 small teams of scientists at an ecological field station• Teams differed across: • PI career stage • Methodological approach to research • Length of study • Funding source
    • 8. NUTRIENT UPTAKE IN STREAMS (NUS) TEAMName* Career Stage Discipline Project RoleElizabeth Assistant prof. Biogeochemistry PIJessica Assistant prof. Stream ecology PITina Graduate student Hydrogeology Graduate researcherCarolyn Undergraduate student Environmental studies Undergraduate researcherJanet Undergraduate student Chemistry Undergraduate researcher*pseudonyms are used to protect identities
    • 11. CONCEPTIONS OF DATA’S VALUE• Data exhibited primarily an instrumental value• Value conceptions made up of: • Assumptions about purposes for specific data at hand • Characteristics data needed to exhibit to meet those ends • Beneficiaries of data’s value • Timespan over which data would be valuable
    • 12. [. . .] if you think about it short-term it almost kindof seems meaningless. Like sometimes I actuallyfind myself getting caught up in that. I‟mlike, „Does it really matter what this exact sedgeis?‟ Like if it‟s Juncus balticus or Juncusnodosus, does it matter? But if you think about it inlong-term, it’s not just about that. [. . .] It’s notabout the little identifying plants [. . .] (Brooke, IM-UR).
    • 13. PURPOSE OF NUS STUDY• Addressing a gap in knowledge • How leaf litter affects nutrient uptake in streams• Supporting Hypotheses • Nutrient uptake depends on N:P on the leaves • As the leaves decompose, C:N and C:P increase and nutrient uptake in the different leaf treatments becomes more similar
    • 14. “Were doing it in this situation because we wantto test the mechanism. […] If it wasn‟t amechanism-driven question then it wouldn‟t beappropriate to ask it in this setting.” (Jessica-PI)
    • 15. The problem […] is that that wasnt the microbeson the leaves that was [taking up the nutrients]. Itwas algae and microbes and all that fineparticulate organic matter. Thats why we had toswitch to ground water last Wednesday. Becausethats […] not what were interested in. That wasn’tthe whole point of why we built all theseexperimental channels: to grow algae and fineparticulate organic matter of unknown C to P to Nratios. (Jessica-PI)
    • 16. TYPE DESIGNATIONS & DATA VALUATION• Raw vs. Derived Data• Baseline Data• Ancillary Data• Field vs. Controlled Experiment Data
    • 17. “[. . .] the whole experiment was designed aroundtwo questions, and you dont have other sorts ofvariabilities. You dont have differences inambient concentrations, or differences in site, ordifferences in channel dynamics that . . . Youknow, theyre all . . . It’s all for the exact samething . . . all designed just to answer twoquestions” (Jessica, PI).
    • 18. “[…] this is an isolated experiment designed toanswer a simple question. […] its done in theseartificial stream channels. I think, to a largeextent, the useful life of our actual numbers willprobably end when the paper comes out. If wewere doing something like this in a stream, or likewhat we did last year, I think the useful life of thatdata is a lot longer […]” (Elizabeth-PI)
    • 19. “[…] theyre not comparable, really, to anythingelse outside the system that were working in.”
    • 20. “I probably would not reach out to them aboutthis kind of data, because its an experiment inthese channels as opposed to observations of thenatural system, which I might be more inclinedthen to say, „Would you like some component ofthis data?‟ because it would contribute tobaseline information or something. (Elizabeth-PI)
    • 21. FIELD VS. CONTROLLED EXPERIMENT• Data gathered through the study of a “natural” system seen as having more broad value • Could go back to the system • Could combine with other data gathered from same place• Controlled experiment data • Only valuable within the context studied, which is transient and deliberately unnatural
    • 22. NEXT STEPS• Cross-case comparison• Further exploration of categories of data meaning and those meanings implications in data practices
    • 23. Thank you.dharma.akmon@gmail.comTwitter: @dharmaakmon