1. THE ROLE OF VALUE IN
DATA PRACTICES
DHARMA AKMON
RDAP13 APRIL 5, 2013

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 QUESTION
How do scientists conceive of the value of their data,
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
data's 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

7. “ECOLOGIST” FASHION

8. NUTRIENT UPTAKE IN STREAMS (NUS)
TEAM
Name* Career Stage Discipline Project Role
Elizabeth Assistant prof. Biogeochemistry PI
Jessica Assistant prof. Stream ecology PI
Tina Graduate student Hydrogeology Graduate
researcher
Carolyn Undergraduate student Environmental studies Undergraduate
researcher
Janet Undergraduate student Chemistry Undergraduate
researcher
*pseudonyms are used to protect identities

9. AN EXPERIMENTAL STREAM CHANNEL

10. TAKING WATER SAMPLES

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 kind
of seems meaningless. Like sometimes I actually
find myself getting caught up in that. I‟m like,
„Does it really matter what this exact sedge is?‟
Like if it‟s Juncus balticus or Juncus nodosus, does
it matter? But if you think about it in long-term, it’s
not just about that. [. . .] It’s not about 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. “We're doing it in this situation because we want
to test the mechanism. […] If it wasn‟t a
mechanism-driven question then it wouldn‟t be
appropriate to ask it in this setting.” (Jessica-PI)

15. The problem […] is that that wasn't the microbes
on the leaves that was [taking up the nutrients]. It
was algae and microbes and all that fine
particulate organic matter. That's why we had to
switch to ground water last Wednesday. Because
that's […] not what we're interested in. That wasn’t
the whole point of why we built all these
experimental channels: to grow algae and fine
particulate organic matter of unknown C to P to N
ratios. (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 around
two questions, and you don't have other sorts of
variabilities. You don't have differences in
ambient concentrations, or differences in site, or
differences in channel dynamics that . . . You
know, they're all . . . It’s all for the exact same
thing . . . all designed just to answer two
questions” (Jessica, PI).

18. “[…] this is an isolated experiment designed to
answer a simple question. […] it's done in these
artificial stream channels. I think, to a large extent,
the useful life of our actual numbers will probably
end when the paper comes out. If we were doing
something like this in a stream, or like what we did
last year, I think the useful life of that data is a lot
longer […]” (Elizabeth-PI)

19. “[…] they're not comparable, really, to anything
else outside the system that we're working in.”

20. “I probably would not reach out to them about
this kind of data, because it's an experiment in
these channels as opposed to observations of the
natural system, which I might be more inclined
then to say, „Would you like some component of
this data?‟ because it would contribute to
baseline 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.com
Twitter: @dharmaakmon