Exploiting Diverse Sources of Scientific Data the vision, what has been achieved
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Exploiting Diverse Sources of Scientific Data the vision, what has been achieved and what next… Prof. Jessie Kennedy e-SI Theme: Exploiting Diverse Sources of Scientific Data Science & Scientific Data Science and Scientific Data are Complex… Exploiting Diverse Sources of Scientific Data Climatology Hydrology Meteorology Geography Geology Ecology Paleontology Genomics Taxonomy Nomenclature Proteomics Morphology Biochemistry Climatology Hydrology Meteorology Geography Temperature Geology Organism Ecology Taxon concept Paleontology Gene sequence Genomics Taxonomy Proteomics Name Nomenclature Protein Morphology Pathway Biochemistry Scientific Community: complex Small Scientific Community Individual Scientist Large Scientific Community Exploiting Diverse Sources of Scientific Data Scientific Laboraotory Meteorology Meteorology Meteorology Meteorology Geology Geology Geology Geology Climatology Climatology Climatology Climatology Temperature Temperature Temperature Temperature Hydrology Hydrology Hydrology Hydrology Geography Geography Geography Geography Organism Organism Organism Organism Ecology Ecology Ecology Ecology Taxon Gene Taxon concept Gene Paleontology Taxon sequence concept Gene Paleontology Taxon sequence concept Genomics Gene Paleontology Taxonomy sequence concept Genomics Proteomics Paleontology Taxonomy Name sequence Genomics Proteomics Taxonomy Name Genomics Proteomics Protein Taxonomy Name Proteomics Protein Morphology Name Nomenclature Protein Morphology Nomenclature Pathway Protein Morphology Nomenclature Pathway Morphology Nomenclature Pathway Biochemistry Pathway Biochemistry Biochemistry Biochemistry 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 conclusion observation experiment hypothesis Exploiting Diverse Sources of Scientific Data Exploiting Diverse Sources of Scientific Data: the vision To 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 Exploiting Diverse Sources of Scientific Data Projects in most sciences: ESG Exploiting Diverse Sources of Scientific Data 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 approaches Data 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 Exploiting Diverse Sources of Scientific Data: the approaches Data 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 Exploiting Diverse Sources of Scientific Data: the approaches Data 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 Exploiting Diverse Sources of Scientific Data: the technologies Standardisation of formats Metadata Ontologies Life Science Identifiers (LSIDs) Provenance Workflow Tools GRID/Web technology Exploiting Diverse Sources of Scientific Data Exploiting Diverse Sources of Scientific Data: the technologies Standardisation of formats Metadata Ontologies Life Science Identifiers (LSIDs) Provenance Workflow Tools GRID/Web technology Exploiting Diverse Sources of Scientific Data 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…. Exploiting Diverse Sources of Scientific Data 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 Exploiting Diverse Sources of Scientific Data 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 Exploiting Diverse Sources of Scientific Data Ecological Metadata Language A look inside the meta-utopia of ecology knb.ecoinformatics.org Identification: dataset elements knb.ecoinformatics.org Identification: resource elements knb.ecoinformatics.org Identification: party elements knb.ecoinformatics.org Discovery: coverage elements Geographic Temporal Taxonomic knb.ecoinformatics.org Evaluation Level Information knb.ecoinformatics.org Evaluation: Method Information knb.ecoinformatics.org Evaluation: Project Information L3 knb.ecoinformatics.org Access: Permissions Information L4 knb.ecoinformatics.org Access: Physical Information knb.ecoinformatics.org Access: Physical formatting details knb.ecoinformatics.org Access: Distribution Information L4 knb.ecoinformatics.org Integration Level Information knb.ecoinformatics.org Integration Level: Attribute structure knb.ecoinformatics.org Integration Level: attribute domains knb.ecoinformatics.org Integration Level: attribute domains knb.ecoinformatics.org Integration Level: measurementScale knb.ecoinformatics.org 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… Exploiting Diverse Sources of Scientific Data 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. Exploiting Diverse Sources of Scientific Data 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 Exploiting Diverse Sources of Scientific Data 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 Exploiting Diverse Sources of Scientific Data 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 entity An extension point for domain-specific terms Josh Madin Characteristic Josh Madin Measurement standard Similar to… All the units, scales, indices, classifications, and lists used for ‘measuring’ a characteristic Josh Madin Semantic Web for Earth and Environmental Terminology (SWEET) Ontologies revised and validated Jan 26, 2006 Earth Realm Physical Phenomena Physical Process Physical Property Physical Substance Sun Realm Biosphere Data Data Center Human Activity Material Thing Numerics Sensor Space Time Units Exploiting Diverse Sources of Scientific Data Takes us back to… BDI Taxon Concept Ontology …is really just a schema for representing … Biological Taxonomy Classify 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 Exploiting Diverse Sources of Scientific Data Taxonomic history of imaginary genus Aus L. 1758 Linneaus 1758 Aus L.1758 Archer 1965 Aus L.1758 Aus aus L.1758 Aus aus L.1758 Aus bea Archer 1965 Fry 1989 Tucker 1991 Pargiter 2003 Aus L.1758 Aus L.1758 Aus L.1758 Aus aus L.1758 Aus aus L. 1758 Aus aus L.1758 Aus ceus BFry 1989 Aus bea Archer 1965 Aus cea BFry 1989 Aus cea BFry 1989 (vi) Xus Pargiter 2003 Xus beus (Archer) Pargiter 2003. Pyle 1990 5 Revisions of Aus 1 name spelling change Aus bea and Aus cea noted as invalid names and replaced with Aus beus and Aus ceus. Exploiting Diverse Sources of Scientific Data Taxonomic history of imaginary genus Aus L. 1758 Linneaus 1758 Aus L.1758 Archer 1965 Aus L.1758 Aus aus L.1758 Aus aus L.1758 Aus bea Archer 1965 Fry 1989 Tucker 1991 Pargiter 2003 Aus L.1758 Aus L.1758 Aus L.1758 Aus aus L.1758 Aus aus L. 1758 Aus aus L.1758 Aus ceus BFry 1989 Aus bea Archer 1965 Aus cea BFry 1989 Aus cea BFry 1989 (vi) Xus Pargiter 2003 Xus beus (Archer) Pargiter 2003. • 8 Names • 2 genus • 6 species Pyle 1990 Aus bea and Aus cea noted as invalid names and replaced with Aus beus and Aus ceus. Exploiting Diverse Sources of Scientific Data C0.1 C0.2 N0 Results in many concepts for each name N0 - Aus L.1758 C0.3 C0.4 C0.5 C1.1 N1 N1 - Aus aus L.1758 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 C1.1 - Aus aus L.1758 sec. Linneaeus 1758 C1.2 C1.2 - Aus aus L.1758 sec. Archer 1965 C1.3 C1.3 - Aus aus L.1758 sec. Fry 1989 C1.4 C1.5 C2.2 N2 C1.4 - Aus aus L.1758 sec. Tucker 1991 C1.5 - Aus aus L.1758 sec. Pargiter 2003 C2.2 - Aus bea Archer 1965 sec. Archer 1965 C2.3 C2.3 - Aus bea Archer 1965 sec. Fry 1989 C3.3 C3.3 - Aus cea Fry 1989 sec. Fry 1989 C3.4 C3.4 - Aus cea Fry 1989 sec. Tucker 1991 N5 N5 - Aus ceus Fry 1989 C5.5 C5.5 - Aus ceus Fry 1989 sec. Fry 1989 N6 N6 - Xus beus Pargiter 2003 C6.5 C6.6 - Xus beus Pargiter 2003 sec. Pargiter 2003 C7.5 C7.6 - Xus Pargiter 2003 sec. Pargiter 2003 N2 - Aus bea Archer 1965 N3 N3 - Aus cea Fry 1989 N4 N4 - Aus beus Archer 1965 N7 N7 - Xus Pargiter 2003 8 Names 17 Concepts Possible interpretations of Aus aus L. 1758 Request data sets about Aus aus (N1) what’s returned? C1.1 Original concept: C1.1 N1 - Aus aus L.1758 Most recent concept: C1.5 Preferred Authority (e.g. Fry 1989): C1.3 Everything ever named N1: 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) C1.2 C1.3 C1.4 Is it appropriate to link or merge data on this? Depends on the user’s purpose Level of precision required Exploiting Diverse Sources of Scientific Data C1.5 Classifications synonymy relationships between concepts and names. N7 N0 Parent child relationships in 5 revisions C0.1 C1.1 C0.2 C1.2 C0.3 C2.2 C1.3 C2.3 C0.5 C0.4 C3.3 C1.4 C3.4 C1.5 C7.5 C5.5 C6.5 N5 N6 Names for each of the concepts N1 N2 N3 N4 In the literature taxonomists tell us names that are synonymous with their concepts Exploiting Diverse Sources of Scientific Data Classifications synonymy relationships between concepts and names. N7 N0 C0.1 C1.1 C0.2 C1.2 C0.3 C2.2 C1.3 C2.3 C0.5 C0.4 C3.3 C1.4 C3.4 C1.5 C7.5 C5.5 C6.5 Which can result in anything being returned for Aus aus by traversing the synonymy links N1 N2 N3 N4 Exploiting Diverse Sources of Scientific Data N5 N6 Classifications with set relationships between concepts. We can build systems to return data suit for purpose N7 N0 What we need are the set relationships from concepts in a revision to earlier concepts C0.2 C0.1 C1.1 C1.2 C0.3 C2.2 C1.3 C2.3 C3.3 C1.4 C0.5 C0.4 C3.4 C1.5 C7.5 C5.5 C6.5 N5 N6 and name changes related to earlier names N1 N3 N2 = N4 = Exploiting Diverse Sources of Scientific Data Real Taxonomic Revisions German 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 taxa Taxonomic Revision of genus Alteromonas 34 years: from 1972 to 2006 Thanks to George Garrity, Michigan State Univ. Exploiting Diverse Sources of Scientific Data 1972 Alteromonas macleodii(T) communis vaga 1972 1973 Alteromonas macleodii(T) communis vaga haloplanktis 1972 1973 1976 Alteromonas macleodii(T) communis vaga haloplanktis rubra 1972 1973 1976 1977 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea 1972 1973 1976 1977 1978 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina 1972 1973 1976 1977 1978 1979 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia 1972 1973 1976 1977 1978 1979 1981 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai 1972 1973 1976 1977 1978 1979 1981 1982 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae 1972 1973 1976 1977 1978 1979 1981 1982 1984 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae 1972 1973 1976 1977 1978 1979 1981 1982 1984 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum 1986 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae Shewanella putrifaciens(T) benthica hanedai 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum 1987 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans Shewanella putrifaciens(T) benthica hanedai 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum 1988 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana Shewanella putrifaciens(T) benthica hanedai 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense 1990 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis Shewanella putrifaciens(T) benthica hanedai colwelliana 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense 1992 Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora Shewanella putrifaciens(T) benthica hanedai colwelliana algae 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora distincta fuliginea 1995 Shewanella putrifaciens(T) benthica hanedai colwelliana algae 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora distincta fuliginea 1995 Shewanella putrifaciens(T) benthica hanedai colwelliana algae Pseudoalteromonas haloplanktis haloplanktis(T) haloplanktis tetradonis atlantica aurantia carrageenovora citrea esperjiana luteoviolacea nigrifaciens pisicida rubra undina 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora distincta fuliginea elyakoviii 1997 Shewanella putrifaciens(T) benthica hanedai colwelliana algae Pseudoalteromonas haloplanktis haloplanktis(T) haloplanktis tetradonis atlantica aurantia carrageenovora citrea esperjiana luteoviolacea nigrifaciens pisicida rubra undina antartica 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium mediterannea biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora distincta fuliginea elyakoviii 2000 Shewanella putrifaciens(T) benthica hanedai colwelliana algae fridgidimarina geldimarina woodyii amazonensis baltica oneidensis pealeana violacea Pseudoalteromonas haloplanktis haloplanktis(T) haloplanktis tetradonis atlantica aurantia carrageenovora citrea esperjiana luteoviolacea nigrifaciens pisicida rubra undina antartica bacteriolytica prydzensis tunicata distincta elyakovii peptidolytica 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium mediterannea biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora distincta fuliginea elyakoviii 2001 Shewanella putrifaciens(T) benthica hanedai colwelliana algae fridgidimarina geldimarina woodyii amazonensis baltica oneidensis pealeana violacea japonica Pseudoalteromonas haloplanktis haloplanktis(T) haloplanktis tetradonis atlantica aurantia carrageenovora citrea esperjiana luteoviolacea nigrifaciens pisicida rubra undina antartica bacteriolytica prydzensis tunicata distincta elyakovii peptidolytica tetrodonis 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 2001 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium mediterannea biejerinckii maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora distincta fuliginea elyakoviii 2002 Shewanella putrifaciens(T) benthica hanedai colwelliana algae fridgidimarina geldimarina woodyii amazonensis baltica oneidensis pealeana violacea japonica denitrificans livingstonensis alleyanna Pseudoalteromonas haloplanktis haloplanktis(T) haloplanktis tetradonis atlantica aurantia carrageenovora citrea esperjiana luteoviolacea nigrifaciens pisicida rubra undina antartica bacteriolytica prydzensis tunicata distincta elyakovii peptidolytica tetrodonis 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 2001 2002 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium mediterannea biejerinckii primoryensis maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora distincta fuliginea elyakoviii stellipolaris litorea Shewanella putrifaciens(T) benthica hanedai colwelliana algae fridgidimarina geldimarina woodyii amazonensis baltica oneidensis pealeana violacea japonica denitrificans livingstonensis alleyanna mariniintestina saire schlegeliana gaetbuli 5 others 2004 Pseudoalteromonas haloplanktis haloplanktis(T) haloplanktis tetradonis atlantica aurantia carrageenovora citrea esperjiana luteoviolacea nigrifaciens pisicida rubra undina antartica bacteriolytica prydzensis tunicata distincta elyakovii peptidolytica tetrodonis 12 others 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 2001 2002 2004 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium mediterannea biejerinckii primoryensis maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora distincta fuliginea elyakoviii stellipolaris litorea 2 others Shewanella putrifaciens(T) benthica hanedai colwelliana algae fridgidimarina geldimarina woodyii amazonensis baltica oneidensis pealeana violacea japonica denitrificans livingstonensis alleyanna mariniintestina saire schlegeliana gaetbuli 8 others 2005 Pseudoalteromonas haloplanktis haloplanktis(T) haloplanktis tetradonis atlantica aurantia carrageenovora citrea esperjiana luteoviolacea nigrifaciens pisicida rubra undina antartica bacteriolytica prydzensis tunicata distincta elyakovii peptidolytica tetrodonis 14 others 1972 1973 1976 1977 1978 1979 1981 1982 1984 1986 1987 1988 1990 1992 1995 1997 2000 2001 2002 2004 2005 Oceanosprillum Marinomonas linum(T) communis(T) japonicum vaga minutium mediterannea biejerinckii primoryensis maris maris maris williamsae hiroshimense multiglobiferum pelagicum pusillum commune jannaschii kreigii vagum biejerinckii pelagicum maris hiroshimense Alteromonas macleodii(T) communis vaga haloplanktis rubra citrea esperjiana undina aurantia putrifaciens hanedai luteoviolaceae denitrificans colwelliana tetradonis atlantica carageenovora distincta fuliginea elyakoviii stellipolaris litorea 2 others Shewanella putrifaciens(T) benthica hanedai colwelliana algae fridgidimarina geldimarina woodyii amazonensis baltica oneidensis pealeana violacea japonica denitrificans livingstonensis alleyanna mariniintestina saire schlegeliana gaetbuli 13 others 2006 Pseudoalteromonas haloplanktis haloplanktis(T) haloplanktis tetradonis atlantica aurantia carrageenovora citrea esperjiana luteoviolacea nigrifaciens pisicida rubra undina antartica bacteriolytica prydzensis tunicata distincta elyakovii peptidolytica tetrodonis 14 others May 2004 November 2004 Gammaproteobacteria Alteromonadales Alteromonadacea Colwelliaceae Alteromonas Colwelliaceae Aestuariibacter Thalassomonas Alishewanella Colwellia At the species Ferrimonas level 18 “emendations” Glaciecola 21 new species Idiomarina 19 species reassigned to 4 genera Marinobacter 3 new combinations Marinobacterium 6 synonyms 2 speciesMicrobulbifer to subspecies 2 subspecies to species Moritella 50 names, five genera, five families, and two classes but….Pseudoalteromonas only 5 validlyPsychromonas published species. At the higher level Shewanella 1 Family 16 genera -> 8 families 12 genera Thalassomonas 1 unclassified genus -> 7 unclassified genera Incertae sedis Which is correct? Teredinibacter Which is supported/recorded in the data? What is the impact on Analysis? Ferrimonadacea Ferrimonas Pseudoalteromonadaceae Pseudoalteromonas Algicola Idiomarinaceae Idiomarina Psychromonadaceae Psychromonas Incertae sedis Agarvorans Alishewanella Shewanellaceae Shewanella Moritellaceae Moritella Marinobacter Marinobacterium Microbulbifer Salinomonas Teredinibacter Meta-utopia - a pipe dream? What is meta-data? Ecological Data set Your meta data is my data… Depends on your perspective But it’s useful to differentiate for certain purposes META DATA It’s all data anyway….. Taxonomic Data DATA How you see the world What’s important to you What you want to do with the “data” Meta data Name: Year: Linnaeus 1758 Taxon Higher Taxon Picea Pinaceae Picea abies Picea Picea rubens Picea Exploiting Diverse Sources of Scientific Data Data Meta-utopia - a pipe dream? 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. Exploiting Diverse Sources of Scientific Data Geographic/cartographic perspective Instance of Picea rubens is-a feature that can be mapped Features inherently have geospatial coordinates. Taxonomic perspective Instance of Picea rubens is a specimen of some biological taxon Taxa inherently have characteristics used in classification Feature Building Observation Organism occurrence Pinaceae Picea Picea abies Exploiting Diverse Sources of Scientific Data Picea rubens Picea rubens Meta-utopia - a pipe dream? There's more than one way to describe something Exploiting Diverse Sources of Scientific Data Exploiting Diverse Sources of Scientific Data Meta-utopia - a pipe dream? 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… Exploiting Diverse Sources of Scientific Data Meta-utopia - a pipe dream? 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. Exploiting Diverse Sources of Scientific Data Meta-utopia - a pipe dream? 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 Exploiting Diverse Sources of Scientific Data Meta-utopia - a pipe dream? 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 Exploiting Diverse Sources of Scientific Data Life Science Identifiers (LSIDs): the vision WWW provides a globally distributed communication framework LSID 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 Exploiting Diverse Sources of Scientific Data Bioinformatics 5.1:59-70, March 1, 2004. Life Science Identifiers URI based naming scheme An LSID has data - gene sequence in GenBank urn:lsid:ipni.org:names:1234-1An LSID has metadata - ecological data set (in - format oftext the file) data excel, or in a - display title for clients - image - Dublin metadata The data shouldcore never change -anything - can version you want The metadata can change retrieval framework Get data Data record Get metadata RDF LSID resolver http://lsid.sourceforge.net/ Exploiting Diverse Sources of Scientific Data 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 Exploiting Diverse Sources of Scientific Data Issues For Each Community Who 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 Exploiting Diverse Sources of Scientific Data Organizations Using LSIDs Biopathways 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 device TDWG (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… Exploiting Diverse Sources of Scientific Data Use of LSIDs Ecological Data Sets Hippocampus tetragonous Mitchill, 1814 Lined seahorse 347 347 Hippocampus erectus Hippocampus marginalis Kaup, 1856 347 347 Hippocampus erectus Perry 1810 urn:lsid:biocast.org:concept:347 TAX 347 347 347 Moving to a world of LSIDs Using LSIDs alone will not address all issues of data sharing Data 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… Exploiting Diverse Sources of Scientific Data Convert Data Provider to use LSIDs Hexacorallia Data Provider Map to ontology Linker Tool Original data RDF Data to be repository (target) updated with LSIDs Authority LSID from authority resolution providersservices (source) Hexacorallia Data Triple Store Match data data from from repository repository with with data data in in LSID LSID resolvers resolvers Match and return return LSID LSID to to repository repository and Specimen LSID + RDF Name LSID + RDF Concept Publication LSID + RDF LSID + RDF Exploiting Diverse Sources of Scientific Data Person LSID + RDF Linking…. WASABI Service Request Dispatcher SPARQL LSID OAI local (“target”) provider Hexacorallia Thematic Triple Store Linker Client Request linkable classes and select one to be linked Linker SPARQL LSID OAI WASABI Service Request Dispatcher Exploiting Diverse Sources of & Scientific authoritative (“source”) provider linker Data Person Triple Store Linking…. WASABI Service Request Dispatcher SPARQL LSID OAI local (“target”) provider Hexacorallia Thematic Triple Store Select class to be linked Linker Client Linker SPARQL LSID OAI WASABI Service Request Dispatcher Exploiting Diverse Sources of & Scientific authoritative (“source”) provider linker Data Person Triple Store Linking…. WASABI Service Request Dispatcher SPARQL LSID OAI local (“target”) provider Request possible LSIDs Linker Hexacorallia Thematic Triple Store Linker Client SPARQL LSID OAI WASABI Service Request Dispatcher Exploiting Diverse Sources of & Scientific authoritative (“source”) provider linker Data Person Triple Store Confirm/Skip Annotations Person to find LSID for Choice of possible persons with LSIDs Exploiting Diverse Sources of Scientific Data Issues in converting to LSIDs Mapping 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 Exploiting Diverse Sources of Scientific Data Issues in converting to LSIDs What 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? Exploiting Diverse Sources of Scientific Data To Summarise…. We have seen that (Life) Science is Complex & Changing The 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 Exploiting Diverse Sources of 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 Exploiting Diverse Sources of Scientific Data 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 Exploiting Diverse Sources of Scientific Data 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. Exploiting Diverse Sources of Scientific Data 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… Exploiting Diverse Sources of Scientific Data 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. Exploiting Diverse Sources of Scientific Data Support Community 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 Exploiting Diverse Sources of Scientific Data Tools Simple tools would go a long way to help Contextual 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? Exploiting Diverse Sources of Scientific Data 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 evolving Observe whether they benefit the community or act as a straight jacket A 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 Exploiting Diverse Sources of Scientific Data Acknowledgements E-Science Institute for sponsoring theme leadership Malcolm 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
