Making Data and Workflows Findable for Machines

作     者：Tobias Weigel Ulrich Schwardmann Jens Klump Sofiane Bendoukha Robert Quick Tobias Weigel;Ulrich Schwardmann;Jens Klump;Sofiane Bendoukha;Robert Quick

作者机构：Deutsches KlimarechenzentrumBundesstrasse 45aHamburg 20146Germany Gesellschaft für wissenschaftliche Datenverarbeitung GottingenAm Faßberg 1137077 GottingenGermany CSIROKensingtonWA 6151CanberraAustralia Indiana University BloomingtonBloomingtonIN 47405USA 

出 版 物：《Data Intelligence》 (数据智能（英文）)

年 卷 期：2020年第2卷第1期

页      面：40-46,303页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

主　　题：Findability Workflows Automation FAIR data Data infrastructures Data services 

摘      要：Research data currently face a huge increase of data objects with an increasing variety of types(data types,formats)and variety of workflows by which objects need to be managed across their lifecycle by data *** desire to shorten the workflows from data generation to analysis and publication,and the full workflow needs to become transparent to multiple stakeholders,including research administrators and *** poses challenges for research infrastructures and user-oriented data services in terms of not only making data and workflows findable,accessible,interoperable and reusable,but also doing so in a way that leverages machine support for better *** primary need to be addressed is that of findability,and achieving better findability has benefits for other aspects of data and workflow *** this article,we describe how machine capabilities can be extended to make workflows more findable,in particular by leveraging the Digital Object Architecture,common object operations and machine learning techniques.