ÆKOS: A new paradigm for discovery and access to complex ecological data David Turner, Paul...
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ÆKOS: A new paradigm for discovery and access to complex ecological data David Turner, Paul Chinnick, Andrew Graham, Matt Schneider, Craig Walker Logos
KOS: A new paradigm for discovery and access to complex
ecological data David Turner, Paul Chinnick, Andrew Graham, Matt
Schneider, Craig Walker Logos used with consent. Content of this
presentation except logos is released under TERN Attribution
Licence Data Licence v1.0
Slide 2
Goal: To enable the preservation and appropriate re-use of rich
ecological information www.nswrail.net Fragmentation: Many
different ways of measuring/observing/ expressing similar
ecological concepts * Rapidly evolving Dispersal: Data is stored in
many storage locations and formats Diversity: Ecological Data
covers a wide range of topics www.derm.qld.gov.au Alamay
Source:Forestcheck: www.dec.wa.gov.au Complexity: Data usually
needs explanation and context before it can be accurately used
Slide 3
Needs and expectations of users An interface to allow for: Data
discovery Immediate or facilitated data access Comprehension A
common information platform Easy manipulation Familiar tools and or
intuitive interface Cater for individuality Each user will have
different data requirements
Slide 4
The Information Landscape
Slide 5
Preservation?
Slide 6
The ecological data delivery paradox IT solutions require rigid
models to process data hence tend to force things into narrow
structure BUT flexible models are required to preserve the full
richness of data and context Software is not intelligent and
requires fixed structures and fixed rules (logic) Understanding
data involves many subtleties which are complex to model Models and
processing dependent on the question
Slide 7
Towards the new paradigm Accept that flexible data needs
flexible information models A sparse semi-structured graph oriented
knowledge model (not a data model) Model must be extensible
(ontology based) Create descriptive artifacts for consumers Provide
the understanding of context (express subtleties) and avoid the
significant complexity of modelling By capturing the full richness,
we enable informed re-use for the broadest range of questions The
user can assess suitability for their purpose and can obtain
complete information
Slide 8
KOS Operational Model Data Source Data Source KOS Repository
KOS Repository KOS Portal Common Information Model (Ontology)
Contextual Description ETL Script KOS DSL Engine Data Portal Traits
(tags from controlled vocabulary) Data under Common Model Data
Custodian Subject Expertise Integration via a common structure
Enrichment Search Index Semantics Data Ingestion Graph
visualisation Description products
Slide 9
The user experience Key features Access and licensing Build
complex queries From simple blocks Site context
Slide 10
The information model Select my site Establish it, and make
some observations Visit 1 Etc... Make more observations Visit 3
Make more observations Visit 2
Slide 11
The user experience Record instances e.g. visits Observation
instances Observation instance details Search level controls
granularity
Slide 12
Concept alignment We integrate data from many different
sources, using different classification systems All datasets
indexed with common set of search terms (vocabularies) Taxonomy is
a good example Many other themes for search from project metadata
to observation level data
Slide 13
Descriptive artefacts Context represents a communication
challenge not a modelling problem Standard structure Bite sized
chunks Direct linkage between data and description Authored
synthesis based on information derived from multiple sources
(published and unpublished) Image of descriptive doc Once
available
Slide 14
Current status
Slide 15
Live system demo... Members Dining Room 3 15:30 17:10