Exploiting Diverse Sources of Scientific Data the vision, what has been achieved and what next…

e-SI Theme: Exploiting Diverse Sources of Scientific Data

Exploiting Diverse Sources of Scientific Data the vision, what has been achieved and what next…

Prof. Jessie Kennedy

Exploiting Diverse Sources of Scientific Data

Science & Scientific Data

Science and Scientific Data are Complex…

Biochemistry

Climatology

Taxonomy

Meteorology

Nomenclature

Paleontology

GenomicsProteomics

Hydrology

Morphology

Geology

Oceanography

Geography

Ecology

Biochemistry

Climatology

Taxonomy

Meteorology

Nomenclature

Paleontology

GenomicsProteomics

Hydrology

Morphology

Geology

Oceanography

Ecology

Geography

Organism

Name

Taxon concept Gene

sequence

Pathway

Protein

Location

TemperatureDepth


Individual Scientist

Small Scientific Community

Large Scientific Community Scientific Laboraotory

Scientific Community: complex

Biochemistry

Climatology

Taxonomy

Meteorology

Nomenclature

Paleontology

GenomicsProteomics

Hydrology

Morphology

Geology

Oceanography

Ecology

Geography

Organism

Name

Taxon concept Gene

sequence

Pathway

Protein

Location

TemperatureDepth

Biochemistry

Climatology

Taxonomy

Meteorology

Nomenclature

Paleontology

GenomicsProteomics

Hydrology

Morphology

Geology

Oceanography

Ecology

Geography

Organism

Name

Taxon concept Gene

sequence

Pathway

Protein

Location

TemperatureDepth

Biochemistry

Climatology

Taxonomy

Meteorology

Nomenclature

Paleontology

GenomicsProteomics

Hydrology

Morphology

Geology

Oceanography

Ecology

Geography

Organism

Name

Taxon concept Gene

sequence

Pathway

Protein

Location

TemperatureDepth

Biochemistry

Climatology

Taxonomy

Meteorology

Nomenclature

Paleontology

GenomicsProteomics

Hydrology

Morphology

Geology

Oceanography

Ecology

Geography

Organism

Name

Taxon concept Gene

sequence

Pathway

Protein

Location

TemperatureDepth


Science & Scientific Data

Are continually changing Conclusions become

foundations for new hypotheses

New experiments invalidate existing knowledge

Knowledge is open to interpretation Different opinions

World continually changing

observation

experiment hypothesis

conclusion


Exploiting Diverse Sources of Scientific Data: the visionTo provide scientists with technological

solutions to exploit the wealth and diversity of Scientific Data Discovery Access Sharing Integration/Linking Analysis

Which would thereby improve the potential for new scientific discovery


Projects in most sciences:

ESG

SEEK (Scientific Environment for Ecological Knowledge): Vision

• Research, develop, and capitalize upon advances in information technology to radically improve the type and scale of ecological science that can be addressed

– Scalable synthesis

Michener

Data Dispersion Challenges

• Data are massively dispersed– Ecological field stations and research centers (100’s)– Natural history museums and biocollection facilities (100’s)– Agency data collections (10’s to 100’s)– Individual scientists (1000’s)

– Maintenance must be local

Michener

Data Integration Challenges

• Data are heterogeneous– Syntax

• (format)

– Schema• (model)

– Semantics• (meaning)

Jones

Ecological Modeling Challenges

• Analysis and modeling tools are: – Specialized– Disconnected– Proprietary

• It is:– Difficult to revise analyses– Hard to document analyses– Impossible to reliably publish models to share with

colleagues– Hard to re-use models and analyses from colleagues– Difficult to use grid-computing for demanding computations– Labor-intensive to manage data in popular analysis software

Michener


Exploiting Diverse Sources of Scientific Data: the approachesData Discovery/Access

Metadata To describe the data sets

Ontologies To define the terminology used

Standardisation of formats For the exchange of data

Life Science Identifiers (LSIDs) To uniquely identify and resolve data objects

Provenance of data To record where the data has come from And what has happened to it en route.

GRID/Web technology Distributed data management


Exploiting Diverse Sources of Scientific Data: the approachesData Integration/Linking

Metadata To know how to interpret the data sets

Ontologies To know how data in the data sets might be related To aid automatic transformation of the data

Standardisation of formats To ease integration

Life Science Identifiers (LSIDs) To know when 2 things are the same

Workflows To enable refinement and repetition of integration


Exploiting Diverse Sources of Scientific Data: the approachesData Analysis

Metadata To know how to interpret the data sets

Ontologies To know analytical/transformation processes appropriate

Workflow Tools To ease analytical processes Recording/reuse of analytical processes

Provenance Recording life history of data To enable validation


Exploiting Diverse Sources of Scientific Data: the technologiesStandardisation of formatsMetadata OntologiesLife Science Identifiers (LSIDs)ProvenanceWorkflow Tools GRID/Web technology


Meta Data: the vision

Meta data - "data about data" keywords, title, creator ….

If scientists marked up their data with the agreed meta data it would be trivial to find highly relevant data (sub-)sets for analysis…

Meta-utopia….


Meta-utopia

A world of complete, reliable metadata. In meta-utopia,

Everyone uses the same language and means the same thing…

The guardians of epistemology have rationally mapped out a schema or hierarchy of ideas. that everyone adheres to…

Scientists accurately describe their methods, processes and results. so anyone can do anything with it in the future…

Cory Doctorow


Meta Data: the approach

Common language XML Schemas to describe data/meta data

Domain specific exchange schemas Explosion of these in every domain

Exchanging data Archiving data

knb.ecoinformatics.org

Ecological Metadata Language

A look inside the meta-utopia of ecology


Identification: dataset elements


Identification: resource elements


Identification: party elements


Discovery: coverage elements

GeographicTemporal Taxonomic


Evaluation Level Information


Evaluation: Method Information


Evaluation: Project Information

L3


Access: Permissions Information

L4


Access: Physical Information


Access: Physical formatting details


Access: Distribution Information

L4


Integration Level Information


Integration Level: Attribute structure


Integration Level: attribute domains


Integration Level: measurementScale


Meta Data: the approach

Common language XML Schemas to describe data/meta data

Domain specific exchange schemas Explosion of these in every domain

Exchanging data Archiving data

Turned into extensive specifications Difficult to know where to stop…


but even this wasn’t enough…..

It’s not good enough to have meta-data, we need to know what the terms in the meta-data (schema or data values) mean.


Ontologies – the vision

If we understood the meaning of the schema and the terms used in the meta-data or databases we would be able to: find things more reliably, integrate things more easily, reason about what things are comparable….

because we have support for automatic inference


Ontologies – the approach

Common Language… OWL?

RDF, OWL lite, OWL DL, OWL full…..

Domain specific ontologies or project specific?

Map different ontologies Modularise the ontologies

Reuse..Build upper ontologies to which domain

ontologies extend/link

Biodiversity Base Ontology

Core Layer

BDI Core Taxon Name

BDI Core Taxon Concept

BDI Core BioSpecimen

BDI Core BioObservation

Similar to…

SEEK Observation ontology

Josh Madin

An extension point for domain-specific terms

entity

Josh Madin

Characteristic

Josh Madin

All the units, scales, indices, classifications, and lists used for ‘measuring’ a characteristic

Measurement standard

Similar to…

Josh Madin


Semantic Web for Earth and Environmental Terminology (SWEET)

Ontologies revised and validated Jan 26, 2006

Biosphere Data

Data Center Human Activity Material Thing

Numerics Sensor Space Time Units

Earth Realm Physical Phenomena Physical Process Physical Property Physical Substance Sun Realm

Takes us back to…

http://sweet.jpl.nasa.gov/ontology/biosphere.owl

http://sweet.jpl.nasa.gov/ontology/data.owl

http://sweet.jpl.nasa.gov/ontology/data_center.owl

http://sweet.jpl.nasa.gov/ontology/human_activities.owl

http://sweet.jpl.nasa.gov/ontology/material_thing.owl

http://sweet.jpl.nasa.gov/ontology/numerics.owl

http://sweet.jpl.nasa.gov/ontology/sensor.owl

http://sweet.jpl.nasa.gov/ontology/space.owl

http://sweet.jpl.nasa.gov/ontology/time.owl

http://sweet.jpl.nasa.gov/ontology/units.owl

http://sweet.jpl.nasa.gov/ontology/earthrealm.owl

http://sweet.jpl.nasa.gov/ontology/phenomena.owl

http://sweet.jpl.nasa.gov/ontology/process.owl

http://sweet.jpl.nasa.gov/ontology/property.owl

http://sweet.jpl.nasa.gov/ontology/substance.owl

http://sweet.jpl.nasa.gov/ontology/sunrealm.owl

BDI Taxon Concept Ontology

…is really just a schema for representing

…


Biological TaxonomyClassify and name all organisms in the world

So we can talk about them, experiment with them Do life science…

The longest running attempt at building an ontology? Linnaeus binomial system of nomenclature started in 1758

An attempt to resolve a long standing problem in biology

Many ways to classify things Understanding continually changes with new discoveries &

technologies Classifications continually being redone

New things defined, New definitions given for things in existence

Lots of classifications over time Many compete at any one point in time


Aus aus L.1758

Aus L.1758

Aus bea Archer 1965

Archer 1965

Aus L.1758

Aus aus L.1758

Linneaus 1758

Aus L.1758

Aus aus L.1758

Aus bea Archer 1965

Aus cea BFry 1989

Fry 1989

Aus L.1758

Xus beus (Archer) Pargiter 2003.

Aus ceus BFry 1989

(vi) Xus Pargiter 2003

Pargiter 2003

Aus aus L. 1758

Aus bea and Aus cea noted as invalid names and replaced with Aus beus and Aus ceus.

Aus aus L.1758

Tucker 1991

Aus L.1758

Aus cea BFry 1989

Taxonomic history of imaginary genus Aus L. 1758

Pyle 1990

5 Revisions of Aus 1 name spelling change


Aus aus L.1758

Aus L.1758

Aus bea Archer 1965

Archer 1965

Aus L.1758

Aus aus L.1758

Linneaus 1758

Aus L.1758

Aus aus L.1758

Aus bea Archer 1965

Aus cea BFry 1989

Fry 1989

Aus L.1758

Xus beus (Archer) Pargiter 2003.

Aus ceus BFry 1989

(vi) Xus Pargiter 2003

Pargiter 2003

Aus aus L. 1758

Aus bea and Aus cea noted as invalid names and replaced with Aus beus and Aus ceus.

Aus aus L.1758

Tucker 1991

Aus L.1758

Aus cea BFry 1989

Taxonomic history of imaginary genus Aus L. 1758

Pyle 1990

• 8 Names• 2 genus• 6 species

N4 - Aus beus Archer 1965

N1 - Aus aus L.1758

N1

C1.5

C1.4

C1.3

C1.2

C1.1 C1.1 - Aus aus L.1758 sec. Linneaeus 1758

C1.2 - Aus aus L.1758 sec. Archer 1965

C1.3 - Aus aus L.1758 sec. Fry 1989

C1.4 - Aus aus L.1758 sec. Tucker 1991

C1.5 - Aus aus L.1758 sec. Pargiter 2003

N2 - Aus bea Archer 1965

N5 C5.5N5 - Aus ceus Fry 1989

C5.5 - Aus ceus Fry 1989 sec. Fry 1989

C6.5N6N6 - Xus beus Pargiter 2003

C6.6 - Xus beus Pargiter 2003 sec. Pargiter 2003

N2

C2.3

C2.2 C2.2 - Aus bea Archer 1965 sec. Archer 1965

C2.3 - Aus bea Archer 1965 sec. Fry 1989

N3

N4C3.4

C3.3N3 - Aus cea Fry 1989 C3.3 - Aus cea Fry 1989 sec. Fry 1989

C3.4 - Aus cea Fry 1989 sec. Tucker 1991

N0 - Aus L.1758

N0

C0.5

C0.4

C0.3

C0.2

C0.1 C0.1 - Aus L.1758 sec. Linneaeus 1758

C0.2 - Aus L.1758 sec. Archer 1965

C0.3 - Aus L.1758 sec. Fry 1989

C0.4 - Aus L.1758 sec. Tucker 1991

C0.5 - Aus L.1758 sec. Pargiter 2003

C7.5N7

N7 - Xus Pargiter 2003

C7.6 - Xus Pargiter 2003 sec. Pargiter 2003

8 Names 17 Concepts

Results in many

concepts for each name


Possible interpretations of Aus aus L. 1758 Request data sets about Aus aus (N1)

what’s returned?

Original concept: C1.1 Most recent concept: C1.5 Preferred Authority (e.g. Fry 1989): C1.3 Everything ever named N1:

Union(C1.1,C1.2,C1.3,C1.4,C1.5) Best fit according to some matching algorithm

Best(C1.1,C1.2,C1.3,C1.4,C1.5) New concept containing only those features

common to all concepts with the name N1: Intersection(C1.1,C1.2,C1.3,C1.4,C1.5)

Is it appropriate to link or merge data on this? Depends on the user’s purpose Level of precision required

N1 - Aus aus L.1758

N1

C1.5

C1.4

C1.3

C1.2

C1.1


C1.5 C5.5

C0.5

C1.4 C3.4

C0.4

C1.1

C0.1

C1.2 C2.2

C0.2

C1.3 C2.3 C3.3

C0.3

C6.5

C7.5

N0 N7

N1 N2N5 N6N3 N4

Classifications synonymy relationships between concepts and names.

In the literature taxonomists tell us names that are synonymous with their concepts

Parent child relationships in 5 revisions

Names for each of the concepts


C1.5 C5.5

C0.5

C1.4 C3.4

C0.4

C1.1

C0.1

C1.2 C2.2

C0.2

C1.3 C2.3 C3.3

C0.3

C6.5

C7.5

N0 N7

N1 N2N5 N6N3 N4

Classifications synonymy relationships between concepts and names.

Which can result in anything being returned for Aus aus by traversing the synonymy links


C1.5 C5.5

C0.5

C1.4 C3.4

C0.4

C1.1

C0.1

C1.2 C2.2

C0.2

C1.3 C2.3 C3.3

C0.3

C6.5

C7.5

N1N5 N6

N2 N3 N4

N0 N7

= =

Classifications with set relationships between concepts.

What we need are the set relationships from concepts in a revision to earlier concepts

and name changes related to earlier names

We can build systems to return data suit for purpose


Real Taxonomic RevisionsGerman mosses

14 classifications in 73 years covering 1548 taxa only 35% thought to be stable concepts

65% of names used in legacy data sets are ambiguous and we don’t know which ones?? we need computers to help understand this…

Smaller classifications are combined into large classifications ITIS – integrated taxonomy (also changing) approx. 250,000

taxaTaxonomic Revision of genus Alteromonas

34 years: from 1972 to 2006 Thanks to George Garrity, Michigan State Univ.

macleodii(T)

communis

Alteromonas

1972

vaga

communisvagahaloplanktis

Alteromonasmacleodii(T)

1972 1973

communisvagahaloplanktisrubra

Alteromonas

1972 1973 1976

macleodii(T)

communisvagahaloplanktisrubracitrea

Alteromonas

1972 1973 1976 1977

macleodii(T)

communisvagahaloplanktisrubracitreaesperjianaundina

Alteromonas

1972 1973 1976 1977 1978

macleodii(T)

communisvagahaloplanktisrubracitreaesperjianaundinaaurantia

Alteromonas

1972 1973 1976 1977 1978 1979

macleodii(T)

communisvagahaloplanktisrubracitreaesperjianaundinaaurantiaputrifacienshanedai

Alteromonas

1972 1973 1976 1977 1978 1979 1981

macleodii(T)

communisvagahaloplanktisrubracitreaesperjianaundinaaurantiaputrifacienshanedailuteoviolaceae

Alteromonas

1972 1973 1976 1977 1978 1979 1981 1982

macleodii(T)

communisvagahaloplanktisrubracitreaesperjianaundinaaurantiaputrifacienshanedailuteoviolaceae

vagacommunis(T)

Marinomonas Alteromonas

commune

vagum

1972 1973 1976 1977 1978 1979 1981 1982 1984

multiglobiferum

japonicumminutiumbiejerinckiimaris

maris

hiroshimense

pelagicumpusillum

jannaschiikreigii

Oceanosprillum

mariswilliamsae

linum(T) macleodii(T)

communisvagahaloplanktisrubracitreaesperjianaundinaaurantiaputrifacienshanedai

vaga benthicahanedai

Marinomonas Alteromonasputrifaciens(T)

Shewanella


maris

hiroshimensemultiglobiferumpelagicumpusillumcommunejannaschiikreigiivagum

Oceanosprillum

mariswilliamsae

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986

luteoviolaceae

communis(T)linum(T) macleodii(T)

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987

communisvagahaloplanktisrubracitreaesperjianaundinaaurantia

hanedailuteoviolaceaedenitrificans


Marinomonas Alteromonas Shewanella


maris


Oceanosprillum

mariswilliamsae

putrifaciens

putrifaciens(T)communis(T)linum(T) macleodii(T)

communisvagahaloplanktisrubracitreaesperjianaundinaaurantiaputrifacienshanedailuteoviolaceaedenitrificans


Marinomonas Alteromonas Shewanella


maris


Oceanosprillum

mariswilliamsae

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988

colwelliana



Marinomonas Shewanella


maris

hiroshimensemultiglobiferumpelagicumpusillumcommunejannaschiikreigiivagumbiejerinckiipelagicummaris

hiroshimense

Oceanosprillum

mariswilliamsae


tetradonis

Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990

colwelliana


vaga benthicahanedaicolwellianaalgae



tetradonisatlanticacarageenovora

Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992


maris


hiroshimense

Oceanosprillum

mariswilliamsae





putrifacienshanedai

denitrificans

rubracitreaesperjianaundinaaurantia

luteoviolaceae


Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995


maris


hiroshimense

Oceanosprillum

mariswilliamsae

distinctafuliginea





putrifacienshanedai

denitrificans

rubracitreaesperjianaundinaaurantia

luteoviolaceae


Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995


maris


hiroshimense

Oceanosprillum

mariswilliamsae

distinctafuliginea

atlanticaaurantiacarrageenovoracitreaesperjianaluteoviolaceanigrifacienspisicidarubra

haloplanktishaloplanktis(T)

Pseudoalteromonas

undina

haloplanktistetradonis






Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997


maris


hiroshimense

Oceanosprillum

mariswilliamsae

distinctafuliginea


Pseudoalteromonas

undinaantartica

elyakoviii








Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000


maris


hiroshimense

Oceanosprillum

mariswilliamsae

distinctafuliginea


Pseudoalteromonas

undinaantartica

elyakoviii

fridgidimarinageldimarinawoodyiiamazonensisbalticaoneidensispealeanaviolacea

bacteriolyticaprydzensistunicatadistinctaelyakoviipeptidolytica


mediterannea







Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 2001


maris


hiroshimense

Oceanosprillum

mariswilliamsae

distinctafuliginea


Pseudoalteromonas

undinaantartica

elyakoviii

fridgidimarinageldimarinawoodyiiamazonensisbalticaoneidensispealeanaviolacea

bacteriolyticaprydzensistunicatadistinctaelyakoviipeptidolyticatetrodonis

japonica


mediterannea







Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 2001 2002


maris


hiroshimense

Oceanosprillum

mariswilliamsae

distinctafuliginea

Pseudoalteromonas

elyakoviii

fridgidimarinageldimarinawoodyiiamazonensisbalticaoneidensispealeanaviolaceajaponicadenitrificanslivingstonensisalleyanna

atlanticaaurantiacarrageenovoracitreaesperjianaluteoviolaceanigrifacienspisicidarubraundinaantarticabacteriolyticaprydzensistunicatadistinctaelyakoviipeptidolyticatetrodonis


mediterannea







Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 2001 2002 2004


maris


hiroshimense

Oceanosprillum

mariswilliamsae

distinctafuliginea

Pseudoalteromonas

elyakoviii




12 others

mariniintestinasaireschlegelianagaetbuli

mediteranneaprimoryensis



stellipolarislitorea 5 others





Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 2001 2002 2004 2005


maris


hiroshimense

Oceanosprillum

mariswilliamsae

distinctafuliginea

Pseudoalteromonas

elyakoviii




14 others





stellipolarislitorea 8 others2 others





Alteromonas

colwelliana

1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 2001 2002 2004 2005 2006


maris


hiroshimense

Oceanosprillum

mariswilliamsae

distinctafuliginea

Pseudoalteromonas

elyakoviii




14 others





stellipolarislitorea 13 others2 others

Alteromonas

Alteromonadacea

Alteromonadales

Gammaproteobacteria

Alishewanella

Aestuariibacter

FerrimonasColwellia

Idiomarina

Glaciecola

Marinobacterium

Marinobacter

Pseudoalteromonas

Microbulbifer

Incertae sedis

Psychromonas

Teredinibacter

Shewanella

Thalassomonas

Ferrimonadacea

Idiomarinaceae

Moritella

Moritellaceae

Pseudoalteromonadaceae

Ferrimonas

Idiomarina

Pseudoalteromonas

Psychromonadaceae

Algicola

Psychromonas

Moritella

Shewanellaceae Shewanella

Incertae sedis

Teredinibacter

Agarvorans

Alishewanella

Marinobacterium

Marinobacter

Microbulbifer

Salinomonas

Colwelliaceae

Colwelliaceae

Thalassomonas

May 2004 November 2004

At the species level 18 “emendations”

21 new species19 species reassigned to 4 genera

3 new combinations6 synonyms 2 species to subspecies2 subspecies to species

50 names, five genera, five families, and two classes but….only 5 validly published species.

At the higher level1 Family 16 genera -> 8 families 12 genera

1 unclassified genus -> 7 unclassified generaWhich is correct?Which is supported/recorded in the data?What is the impact on Analysis?


Meta-utopia - a pipe dream?

What is meta-data? Your meta data is my

data… Depends on your

perspective How you see the world What’s important to you What you want to do with

the “data”

Ecological Data set

Meta data

Taxonomic Data

ME

TA

DA

TA

DA

TA

PinaceaePicea

PiceaPicea rubens

PiceaPicea abies

Higher TaxonTaxon

Name: LinnaeusYear: 1758

Data

It’s all data anyway….. But it’s useful to

differentiate for certain purposes



Schemas aren't neutral Presumes there is a "correct" way of modelling or

categorising ideas that, given enough time and incentive, people can agree

on the correct way…

Any hierarchy of concepts necessarily implies the importance of some axes over others.


Geographic/cartographic perspective Instance of Picea rubens

is-a feature that can be mapped

Features inherently have geospatial coordinates.

Pinaceae

Picea

Picea rubensPicea abies

Building

Feature

Observation

Organismoccurrence

Picea rubens

Taxonomic perspective Instance of Picea rubens is a

specimen of some biological taxon

Taxa inherently have characteristics used in classification



There's more than one way to describe something



There's more than one way to describe something Reasonable people can disagree forever on how to

describe something. Requiring scientists to use the same vocabulary to

describe their data enforces homogeneity in ideas. Which could limit science…



Metrics influence results Agreeing to a common metric for measuring

important things in a domain necessarily privileges the items that score high on that metric, regardless of those items' overall suitability.

Ranking axes are mutually exclusive software that scores high for security scores low for

convenience, Everyone wants to emphasize their high-scoring

axes and de-emphasize (or, if possible, ignore altogether) their

low-scoring axes.



People are not altruistic Scientists have their own immediate deliverables

Doesn’t leave time for thinking about who else might do what with their data

Metadata exists in a competitive world. People want their work cited and will (ab)use meta-data to do so.

People are busy e-Scientists understand the importance of excellent

metadata Jo-scientist is mainly concerned about publishing the results.

No time for added extras



People make mistakes Even when there's a positive benefit to creating

good metadata, people don’t exercise enough care and diligence in their metadata creation.

Mission Impossible? Simple observation demonstrates people are poor

observers of their own behaviours. Therefore any meta data will be a poor representation


Life Science Identifiers (LSIDs): the visionWWW provides a globally distributed communication frameworkLSID and the LSID Resolution System

will provide a simple mechanism to globally resolve locally named objects distributed over the WWW.

LSIDs will allow us to know what kind of object it is, who originated it, who is responsible for it, how to interface to it and what computations might be carried out on it.

Adoption of LSIDs will facilitate more reliable integration of multiple knowledge bases,

each of which has partial information of a shared domain will encourage stronger global collaboration in life sciences.

Clark T., Martin S., Liefeld T. Globally Distributed Object Identification for Biological Knowledgebases Briefings in Bioinformatics 5.1:59-70, March 1, 2004.


URI based naming scheme urn:lsid:ipni.org:names:1234-1

retrieval framework

http://lsid.sourceforge.net/

Life Science Identifiers

LSID resolver

Get data

Get metadata

Data record

RDF

An LSID has data- gene sequence in GenBank- ecological data set (in excel, or in a text file)- image

The data should never change- can version

An LSID has metadata- format of the data- display title for clients - Dublin core metadata-anything you want

The metadata can change





Issues For Each Community

What gets an LSID? Real life objects

Biological specimen

Abstract concepts Taxon concept or name – Bellis perennis

Electronic representations of things Image of specimen, description of specimen or concept

For each thing, what’s the data and metadata? LSIDs

Data doesn’t change but Meta data can Should all data become meta data? Maybe it implies a temporal database approach


Issues For Each CommunityWho issues LSIDs?

Owner of data Not always clear who owns data especially legacy data

A central authority One authority responsible for issuing LSID for specific types of

information This would help enforce a 1:1 mapping of LSIDs and data items It MAY also reduce the likelihood of LSIDs becoming unresolvable

A respected authority This would help enforce a 1:1 mapping for those who use the authority It may also be more feasible

Free for all (possibly with an index) List your LSID authority in an index so your LSIDs are easy to find

Perhaps structured delegation has best potential to globally unite science


Organizations Using LSIDsBiopathways consortium

National Center for Biotech Information (NCBI) Pubmed, Genbank

European Bioinformatics Institute (EBI)BioMOBY – an biological database interoperability program

(biomoby.org) represent all entities in MOBY Ontologies (Object, Service, and

Namespace), as well as all instances of BioMOBY services. myGrid (mygrid.org.uk)

used throughout as object naming deviceTDWG (tdwg.org)

IPNI – plant names Index Fungorum – fungi names

US Long Term Ecological Research Network (LTER) SEEK (seek.ecoingformatics.org) Used in Kepler – actors, components, TOS – taxon concepts…

Use of LSIDsUse of LSIDs

Linedseahorse

Hippocampus erectus Perry 1810urn:lsid:biocast.org:concept:347

HippocampusmarginalisKaup, 1856

Hippocampustetragonous

Mitchill, 1814

Hippocampuserectus

347347347

TAX

347

347

347

347

Ecological Data Sets


Moving to a world of LSIDs

Using LSIDs alone will not address all issues of data sharingData repositories must (re)use LSIDs to cross reference data

within and outwith their own repository. it is important that we use the same LSID to refer to the same entity

If multiple LSIDs exist for the same entity we would be required to decide whether or not two LSIDs were really the same thing. We would be in a worse situation than we are today,

for example when trying to decide if two taxonomic names mean the same.

Generating LSIDs for any self contained data set is a fairly trivial task

Appointing LSIDs to existing data from an authoritative repository to re-use them is more challenging Investigate what’s involved…


Specimen PublicationConcept Name

Hexacorallia Data

Triple Store

Person

Hexacorallia Data Provider

Map to ontology

Convert Data Provider to use LSIDs

Original data repository (target)RDF Data to be updated with LSIDs

from authority providers

LSID+ RDF

LSID+ RDF

LSID+ RDF

LSID+ RDF

LSID+ RDF

Map to ontology

Match data from repository with data in LSID resolvers and return LSID to repository

LinkerTool

Match data from repository with data in LSID resolvers and return LSID to repository

Authority LSID resolution services

(source)


Linking….WASABI Service Request Dispatcher

LSIDSPARQL OAI

WASABI Service Request Dispatcher

LSIDSPARQLLinker OAI

authoritative (“source”) provider & linker

local (“target”) provider

Linker Client

Hexacorallia Thematic

Triple Store

PersonTriple Store

Request linkable

classes and select one to

be linked



LSIDSPARQL OAI





Linker Client


Triple Store

PersonTriple Store

Select class to be linked



LSIDSPARQL OAI





Linker Client


Triple Store

PersonTriple Store

Request possible LSIDs


Confirm/Skip Annotations

Person to find LSID

forChoice of possible persons with LSIDs


Issues in converting to LSIDsMapping to ontology

LSIDs RDF schema? ontology? agreement on ontology - problem?

Replace or annotate existing data? If we replace an author with a person LSID what is returned when resolving that LSID won’t likely be what data was

stored in DB for an author.Dependencies between objects with LSIDs

If you link via a taxon name LSID – the resolved name should have embedded an LSID for a publication – so there shouldn’t be any need (in principal) to match publications for names

What about authorities that issues LSIDs but don’t map to other authorities e.g. name providers not mapping to either publication or specimen

providers


Issues in converting to LSIDsWhat support would a linking tool need to provide end users?

How would users want to process this data How much automation?

E.g. above a certain confidence level Would this be trusted? Order of matching

E.g. match all instances of persons at once Match of persons by publication?

Other Issues… Performance of existing linking tool approach

Lots of data passing going on Need more efficient approach which matches user needs

Finding authorities that provide linking services How do scientists find out about authorities with linking services? How do you they which ones to use?


To Summarise….We have seen that (Life) Science is

Complex & ChangingThe fundamental challenges of science that have always been

there are still here Now we have additional opportunities associated with the explosion of

scientific information and the move to a virtual world And now the challenge is how best to exploit these….

e-Science uses computation to aid scientists By providing appropriate infrastructure and tool support

Speed up scientific processes Do them repeatedly Re-evaluation

Can give scientists time for more thoughtful science… May require a change of emphasis in how scientists work

Must support the inherent features of science, scientists and scientific data


e-Science: Complex Science Support decomposition of scientific domains,

problems and associated data Fundamental to data & software analysis and design

Support re-composition, linking or building on the components Need to know when components or links have changed

Identify the overlaps/linkages in the different domains Need useful approximations of things to simplify linked

domain Need to understand the approximations or linking points well

Raise level of abstraction Artefact of storage mechanisms Implies lingua franca Need more evaluation of the different approaches


e-Science: Changing Science

Science is full of legacy data Today’s scientific research is tomorrow’s legacy data

Provide long-term persistent storage Any published scientific discovery should store the data as

evidence Data needs to be accurately annotated

Sufficient to repeat analyses to test hypotheses

e-Science already changing the way scientists do science But to be effective it needs to change even more… More emphasis on well curated, accessible, persistent data

Evidence for results


Meta Data & Ontologies?Do we throw out meta data/ontologies, then?

No… To benefit from stored data we need to know what it means!

However, there are no large-scale benefits while there is insufficient coverage of meta data if only 10% data has meta data people won’t use meta

data… Need to reach the tipping point…

Controlled vocabulary and schemas shown useful for large projects or small communities with common goal Need long-term projects to see if they sustain their value as

the community and the science evolves.


Describe or Prescribe?

Descriptions become a vocabularies used by others

Folksonomy or ontologies? Informal versus formal or free versus constrained Informal can be basis for something formal

Move towards common vocabularies with built in flexibility and extensibility

Issue of what language(s)…Need more research evaluating these issues…


Reliability of Meta Data

Automatic recording of meta data From machines, software, workflows… Avoids labour Starting to happen Helps reach critical mass of available meta data

Still need to decide what it is that the machines/software are collecting… Human input still needed

Purpose of experiment, deviations from planned protocol etc.


SupportCommunity ontologies need to be easily available to

all scientists Listing the known ontologies on a web site is not enough

Need to understand when (meta) data is fit for purpose Accurate enough, not overly precise

Need collaborative approaches to extending ontologies Allow users to be involved to achieve community buy-in

Ontologies are difficult for people to comprehend Need good visualisation Need to trust system


ToolsSimple tools would go a long way to helpContextual data is consistent for many data sets

e.g. observer/location Tools should support collection and re-use of this data

Make use of (incorporate) existing ontologies into tools

Get the software to do as much work as possible Good at repetitive tasks, faster than humans

Personalisation How application specific do tools have to be to be useful Generic/ Domain specific/ Individual? The more generic the more widely applicable

Pluggable components for personalisation?


Finally… It will take time and commitment for any of these approaches to

work.Focus on central important resources that are reused in many

(sub-)domains Ensure the data are well managed and curated, identified, described,

easily available, lasting and evolvingObserve whether they benefit the community or act as a

straight jacketA good test case for this approach is the development of a

taxon concept name resolution service To allow scientists to find correct names for the concepts they are

working with, Mark up their data, Resolve their concepts against other scientists’ data so they know they

are talking about the same thing. Is central to communication in all life sciences Poses many computational, social and data research issues


Acknowledgements

E-Science Institute for sponsoring theme leadershipMalcolm Atkinson

For support and many interesting discussions on exploiting scientific data.

Collaborators on SEEK project,

Matt Jones, Bill Michener, Aimee Stewart, Robert Gales, Josh Madin, Shaun Bowers

Collaborators in TDWG/GBIF Robert Kukla, Roger Hyam,

funding, slides, interesting problems

Exploiting Diverse Sources of Scientific Data the vision, what has been achieved and what next…

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