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作者机构:TIB-Leibniz Information Centrefor Science and TechnologyWelfengarten 1B30167 HannoverGermany Center for Empirical Research in Economics and Behavioral Sciences(CEREB)University of ErfurtNordhauser Str.6399089 ErfurtGermany DONA FoundationC/O Universitede GeneveRuedu General-Dufour 241204 GeneveSwitzerland Statistics and Data Science in Social Sciences and the HumanitiesDepartment of StatisticsLudwig-Maximilians-University MunchenLudwigstr.3380539 MunchenGermany GWDGBurckhardtweg 437077 GotingenGermany FDO ForumGemeindeweg 5547533 KleveGermany 7KAIROS BochumUniversitatsstraBe 13644799 BochumGermany
出 版 物:《Data Intelligence》 (数据智能(英文))
年 卷 期:2022年第4卷第2期
页 面:155-172页
核心收录:
学科分类:08[工学] 0812[工学-计算机科学与技术(可授工学、理学学位)]
主 题:Experiments Experimental economics FAIR digital objects Information science FAIR research output
摘 要:The overall expectation of introducing Canonical Workflow for Experimental Research and FAIR digital objects(FDOs)can be summarised as reducing the gap between workflow technology and research practices to make experimental work more efficient and improve FAIRness without adding administrative load on the *** this document,we will describe,with the help of an example,how CWFR could work in detail and improve research *** have chosen the example ofexperiments with human subjectswhich stretches from planning an experiment to storing the collected data in a *** we focus on experiments with human subjects,we are convinced that CWFR can be applied to many other data generation processes based on *** main challenge is to identify repeating patterns in existing research practices that can be abstracted to create *** this document,we will include detailed examples from different disciplines to demonstrate that CWFR can be implemented without violating specific disciplinary or methodological *** do not claim to be comprehensive in all aspects,since these examples are meant to prove the concept of CWFR.