FAIR Software as a Service: Combining tools to implement FAIR for clinical data platforms

Rick Overkleeft; Sander van Boom; Eric Prud’Hommeaux; Kees Burger; Jip Fransen; Teus Kappen; Luiz Bonino da Silva Santos; Rajaram Kaliyaperumal; Marco Roos

FAIR Software as a Service: Combining tools to implement FAIR for clinical data platforms

Rick Overkleeft
, Sander van Boom
, Eric Prud’Hommeaux
, Kees Burger
, Jip Fransen
, Teus Kappen
, Luiz Bonino da Silva Santos
, Rajaram Kaliyaperumal
, Marco Roos

Research output: Contribution to journal › Article › Academic

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Abstract

Many DCCs have operational expertise in deploying software services, but lack expertise in deploying and exploiting FAIR services that use Semantic Web technology for machine actionability. Although local deployment is lightweight and non-invasive for institutional infrastructure, engineers are not yet familiar with the underlying web technologies of FAIR services. This poses a risk for delivering the FAIR foundation of a federated health data infrastructure that is ready for federated analytics, AI, and machine learning. This project is making deployment by institutional DCCs of FAIR services cost-effective, scalable, and sustainable by delivering ‘FAIR Software as a Service’ (FAIR-SaaS).

Original language	English
Pages (from-to)	208-209
Number of pages	2
Journal	CEUR Workshop Proceedings
Volume	3890
Publication status	Published - 3 Jan 2025
Event	15th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2024 - Hybrid, Leiden, Netherlands Duration: 20 Feb 2024 → 26 Feb 2024

Keywords

architecture
FAIR
FAIR Data Point
implementation
SaaS

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FAIR Software as a Service: Combining tools to implement FAIR for clinical data platformsFinal published version, 387 KBLicence: CC BY

https://ceur-ws.org/Vol-3890/Licence: CC BY

Cite this

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title = "FAIR Software as a Service: Combining tools to implement FAIR for clinical data platforms",

abstract = "Many DCCs have operational expertise in deploying software services, but lack expertise in deploying and exploiting FAIR services that use Semantic Web technology for machine actionability. Although local deployment is lightweight and non-invasive for institutional infrastructure, engineers are not yet familiar with the underlying web technologies of FAIR services. This poses a risk for delivering the FAIR foundation of a federated health data infrastructure that is ready for federated analytics, AI, and machine learning. This project is making deployment by institutional DCCs of FAIR services cost-effective, scalable, and sustainable by delivering {\textquoteleft}FAIR Software as a Service{\textquoteright} (FAIR-SaaS).",

keywords = "architecture, FAIR, FAIR Data Point, implementation, SaaS",

author = "Rick Overkleeft and \{van Boom\}, Sander and Eric Prud{\textquoteright}Hommeaux and Kees Burger and Jip Fransen and Teus Kappen and \{Bonino da Silva Santos\}, Luiz and Rajaram Kaliyaperumal and Marco Roos",

note = "Publisher Copyright: {\textcopyright} 2024 Copyright for this paper by its authors.; 15th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2024 ; Conference date: 20-02-2024 Through 26-02-2024",

year = "2025",

month = jan,

day = "3",

language = "English",

volume = "3890",

pages = "208--209",

journal = "CEUR Workshop Proceedings",

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TY - JOUR

T1 - FAIR Software as a Service

T2 - 15th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2024

AU - Overkleeft, Rick

AU - van Boom, Sander

AU - Prud’Hommeaux, Eric

AU - Burger, Kees

AU - Fransen, Jip

AU - Kappen, Teus

AU - Bonino da Silva Santos, Luiz

AU - Kaliyaperumal, Rajaram

AU - Roos, Marco

PY - 2025/1/3

Y1 - 2025/1/3

N2 - Many DCCs have operational expertise in deploying software services, but lack expertise in deploying and exploiting FAIR services that use Semantic Web technology for machine actionability. Although local deployment is lightweight and non-invasive for institutional infrastructure, engineers are not yet familiar with the underlying web technologies of FAIR services. This poses a risk for delivering the FAIR foundation of a federated health data infrastructure that is ready for federated analytics, AI, and machine learning. This project is making deployment by institutional DCCs of FAIR services cost-effective, scalable, and sustainable by delivering ‘FAIR Software as a Service’ (FAIR-SaaS).

AB - Many DCCs have operational expertise in deploying software services, but lack expertise in deploying and exploiting FAIR services that use Semantic Web technology for machine actionability. Although local deployment is lightweight and non-invasive for institutional infrastructure, engineers are not yet familiar with the underlying web technologies of FAIR services. This poses a risk for delivering the FAIR foundation of a federated health data infrastructure that is ready for federated analytics, AI, and machine learning. This project is making deployment by institutional DCCs of FAIR services cost-effective, scalable, and sustainable by delivering ‘FAIR Software as a Service’ (FAIR-SaaS).

KW - architecture

KW - FAIR

KW - FAIR Data Point

KW - implementation

KW - SaaS

UR - https://www.scopus.com/pages/publications/85214890447

M3 - Article

AN - SCOPUS:85214890447

SN - 1613-0073

VL - 3890

SP - 208

EP - 209

JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 20 February 2024 through 26 February 2024

ER -

FAIR Software as a Service: Combining tools to implement FAIR for clinical data platforms

Abstract

Keywords

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