TY - JOUR
T1 - The need of industry to go fair
AU - van Vlijmen, Herman
AU - Mons, Albert
AU - Waalkens, Arne
AU - Franke, Wouter
AU - Baak, Arie
AU - Ruiter, Gerbrand
AU - Kirkpatrick, Christine
AU - Santos, Luiz Olavo Bonino da Silva
AU - Meerman, Bert
AU - Jellema, Renger
AU - Arts, Derk
AU - Kersloot, Martijn
AU - Knijnenburg, Sebastiaan
AU - Lusher, Scott
AU - Verbeeck, Rudi
AU - Neefs, Jean Marc
N1 - Publisher Copyright:
© 2019 Chinese Academy of Sciences Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - The industry sector is a very large producer and consumer of data, and many companies traditionally focused on production or manufacturing are now relying on the analysis of large amounts of data to develop new products and services. As many of the data sources needed are distributed and outside the company, FAIR data will have a major impact, both by reducing the existing internal data silos and by enabling the efficient integration with external (public and commercial) data. Many companies are still in the early phases of internal data ”FAIRification”, providing opportunities for SMEs and academics to apply and develop their expertise on FAIR data in collaborations and public-private partnerships. For a global Internet of FAIR Data & Services to thrive, also involving industry, professional tools and services are essential. FAIR metrics and certifications on individuals, data, organizations, and software, must ensure that data producers and consumers have independent quality metrics on their data. In this opinion article we reflect on some industry specific challenges of FAIR implementation to be dealt with when choices are made regarding ”Industry GOing FAIR”.
AB - The industry sector is a very large producer and consumer of data, and many companies traditionally focused on production or manufacturing are now relying on the analysis of large amounts of data to develop new products and services. As many of the data sources needed are distributed and outside the company, FAIR data will have a major impact, both by reducing the existing internal data silos and by enabling the efficient integration with external (public and commercial) data. Many companies are still in the early phases of internal data ”FAIRification”, providing opportunities for SMEs and academics to apply and develop their expertise on FAIR data in collaborations and public-private partnerships. For a global Internet of FAIR Data & Services to thrive, also involving industry, professional tools and services are essential. FAIR metrics and certifications on individuals, data, organizations, and software, must ensure that data producers and consumers have independent quality metrics on their data. In this opinion article we reflect on some industry specific challenges of FAIR implementation to be dealt with when choices are made regarding ”Industry GOing FAIR”.
KW - FAIR application
UR - http://www.scopus.com/inward/record.url?scp=85107329559&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107329559&partnerID=8YFLogxK
U2 - 10.1162/dint_a_00050
DO - 10.1162/dint_a_00050
M3 - Article
AN - SCOPUS:85107329559
SN - 2096-7004
VL - 2
SP - 276
EP - 284
JO - Data Intelligence
JF - Data Intelligence
IS - 1-2
ER -