THE VODAN FAIR DATA POINT FAIR DATA THE UNDERLYING PROBLEM MOST - - PowerPoint PPT Presentation

THE VODAN FAIR DATA POINT

FAIR DATA

THE UNDERLYING PROBLEM

FRAGMENTATION

• f…

Data sample collections image collections Regulations software tools research initiatives Funding Expertise etc.

WE NEED ACTIONABLE DATA!! MOST DATA DON’T ‘TALK’ TO EACH OTHER

Findable:

• F1. (meta)data are assigned a globally unique and persistent

identifier;

• F2. data are described with rich metadata;
• F3. metadata clearly and explicitly include the identifier of

the data it describes;

• F4. (meta)data are registered or indexed in a searchable

resource;

Accessible:

• A1. (meta)data are retrievable by their identifier using a

standardized communications protocol; A1.1 the protocol is open, free, and universally implementable; A1.2. the protocol allows for an authentication and authorization procedure, where necessary;

• A2. metadata are accessible, even when the data are no longer

available;

Interoperable:

• I1. (meta)data use a formal, accessible, shared, and broadly

applicable language for knowledge representation.

• I2. (meta)data use vocabularies that follow FAIR principles;
• I3. (meta)data include qualified references to other

(meta)data;

Reusable:

• R1. (meta)data are richly described with a plurality of accurate

and relevant attributes; R1.1. (meta)data are released with a clear and accessible data usage license; R1.2. (meta)data are associated with detailed provenance; R1.3. (meta)data meet domain-relevant community standards;

https://www.nature.com/articles/sdata201618

FAIR DATA PRINCIPLES

• Sci. Data 3: 160018 doi: 10.1038/sdata.2016.18 (2016

Accessible:

• A1. (meta)data are retrievable by their identifier using a

standardized communications protocol; A1.1 the protocol is open, free, and universally implementable; A1.2. the protocol allows for an authentication and authorization procedure, where necessary;

• A2. metadata are accessible, even when the data are no longer

available;

ACCESSIBLE UNDER WELL DEFINED CONDITIONS: NOT ALWAYS OPEN AND FREE!

STAY IN CONTROL OF YOUR DATA

THE INTERNET OF FAIR DATA AND SERVICES

0101110101 00111010111000111 010001110101001010101 101010101010101010011101 11000111010101101010001110 10100101010111010101010101 01010101001110101110001110 10101101010001110101001010 10111010101010101010101010 01110101110001110101011010 100011101010010101011101 010101010101010100111 100011101010110101 01001010101

DATA STATION

Provides FAIR access to data and metadata Allows train to access and interact with data

TRAIN

Interacts with data (process, integrate, analyze, …)

DATA GATEWAY

Provides access and control to the data authority regardless of where the data is located/stored

TRACKS

The routing and transport infrastructure

IFDS MAIN ELEMENTS

FDS FAIR Data Station (FDS) FDS FDS FDS FDS Algorithm

THE FAIR DATA TRAIN: ALGORITHMS TO DATA - TRAINS TO STATIONS

DATA DO NOT LEAVE THE STATION!

THE FAIR DATA TRAIN: ALGORITHMS TO DATA - TRAINS TO STATIONS

DATA VISTING VERSUS DATA SHARING

THE INTERNET FOR MACHINES

The Machine knows what I mean As open as possible, as closed as necessary As distributed as possible, as central as needed Global: FAIR aka Fully AI-Ready

FAIR DATA AND THE INTERNET OF FAIR DATA AND SERVICES

Data machine readable Principles generic; so applicable in all domains Principles; not standards so no replacement or threat Honoring what is already there, connecting at a higher level Local FAIR Data Points visited by algorithms Data do NOT leave the source or country Only use relevant data for exchange and analytics Controlled access

• FINDABLE

ACCESSIBLE INTEROPERABLE REUSABLE

Without good metadata NO effective interoperability Metadata ‘describe’ the data source in detail

Structure and Internal coherence Source reference and Licenses Time stamped changes to the data Quality Context Provenance and maintenance

META DATA ESSENTIAL FOR INTEROPERABILITY

META DATA

APPLIED INTELLIGENCE POSSIBILITIES

FAIR Data well

• rganized as basis for

AI (Fully AI Ready) Underlying ontology provides relationships Allows for creating semantic triples Enables knowledge graph based in silico discovery

THE VODAN FDP

VIRUS OUTBREAK DATA NETWORK (VODAN) PROJECT

Uganda China

VIRUS OUTBREAK DATA NETWORK (VODAN) PROJECT

Uganda China Italy Ireland USA

THE WORLD HEALTH ORGANIZATION ELECTRONIC CASE RECORD FORM

THE WORLD HEALTH ORGANIZATION ELECTRONIC CASE RECORD FORM

WHO provided form

THE WORLD HEALTH ORGANIZATION ELECTRONIC CASE RECORD FORM

VODAN team created a semantic model

THE WORLD HEALTH ORGANIZATION ELECTRONIC CASE RECORD FORM

VODAN project provides RDF

WHO eCRF as RDF

Based upon semantic model RDF is created so the input becomes available as machine readable FAIR data

FAIR DATA POINT

Current situation

W h a t ? Where? H

• w

?

VODAN data approach

Data Metadata Real-world phenomena Represents Describes

• Provenance
• License
• Access conditions
• Semantic description
• …

FAIR Data Point

• Provides access to structured and semantically-rich metadata describing:
• The data source itself;
• Groups of datasets (catalogs);
• Datasets;
• The accessible method(s) of each dataset (distributions);
• Common interface to access the metadata (REST API);

VODAN data infrastructure

A network of FDPs to:

• Facilitate exposure/publication
• f metadata about COVID-19

data;

• Provide rich metadata;
• Provide semantic descriptions
• f both metadata and data;
• Improve machine-actionability
• n metadata and data;

WHO’s COVID-19 CRF semantic data model

• Based on WHO’s COVID 19 CRF, rapid version - https://www.who.int/docs/default-

source/coronaviruse/who-ncov-crf.pdf?sfvrsn=84766e69_2 ;

• Provide machine-actionable semantic references for the form’s questions and answers;
• Open access: CC BY 4.0;
• Source file available at: https://github.com/FAIRDataTeam/WHO-COVID-CRF
• Documentation: https://vodan-ontology.github.io
• Published in BioPortal: http://bioportal.bioontology.org/ontologies/COVIDCRFRAPID

WHO’s COVID-19 CRF semantic data model

Antiviral à SNOMEDCT:372701006 Antiviral Agent à NCIT:C281 Antiviral Agents à MESH:D000998 … Ribavirin à SNOMEDCT:387188005 Ribavirin à NCIT:C807 Ribavirin à MESH:D012254 … Lopinavir- and ritonavir-containing product à SNOMEDCT:387067003 Lopinavir/Ritonavir à NCIT:C2096 Lopinavir-ritonavir drug combination à MESH:D558899 …

Albert Mons

International Project Consultant GO FAIR CEO FAIR Solutions

albert.mons@fairsolutions.com

THANK YOU AND Q&A

Luiz Bonino

Technical Advisor to GO FAIR Associate Professor at University of Twente And Leiden University Medical Center

luiz.bonino@go-fair.org

GLOSSARY OF FAIR TERMS: CONTENT

FAIR: Enabling both humans and machines to use data more efficiently by making data Findable, Accessible, Interoperable and Reusable GO FAIR: A global collaborative community implementing FAIR Data and Services using good practices Metadata: A set of data about a given object/resource that helps to describe it Semantic Interoperability: the ability of different systems to share meaning Conceptual Modelling: a discipline to support the formal description of conceptualizations Conceptual model: a formal representation of concepts to support the understanding of a given universe of discourse Ontology: a formal description of a shared conceptualization FAIR Implementation Profile (FIP): the collection of FAIR implementation choices made in a community of practice FAIR Data Stewardship: maximizing the re-use of data

GLOSSARY OF FAIR TERMS: EVENTS

FAIR awareness Event (FAE): A half day high level introduction to FAIR and the GO FAIR ecosystem FAIR Value Event (FVE): A three day event demonstrating the value of FAIR with a real use case Bring Your Own Data Event (BYOD): A 2 day event making data FAIR and answering research questions FAIR Data Stewardship Course (FDS): a 5 day course on FAIR data and FAIR Data Stewardship Ontology Modelling Course (OM): a 5 day course on Conceptual and Ontology Modelling FAIR Implementation Profile Workshop (FIP): A hands-on activity profiling the collection of FAIR implementation choices made in a community of practice Meta Data for Machines Workshop (M4M): A hands-on activity creating (or re-using) machine-actionable metadata templates and instances for deployment of FAIR Data and Services

GLOSSARY OF FAIR TERMS: TOOLS

FAIR Data Stewardship Wizard (FDSW): tool to create FAIR Data Policy plans FAIRifier: an application to support data wrangling, semantic data modelling, metadata definition and FAIR publication FAIR Data Station: a server application to publish metadata and allow the interaction with data in a FAIR way FAIR Evaluator: an application to assess the FAIRness levels of different types of resources, namely, metadata, data,

• ntologies, applications, etc.

FAIR Meta Data Search Engine: a server application enabling humans and machines to search for FAIR objects/resources based

• n the indexing of their metadata

Ontology Modelling tool: an application to support the modelling/definition of ontologies Ontology Management System: a server application to support the management of the lifecycle of models (data models,

• ntologies, semantic data models, etc.)