分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-28 合作期刊: 《数据智能（英文）》
摘要： Since their introduction by James Dixon in 2010, data lakes get more and more attention, driven by the promise of high reusability of the stored data due to the schema-on-read semantics. Building on this idea, several additional requirements were discussed in literature to improve the general usability of the concept, like a central metadata catalog including all provenance information, an overarching data governance, or the integration with (high-performance) processing capabilities. Although the necessity for a logical and a physical organisation of data lakes in order to meet those requirements is widely recognized, no concrete guidelines are yet provided. The most common architecture implementing this conceptual organisation is the zone architecture, where data is assigned to a certain zone depending on the degree of processing. This paper discusses how FAIR Digital Objects can be used in a novel approach to organize a data lake based on data types instead of zones, how they can be used to abstract the physical implementation, and how they empower generic and portable processing capabilities based on a provenance-based approach.
分类： 计算机科学 >> 计算机科学的集成理论 提交时间： 2022-11-28 合作期刊: 《数据智能（英文）》
Dirk, Betz
Claudia, Biniossek
Christophe, Blanchi
Felix, Henninger
Thomas, Lauer
Philipp, Wieder
Peter, Wittenburg
Martin, Zünkeler
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 researchers. In this document, we will describe, with the help of an example, how CWFR could work in detail and improve research procedures. We have chosen the example of experiments with human subjects which stretches from planning an experiment to storing the collected data in a repository. While we focus on experiments with human subjects, we are convinced that CWFR can be applied to many other data generation processes based on experiments. The main challenge is to identify repeating patterns in existing research practices that can be abstracted to create CWFR. In this document, we will include detailed examples from different disciplines to demonstrate that CWFR can be implemented without violating specific disciplinary or methodological requirements. We do not claim to be comprehensive in all aspects, since these examples are meant to prove the concept of CWFR.
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