The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem.Persistent,globally unique identifiers,resolvable on the Web,and associate...The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem.Persistent,globally unique identifiers,resolvable on the Web,and associated with a set of additional descriptive metadata,are foundational to FAIR data.Here we describe some basic principles and exemplars for their design,use and orchestration with other system elements to achieve FAIRness for digital research objects.展开更多
One of the key goals of the FAIR guiding principles is defined by its final principle-to optimize data sets for reuse by both humans and machines.To do so,data providers need to implement and support consistent machin...One of the key goals of the FAIR guiding principles is defined by its final principle-to optimize data sets for reuse by both humans and machines.To do so,data providers need to implement and support consistent machine readable metadata to describe their data sets.This can seem like a daunting task for data providers,whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used.Additionally,for existing data sets it is often unclear what steps should be taken to enable maximal,appropriate reuse.Data citation already plays an important role in making data findable and accessible,providing persistent and unique identifiers plus metadata on over 16 million data sets.In this paper,we discuss how data citation and its underlying infrastructures,in particular associated metadata,provide an important pathway for enabling FAIR data reuse.展开更多
基金This work was supported in part by the European Union’s Horizon 2020 program under grant agreements 777523,FREYA“Connected Open Identifiers for Discovery,Access and Use of Research Resources”,654248,CORBEL+1 种基金“Coordinated Research Infrastructures Building Enduring Life-science services”,and 823830Bioexcel2,"BioExcel-2 Centre of Excellence for Computational Biomolecular Research".Many thanks to Paul Groth for his helpful comments on the manuscript.
文摘The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem.Persistent,globally unique identifiers,resolvable on the Web,and associated with a set of additional descriptive metadata,are foundational to FAIR data.Here we describe some basic principles and exemplars for their design,use and orchestration with other system elements to achieve FAIRness for digital research objects.
基金This work was partially supported by Horizon 2020,INFRADEV-4-2014-2015,654248,CORBEL,Coordinated Research Infrastructures Building Enduring Life-science services.
文摘One of the key goals of the FAIR guiding principles is defined by its final principle-to optimize data sets for reuse by both humans and machines.To do so,data providers need to implement and support consistent machine readable metadata to describe their data sets.This can seem like a daunting task for data providers,whether it is determining what level of detail should be provided in the provenance metadata or figuring out what common shared vocabularies should be used.Additionally,for existing data sets it is often unclear what steps should be taken to enable maximal,appropriate reuse.Data citation already plays an important role in making data findable and accessible,providing persistent and unique identifiers plus metadata on over 16 million data sets.In this paper,we discuss how data citation and its underlying infrastructures,in particular associated metadata,provide an important pathway for enabling FAIR data reuse.
