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IBC FAIR Data Prototype Implementation slideshow

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Discussion about ways of achieving FAIRness of both metadata and data. Brute force approaches, and more elegant "projection" approaches are shown.

Relevant papers are at:
doi: 10.7717/peerj-cs.110 (https://peerj.com/articles/cs-110/)
doi: 10.3389/fpls.2016.00641 (https://doi.org/10.3389/fpls.2016.00641)


Spanish Ministerio de Economía y Competitividad grant number TIN2014-55993-R

Published in: Science
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IBC FAIR Data Prototype Implementation slideshow

  1. 1. Mark D. Wilkinson CBGP-UPM/INIA, Madrid markw@illuminae.com A novel, API-free approach to interoperability leads to FAIRness for legacy and prospective data. IBC Scientific Days January 17-18, 2017
  2. 2. The Problem ...one recent survey of 18 microarray studies found that only two were fully reproducible using the archived data. Another study of 19 papers in population genetics found that 30% of analyses could not be reproduced from the archived data and that 35% of datasets were incorrectly or insufficiently described. “ ” Dominique G. Roche , Loeske E. B. Kruuk, Robert Lanfear, Sandra A. Binning (2015) http://dx.doi.org/10.1371/journal.pbio.1002295
  3. 3. The Problem We surveyed 100 datasets associated with nonmolecular studies in journals that commonly publish ecological and evolutionary research and have a strong PDA policy. Out of these datasets, 56% were incomplete, and 64% were archived in a way that partially or entirely prevented reuse. “ ” Dominique G. Roche , Loeske E. B. Kruuk, Robert Lanfear, Sandra A. Binning (2015) http://dx.doi.org/10.1371/journal.pbio.1002295
  4. 4. The Problem Is that data, therefore... Useless?
  5. 5. The Problem NO!!
  6. 6. The Problem It’s Reuseless!  h.t. to Barend Mons
  7. 7. FAIR Findable → Globally unique, resolvable, and persistent identifiers → Machine-actionable contextual information supporting discovery Accessible → Clearly-defined access protocol → Clearly-defined rules for authorization/authentication Interoperable → Use shared vocabularies and/or ontologies → Syntactically and semantically machine-accessible format Reusable → Be compliant with the F, A, and I Principles → Contextual information, allowing proper interpretation → Rich provenance information facilitating accurate citation The Four Principles
  8. 8. “Skunkworks” Task: Build a prototype
  9. 9. Skunkworks Participants Mark Wilkinson Michel Dumontier Barend Mons Tim Clark Jun Zhao Paolo Ciccarese Paul Groth Erik van Mulligen Luiz Olavo Bonino da Silva Santos Matthew Gamble Carole Goble Joël Kuiper Morris Swertz Erik Schultes Erik Schultes Mercè Crosas Adrian Garcia Philip Durbin Jeffrey Grethe Katy Wolstencroft Sudeshna Das M. Emily Merrill
  10. 10. The Hourglass Concept We want a large ecosystem of apps that use FAIR Data
  11. 11. The Hourglass Concept We want to support a wide range of source providers
  12. 12. The Hourglass Concept The FAIR solution between them must be THIN!
  13. 13. Skunkworks participants had tons of experience v.v. metadata around scholarly publication
  14. 14. Skunkworks participants had tons of experience v.v. metadata around scholarly publication RDA, Force11, Dataverse, Research Objects, NanoPubs, Semantic Science, SADI, AlzForum, SWAN, LSID, … … … ...
  15. 15. There was very little disagreement about F, about A, or about R
  16. 16. The “I” is the big problem
  17. 17. Interoperability is Hard!! The “I” is the big problem
  18. 18. Keeping the history brief A series of teleconferences led to the concept of putting metadata into an iterative set of ~identical “containers”
  19. 19. Skunkworks Hackathons The “containers of containers of containers” idea was elaborated by the belief that we should also reject any solution that required a new API ProgrammableWeb.com already catalogues >16,000 different Web APIs
  20. 20. Skunkworks Hackathons The “containers of containers of containers” idea was elaborated by the belief that we should also reject any solution that required a new API ProgrammableWeb.com already catalogues >16,000 different Web APIs APIs DO NOT MAKE YOU INTEROPERABLE!
  21. 21. Skunkworks Hackathons The “containers of containers of containers” idea was elaborated by the belief that we should also reject any solution that required a new API
  22. 22. Skunkworks Hackathons Are there existing standards that are And have the properties of ?
  23. 23. Uses machine-accessible standards and representations, following a REST paradigm LDP Useful Features I I + R F + A I Defines HTTP-resolvable URIs for each of these containers Defines the concept of a “Container” - a machine-actionable way to represent repositories, data deposits, data files, data points, and their metadata Uses a widely accepted standard (DCAT) to relate metadata to data → machine- actionable data mining
  24. 24. Uses machine-accessible standards and representations, following a REST paradigm LDP Useful Features I I + R F + A I Defines HTTP-resolvable URIs for each of these containers Defines the concept of a “Container” - a machine-actionable way to represent repositories, data deposits, data files, data points, and their metadata Uses a widely accepted standard (DCAT) to relate metadata to data → machine- actionable data mining
  25. 25. The FAIR Accessor In incremental detail
  26. 26. What can we describe with FAIR Accessors? FAIR Accessors provide a machine-actionable, structured, REST-oriented way to publish Metadata about a wide range of scholarly “entities”
  27. 27. What can we describe with FAIR Accessors? Warehouses (e.g. EBI) Databases (e.g. UniProt) Repositories (e.g. Zenodo, INRA-URGI Wheat Repo, UniProt) Datasets (e.g. output from a workflow) Research Objects (data a/o workflow a/o results a/o publications) Data “slices” (e.g. the result of a database query) Data Records (e.g. image, excel file, patient clinical record) Other…
  28. 28. HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Container Resource
  29. 29. (a “resource” is a URI / URL) HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”?
  30. 30. HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Container Resource
  31. 31. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”?
  32. 32. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”? There is a URI for the “Container” (of any of the kinds listed in the previous slide)
  33. 33. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”? Resources are manipulated using the HTTP protocol on the Resource URI For the FAIR Accessor, the only HTTP method we currently require is HTTP GET
  34. 34. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”? What is returned is a document full of metadata richly describing that Container (warehouse, database, dataset, slice, etc.) And a list of Resources (URIs) that represent the contained “things”
  35. 35. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... What does a FAIR Accessor “look like”? Looking more closely at one of those contained things...
  36. 36. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? The contained thing is a Resource
  37. 37. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? That Resource can be resolved by HTTP GET
  38. 38. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? To retrieve a Metadata document describing that resource (e.g. a single record)
  39. 39. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Which record does this Metadata describe? The foaf:primaryTopic attribute defines this
  40. 40. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Using the metadata structures defined by DCAT the FAIR Accessor may also tell you how to get the content of the record, and what formats are available
  41. 41. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? In this case, the record is available in XML format By calling HTTP GET on URL_U2
  42. 42. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”? Or in RDF format by calling HTTP GET on URL_U1
  43. 43. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET What does a FAIR Accessor “look like”?
  44. 44. Or you may add additional layers... Metadata Metadata Metadata Metadata DATA - format 1 DATA - format 2
  45. 45. Features of the FAIR Accessor 1: There is no API GET Interpret the Metadata Select the desired Resource GET
  46. 46. Features of the FAIR Accessor 1: There is no API GET Interpret the Metadata Select the desired Resource GET ANY Web agent can explore/index a FAIR Accessor (e.g. Google) An agent that understands globally-accepted vocabularies can explore it “intelligently”
  47. 47. Features of the FAIR Accessor 49 1: There is no API It’s difficult to get thinner than nothing...
  48. 48. Features of the FAIR Accessor 2: Identifiers for unidentifi-ed/-able things HTTP GET <FAIR metadata/> This is the ArrayExpress query I did for paper doi:10/1234.56 Results: MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
  49. 49. Features of the FAIR Accessor 2: Identifiers for unidentifi-ed/-able things HTTP GET <FAIR metadata/> This is the ArrayExpress query I did for paper doi:10/1234.56 Results: MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... Should assist with reproducibility and transparency
  50. 50. Features of the FAIR Accessor 3: A predictable “place” for metadata PrimaryTopic: record 1A445 Record Metadata... DATA - format 1 DATA - format 2 Different “kinds” of metadata have distinct ontological types, and distinct document structures. There is no ambiguity regarding what the metadata is describing - a repository or a record. Repository metadata MetaRecordURL
  51. 51. Features of the FAIR Accessor 3: Symmetry & predictable path to citation XXX Part of dataset XXX Metadata... DATA - format 1 DATA - format 2 The record metadata contains an “upward” link to the Repository- level metadata, which should contain license and citation information Repository metadata: Cite: doi:10/8847.384 License: cc-by
  52. 52. Features of the FAIR Accessor 4: Granularity of Access/Privacy/Security Container Resource HTTP GET <FAIR metadata/> Contains <<184 Records>> Contact Mark Wilkinson For more information about These records
  53. 53. Features of the FAIR Accessor 4: Granularity of Access/Privacy/Security Container Resource HTTP GET <FAIR metadata/> Contains <<184 Records>> Contact Mark Wilkinson For more information about These records
  54. 54. Features of the FAIR Accessor Container HTTP GET <FAIR metadata/> Contains MetaRecordResource3 MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:distribution <<NONE>> HTTP GET 4: Granularity of Access/Privacy/Security
  55. 55. Features of the FAIR Accessor Container HTTP GET <FAIR metadata/> Contains MetaRecordResource3 MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:distribution <<NONE>> HTTP GET 4: Granularity of Access/Privacy/Security
  56. 56. Container HTTP GET <FAIR metadata/> Contains MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml HTTP GET Features of the FAIR Accessor 4: Granularity of Access/Privacy/Security
  57. 57. Container HTTP GET <FAIR metadata/> Contains MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml HTTP GET Features of the FAIR Accessor 4: Granularity of Access/Privacy/Security
  58. 58. Features of the FAIR Accessor 4: Granularity of Access/Privacy/Security Thin solution - if it’s private, do nothing! Literally!
  59. 59. The Real Thing A working FAIR Accessor Serving a “Slice” of UniProt
  60. 60. A real-world scenario... You are publishing a paper describing the evolution of proteins in the RNA Processing machineries of the fungus Aspergillus nidulans. You want to be a good scholarly publisher interested in transparency and reproducibility So you must describe, in detail, the inclusion/exclusion criteria for selecting proteins for your dataset (today, this is generally done either in the text of the paper, or not at all...)
  61. 61. The query that returns the relevant proteins WHERE { ?protein a up:Protein . ?protein up:organism ?organism . ?organism rdfs:subClassOf taxon:162425 . ?protein up:classifiedWith ?go . ?go rdfs:subClassOf* <http://purl.obolibrary.org/obo/GO_0006396> . bind(replace(str(?protein), "http://purl.uniprot.org/uniprot/", "", "i") as ?id) }
  62. 62. The query that returns the relevant proteins WHERE { ?protein a up:Protein . ?protein up:organism ?organism . ?organism rdfs:subClassOf taxon:162425 . ?protein up:classifiedWith ?go . ?go rdfs:subClassOf* <http://purl.obolibrary.org/obo/GO_0006396> . bind(replace(str(?protein), "http://purl.uniprot.org/uniprot/", "", "i") as ?id) } NCBI Taxonomy: Aspergillus nidulans
  63. 63. The query that returns the relevant proteins WHERE { ?protein a up:Protein . ?protein up:organism ?organism . ?organism rdfs:subClassOf taxon:162425 . ?protein up:classifiedWith ?go . ?go rdfs:subClassOf* <http://purl.obolibrary.org/obo/GO_0006396> . bind(replace(str(?protein), "http://purl.uniprot.org/uniprot/", "", "i") as ?id) } Gene Ontology: RNA Processing
  64. 64. Create and publish a FAIR Accessor for that query http://linkeddata.systems/Accessors/UniProtAccessor Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
  65. 65. Create and publish a FAIR Accessor for that query http://linkeddata.systems/Accessors/UniProtAccessor Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... Resolve the URI (in software or in your browser)
  66. 66. Create and publish a FAIR Accessor for that query Returns a page of metadata (in this example, in RDF) Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
  67. 67. 70
  68. 68. 71 Note that this Metadata is about ME! I am the creator of this dataset, and may be credited for it.
  69. 69. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
  70. 70. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET Step down to individual Record metadata
  71. 71. Step down to individual Record metadata MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET Software calls HTTP GET on the URL representing the MetaRecord Resource for the desired record in the Container (or just click on it, or type it into your browser)
  72. 72. <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml The document that is returned
  73. 73. Note the change in metadata focus! This metadata is about the UniProt Record (not about Mark Wilkinson). The record described in this metadata was created by UniProt, so the citation and authorship information is now THEIRS, not MINE.
  74. 74. Container Resource Symmetrical Link back upward to the Accessor Container, for additional metadata
  75. 75. <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml
  76. 76. Two ways to retrieve the record - RDF or HTML (in REST-speak, two Representations of that Resource)
  77. 77. Note that this metadata record is somewhat more FAIR, than what you can (easily) retrieve from UniProt itself! e.g. the UniProt record does not include the citation or license information - you have to manually surf around the UniProt Web page to find that. So the Accessor makes UniProt’s already notably FAIR data, even more FAIR (with respect to “R”)
  78. 78. How FAIR are we now? What does the Accessor give us?
  79. 79. What we have achieved We have created a FAIR record for something - i.e. a slice of a database - that was, historically, un-recordable and un-identifiable in any formal way.F F + A F + R Accessors are a standard approach to providing human & machine accessible metadata to facilitate appropriate discovery (contextual, biological), proper usage (license) and proper citation for any kind of data. The discovery, accessibility, and drill-down/up behaviors do not require any novel API, rather simply rely on global Web standards; this allows them to be indexed by existing Web search engines
  80. 80. What we have achieved F + AI The metadata itself uses machine-accessible syntaxes, and widely adopted ontologies and vocabularies, thus easily integrates with other metadata A Accessors provide a lightweight means to protect privacy while still providing the maximum degree of transparency possible + Accessors can be static, or dynamic. i.e. we can provide template Accessor file(s) that are edited in Notepad, then published together with the data; or Accessors can dynamically generate their output from code (e.g. layered on a database server)
  81. 81. So far, we have focused on FAIR Metadata
  82. 82. Are there approaches to making the DATA FAIR?
  83. 83. Making a Plant-related Resource FAIR FAIR reformatting of the plant component of the Pathogen Host Interaction Database (PHI-base)
  84. 84. Making a Plant-related Resource FAIR Dr. Mikel Egaña Aranguren Ontologist Dr. Alejandro Rodríguez González Database Expert Dr. Alejandro Rodríguez Iglesias (PhD student at the time) Rodriguez-Iglesias A., Rodriguez-González A., Irvine AG., Sesma A., Urban M., Hammond-Kosack KE., Wilkinson MD. 2016. Publishing FAIR Data: An Exemplar Methodology Utilizing PHI-Base. Frontiers in Plant Science 7.
  85. 85. Extract  Transform  Load A “Brute Force” approach to FAIRness Requires a ~comprehensive data/semantic model Making a Plant-related Resource FAIR
  86. 86. The Plant Pathogen Interaction Ontology (PPIO) Written in OWL2 Many of the Classes are defined by rich logical axioms Designed for automated classification and enrichment of data through logical reasoning (e.g. if attached to a data stream) Semantic Modeling of Plant Pathogen Interaction Data
  87. 87. General introduction 95 The Disease Triangle – Pathogen/Host/Environment http://fyi.uwex.edu/fieldcroppathology/field-crops-fungicide-information/
  88. 88. General introduction 96 The Disease Triangle – Pathogen/Host/Environment This concept has evolved over decades of domain-expert thought and discussion Why not use this as the basis for our Semantic model of Pathogenicity?
  89. 89. The Disease Triangle, Modelled as “Contexts” Interaction Context Environmental Context Host Context Pathogen Context Resulting phenotype
  90. 90. Interaction Context Phenotype Resistance phenotype Susceptibility phenotype Phenotypic Process 1. Abnormal growth development phenotype. 2. Color variation phenotype. 3. Tissue disintegration phenotype. 4. Vascular system damage phenotype.
  91. 91. 99 Interaction Context Phenotypic Process Branch
  92. 92. 100 + Interaction Context Phenotypic Process Branch
  93. 93. The Disease Triangle, Modelled as “Contexts” Interaction Context Environmental Context Host Context Pathogen Context Resulting phenotype
  94. 94. Environmental Context manually extracted from the literature Environmental Context
  95. 95. Environmental Context manually extracted from the literature Environmental Context
  96. 96. Environmental Context manually extracted from the literature Environmental Context
  97. 97. A pathogen that enters through the stomata will be more successful in high humidity, and have higher pathogenicity Environmental Context manually extracted from the literature Environmental Context
  98. 98. The Disease Triangle, Modelled as “Contexts” Interaction Context Environmental Context Host Context Pathogen Context Resulting phenotype
  99. 99. • Plant-pathogen interaction data, including: • Resulting phenotypes • Molecular/genetic basis of pathogenicity • Experimental approaches • Provenance information Host Context Pathogen Context
  100. 100. • 4800 interactions • 3300 gene-mutant records • 220 pathogens • 130 hosts • 261 registered diseases • 1700 references Host Context Pathogen Context
  101. 101. Interaction Context Interaction Context [WT] Interaction Context [mutant] Host Context Host Context Pathogen Context Pathogen Context Description DescriptionProtocol Protocol “Historical observation” “Base state” “reduced virulence” “soft rot” Protocol descriptionCitation PubMed ID “PMID:1234” “gene deletion” Environmental Context Environmental Context Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)
  102. 102. Interaction Context Interaction Context [WT] Interaction Context [mutant] Host Context Host Context Pathogen Context Pathogen Context Description DescriptionProtocol Protocol “Historical observation” “Base state” “reduced virulence” “soft rot” Protocol descriptionCitation PubMed ID “PMID:1234” “gene deletion” Environmental Context Environmental Context Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)
  103. 103. Pathogen Context Allele Gene Locus ID Gene function Gene name Gene accession “AEQ95741” “Effector protein” “TAL2G” “G7TJZ8” Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)
  104. 104. Pathogen Context Allele Gene Locus ID Gene function Gene name Gene accession “AEQ95741” “Effector protein” “TAL2G” “G7TJZ8” Rodríguez-Iglesias A. et al Front. Plant Sci., (2016) http://identifiers.org/*/*
  105. 105. Pathogen Context Allele Gene Locus ID Gene function Gene name Gene accession “AEQ95741” “Effector protein” “TAL2G” “G7TJZ8” Rodríguez-Iglesias A. et al Front. Plant Sci., (2016) rdfs:label
  106. 106. Pathogen Context Allele Gene Locus ID Gene function Gene name Gene accession “AEQ95741” “Effector protein” “TAL2G” “G7TJZ8” Rodríguez-Iglesias A. et al Front. Plant Sci., (2016) http://identifiers.org/*/*
  107. 107. Pathogen Context Allele Gene Locus ID Gene function Gene name Gene accession “AEQ95741” “Effector protein” “TAL2G” “G7TJZ8” Rodríguez-Iglesias A. et al Front. Plant Sci., (2016) http://identifiers.org/*/* KEY MESSAGE: Because we use identifiers.org URIs, and AgroLD does also, we can query our Pathogen Host Interaction database, and DYNAMICALLY RETRIEVE additional information from AgroLD with NO additional effort!!
  108. 108. Transform PHI-base data into RDF compliant with the PPIO Ontology Load into Virtuoso Triplestore
  109. 109. Transform PHI-base data into RDF compliant with the PPIO Ontology Load into Virtuoso Triplestore (this was a LOT of work!!)
  110. 110. Transform PHI-base data into RDF compliant with the PPIO Ontology Load into Virtuoso Triplestore …but are we now FAIR?
  111. 111. Transform PHI-base data into RDF compliant with the PPIO Ontology Load into Virtuoso Triplestore …but are we now FAIR? …Not really….
  112. 112. Findable Accessible Interoperable ReusableX X HTTP GET, SPARQL, open access RDF with published ontologies
  113. 113. Findable Accessible Interoperable ReusableX X • How would you find this database? • How would you know if anything interesting is in it? • How would you (your machine) find a record? • Who do you cite if you reuse a piece of data? • What are the license conditions? • Can I reuse the data at all?? HTTP GET, SPARQL, open access RDF with published ontologies
  114. 114. Build a FAIR Accessor http://linkeddata.systems/SemanticPHIBase/Metadata Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic SemPHI #1 dcat:Distribution_1 Source URL_U1 Format rdf+xml dcat:Distribution_2 Source URL_U2 Format HTML HTTP GET
  115. 115. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic SemPHI #1 dcat:Distribution_1 Source URL_U1 Format rdf+xml dcat:Distribution_2 Source URL_U2 Format HTML HTTP GET Build a FAIR Accessor http://linkeddata.systems/SemanticPHIBase/Metadata The URL of the record in “native” PHI-base
  116. 116. Container Resource HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic SemPHI #1 dcat:Distribution_1 Source URL_U1 Format rdf+xml dcat:Distribution_2 Source URL_U2 Format HTML HTTP GET Build a FAIR Accessor http://linkeddata.systems/SemanticPHIBase/Metadata The URL of the (RDF) record in Semantic PHI-base
  117. 117. This allows us to find Semantic PHI-base based on its Repository-level Metadata “what kind of data does Semantic PHI-base Contain?” “Does it have any information about my gene of interest?” And “drill-down” to a record of interest selected based on its Record Metadata
  118. 118. But… FAIR Accessors should be symmetrical How do I go from data back “upwards” to metadata?
  119. 119. But… FAIR Accessors should be symmetrical How do I go from data back “upwards” to metadata? To allow the retrieval of the Metadata for any piece of data in Semantic PHI Base Use the URL of the FAIR Accessor (Container Resource) as the URL of the “Named Graph” in the triplestore using RDF “Quads” SubjectURI  PredicateURI  ObjectURI  ContextURL Container URL HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ...
  120. 120. But… FAIR Accessors should be symmetrical How do I go from data back “upwards” to metadata? Container URL HTTP GET <FAIR metadata/> Contains MetaRecordResource1 MetaRecordResource2 MetaRecordResource3 ... To allow the retrieval of the Metadata for any piece of data in Semantic PHI Base Use the URL of the FAIR Accessor (Container Resource) as the URL of the “Named Graph” in the triplestore using RDF “Quads” SubjectURI  PredicateURI  ObjectURI  Container URL
  121. 121. Findable Accessible Interoperable Reusable
  122. 122. The Brute Force approach is… a lot of work! Worthwhile for community-critical resources and databases like AgroLD, UniProt, PHI-base, ChEMBL, etc.
  123. 123. Is there a more “elegant” & lightweight way to be FAIR?
  124. 124. FAIR Projection: Providing FAIR Data from non-FAIR Data Dynamically
  125. 125. This is going to be a bit complicated, but please be patient
  126. 126. Imagine the data we need to integrate is in a CSV file in FigShare or Zenodo How do we discover and integrate that data?
  127. 127. Things we need to do: We need a way to query “opaque” data blobs (like CSV) about their content We need a way to retrieve that content in a FAIR format We need, therefore, to model semantics for that opaque data content We need to model various semantics for that content (one “size” doesn’t fit all!) We need to associate those semantic models with a record or record-sets We need a way to query those semantics determine which “size” fits our req’s We would like to reuse semantic definitions as much as possible We need to do all of this without creating a new API :-)
  128. 128. Triple Pattern Fragments + RDF Mapping Language Ruben Verborgh Ghent University Anastasia Dimou Ghent University
  129. 129. Triple Pattern Fragments (TPF) A REST interface for requesting/retrieving RDF Triples (from any source) Ruben Verborgh “Slices” of data, from any source, are considered Resources and are therefore represented by a distinct URL: http://some.database.org/dataset?s=___;p=___;o=___ Calling HTTP GET on a TPF URL returns the set of Triples matching {?s, ?p, ?o} PLUS hypermedia instructions and Resource URLs for other relevant slices.
  130. 130. Triple Pattern Fragments (TPF) A REST interface for retrieving RDF Triples (from any source) Ruben Verborgh For example, the “BMI” column from a patient registry is a Resource with the URL: http://my.registry.org/patients?p=CMO:0000105 (CMO:0000105 = “body mass index””) HTTP GET gives me all BMI triples in the registry, together with other Resource URLs representing other “slices” that might be useful, for example: http://my.registry.org/patients?p=CMO:0000004 (CMO:0000004 = “systolic B.P.”)
  131. 131. Triple Pattern Fragments (TPF) A REST interface for retrieving RDF Triples (from any source) Ruben Verborgh For example, the “BMI” column from a patient registry is a Resource with the URL: http://my.registry.org/patients?p=CMO:0000105 (CMO:0000105 = “body mass index””) HTTP GET gives me all BMI triples in the registry, together with other Resource URLs representing other “slices” that might be useful, for example: http://my.registry.org/patients?p=CMO:0000004 (CMO:0000004 = “systolic B.P.”)
  132. 132. We have a standard, RESTful way to request triples from any data source i.e. every slice of every dataset will be considered a distinct Resource → simply call HTTP GET on that Resource to get the Triples
  133. 133. But... We have no way to know what TPF Resources are available for any given dataset or what those Resources “are” (proteins? genes? patients? articles?)
  134. 134. RML A way to describe the structure of an RDF document Anastasia Dimou RML allows us to create models of (meta)data structures “What could this data look like, if it were mapped to RDF?” RML fulfills similar objectives to DCAT Profiles, the Dublin Core Application Profile, and ISO 11179 - Metadata Registries; but has added advantages! http://rml.io/RMLmappingLanguage.html
  135. 135. Using RML to describe the structure and semantics of a single Triple Map1 Predicate Object Map Subject Map Object Map ex:Patient Record subjectMap template “http://example.org/patient/{id}” predicate ex:has Variant Map2 Subject Map2 SO:0000694 (“SNP”) template “http://identifiers.org/dbsnp/{snp}” T H E M O D E L
  136. 136. Using RML to describe the structure and semantics of a single Triple Map1 Predicate Object Map Subject Map Object Map ex:Patient Record subjectMap template “http://example.org/patient/{id}” predicate ex:has Variant Map2 Subject Map2 SO:0000694 (“SNP”) template “http://identifiers.org/dbsnp/{snp}” T H E M O D E L We call this a “Triple Descriptor” These are used to describe the structure of data “slices” in which all Triples have the same structure
  137. 137. T H E M O D E L Using RML to describe the structure and semantics of a single Triple Map1 Predicate Object Map Subject Map Object Map ex:Patient Record subjectMap template “http://example.org/patient/{id}” predicate ex:has Variant Map2 Subject Map2 SO:0000694 (“SNP”) template “http://identifiers.org/dbsnp/{snp}” Patient:123 rdf:type ex:Patient Record snp: rs0020394 ex:hasVariant rdf:type ex:Patient Record The Data
  138. 138. Where are we now? TPF - A standard, RESTful way to request Triples Triple Descriptors - A standard way to describe the structure and meaning of a Triple
  139. 139. Where are we now? TPF - A standard, RESTful way to request Triples Triple Descriptors - A standard way to describe the structure and meaning of a Triple We need a way to associate these with each other We need a way to associate these with a dataset or record
  140. 140. Luckily, we have already solved this!
  141. 141. MetaRecord Resource3 <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml HTTP GET The FAIR Accessor can do this Using the metadata structures defined by DCAT the FAIR Accessor also tells you how to get the content of the record, and what formats are available
  142. 142. If we consider the TPF Resource URL to be just another DCAT Distribution, we get... <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml
  143. 143. <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml If we consider the TPF Resource URL to be just another DCAT Distribution, we get... URL representing the Triple Pattern Fragment Resource
  144. 144. If we consider the TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple Descriptor <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPFrag_URL_1 format rdf+xml Model: Triple_Desc_URL <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml Model: Triple_Desc_URL
  145. 145. <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL_1 Format rdf+xml Model: Triple_Desc_URL If we consider the TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple Descriptor HTTP GET on that URL returns:
  146. 146. <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml Model: Triple_Desc_URL If we consider the TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple Descriptor Record + TPF Server + RML Model = FAIR Projector
  147. 147. If we consider the TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple Descriptor HTTP GET on TPF_URL returns rdf+xml triples from Record R That look like Interoperability without Brute Force <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml Model: Triple_Desc_URL
  148. 148. I hear you objecting… I skipped something important!!! We still have not defined a way to CREATE these triples
  149. 149. <FAIR metadata/> foaf:primaryTopic Record R dcat:Distribution_1 Source URL_U1 format rdf+xml dcat:Distribution_2 Source URL_U2 format application/xml dcat:Distribution_3 Source TPF_URL Format rdf+xml Model: Triple_Desc_URL I hear you objecting… I skipped something important!!! How does this return Triples? We still have not defined a way to CREATE these triples
  150. 150. Sadly, there is no magic wand to create interoperability
  151. 151. Sadly, there is no magic wand to create interoperability Someone has to write the TPF server that converts the data Interoperability will never come “for free” (because semantics will never come “for free”)
  152. 152. However, there are reasons for optimism! 1. Researchers transform data anyway to integrate it - this is a daily routine in most bioinformatics labs 2. For the most common file formats (e.g. CSV or Excel), there are RML-based tools to automate the RDF transformation; simply create an RML model of what you want, and ask the tool to covert the file. 3. Investing time into creating an RML model is more FAIR than ad hoc “re-useless” brute-force transformation. When you create a FAIR Projector for your own data transformation needs, it is reusable!
  153. 153. However, there are reasons for optimism! AND 4. RML Triple Descriptors are very simple (one triple!) so we can also templatize their construction  creating a FAIR Projector is quite easy in many cases! 5. Citations Citations Citations! FAIR Accessors/Projectors are FAIR objects - You can get credit if other people use your Projector for their analyses
  154. 154. Summary of FAIR Projectors FAIR Projectors provide a discoverable and standardized REST interface to retrieve interoperable data, and its interoperable metadataF+I+R A + I I FAIR Projectors can convert non-FAIR data into FAIR data, or can change the structure, URL format, or semantics of existing FAIR data sources FAIR Projectors can be deployed over, and provide a common interface to: - Static Data Deposits, in any format, anywhere - Databases - Triplestores - Certain (common) types of Web Services R+++ Triple Descriptors are FAIR entities, intended for reuse, & None of this required a new API
  155. 155. Siri… I need data about the expression of the Oryza dwarf-1 gene under high salt conditions. Please find that data, regardless of location and format If possible, please reformat it automatically to match my local dataset Also, please collect the citation information for each piece of data If the data is not under an open access license, or if any of the data is behind a firewall or paywall, please provide me the contact information of the data owner so that I can ask them for a copy. The (near!) future of FAIR
  156. 156. Thanks to: Michel Dumontier - Stanford Center for Biomedical Informatics Research, Stanford, California. Ruben Verborgh – Ghent University – imec, Ghent, Belgium Luiz Olavo Bonino da Silva Santos - Dutch Techcentre for Life Sciences, Utrecht, The Netherlands - Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. Tim Clark - Department of Neurology, Massachusetts General Hospital Boston MA and Harvard Medical School, Boston, MA, USA Morris A. Swertz - Genomics Coordination Center and Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands Fleur D.L. Kelpin - Genomics Coordination Center and Department of Genetics, University Medical Center Groningen, Groningen, The Netherlands Alasdair J. G. Gray - Department of Computer Science, School of Mathematical and Computer Sciences, Heriot-Watt University, Edinburgh, UK Erik A. Schultes - Department of Human Genetics, Leiden University Medical Center, The Netherlands Erik M. van Mulligen - Department of Medical Informatics, Erasmus University Medical Center Rotterdam, The Netherlands Paolo Ciccarese - Perkin Elmer Innovation Lab, Cambridge MA and Harvard Medical School, Boston MA, USA Mark Thompson - Leiden University Medical Center, Leiden, The Netherlands Jerven T. Bolleman - Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva, Switzerland
  157. 157. Thanks to my former lab members… I MISS YOU!!! Dr. Mikel Egaña Aranguren Ontologist Dr. Alejandro Rodríguez González Database Expert Dr. Alejandro Rodríguez Iglesias (PhD student at the time)
  158. 158. Funding for Mark Wilkinson from: Fundacion BBVA and the UPM Isaac Peral programme, and the Spanish Ministerio de Economía y Competitividad grant number TIN2014-55993-R. Additional support for FAIR Skunkworks members comes from: European Union funded projects ELIXIR-EXCELERATE (H2020 no. 676559), ADOPT BBMRI-ERIC (H2020 no. 676550) CORBEL (H2020 no. 654248) Netherlands Organisation for Scientific Research (Odex4all project) Stichting Topconsortium voor Kennis en Innovatie High Tech Systemen en Materialen (FAIRdICT project) BBMRI-NL RD-Connect and ELIXIR (Rare disease implementation study FP7 no. 305444).
  159. 159. You are not only welcome to share and reuse this presentation... ...You are encouraged to! http://tinyurl.com/IBC-FAIR
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