FAIR Data Reuse-the Path through Data Citation  

作　　者：Paul Groth Helena Cousijn Tim Clark Carole Goble 

机构地区：[1]Informatics Institute,University of Amsterdam,Amsterdam 1090 GH,The Netherlands [2]DataCite,Welfengarten 1B,Hannover 30167,Germany [3]Data Science Institute,University of Virginia,Charlottesville,VA 22903-1738,USA [4]Department of Computer Science,The University of Manchester,Oxford Road,Manchester M139PL,UK

出　　处：《Data Intelligence》2020年第1期78-86,305,共10页数据智能（英文）

基　　金：This work was partially supported by Horizon 2020,INFRADEV-4-2014-2015,654248,CORBEL,Coordinated Research Infrastructures Building Enduring Life-science services.

摘　　要：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.

关 键 词：FAIR data Data citation Research objects Data provenance 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]