"Building a Sustainable Knowledge Base for the Marine Protected Areas Monitoring Enterprise" a presentation to the California Ocean Science Trust, Oakland, California March 16, 2010

1. Building a Sustainable
Knowledge Base
for the
Marine Protected
Areas
Monitoring Enterprise

2. Mission?
“The mission of the California Ocean Protection
Council is to ensure that California maintains
healthy, resilient, and productive ocean and
coastal ecosystems for the benefit of current
and future generations.”

3. Mission?(2)
“Our vision is that timely, useful information about
the effects and performance of California’s
marine protected areas network is provided to
decision-makers, resource managers,
stakeholders and the public to inform
management decisions, improve stewardship,
and build understanding of ocean ecosystems…”
--MPA Monitoring Enterprise
Draft NCC MPA Monitoring Plan

4. “The ocean has been chronically under-sampled
for as long as humans have been trying to
characterize its innate complexity.”
“…the current suite of computationally intensive
numerical/theoretical models of ocean
behavior has outstripped the requisite level of
actual data necessary to ground those models
in reality.”
J R Delaney & R S Barga, “A 2020 Vision for Ocean Science” in The Fourth Paradigm: Data-Intensive Scientific Discovery (T.
Hey, S. Tansley and K. Tolle, eds) Microsoft Research Redmond, WA., Version 1.1 Oct. 2009.
http://research.microsoft.com/en-us/collaboration/fourthparadigm/

5. Vision
for Knowledge Management
• Basic Values
• Basic Concepts

6. Basic Values :
• “Data as Evidence” – evidenced-based policy and decision-making
• Free open and effective access and use of all knowledge resources
• Scientific rigor and integrity
• Transparency / accountability / effective access
• Full-cycle collaboration / peer review
• Sustained Contribution to Well-Informed Public Discourse
• “K to Gray” Science Literacy / Civic Literacy
• Support for Education
• Economic Awareness
• Respect for spiritual and emotional values
• Includes “working with emotional intelligence” and “working with cultural
sensitivity”
• Public Domain – free use by all for all purposes
• Open source / proprietary applications

7. Basic Concepts
• Epistemology: Knowledge Evolves and Develops
• Recognition of Developmental Phases: R&D, Prototyping, Full Implementation
• High Level Constraints on Development
• “Knowledge Resources”:
– Data
– Experience
– Information
– Knowledge
– (Wisdom / Belief )
• Full-life-cycle data curation
• “Legacy” Resources / “Prospective” Resources – reconnaissance/survey/inventory
• Standards
• Canonical / “Reference” Data and Systemic Data
• Ecology of Science – relative “maturity” of domains / data set?
• Probability/ confidence levels / risk assessment?
• Logical expression of multiple working hypotheses?
• “Stakeholders” / Communities of practice / Communities of interest

8. Lawrence Lessig, “The New Chicago School,” The Journal of Legal Studies V.27 2 (Pt.2) June,
1998, p.664 Univ of Chicago law School, Univ of Chicago Press.
www.lessig.org/content/articles/works/LessigNewchicschool.pdf
Lessig: “Modalities of Constraint”

9. Repatriation of biodiversity information through Clearing House Mechanism of the Convention on Biological Diversity and Global
Biodiversity Information Facility; Views and experiences of Peruvian and
Bolivian non-governmental organizations. Ulla Helimo Master’s Thesis University of Turku Department of Biology 6.10. 2004
p.11. http://enbi.utu.fi/Documents/Ulla%20Helimo%20PRO%20GRADU.pdf [06-06-05]
KNOWLEDGE RESOURCES:
Technology

10. Research Information Network and British Library “Patterns of information use and exchange: case
studies of researchers in the life sciences”
http://www.rin.ac.uk/system/files/attachments/Patterns_information_use-REPORT_Nov09.pdf

11. US NSF “DataNet” Program
“the full data preservation and access lifecycle”
• “Acquisition”
• “Documentation”
• “Protection”
• “Access”
• “Analysis and Dissemination”
• “Migration”
• “Disposition”
“Sustainable Digital Data Preservation and Access Network Partners (DataNet) Program Solicitation” NSF 07-
601 US National Science Foundation Office of Cyberinfrastructure Directorate for Computer & Information
Science & Engineering

12. “Data”? (1)
“Any information that can be stored in digital
form and accessed electronically, including,
but not limited to, numeric data, text,
publications, sensor streams, video, audio,
algorithms, software, models and simulations,
images, etc.”
Sustainable Digital Data Preservation and Access Network Partners (DataNet)
Program Solicitation NSF 07-601, p.5
http://www.nsf.gov/pubs/2007/nsf07601/nsf07601.pdf

13. “Data” (2)
• “Precise, well-defined representations of
observations, descriptions or measurements of
a referent (object, phenomena or event)
recorded in some standard, well-specified
way”.
Report of the AnthroDPA MetaData Working Group May, 2009 sponsored by the
Wenner-Gren Foundation and the US NSF. [In press.]

14. Specification KMS
• Goals
• Features
• Functions
• Attributes

15. CONTEXT
• A bounded and delimited project
• But with clear links to projects at differing scales and
with different foci
• Wherever possible use methodologies and applications
already proven
• Maintain strong current awareness of ITC and domain
developments
• Use of common standards and tools with
demonstrated or planned capacity for migration
• For flexibility of adaptation, modularity is essential

16. http://www.cec.org/Storage/57/4984_B2B_PCAs_en.pdf

17. W. Michener, “Meta-information concepts for ecological data
management”, Ecological Informatics 1 (2006), 6.

18. Goals/ Objectives
• Primary data types must be clearly specified
• Data formats should be registered
• Provenance/lineage/transformations of data must be specified
• Scientific workflow must be specified
• Users of primary data should be registered (as through
authentication control application – Shibboleth-like?)
• Support for descriptive metadata minimally necessary for
discovery/ retrieval
• Full descriptive metadata is a process not an event
• Support for “qualified / expert tagging”
• Support for iterative cycles of comment and evaluation
• Public access to knowledge products and support for public
comment

19. Process for developing the KMS?
• Who should be involved, when, and in what
role or for what purpose?
• What are the greatest challenges to the
process and how would you overcome them?
• What are the attributes of a good process?
• What would be attributes of a bad process?
• What potential pitfalls do you see and how
would you avoid them?

20. Who?
• CalOST Staff
• Designated Advisory Bodies
• Regulatory Bodies
• Legislative Bodies (?)
• Academic and Research Community
– Institutions
– Organizations
– Individuals
– Library / i-Schools
– Marine Labs
– CS Departments / Data Centers
• Conservation Community
• General Stakeholder Groups
• Allied Projects and Jurisdictions (at all scales)
• Sponsors and potential sponsors (public/private sector – including corporate?)
• Education community (formal/informal)
• Journalism/ publishing community
• Commons community

21. When to involve…?
• A broadly representative technical advisory
body… (within 3 months)
• Expert reviewers – domain specialists…
(ongoing – drawing on existing links)
• MPA Managers group (link to IUCN?/ WCPA?)
(within 9 months)
• Journalist / Communication Committee (soon)
• Education Committee (soon)

22. Challenges?
Pitfalls?
Failure to observe bounds/limits…
Perception that the process is not objective or (worse) biased…
“Politically”
“Jurisdictionally”
“Intellectually”
In any way…?
Overly ambitious goals…
Costs…
Good Process?
Well specified with realistic– well-budgeted -- goals/ objectives…
Sustained collaborations…
Bad process?
Failure to achieve goals hit targets on time (or close!) within budget…

23. Development of the KMS:
• Elements or steps of developing the
system?
• What milestones might be used to judge
progress?
• What timelines?
• How will the success or effectiveness be
assessed?
• Type, number, and timing of additional
resources your approach would require

24. Elements or steps of developing the KMS ?
(All have an iterative, ongoing dimension…)
• Survey / Research / Reconnaissance (6-9 months? )
• Infrastructure specification/ negotiation /
implementation (6-12 months)
– Full life cycle curation / data repositories
– Data registry
• Initial applications development (9-18 months)
– User Registration / Authentication
– Data registry
– Access/ visualization applications
• Collaborative models assessment/ implementation
(18- 24 months)

25. How will “success” or “effectiveness” be assessed?
• Policy formation
• Decisions consistent with knowledge and
recommendations
• Funding / sponsorship
• Peer review mechanisms (“360 degree” reviews?)
• Sustained engagement of partners and
stakeholders.
– Metrics of use
– Publications / citation