ASIST2006: Access to Scientific Data (1) Access to Scientific Data: The Social and Technical Challenges & Strategies (session 1) (STI)
Panel 1 of 2 large view

Economic Considerations for Access to Scientific Data
Yale Braunstein
Berkeley

Why do we need to protect intellectual property?
- difficulty in excluding non-payers, presence of economies of scale but that’s not the whole story
- restrict or prevent duplication and distribution of content, products… and reward producers of new content
(aside: do we need incentive when attention economy?)
(aside2: yes, he is from the dark side)

Incentive effect
- to encourage
- grants to creators and property rights are the two ways

Optimal duration
- balancing like in US Constitution Art1 Sect8 Clause8
- the number of years, to balance has to be < number of useful years of work
- Sonny Bono Act – there is no economic justification for 20 year extension, no incentive for existing works, present value of final 20 years probably not significant for new works (see slides on the wiki for the slides for evidence)

Database Production
- Coase Theorem – market failures arise from inadequate assignment of property rights
- Protection of databases in EU – for databases with non-original content (so not covered by copyright)
- Assumptions of pricing rules: databases sold to profit and not-for-profit orgs, willingness and ability to pay vary.

{left and came back}

Data Access. The Human Element.
Mark Parsons
National Snow and Ice Data Center
http://www.nsidc.org/ (very, very cool web site)
Parsons and Duerr (2005) Designing user communities…Data Science Journal
OAIS definition includes “designated community” – what does that mean?
- experiment designers/science team
- related applications community
- broader scientific community
- non-expert community
- “General Public”
(broad but appropriate use will open to many many more people/uses)
Example from his world: remote sensing data obtained by military-designed sensors for operational weather prediction is now used for studies of caribou calving, global warming, polar bears.

“We must not … start from any and every accepted opinion, but only from those we have defined – those accepted by our judges or by those whose authority they recognize.” – Aristotle

Uncertainty is inherent in scientific data – compare and contrast documents, special page for the press, separate educational page, different products for different communities, scientists are involved to understand and represent data as is necessary for different communities. Data managers out in the field with scientists reduces data uncertainty by 15-20% (this needs further explanation, Parsons et al Hydrological Processes 2004 – he and other data managers processed the data in the field and were able to clarify and correct omissions, handwriting problems, etc. – I checked with Parsons and it is: http://dx.doi.org/10.1002/hyp.5801 ?)

Design for durability
- format needs to be transparent, interoperable, extensible, compact, systems need to be simple, robust, and flexible

Case Study: International Polar Year 2007-2008, http://www.ipy.org/
Themes this year include human/social aspects and impacts
50,000 participants from 60+ countries
Data policy considerations
- difference between data and databases
- free and open access international consensus
- see policy on their web page
- special cases: human subjects, intellectual property (local and traditional knowledge), where data release may cause harm
- applies to data generated by IPY

LTK
Fair and multilevel access/use
Local control of LTK but broad sharing
In Ca there may be constitutional requirements for protection of LTK


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