THE VODAN FAIR DATA POINT
FAIR DATA
THE UNDERLYING PROBLEM MOST DATA DON’T ‘TALK’ TO EACH OTHER FRAGMENTATION of… Data sample collections image collections Regulations software tools research initiatives Funding Expertise etc. WE NEED ACTIONABLE DATA!!
FAIR DATA PRINCIPLES Findable: Accessible: F1. (meta)data are assigned a globally unique and persistent A1. (meta)data are retrievable by their identifier using a identifier; standardized communications protocol; F2. data are described with rich metadata; A1.1 the protocol is open, free, and universally implementable; F3. metadata clearly and explicitly include the identifier of the data it describes; A1.2. the protocol allows for an authentication and authorization procedure, where necessary; F4. (meta)data are registered or indexed in a searchable resource; A2. metadata are accessible, even when the data are no longer available; Interoperable: Reusable: I1. (meta)data use a formal, accessible, shared, and broadly R1. (meta)data are richly described with a plurality of accurate applicable language for knowledge representation. and relevant attributes; I2. (meta)data use vocabularies that follow FAIR principles; R1.1. (meta)data are released with a clear and accessible data usage license; I3. (meta)data include qualified references to other (meta)data; R1.2. (meta)data are associated with detailed provenance; R1.3. (meta)data meet domain-relevant community standards; https://www.nature.com/articles/sdata201618 Sci. Data 3: 160018 doi: 10.1038/sdata.2016.18 (2016
ACCESSIBLE UNDER WELL DEFINED CONDITIONS: NOT ALWAYS OPEN AND FREE! Accessible: STAY IN CONTROL A1. (meta)data are retrievable by their identifier using a OF YOUR DATA 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;
THE INTERNET OF FAIR DATA AND SERVICES
IFDS MAIN ELEMENTS DATA STATION TRAIN Provides FAIR access to Interacts with data data and metadata (process, integrate, analyze, …) Allows train to access and interact with data 0101110101 00111010111000111 010001110101001010101 101010101010101010011101 11000111010101101010001110 10100101010111010101010101 DATA GATEWAY TRACKS 01010101001110101110001110 10101101010001110101001010 Provides access and 10111010101010101010101010 The routing and 01110101110001110101011010 control to the data transport infrastructure 100011101010010101011101 authority regardless of 010101010101010100111 100011101010110101 where the data is 01001010101 located/stored
THE FAIR DATA TRAIN: ALGORITHMS TO DATA - TRAINS TO STATIONS DATA DO NOT LEAVE THE STATION! FDS FAIR Data Station (FDS) Algorithm FDS FDS FDS FDS
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 FINDABLE Honoring what is already there, connecting at a higher level ACCESSIBLE Local FAIR Data Points visited by algorithms INTEROPERABLE Data do NOT leave the source or country REUSABLE Only use relevant data for exchange and analytics Controlled access o
META DATA ESSENTIAL FOR INTEROPERABILITY Without good metadata NO effective interoperability Metadata ‘describe’ the data source in detail Structure and Internal coherence Source reference and Licenses META Time stamped changes to the data DATA Quality Context Provenance and maintenance
APPLIED INTELLIGENCE POSSIBILITIES FAIR Data well organized as basis for AI ( F ully AI R eady) 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 China Uganda
VIRUS OUTBREAK DATA NETWORK (VODAN) PROJECT USA China Ireland Italy Uganda
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 Based upon semantic model RDF is created so the input becomes available as machine readable FAIR data WHO eCRF VODAN project as provides RDF RDF
FAIR DATA POINT
Current situation ? t a h W Where? H o w ?
VODAN data approach Real-world phenomena Data Metadata Provenance • License • Represents Describes 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 of metadata about COVID-19 data; • Provide rich metadata; • Provide semantic descriptions of both metadata and data; Improve machine-actionability • on 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 Ribavirin à SNOMEDCT:387188005 Antiviral Agent à NCIT:C281 Ribavirin à NCIT:C807 Antiviral Agents à MESH:D000998 Ribavirin à MESH:D012254 … … Lopinavir- and ritonavir-containing product à SNOMEDCT:387067003 Lopinavir/Ritonavir à NCIT:C2096 Lopinavir-ritonavir drug combination à MESH:D558899 …
THANK YOU AND Q&A Albert Mons Luiz Bonino International Project Consultant GO FAIR Technical Advisor to GO FAIR CEO FAIR Solutions Associate Professor at University of Twente albert.mons@fairsolutions.com 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, ontologies, applications, etc . FAIR Meta Data Search Engine: a server application enabling humans and machines to search for FAIR objects/resources based on 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, ontologies, semantic data models, etc.)
Recommend
More recommend
