IBC FAIR Data Prototype Implementation slideshow

Mark D. WilkinsonCBGP-UPM/INIA,

[email protected]

A novel, API-free approach to interoperability leads to FAIRness for legacy and prospective data.

IBC Scientific DaysJanuary 17-18, 2017

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

The ProblemWe 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

The ProblemIs that data, therefore...

Useless?

The Problem

NO!!

The Problem

It’s Reuseless!

h.t. to Barend Mons

FAIRFindable

→ 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

“Skunkworks”

Task: Build a prototype

Skunkworks ParticipantsMark WilkinsonMichel DumontierBarend MonsTim ClarkJun ZhaoPaolo CiccaresePaul GrothErik van MulligenLuiz Olavo Bonino da Silva

SantosMatthew GambleCarole GobleJoël KuiperMorris SwertzErik Schultes

Erik SchultesMercè CrosasAdrian GarciaPhilip DurbinJeffrey GretheKaty WolstencroftSudeshna DasM. Emily Merrill

The Hourglass Concept

We want a large ecosystem of apps that use FAIR Data


We want to support a wide range of source providers


The FAIR solution between them must be THIN!

Skunkworks participants had

tons of experience v.v.

metadata around scholarly

publication

Skunkworks participants had

tons of experience v.v.

metadata around scholarly

publication RDA,Force11,Dataverse,Research Objects,NanoPubs,Semantic Science,SADI, AlzForum, SWAN,LSID, ………...

There was very little

disagreement about F, about A,

or about R

The “I” is the big problem

Interoperability is Hard!!

The “I” is the big problem

Keeping the history brief

A series of teleconferences led to the concept of putting metadata into an iterative set of

~identical “containers”

Skunkworks HackathonsThe “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



a new API

ProgrammableWeb.com already catalogues >16,000 different Web APIs

APIs DO NOT MAKE YOU INTEROPERABLE!



a new API

Skunkworks Hackathons

Are there existing standards that are

And have the properties of

?

Uses machine-accessible standards and representations, following a REST paradigm

LDPUseful 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

Uses machine-accessible standards and representations, following a REST paradigm

LDPUseful 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

Inspiration

Not Adoption

The FAIR Accessor

In incremental detail

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”

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…

HTTP GET

<FAIR metadata/>

Contains

MetaRecordResource1 MetaRecordResource2 MetaRecordResource3

...

MetaRecord Resource3

<FAIR metadata/>

foaf:primaryTopic Record R

dcat:Distribution_1Source URL_U1format rdf+xml

dcat:Distribution_2Source URL_U2

format application/xml

HTTP GET

What does a FAIR Accessor “look like”?

Container Resource

(a “resource” is a URI / URL) HTTP GET

<FAIR metadata/>

Contains


...


<FAIR metadata/>





HTTP GET


HTTP GET

<FAIR metadata/>

Contains


...


<FAIR metadata/>





HTTP GET


Container Resource

Container Resource HTTP GET

<FAIR metadata/>

Contains


...



<FAIR metadata/>

Contains


...


There is a URI for the “Container”

(of any of the kinds listed in the previous slide)


<FAIR metadata/>

Contains


...


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


<FAIR metadata/>

Contains


...


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”


<FAIR metadata/>

Contains


...


Looking more closely at one of those contained things...


<FAIR metadata/>





HTTP GET


The contained thing is a Resource


<FAIR metadata/>





HTTP GET


That Resource can be resolved by HTTP GET


<FAIR metadata/>





HTTP GET


To retrieve a Metadata document describing that

resource (e.g. a single record)


<FAIR metadata/>





HTTP GET


Which record does this Metadata describe?

The foaf:primaryTopic attribute defines this


<FAIR metadata/>





HTTP GET


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


<FAIR metadata/>





HTTP GET


In this case, the record is available in XML formatBy calling HTTP GET on URL_U2


<FAIR metadata/>





HTTP GET


Or in RDF format by calling HTTP GET on URL_U1


<FAIR metadata/>

Contains


...


<FAIR metadata/>





HTTP GET


Or you may add additional layers...

Metadata

Metadata

Metadata

MetadataDATA - format 1DATA - format 2

Features of the FAIR Accessor

1: There is no APIGET

Interpret the Metadata

Select the desired Resource

GET


1: There is no APIGET

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”


49

1: There is no API

It’s difficult to get thinner than nothing...


2: Identifiers for unidentifi-ed/-able things

HTTP GET

<FAIR metadata/>

This is the ArrayExpress queryI did for paper doi:10/1234.56

Results: MetaRecordResource1 MetaRecordResource2 MetaRecordResource3

...


2: Identifiers for unidentifi-ed/-able things

HTTP GET

<FAIR metadata/>

This is the ArrayExpress queryI did for paper doi:10/1234.56

Results: MetaRecordResource1 MetaRecordResource2 MetaRecordResource3

...

Should assist with reproducibility and transparency


3: A predictable “place” for metadata

PrimaryTopic: record 1A445Record Metadata...

DATA - format 1DATA - 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


3: Symmetry & predictable path to citation

XXX

Part of dataset XXXMetadata...

DATA - format 1DATA - 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:10/8847.384License: cc-by


4: Granularity of Access/Privacy/Security


<FAIR metadata/>

Contains

<<184 Records>>

Contact Mark Wilkinson For more information about These records




<FAIR metadata/>

Contains

<<184 Records>>

Contact Mark Wilkinson For more information about These records


Container HTTP GET

<FAIR metadata/>Contains

MetaRecordResource3


<FAIR metadata/>


dcat:distribution

<<NONE>>

HTTP GET



Container HTTP GET

<FAIR metadata/>Contains

MetaRecordResource3


<FAIR metadata/>


dcat:distribution

<<NONE>>

HTTP GET


Container HTTP GET

<FAIR metadata/>

Contains MetaRecordResource3

...


<FAIR metadata/> foaf:primaryTopic Record R


HTTP GET



Container HTTP GET

<FAIR metadata/>

Contains MetaRecordResource3

...




HTTP GET





Thin solution - if it’s private, do nothing! Literally!

The Real ThingA working FAIR Accessor

Serving a “Slice” of UniProt

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 publisherinterested 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...)

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)}


WHERE{




NCBI Taxonomy:Aspergillus nidulans


WHERE{




Gene Ontology:RNA Processing

Create and publish a FAIR Accessor for that query

http://linkeddata.systems/Accessors/UniProtAccessor


<FAIR metadata/>

Contains


...


http://linkeddata.systems/Accessors/UniProtAccessor


<FAIR metadata/>

Contains


...

Resolve the URI(in software or in your browser)


Returns a page of metadata (in this example, in RDF)


<FAIR metadata/>

Contains


...

70

71

Note that this Metadata is about ME! I am the creator of this dataset, and may be credited for it.


<FAIR metadata/>

Contains


...


<FAIR metadata/>

Contains


...


<FAIR metadata/>





HTTP GET

Step down to individual Record metadata

Step down to individual Record metadata


<FAIR metadata/>





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)

<FAIR metadata/>





The document that is returned

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.

Container Resource

Symmetrical Linkback upward to the Accessor

Container, for additional metadata

<FAIR metadata/>





Two ways to retrieve the record - RDF or HTML

(in REST-speak, two Representations of that Resource)

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”)

How FAIR are we now?

What does the Accessor give us?

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

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

AAccessors 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)

So far, we have focused on FAIR Metadata

Are there approaches to making the DATA FAIR?

Making a Plant-related Resource FAIR

FAIR reformatting of the plant component of the

Pathogen Host Interaction Database (PHI-base)


Dr. Mikel Egaña ArangurenOntologist

Dr. Alejandro Rodríguez GonzálezDatabase 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.

Extract Transform Load

A “Brute Force” approach to FAIRness

Requires a ~comprehensive data/semantic model


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

General introduction 95

The Disease Triangle – Pathogen/Host/Environment

http://fyi.uwex.edu/fieldcroppathology/field-crops-fungicide-information/

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?

The Disease Triangle, Modelled as “Contexts”

Interaction Context

Environmental Context

Host Context

Pathogen ContextResulting phenotype

Interaction Context

PhenotypeResistance phenotype

Susceptibility phenotype

Phenotypic Process

1. Abnormal growth development phenotype.

2. Color variation phenotype.

3. Tissue disintegration phenotype.

4. Vascular system damage phenotype.

99

Interaction ContextPhenotypic Process Branch

100

Plant Trait Ontology +

Interaction ContextPhenotypic Process Branch


Interaction Context


Host Context


Environmental Context manually extracted from the literature






A pathogen that enters throughthe stomata will be more successful

in high humidity, and have higher pathogenicity




Interaction Context


Host Context


• Plant-pathogen interaction data, including:

• Resulting phenotypes• Molecular/genetic basis of pathogenicity• Experimental approaches• Provenance information

Host Context

Pathogen Context

• 4800 interactions• 3300 gene-mutant records• 220 pathogens• 130 hosts• 261 registered diseases• 1700 references

Host Context

Pathogen Context

Interaction Context

Interaction Context

[WT]

Interaction Context[mutant]

Host Context

Host Context

PathogenContext

Pathogen Context

Description DescriptionProtocol Protocol

“Historical observation”

“Base state” “reduced

virulence”

“soft rot”

Protocol descriptionCitation

PubMedID“PMID:1234”

“gene deletion”



Rodríguez-Iglesias A. et al Front. Plant Sci., (2016)

Interaction Context

Interaction Context

[WT]

Interaction Context[mutant]

Host Context

Host Context

PathogenContext

Pathogen Context

Description DescriptionProtocol Protocol

“Historical observation”

“Base state” “reduced

virulence”

“soft rot”

Protocol descriptionCitation

PubMedID“PMID:1234”

“gene deletion”




Pathogen Context

Allele

Gene

Locus ID

Gene function

Gene name

Gene accession

“AEQ95741”

“Effector protein”

“TAL2G”

“G7TJZ8”


Pathogen Context

Allele

Gene

Locus ID

Gene function

Gene name

Gene accession

“AEQ95741”


“TAL2G”

“G7TJZ8”


http://identifiers.org/*/*

Pathogen Context

Allele

Gene

Locus ID

Gene function

Gene name

Gene accession

“AEQ95741”


“TAL2G”

“G7TJZ8”


rdfs:label

Pathogen Context

Allele

Gene

Locus ID

Gene function

Gene name

Gene accession

“AEQ95741”


“TAL2G”

“G7TJZ8”



Pathogen Context

Allele

Gene

Locus ID

Gene function

Gene name

Gene accession

“AEQ95741”


“TAL2G”

“G7TJZ8”



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!!

Transform PHI-base data into RDF compliant with the PPIO Ontology

Load into Virtuoso Triplestore



(this was a LOT of work!!)



…but are we now FAIR?



…but are we now FAIR?

…Not really….

Findable

Accessible

Interoperable

ReusableX

X

HTTP GET, SPARQL, open access

RDF with published ontologies

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

Build a FAIR Accessorhttp://linkeddata.systems/SemanticPHIBase/Metadata


<FAIR metadata/>

Contains


...


<FAIR metadata/>

foaf:primaryTopic SemPHI #1

dcat:Distribution_1Source URL_U1Format rdf+xml

dcat:Distribution_2Source URL_U2Format HTML

HTTP GET


<FAIR metadata/>

Contains


...


<FAIR metadata/>




HTTP GET


The URL of the record in“native” PHI-base


<FAIR metadata/>

Contains


...


<FAIR metadata/>




HTTP GET


The URL of the (RDF) record inSemantic PHI-base

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 interestselected based on its Record Metadata

But… FAIR Accessors should be symmetrical

How do I go from data back “upwards” to metadata?


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


...


How do I go from data back “upwards” to metadata?

Container URL HTTP GET

<FAIR metadata/>

Contains


...

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

Findable

Accessible

Interoperable

Reusable

The Brute Force approach is…

a lot of work!

Worthwhile for community-critical resources and

databases like AgroLD, UniProt, PHI-base,

ChEMBL, etc.

Is there a more “elegant” & lightweight

way to be FAIR?

FAIR Projection:

Providing FAIR Datafrom non-FAIR Data

Dynamically

This is going to be a bit complicated, but please be patient

Imagine the data we need to integrate

is in a CSV filein FigShare or Zenodo

How do we discover and integrate that data?

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 :-)

Triple Pattern Fragments+

RDF Mapping Language

Ruben VerborghGhent University

Anastasia DimouGhent University

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.

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.”)

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.”)

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

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?)

RMLA 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

Using RML to describe the structure

and semantics of a single Triple

Map1

PredicateObject Map

SubjectMap

ObjectMap

ex:PatientRecord

subjectMap template“http://example.org/patient/{id}”

predicateObjectM

ap

predicate ex:hasVariant

objectMa

p

class

Map2

parentTriplesMap

SubjectMap2

SO:0000694(“SNP”)

subjectMap template“http://identifiers.org/dbsnp/{snp}”

class

THE MODEL



Map1

PredicateObject Map

SubjectMap

ObjectMap

ex:PatientRecord


predicateObjectM

ap


objectMa

p

class

Map2

parentTriplesMap

SubjectMap2

SO:0000694(“SNP”)


class

THE MODEL

We call this a “Triple Descriptor”

These are used to describe the structure of data

“slices” in which all Triples have the same structure

THE MODEL



Map1

PredicateObject Map

SubjectMap

ObjectMap

ex:PatientRecord


predicateObjectM

ap


objectMa

p

class

Map2

parentTriplesMap

SubjectMap2

SO:0000694(“SNP”)


class

Patient:123

rdf:type

ex:PatientRecord

snp:rs0020394

ex:hasVariant

rdf:type

ex:PatientRecord

The Data

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

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

Luckily, we have already solved this!


<FAIR metadata/>



dcat:Distribution_2Source URL_U2format 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

If we consider the TPF Resource URL to be just another DCAT Distribution, we get...

<FAIR metadata/>




dcat:Distribution_3 Source TPF_URL Format rdf+xml

<FAIR metadata/>




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

If we consider the TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple Descriptor <FAIR metadata/>




format application/xml dcat:Distribution_3

Source TPFrag_URL_1

format rdf+xmlModel: Triple_Desc_URL

<FAIR metadata/>




dcat:Distribution_3 Source TPF_URL Format rdf+xml Model: Triple_Desc_URL

<FAIR metadata/>




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 thatURL returns:

<FAIR metadata/>





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

If we consider the TPF Resource URL to be just another DCAT Distribution, we get… now add the Triple DescriptorHTTP GET on TPF_URL

returns rdf+xml triples from Record R

That look like

Interoperability

without Brute Force

<FAIR metadata/>





I hear you objecting… I skipped something important!!!We still have not defined a

way to CREATE these triples





I hear you objecting… I skipped something important!!!

How does this return Triples?

We still have not defined a way to

CREATE these triples

Sadly, there is no magic wand to create interoperability

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”)

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!

However, there are reasons for optimism!

AND4. 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

Summary of FAIR Projectors

FAIR Projectors provide a discoverable and standardized REST interface to retrieve interoperable data, and its interoperable metadata

F+I+R

A + I

IFAIR 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 ServicesR+

++Triple Descriptors are FAIR entities, intended for reuse, & None of this required a new API

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

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

Thanks to my former lab members… I MISS YOU!!!

Dr. Mikel Egaña ArangurenOntologist

Dr. Alejandro Rodríguez GonzálezDatabase Expert

Dr. Alejandro Rodríguez Iglesias(PhD student at the time)

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-NLRD-Connect and ELIXIR (Rare disease implementation study FP7 no. 305444).

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