Prof. Jessie Kennedy e-SI Theme:
Exploiting Diverse Sources of Scientific Data

 Wealth and diversity of scientific data collected and stored is growing rapidly

Increase in automation
 Genetic sequencing, remote sensing, astronomy satellites

Decrease in technological costs
 Computers more powerful, disk space greater for the same £

Huge potential for scientific discovery by exploiting this data
 especially multi-disciplinary research

Number, complexity and diversity of resources makes this a difficult task
 Case Study
 Data Integration
 Matching data sets on biological names
Exploiting Diverse Sources of Scientific Data 2


Science Environment for Ecological Knowledge
 USA National Science Foundation funding

Multidisciplinary project
 Biology: Ecology, Taxonomy
 Environmental science: Geography, Remote sensing,
Meteorology, Climatology
 Computer Science: Database, GRID/Web, Ontologies,
Workflows, Algorithms, Human Computer Interaction
Exploiting Diverse Sources of Scientific Data 3

The SEEK Prototype: Ecological
Niche Modeling
Biodiversity information e.g. data from museum specimens, ecological surveys
Geographic Space Ecological Space ecological niche modeling
Geospatial and remotely sensed data
Model of niche in ecological dimensions occurrence points on native species distribution temperature
Results taken to integrate with other data realms (e.g., human populations, public health, etc.)
Native range prediction
Project back onto geography
4

Predicted
Distribution:
Amur snakehead
(Channa argus)
Image from http://www.lifemapper.org
Exploiting Diverse Sources of Scientific Data 5


Data is Distributed

Data is Heterogeneous
 Syntax
 e.g. Text, Excel, Relational Database…..

Schema
 e.g. Names of the tables, columns in tables

Semantics  principal focus for SEEK

From many disciplines
 Biodiversity surveys, hydrology, atmospheric chemistry, spatial data, behavioural experiments,…
 Data on economics, demographics, legal issues,…
Exploiting Diverse Sources of Scientific Data 6

EcoGrid:
Making diverse environmental data
Semantic Mediation System:
“Smart” data discovery and integration
BEAM WG:
Biodiversity and Ecological
Analysis and Modelling
Knowledge Representation WG:
Taxonomic name/concept resolution server
Exploiting Diverse Sources of Scientific Data 7

EcoGrid
Exploiting Diverse Sources of Scientific Data 8

Partnership for Interdisciplinary
Studies of Coastal Oceans (4)
Natural History
Collections (>> 100)
NTL
System (36) HBR
VCR
Multi-agency Rocky
Intertidal Network (60)
LTER Network (24)
Organization of Biological
Field Stations (180)
Metacat node
VegBank node
Xanthoria node
LUQ
SRB node
DiGIR node
Legacy system
Exploiting Diverse Sources of Scientific Data 9


EcoGrid registry to discover data sources

EML (Ecological Metadata Language)

Experimental data, survey data, spatial raster and vector data, etc.

XML based
 Discovery information
 Creator, Title, Abstract, Keyword, etc.
 Coverage
 Geographic, temporal, and taxonomic extent

Logical and physical data structure
 Data semantics via unit definitions and typing

Protocols and methods

DarwinCore

Museum collections
10


Service to Analysis and Modelling Layer

Interaction with Kepler – Workflows

Interaction with Grid Computing Facilities
 Distributed computation

Service to Semantic Mediation Layer

Access to Ontologies; Taxon Services

Access to Legacy Apps

LifeMapper
 Spatial Data Workbench
Exploiting Diverse Sources of Scientific Data 11

AMS
Exploiting Diverse Sources of Scientific Data 12


Model the way scientists currently work with data
 coordinate export and import of data among software systems
 Workflows emphasize data flow

Output generation includes creating appropriate metadata

The analysis workflow itself becomes metadata
 The workflow describes the data lineage as it has been transformed
 Derived data sets can be stored in EcoGrid with provenance
Query EcoGrid to find data Exploiting Diverse Sources of Scientific Data
Archive output to EcoGrid with workflow metadata 13


EML provides semi-automated data binding
Exploiting Diverse Sources of Scientific Data 14

(200 to 500 runs per species x
2000 mammal species x
3 minutes/run)
=
833 to 2083 days
Exploiting Diverse Sources of Scientific Data 15

 Utilize distributed computing resources

Execute single steps or sub-workflows on distributed machines
KeplerGrid for Niche
Modeling
(200 to 500 runs per species x
2000 mammal species x
3 minutes/run)
/
100 nodes
=
8 to 20 days
Exploiting Diverse Sources of Scientific Data 16

SMS
Exploiting Diverse Sources of Scientific Data 17

 Key information needed to read and machine process a data file is in the metadata

Physical descriptors (CSV, Excel, RDBMS, etc.)

Logical Entity (table, image..),Attribute (column) descriptions
 Name
 Type (integer, float, string…)
 Codes (missing values, nulls...)
 Integrity constraints
 Semantic descriptions (ontology-based type systems)

Metadata driven data ingestion
Exploiting Diverse Sources of Scientific Data 18


What was measured ( biomass or photosynthetic solar radiation )

Type of quantity measured ( mass, length )

Context of measurement ( Psychotria limonensis, wavelength band )

How it was measured ( dry weight, total solar radiation )
Exploiting Diverse Sources of Scientific Data 19

 Label data with semantic types

Label inputs and outputs of analytical components with semantic types
Data Ontology Workflow Components
 Use reasoning engine to generate transformation step

Use reasoning engine to discover relevant component
Exploiting Diverse Sources of Scientific Data 20

 Homogeneous data integration
 Integration via EML metadata is relatively straightforward

Heterogeneous Data integration

Requires advanced metadata and processing
 Attributes must be semantically typed
 Collection protocols must be known
 Units and measurement scale must be known
 Measurement relationships must be known
 e.g., that ArealDensity=Count/Area
Exploiting Diverse Sources of Scientific Data 21

Exploiting Diverse Sources of Scientific Data 22


Much of the data gathered in ecological studies and used in ecological data analysis is bioreferenced data
 typically organisms are referenced by a Latin name
 e.g. Picea rubens

Many analyses require integrating data
 originating in many locations and
 at various points in time

For most bio-referenced data, integration involves matching on organism name

SEEK Taxon investigating associated issues
Exploiting Diverse Sources of Scientific Data 23


Used for communicating information about known organisms and groups of organisms – taxa
 Framework for all biologists to communicate…

Arise from taxonomists applying them to species and higher taxa following classification

Formalized according to strict codes of nomenclature
 differ depending on kingdom

Use a Latin naming scheme
 polynomial for species + below; monomial for genus + above

Quoted as: LatinName NameAuthors Year
 Example: Carya floridana Sarg. 1913
 Can cause problems in data analysis…..
24

Pile of specimens
Taxon_concept _ a
Genus
Type specimens classify
Taxon_concept _ b
Taxon_concept _ c
Taxon_concept _ d
Species
Taxonomic Hierarchy
Exploiting Diverse Sources of Scientific Data 25

classify
Pile of specimens Taxon_concept_d Taxon_concept_d
Exploiting Diverse Sources of Scientific Data 26

Archer splits Aus aus L. 1758 into two species, of Taxonomic aus L. 1758 and Aus bea Archer. 1965 into one
Revisions species, retains the name.
Genus concept specimen
(v) Aus L.1758
(i) Aus L.1758
(ii) Aus L.1758
(iii) Aus L.1758
(iv) Aus L.1758
Aus aus L.1758
Aus aus L.1758
Species concept
Aus bea
Archer 1965 species name
Aus aus L.1758
Aus bea
Archer 1965
Aus cea
BFry 1989
Aus aus
L.1758
Aus aus L. 1758
Aus ceus
BFry 1989
Xus Pargiter 2003
Aus cea
BFry 1989
Linnaeus 1758 Archer 1965 Fry 1989 Tucker 1991
Xus beus (Archer)
Pargiter 2003.
Pargiter 2003
In Linnaeus 1758 In Archer 1965 publication
A diligent nomenclaturist, Pyle (1990),
In Fry 1989 In Tucker 1991
In Pargiter 2003 notes that the species epithet of Aus
Publications of Purely
Nomenclatural
Observation bea and cea noted bea and Aus cea are of the wrong gender and publishes the corrected names and
Aus beus
Aus ceus corrig. Archer 1965 corrig. BFry 1989 as invalid names and replaced with beus and ceus.
Pyle 1990
In Pyle 1990
Tucker publishes his revision without noting
Pyle’s corrigendum of the name of
Exploiting Diverse Sources of Scientific Data
Aus ceus.
Aus cea to
27


Are not unique
 “Re-use” of names with changed definition

Name is ambiguous without definition/context

Subject to alterations and 'corrections' in time

Often recorded inappropriately in datasets
 No author and/or year (e.g. Carya floridana )

Abbreviated (e.g. C. floridana )
 Internal code (e.g. PicRub for Picea rubens)

Vernacular used (e.g. Scrub Hickory)
 Misspelled
Exploiting Diverse Sources of Scientific Data 28


The published expert opinion defining and describing a group of organisms which are given a (scientific) name
 Scientific names qualified with a reference to the definition of a concept

Should be used for communicating about groups of organisms

Comparing or integrating data based on taxon concepts will be more accurate
Exploiting Diverse Sources of Scientific Data 29


Created by someone - an Author

Described in a Publication

Given a Name

Related to the type specimen

Definition

Referenced by
 Full Scientific name + “according to” (Author +
Publication + Date)

Definition

Carya floridana Sarg. (1913) “according to” Charles
Sprague Sargent, Trees & Shrubs 2:193 plate 177
(1913)
Exploiting Diverse Sources of Scientific Data 30


Defined by
 set of Specimens examined during classification
 set of common Characters
 context dependent; differentiate taxa rather than fully describe them;
 use natural language with all its ambiguities
 relationships to other Taxon Concepts

Taxon circumscription
 the lower level taxa

Congruence, overlap, includes etc. to taxa in other classifications
Exploiting Diverse Sources of Scientific Data 31


Original concept
 1 st use of name as described by the taxonomist
 same author + date in scientific name and “according to”
 Carya floridana Sarg. (1913) Charles Sprague Sargent, Trees &
Shrubs 2:193 plate 177 (1913)

TC_a

Revised concept

Re-classification of a group

Carya floridana Sarg. (1913) “according to” Stone, Flora of North
America 3:424 (1997)
 TC_b

Relationship between the taxon concepts
 TC_b includes TC_a
Exploiting Diverse Sources of Scientific Data 32


In legacy data names often appear in place of concepts
 Names are imprecise
 inappropriate for referring to information regarding taxa
 e.g. observational/collection data
 BUT…sometimes that’s all we have
 How do we interpret names?…..
 potentially multiple definitions
 the sum of all definitions that exist for the name
 one of the existing definitions
 the “attributes” in common to all the definitions
 represented by the type specimen
Exploiting Diverse Sources of Scientific Data 33


Nominal concepts

Sub-set of TaxonConcepts

Name but no AccordingTo
 non-unique (concept) identifier attributes
 can be given a unique concept identifier

No definition
 Explicitly saying it’s something with this name
 but not really sure what is/was meant by the name

Encourage people to understand and address the issue of names

Allowing mark-up of data with names allows them to believe names are really good enough

Will improve long term usefulness of scientific data

Ease integration
Exploiting Diverse Sources of Scientific Data 34


Scientific names are not unique identifiers for biological entities

Integrating data from different sources based on names alone could cause serious errors in analysis of the integrated data

Biologists must reference organisms precisely
 if datasets to be of use long term or to other users

Reference by taxon concept rather than name
 integrate data for analysis on taxon concepts
Exploiting Diverse Sources of Scientific Data 35

 Main taxonomic list servers are still name based
 single perspective on taxonomy
 don’t represent multiple classifications
 unclear what the definition is (don’t even try!)
 provide non-standardised interface (web page, xml download)

SEEK Taxon aims to prototype a concept/name resolution service for ecologists working with SEEK
 Find concepts given a name

Compare concepts

Relate concepts

Mark up ecological data sets with concepts

First

Need data on names and concepts
 Need an exchange standard….
Exploiting Diverse Sources of Scientific Data 36

 TCS standard for exchange of taxonomic names/concept data

Taxonomic Databases Working Group (TDWG)

Global Biodiversity Information Facility (GBIF)
 XML based exchange schema
 Makes heavy use of Globally Unique Identifiers (GUIDs)
 Not designed as the “correct way” to model a Taxon
Concept
 No “rules” as to what a taxon must have

Design to accommodate different models

Includes Taxon Names
 more constrained - the codes of nomenclature

TCS/EML

TCS modifications to EML taxon coverage
Exploiting Diverse Sources of Scientific Data 37


Important to be able to pass names alone

For nomenclatural and some taxonomic purposes

But not for identifications/observations

Taxon Concepts refer to Names

By GUID
 Names must not change
 Can’t record original taxon concept
Exploiting Diverse Sources of Scientific Data 38

 Taxon Object Server
 Schema based on the TCS model

Implements the GUIDs using LSID technology

Tool to import/export data from TCS documents
 TOS Allows
 registration, retrieval of taxonomic datasets

Match concepts given names, concepts, etc.
 Allow users to
 See different taxonomic opinions
 Uses GUIDs to reference concepts (LSIDs)
 Find concepts…

Author new concepts
 Make new relationships between existing concepts

Integrated with Kepler workflow system
Exploiting Diverse Sources of Scientific Data 39


Concept mapper

A desktop tool to assist taxonomists to relate concepts from one source to another
 For use in creating data sets for TOS or TCS
 For creating new relationships between concepts in TOS

Taxonomy comparison visualisation

Visualisation tool to explore different classifications
 Compare concepts
Exploiting Diverse Sources of Scientific Data 40

Query concepts
Concepts
Relationships
Exploiting Diverse Sources of Scientific Data 41

Exploiting Diverse Sources of Scientific Data 42


Environment to support large scale ecological data analysis
 Scientific Workflows: Kepler

Semantic Mediation

Ecological ontology creation/use for data integration

Grid/Wed based data discovery

Resolution of Taxonomic Names/Concepts
 Standards development

Concept matching server
 Visualisation tools
 http://seek.ecoinformatics.org
Exploiting Diverse Sources of Scientific Data 43


I hope I have convinced you that the answer is
NO
 as a general rule…
BUT

Depends on the purpose of the data
 therefore the accuracy required

The degree of automation used in matching
 greater automation – greater potential problem

Expertise of person involved in the matching
Exploiting Diverse Sources of Scientific Data 44

 Educating biologists of the inherent problem in names
 Not limited to the Linnaean system of nomenclature

Lack of good taxon concept data

Widening usage and application of taxon concepts
 Adopting GUIDs
 Provision of reliable ‘look up’ facilities

Cross referencing of GUIDs
 Reuse is vital
 Must not create duplicate GUIDs if possible

Conversion of legacy data

Develop good matching algorithms
 Potential move from XML schema -> semantic web technologies
 ……..
Exploiting Diverse Sources of Scientific Data 45

 This material is based upon work supported by:

The National Science Foundation
 SEEK Collaborators: NCEAS (UC Santa Barbara),
University of New Mexico (Long Term Ecological
Research Network Office), San Diego Supercomputer
Center, University of Kansas (Center for Biodiversity
Research), University of Vermont, University of North
Carolina, Arizona State University, UC Davis

Matt Jones – for many of the slides….

Global biodiversity Information Facility
 eScience Institute

Research Theme Programme
 Malcolm Atkinson
Exploiting Diverse Sources of Scientific Data 46


Upcoming Workshop
 discussing possible technology solutions
RDF, Ontologies and Meta-Data Workshop
7th – 9th June, 2006 e-Science Institute
15 South College Street
Edinburgh http://www.nesc.ac.uk/esi/events/683/
Exploiting Diverse Sources of Scientific Data 47