This document discusses the metaphor of "data publication" and whether it is still the right metaphor given changes in how data is shared and used. It notes that the term bothers some as it implies a process of peer review similar to academic publishing, which may not accurately reflect how data is validated. Alternative metaphors are proposed like "data release," "infrastructure," "ecosystem," and "software production" to reflect different aspects of managing and sharing research data. The document also analyzes several existing models for how they enable key attributes such as trust, discovery, preservation and proposes disaggregating functions between archiving, releasing, and mediating data to fit different needs with multiple metaphors.

1. Is Data Publication the Right Metaphor?
Mark A. Parsons and Peter Fox
Rensselaer Polytechnic Institute
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Research Data Publication in Principle and Practice
International Data Curation Conference Workshop
San Francisco, California
24 February 2014
Unless otherwise noted, the slides in this presentation are licensed by Mark A. Parsons under a Creative Commons Attribution-Share Alike 3.0 License

2. A Community Conversation
okay, I'll say it. The *term* data 'publication' bothers me more and more.
Am leaning toward data release and *maybe* review, #CODATA2010
@taswegian

4. Most people think they can
get along perfectly well
without metaphor.
!
We have found, on the
contrary, that metaphor is
pervasive in everyday life,
not just in language but in
thought and action. Our
ordinary conceptual
system, in terms of which
we both think and act, is
fundamentally metaphorical
in nature.

5. Language is at once a surface phenomenon and a
source of power. It is a means of expressing,
communicating, accessing, and even shaping
thought. […]
Language gets its power because it is defined
relative to frames, prototypes, metaphor, narratives,
images and emotions. Part of its power comes from
unconscious aspects: we are not consciously aware
of all that it evokes in us, but it is there, hidden,
always at work. If we hear the same language over
and over, we will think more and more in terms of the
frames and metaphors activated by that language.
—George Lakoff

6. Data Publication
Moving from the library to the
internet. © photogj –fotolia.com

7. Big Iron
Image courtesy of SITEC

9. Map Making
“The Imperial Cartographer”

10. Science Support

11. Linked Data

12. Some attributes of the ideal system
•
•
•
•
•
•
*critical
Trust (of data, system, and people)*
Discoverable data*
Preserved data*
Data are accessible to humans and machines*
Usable, incl. some level of understandability*
Distributed governance*
• Verifiable
• Citable data
• Simple (in concept)
• Scalable/evolvable
• Ethically open data
• Appropriately transparent (translucent)
• Data are contextually associated
• Handles distributed security, authentication, and legality.
• Defined roles
Difficult
Easy
Easy
Easy
Difficult
Difficult
Difficult
Easy
Difficult
Difficult
Difficult
Difficult
Difficult
Difficult
Difficult

13. How the current models perform
*critical
Data Pub.
Big Iron
Sci. support
Maps
Linked
•
Trust
good
moderate
good
moderate
poor
•
Discovery
poor
moderate
poor
moderate
good
•
Preservation
good
poor
variable
poor
poor
•
Access
moderate
moderate
moderate
good
good
•
Usable
moderate
moderate
good
moderate
moderate
•
Governance
poor
good
poor
moderate
poor
•
Credit/
Accountability
good
moderate
variable
poor
variable

14. Three perceptual frames of concern in
Data Publication
• Peer review
• data review ≠ literature review
• quality is in the eye of the beholder—“Facts all come with points of view. Facts
don’t do what I want them to.” (Talking Heads)
• We can’t keep up with the literature now.
• Data citation
• Does a DOI imply a imprimatur? Why and what kind? What about other
identifiers?
• When do we need a citation vs. a simple pointer? When does credit play a role?
• Copyright and intellectual “property”
• Data are used, referenced, discovered well outside the scholarly article.
• A copyright article should not be a primary path to data

15. Do we need new and alternative metaphors?

16. Infrastructure?
Relationships not just physical
structures.

17. Ecosystem?
Data systems that grow, evolve,
and thrive on diversity.

18. Exploration
Ref: M. Serres—Crossing the
Northwest Passage between
culture and science

19. Grand Bazaar?
Spatial metaphors abound
photo by Frank Kovalchek (CC-BY)

20. Software Production?
“We should be treating data as an
ongoing process” (Schopf, 2012).

21. Contracts?
New forms; new agreements;
new parties

22. Disaggregating the functions
• A new paradigm of Archive, Release, Mediate, ... that disaggregates the
functions? Hence multiple metaphors.
• Formal, sustained archiving (like a museum or archive)
• Rapid, carefully versioned and described releases (like software)
• Simple, Weak (least power), Scalable, Open?
• Active mediation between producers and users (like specialist shop keepers
filling niches)
!
• More metaphors, please.

23. A research agenda based on:
Data Science in Action
• How do roles and relations change with different metaphors and world views?
• What are the new norms and contractual relations?
• What is the spectrum or space of referencing, citing, and relating? How much
does credit really matter? When?
• What approaches can bridge the domain, data, and computer science
disciplines into cohesive collaborations when needed? Is there a maturity model?
• What approaches in the data life cycle need to scale? How?
• How can research collections be discovered beyond the context of the scholarly
article?
• How do we track context instead of quality?
• What does it mean “Data as a first class object”? Is it really necessary?

24. Thank You
parsom3@rpi.edu