eXtended Metadata Registry XMDR

1

• eXtended Metadata Registry (XMDR)
• International Ecoinformatics Technical
  Collaboration
• Berkeley, California
• October 24, 2006

Bruce Bargmeyer Lawrence Berkley National
Laboratory and Berkeley Water Center University
of California, Berkeley Tel 1
510-495-2905 bebargmeyer_at_lbl.gov
2
Topics

• Challenges to address
• A brief tutorial on Semantics and semantic
  computing
• where XMDR fits
• Semantic computing technologies
• Traditional Data Administration
• XMDR project
• Test Bed demonstrations

3
The Internet Revolution
A world wide web of diverse content The
information glut is nothing new. The access to it
is astonishing.
4
Challenge Find and process non-explicit data
Analgesic Agent
For example Patient data on drugs contains
brand names (e.g. Tylenol, Anacin-3,
Datril,) However, want to study patients
taking analgesic agents
Non-Narcotic Analgesic
Analgesic and Antipyretic
Acetominophen
Nonsteroidal Antiinflammatory Drug
Datril
Anacin-3
Tylenol
5
Challenge Specify and compute across Relations,
e.g., within a food web in an Arctic ecosystem
                                       
An organism is connected to another organism for
which it is a source of food energy and material
by an arrow representing the direction of
biomass transfer.
Source http//en.wikipedia.org/wiki/Food_webFood
_web (from SPIRE)
6
Challenge Combine Data, Metadata Concept
Systems
Inference Search Query find water bodies
downstream from Fletcher Creek where chemical
contamination was over 10 micrograms per liter
between December 2001 and March 2003
Concept system
Data
Metadata
7
Challenge Use data from systems that record the
same facts with different terms

• Reduce the human toil of drawing information
  together and performing analysis -gt shift to
  computer processing.

8
Challenge Use data from systems that record the
same facts with different terms
Database Catalogs
Common Content
ISO 11179Registries
UDDIRegistries
Table Column
Data Element
Common Content
Common Content
Business Specification
Country Identifier
OASIS/ebXMLRegistries
CASE Tool Repositories
XML Tag
Attribute
Common Content
Common Content
Business Object
Coverage
TermHierarchy
OntologicalRegistries
Common Content
9
Same Fact, Different Terms
Data Elements
DZ BE CN DK EG FR . . . ZW
012 056 156 208 818 250 . . . 716
Algeria Belgium China Denmark Egypt France . .
. Zimbabwe
LAlgérie Belgique Chine Danemark Egypte La
France . . . Zimbabwe
DZA BEL CHN DNK EGY FRA . . . ZWE
Name Context Definition Unique ID 4572 Value
Domain Maintenance Org. Steward Classification
Registration Authority Others
ISO 3166 English Name
ISO 3166 3-Numeric Code
ISO 3166 2-Alpha Code
ISO 3166 French Name
ISO 3166 3-Alpha Code
10
Challenge Draw information together from a broad
range of studies, databases, reports, etc.
11
Challenge Gain Common Understanding of meaning
between Data Creators and Data Users
A common interpretation of what the data
represents
EEA
USGS
text
data
environ agriculture climate human
health industry tourism soil water air
DoD
123 345 445 670 248 591 308
123 345 445 670 248 591 308
3268 0825 1348 5038 2708 0000 2178
3268 0825 1348 5038 2708 0000 2178
Users
text
data
environ agriculture climate human
health industry tourism soil water air
EPA
123 345 445 670 248 591 308
123 345 445 670 248 591 308
3268 0825 1348 5038 2708 0000 2178
3268 0825 1348 5038 2708 0000 2178
text
data
3268 0825 1348 5038 2708 0000 2178
123 345 445 670 248 591 308
ambiente agricultura tiempo salud
huno industria turismo tierra agua aero
123 345 445 670 248 591 308
3268 0825 1348 5038
Others . . .
Users
Information systems
Data Creation
12
Semantic Computing and XMDR

• We are laying the foundation to make a quantum
  leap toward a substantially new way of computing
  Semantic Computing
• How can we make use of semantic computing for the
  environment and health?
• What do environmental agencies need to do to
  prepare for and stimulate semantic computing?
• What are the ecoinformatics challenges?

13
Coming A Semantic Revolution

• Searching and ranking
• Pattern analysis
• Knowledge discovery
• Question answering
• Reasoning
• Semi-automated
• decision making

14
The Nub of It

• Processing that takes meaning into account
• Processing based on the relations between things
  not just computing about the things themselves.
• Processing that takes people out of the
  processing, reducing the human toil
• Data access, extraction, mapping, translation,
  formatting, validation, inferencing,
• Delivering higher-level results that are more
  helpful for the users thought and action

15
XMDR ISO/IEC 11179

• Managing, harmonizing, and vetting semantics is
  essential to enable semantic computing
• Managing, harmonizing and vetting semantics is
  important for traditional data management.
• In the past we just covered the basics
• We want to maintain compatibility with previous
  MDR purposes (data administration, data
  provenance, data design, )
• Ecoinformatics Test Bed demonstrations of XMDR
  should show more than incremental improvements of
  current applications for metadata registries

16
A Brief Tutorial on Semantics

• What is meaning?
• What are concepts?
• What are relations?
• What are concept systems?
• What is reasoning?

17
Meaning The Semiotic Triangle
C.K Ogden and I. A. Richards. The Meaning of
Meaning.
18
Semiotic TriangleConcepts, Definitions and
Signs
Definition
Sign
19
Semiotic TriangleConcepts, Definitions, Signs,
Designations
Definition
CONCEPT
Designation
Sign
Referent
20
Forms of Definitions
Definition - Define by --Essence
Differentia --Relations --Axioms
Sign
21
Definition of Concept - Rose Dictionary -
Essence Differentia

• 1. any of the wild or cultivated, usually
  prickly-stemmed, pinnate-leaved, showy-flowered
  shrubs of the genus Rosa. Cf. rose family.
• 2. any of various related or similar plants.
• 3. the flower of any such shrub, of a red, pink,
  white, or yellow color.
• --Random House Websters Unabridged Dictionary
  (2003)

22
Definitions in the EPA Environmental Data
Registry
http//www.epa/gov/edr/sw/AdministeredItemMailing
Address The exact address where a mail piece is
intended to be delivered, including urban-style
address, rural route, and PO Box
Mailing Address
State USPS Code
http//www.epa/gov/edr/sw/AdministeredItemStateUS
PSCode The U.S. Postal Service (USPS)
abbreviation that represents a state or state
equivalent for the U.S. or Canada
Mailing Address State Name
http//www.epa/gov/edr/sw/AdministeredItemStateNa
me The name of the state where mail is delivered
23
Definition of Concept - Rose Relations to Other
Concepts
Love Romance Marriage
CONCEPT
Refers To
Symbolizes
Rose, ClipArt
Stands For
Referent
24
SNOMED Terms Defined by Relations
25
Definition of Concept - RoseDefined by Axioms
in OWL
rdfssubClassOf owlequivalentClass
owldisjointWith
CONCEPT
Refers To
Symbolizes
Rose, ClipArt
Stands For
Referent
26
Class Axiom (Definitions)Class Description is
Building Block of Class Axiom

• A class description is the term used in this
  document (and in the OWL Semantics and Abstract
  Syntax) for the basic building blocks of class
  axioms (informally called class definitions in
  the Overview and Guide documents). A class
  description describes an OWL class, either by a
  class name or by specifying the class extension
  of an unnamed anonymous class.
• OWL distinguishes six types of class
  descriptions
• a class identifier (a URI reference)
• an exhaustive enumeration of individuals that
  together form the instances of a class
• a property restriction
• the intersection of two or more class
  descriptions
• the union of two or more class descriptions
• the complement of a class description
• The first type is special in the sense that it
  describes a class through a class name
  (syntactically represented as a URI reference).
  The other five types of class descriptions
  describe an anonymous class by placing
  constraints on the class extension.
• Class descriptions of type 2-6 describe,
  respectively, a class that contains exactly the
  enumerated individuals (2nd type), a class of all
  individuals which satisfy a particular property
  restriction (3rd type), or a class that satisfies
  boolean combinations of class descriptions (4th,
  5th and 6th type). Intersection, union and
  complement can be respectively seen as the
  logical AND, OR and NOT operators. The four
  latter types of class descriptions lead to nested
  class descriptions and can thus in theory lead to
  arbitrarily complex class descriptions. In
  practice, the level of nesting is usually
  limited.

27
Class Descriptions -gt Class Axiom

• Class descriptions form the building blocks for
  defining classes through class axioms. The
  simplest form of a class axiom is a class
  description of type 1, It just states the
  existence of a class, using owlClass with a
  class identifier.
• For example, the following class axiom declares
  the URI reference Human to be the name of an OWL
  class
• ltowlClass rdfID"Human"/gt This is correct OWL,
  but does not tell us very much about the class
  Human. Class axioms typically contain additional
  components that state necessary and/or sufficient
  characteristics of a class. OWL contains three
  language constructs for combining class
  descriptions into class axioms
• rdfssubClassOf allows one to say that the class
  extension of a class description is a subset of
  the class extension of another class description.
  
• owlequivalentClass allows one to say that a
  class description has exactly the same class
  extension as another class description.
• owldisjointWith allows one to say that the class
  extension of a class description has no members
  in common with the class extension of another
  class description.

28
Computable Meaning
rdfssubClassOf owlequivalentClass
owldisjointWith
CONCEPT
Refers To
Symbolizes
Rose, ClipArt
Stands For
Referent
If rose is owldisjointWith daffodil, then a
computer can determine that an assertion is
invalid, if it states that a rose is also a
daffodil (e.g., in a knowledgebase).
29
What are Relations?
WaterBody
Relation
Merced River
Fletcher Creek
isA
isA
Merced Lake
Merced Lake
Fletcher Creek
Concepts and relations can be represented as
nodes and edges in formal graph structures, e.g.,
is-a hierarchies.
30
Concept Systems have Nodes and may have Relations
Nodes represent concepts
A
Lines (arcs) represent relations
2
1
b
a
c
d
Concept systems are concepts and the relations
between them. Concept systems can be represented
queried as graphs
31
A More Complex Concept Graph
Concept lattice of inland water features
From Supervaluation Semantics for an Inland Water
Feature Ontology Paulo Santos and Brandon
Bennett http//ijcai.org/papers/1187.pdfsearch2
2terminology20water20ontology22
32
Types of Concept System Graph Structures

• Trees
• Partially Ordered Trees
• Ordered Trees
• Faceted Classifications
• Directed Acyclic Graphs
• Partially Ordered Graphs
• Lattices
• Bipartite Graphs
• Directed Graphs
• Cliques
• Compound Graphs

33
Types of Concept System Graph Structures
34
Graph Taxonomy
Graph
Directed Graph
Undirected Graph
Directed Acyclic Graph
Clique
Bipartite Graph
Partial Order Graph
Faceted Classification
Lattice
Partial Order Tree
Note not all bipartite graphs are undirected.
Tree
Ordered Tree
35
What Kind of Relations are There?Lots!

• Relationship class A particular type of
  connection existing between people related to or
  having dealings with each other.
• acquaintanceOf - A person having more than slight
  or superficial knowledge of this person but short
  of friendship.
• ambivalentOf - A person towards whom this person
  has mixed feelings or emotions.
• ancestorOf - A person who is a descendant of this
  person.
• antagonistOf - A person who opposes and contends
  against this person.
• apprenticeTo - A person to whom this person
  serves as a trusted counselor or teacher.
• childOf - A person who was given birth to or
  nurtured and raised by this person.
• closeFriendOf - A person who shares a close
  mutual friendship with this person.
• collaboratesWith - A person who works towards a
  common goal with this person.

36
Example of relations in a food web in an Arctic
ecosystem
                                       
An organism is connected to another organism for
which it is a source of food energy and material
by an arrow representing the direction of
biomass transfer.
Source http//en.wikipedia.org/wiki/Food_webFood
_web (from SPIRE)
37
Ontologies are a type of Concept System

• Ontology explicit formal specifications of the
  terms in the domain and relations among them
  (Gruber 1993)
• An ontology defines a common vocabulary for
  researchers who need to share information in a
  domain. It includes machine-interpretable
  definitions of basic concepts in the domain and
  relations among them.
• Why would someone want to develop an ontology?
  Some of the reasons are
• To share common understanding of the structure of
  information among people or software agents
• To enable reuse of domain knowledge
• To make domain assumptions explicit
• To separate domain knowledge from the operational
  knowledge
• To analyze domain knowledge

http//www.ksl.stanford.edu/people/dlm/papers/onto
logy101/ontology101-noy-mcguinness.html
38
What is Reasoning?Inference
Disease
is-a
is-a
Infectious Disease
Chronic Disease
is-a
is-a
is-a
is-a
Heart disease
Polio
Smallpox
Diabetes
Signifies inferred is-a relationship
39
Reasoning Taxonomies partonomies can be used
to support inference queries
E.g., if a database contains information on
events by city, we could query that database for
events that happened in a particular county or
state, even though the event data does not
contain explicit state or county codes.
40
Reasoning Relationship metadata can be used to
infer non-explicit data
Analgesic Agent

• For example
• patient data on drugs currently being taken
  contains brand names (e.g. Tylenol, Anacin-3,
  Datril,)
• (2) concept system connects different drug types
  and names with one another (via is-a, part-of,
  etc. relationships)
• (3) so patient data can be linked and searched
  by inferred terms like acetominophen and
  analgesic as well as trade names explicitly
  stored as text strings in the database

Non-Narcotic Analgesic
Analgesic and Antipyretic
Acetominophen
Nonsteroidal Antiinflammatory Drug
Datril
Anacin-3
Tylenol
41
Reasoning Least Common Ancestor Query
What is the least common ancestor concept in the
NCI Thesaurus for Acetominophen and Morphine
Sulfate? (answer Analgesic Agent)
Analgesic and Antipyretic
42
Reasoning Example sibling queries concepts
that share a common ancestor

• Environmental
• "siblings" of Wetland (in NASA SWEET ontology)
• Health
• Siblings of ERK1 finds all 700 other kinase
  enzymes
• Siblings of Novastatin finds all other statins
• 11179 Metadata
• Sibling values in an enumerated value domain

43
Reasoning More complex sibling queries
concepts with multiple ancestors

• Health
• Find all the siblings of Breast Neoplasm
• Environmental
• Find all chemicals that are a
• carcinogen (cause cancer) and
• toxin (are poisonous) and
• terratogenic (cause birth defects)

site neoplasms
breast disorders
Breast neoplasm
Non-Neoplastic Breast Disorder
Eye neoplasm
Respiratory System neoplasm
44

• End of Tutorial about concept systems
• Where does ISO/IEC 11179 fit?

45
Data Generation and UseCost vs. Coordination
Full Control

Community of Interest
Data Creation
Reporting
Coordination
Autonomous
46
Data Generation and UseCost vs. Coordination
Data Use
Full Control

Community of Interest
Data Creation
Reporting
Coordination
Autonomous
47
ISO/IEC 11179 Metadata Registries Reduce Cost of
Data Creation and Use
Data Use
Full Control

Community of Interest
Data Creation
Reporting
Coordination
Autonomous
48
Metadata Registries Increase the Benefitfrom
Data (Strategic Effectiveness)
Benefit
Full Control
Community of Interest
Autonomous
Reporting
MDR
49
What Can ISO/IEC 11179 MDR Do?

• Traditional Data Management (11179 Edition 2)
• Register metadata which describes datain
  databases, applications, XML Schemas, data
  models, flat files, paper
• Assist in harmonizing, standardizing, and vetting
  metadata
• Assist data engineering
• Provide a source of well formed data designs for
  system designers
• Record reporting requirements
• Assist data generation, by describing the meaning
  of data entry fields and the potential valid
  values
• Register provenance information that can be
  provided to end users of data
• Assist with information discovery by pointing to
  systems where particular data is maintained.

50
Traditional MDRManage Code Sets
Name Country Identifiers Context Definition Un
ique ID 5769 Conceptual Domain Maintenance
Org. Steward Classification Registration
Authority Others
DataElementConcept
Algeria Belgium China Denmark Egypt France . .
. Zimbabwe
Data Elements
DZ BE CN DK EG FR . . . ZW
012 056 156 208 818 250 . . . 716
Algeria Belgium China Denmark Egypt France . .
. Zimbabwe
LAlgérie Belgique Chine Danemark Egypte La
France . . . Zimbabwe
DZA BEL CHN DNK EGY FRA . . . ZWE
Name Context Definition Unique ID 4572 Value
Domain Maintenance Org. Steward Classification
Registration Authority Others
ISO 3166 English Name
ISO 3166 3-Numeric Code
ISO 3166 2-Alpha Code
ISO 3166 French Name
ISO 3166 3-Alpha Code
51
What Can XMDR Do?

• Support a new generation of semantic computing
• Concept system management
• Harmonizing and vetting concept systems
• Linkage of concept systems to data
• Interrelation of multiple concept systems
• Grounding ontologies and RDF in agreed upon
  semantics
• Reasoning across XMDR content
• Provision of Semantic Services

52
Coming A Semantic Revolution
Searching and ranking Pattern analysis Knowledge
discovery Question answering Reasoning Semi-automa
ted decision making
Full Control
Community of Interest
Reporting
Autonomous
53
We are trying to manage semantics in an
increasingly complex content space
Structured data Semi-structured data Unstructured
data Text Pictographic Graphics Multimedia Voice
video
54
11179-3 (E3) Increases MDR Benefit
When communities create information according to
a common vocabulary the value of the resulting
information increases dramatically.
Benefit
Full Control
Community of Interest
Autonomous
Reporting
MDR
55
Example

• Combining Concept Systems, Data, and Metadata to
  answer queries.

56
Linking Concepts Text Document
57
Thesaurus Concept System(From GEMET)
58
Concept System (Thesaurus)
Contamination
chemical pollutant
Chemical
Biological
Radioactive
chemical pollution
cadmium
lead
mercury
59
Chemicals in EPA Environmental Data Registry
Environmental Data Registry
60
Data
Monitoring Stations
Measurements
61
Metadata
Contaminants
Metadata
62
Relations among Inland Bodies of Water
Fletcher Creek
feeds into
Merced River
Merced River
feeds into
fed from
feeds into
Fletcher Creek
Merced Lake
Merced Lake
63
Combining Data, Metadata Concept Systems
Inference Search Query find water bodies
downstream from Fletcher Creek where chemical
contamination was over 2 parts per billion
between December 2001 and March 2003
Concept system
Data
Metadata
64
Example Environmental Text Corpus

• Idea Develop an environmental research corpus
  that could attract RD efforts. Include the
  reports and other material from over 1b EPA
  sponsored research.
• Prepare the corpus and make it available
• Research results from years of ORD RD
• Publish associated metadata and concept systems
  in XMDR
• Use open source software for EPA testing

65
Information Extraction Semantic Computing
Extraction Engine
Segment Classify Associate Normalize Deduplicate
Discover patterns Select models Fit
parameters Inference Report results
11179-3 (E3) XMDR
Actionable Information
Decision Support
66
Extraction Engines

• Find concepts and relations between concepts in
  text, tables, data, audio, video,
• Produce databases (relational tables, graph
  structures), and other output
• Functions
• Segment find text snippets (boundaries
  important)
• Classify determines database field for text
  segment
• Association which text segments belong together
• Normalization put information into standard
  form
• Deduplication collapse redundant information

67
Metadata Registries are Useful

• Registered semantics
• For training extraction engines
• TheNormalize function can make use of standard
  code sets that have mapping between
  representation forms.
• The Classify function can interact with
  pre-established concept systems.
• Provenance
• High precision for proper nouns, less precision
  (e.g., 70) for other concepts -gt impacts
  downstream processing, Need to track precision

68
Normalize Need Registered and Mapped
Concepts/Code Sets
Name Country Identifiers Context Definition Un
ique ID 5769 Conceptual Domain Maintenance
Org. Steward Classification Registration
Authority Others
DataElementConcept
Algeria Belgium China Denmark Egypt France . .
. Zimbabwe
Data Elements
DZ BE CN DK EG FR . . . ZW
012 056 156 208 818 250 . . . 716
Algeria Belgium China Denmark Egypt France . .
. Zimbabwe
LAlgérie Belgique Chine Danemark Egypte La
France . . . Zimbabwe
DZA BEL CHN DNK EGY FRA . . . ZWE
Name Context Definition Unique ID 4572 Value
Domain Maintenance Org. Steward Classification
Registration Authority Others
ISO 3166 English Name
ISO 3166 3-Numeric Code
ISO 3166 2-Alpha Code
ISO 3166 French Name
ISO 3166 3-Alpha Code
69
Example 11179-3 (E3) Support Semantic Web
Applications
XMDR may be used to ground the Semantics of an
RDF Statement.
The address state code is AB. This can be
expressed as a directed Graph e.g., an RDF
statement
Graph
RDF
70
Example Grounding RDF nodes and relations URIs
Reference a Metadata Registry
dbAe0139
ai MailingAddress
dbAma344
ai StateUSPSCode
ABaiStateCode
_at_prefix dbA http/www.epa.gov/databaseA _at_prefix
ai http//www.epa.gov/edr/sw/AdministeredItem
71
Definitions in the EPA Environmental Data
Registry
http//www.epa/gov/edr/sw/AdministeredItemMailing
Address The exact address where a mail piece is
intended to be delivered, including urban-style
address, rural route, and PO Box
Mailing Address
State USPS Code
http//www.epa/gov/edr/sw/AdministeredItemStateUS
PSCode The U.S. Postal Service (USPS)
abbreviation that represents a state or state
equivalent for the U.S. or Canada
Mailing Address State Name
http//www.epa/gov/edr/sw/AdministeredItemStateNa
me The name of the state where mail is delivered
72
Use data from systems that record the same facts
with different terms

• Avoid a combinatorial explosion of data content,
  description, and metadata arrangements for
  information access, exchange, and presentation..

73
Ontologies for Data Mapping
Ontologies can help to capture and express
semantics
74
Example Content Mapping Service

• Collect data from many sources files contain
  data that has the same facts represented by
  different terms. E.g., one system responds with
  Danemark, DK, another with DNK, another with 208
  map all to Denmark.
• XMDR could accept XML files with the data from
  different code sets and return a result mapped to
  a single code set.

75
Ecoinformatics Actions to Manage Semantics

• Define, data, concepts, and relations
• Harmonize and vet data and concept systems
• Ground semantics for RDF, concept systems,
  ontologies
• Provide semantics services

76
Ecoinformatics Concept System Store
Concept systems Keywords Controlled
Vocabularies Thesauri Taxonomies Ontologies Axioma
tized Ontologies (Essentially graphs
node-relation-node axioms)

ISO/IEC 11179 Metadata Registry
77
Ecoinformatics Management of Concept Systems
Concept system Registration Harmonization
Standardization Acceptance (vetting) Mapping
(correspondences)

ISO/IEC 11179 Metadata Registry
78
Ecoinformatics Life Cycle Management

Life cycle management Data and Concept
systems (ontologies)
ISO/IEC 11179 Metadata Registry
79
Ecoinformatics Grounding Semantics
Metadata Registries
Semantic Web RDF Triples Subject (node URI) Verb
(relation URI) Object (node URI)
Ontologies
ISO/IEC 11179 Metadata Registry
80
XMDR Project Collaboration

• Collaborative, interagency effort
• EPA, USGS, NCI, Mayo Clinic, DOD, LBNL others
• Draws on and contributes to interagency/Internatio
  nal Cooperation on Ecoinformatics
• Involves Ecoterm, international, national, state,
  local government agencies, other organizations as
  content providers and potential users
• Interacts with many organizations around the
  world through ISO/IEC standards committees
• Only loosely aligned with Ecoinformatics
  Cooperation

81
XMDR Project

• High risk RD, sponsor expected likelihood of
  failure
• Targeted toward leading-edge semantics
  applications in a highly strategic environment
• Conceptualization of new capabilities, creation
  of designs (expressed as standards), development
  of a software architecture and prototype system
  for demonstrating capabilities and testing
  designs
• Reasoning, inference, linkage of concepts to
  data, .
• Demonstration of fundamental semantic management
  capabilities for metadata registries,
  understanding the potential applications that
  could be built in-house

82
Results to Date

• Completed the first version of designs for next
  generation metadata registriesexpressed as
  figures in a UML model that is proposed for next
  edition of the ISO/IEC 11179 standard
• Developed XMDR Prototype -- available as open
  source software
• Content loaded in prototype broad range of
  traditional metadata and concept systems
• Designs and prototype being explored and used in
  several locations. Potential for facilitating
  development and sharing of content by wide
  diversity of users.
• Starting the next version of designs, taking on
  more challenging content and capabilities

83
Status of Project

• NSF has funded a three-year project, providing a
  funding base
• Strong emphasis on the computer science RD
  results and collaboration with EU and Asia
• Limited staffing
• Proposing further high risk RD
• Developing proposals for collaborative efforts to
  demonstrate capabilities, especially in the area
  of water.
• Opportunity to collaborate with JRC and projects
  under the European Commission 7th Framework
  Program

84
Ecoinformatics Test Bed

• Proposed in Brussels in September 2004
• Project direction and statement developed
• Purpose
• Research and technical informatics to investigate
  metadata management techniques. Practical
  experiment for testing usability.
• Initial Focus
• Use metadata and semantic technologies for air
  quality (transportation) health effects
• Potential for extension to other areas
• Need for engaging ongoing operations and/or
  indicators
• Bruce the unready

85
Ecoinformatics Test Bed

• Extend original charter to Water
• Use Water as example content
• Metadata, concept systems
• Look for opportunities to coordinate with EU
  projects
• WISE, EC Framework Program 7
• Identify and propose possible demonstrations
• Coordinate with Microsoft, LBNL and other efforts
  in UCB/Berkeley Water Center

86
Thanks Acknowledgements

• John McCarthy
• Karlo Berket
• Kevin Keck
• Frank Olken
• Harold Solbrig
• L8 and SC 32/WG 2 Standards Committees
• Major XMDR Project Sponsors and Collaborators
• U.S. Environmental Protection Agency
• Department of Defense
• National Cancer Institute
• U.S. Geological Survey
• Mayo Clinic
• Apelon