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Is Data Publication the Right Metaphor?
Mark A. Parsons and Peter Fox Rensselaer Polytechnic Institute !!Research Data Publication in Principle and Practice International Data Curation Conference Workshop San Francisco, California 24 February 2014
okay, I'll say it. The *term* data 'publication' bothers me more and more. Am leaning toward data release and *maybe* review, #CODATA2010 @taswegian
A Community Conversation
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.
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
Big Iron Image courtesy of SITEC
Map Making “The Imperial Cartographer”
Science Support
Linked Data
Some attributes of the ideal system
*critical• Trust (of data, system, and people)* Difficult• Discoverable data* Easy• Preserved data* Easy• Data are accessible to humans and machines* Easy• Usable, incl. some level of understandability* Difficult• Distributed governance* Difficult• Verifiable Difficult• Citable data Easy• Simple (in concept) Difficult• Scalable/evolvable Difficult• Ethically open data Difficult• Appropriately transparent (translucent) Difficult• Data are contextually associated Difficult• Handles distributed security, authentication, and legality. Difficult• Defined roles Difficult
*critical
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
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
Do we need new and alternative metaphors?
Infrastructure? Relationships not just physical structures.
Ecosystem? Data systems that grow, evolve, and thrive on diversity.
ExplorationRef: M. Serres—Crossing the Northwest Passage between culture and science
Grand Bazaar? Spatial metaphors abound photo by Frank Kovalchek (CC-BY)
Software Production? “We should be treating data as an ongoing process” (Schopf, 2012).
Contracts? New forms; new agreements; new parties
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.
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?