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Towards FAIR protocols and workflows: the OpenPREDICT use case
PeerJ Computer Science ( IF 3.5 ) Pub Date : 2020-09-21 , DOI: 10.7717/peerj-cs.281
Remzi Celebi 1 , Joao Rebelo Moreira 2 , Ahmed A. Hassan 3 , Sandeep Ayyar 4 , Lars Ridder 5 , Tobias Kuhn 2 , Michel Dumontier 1
Affiliation  

It is essential for the advancement of science that researchers share, reuse and reproduce each other’s workflows and protocols. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of research data, and emphasize the importance of making digital objects findable and reusable by others. The question of how to apply these principles not just to data but also to the workflows and protocols that consume and produce them is still under debate and poses a number of challenges. In this paper we describe a two-fold approach of simultaneously applying the FAIR principles to scientific workflows as well as the involved data. We apply and evaluate our approach on the case of the PREDICT workflow, a highly cited drug repurposing workflow. This includes FAIRification of the involved datasets, as well as applying semantic technologies to represent and store data about the detailed versions of the general protocol, of the concrete workflow instructions, and of their execution traces. We propose a semantic model to address these specific requirements and was evaluated by answering competency questions. This semantic model consists of classes and relations from a number of existing ontologies, including Workflow4ever, PROV, EDAM, and BPMN. This allowed us then to formulate and answer new kinds of competency questions. Our evaluation shows the high degree to which our FAIRified OpenPREDICT workflow now adheres to the FAIR principles and the practicality and usefulness of being able to answer our new competency questions.

中文翻译:

迈向公平的协议和工作流程:OpenPREDICT用例

研究人员共享,重用和重现彼此的工作流程和协议对于科学发展至关重要。FAIR原则是一组准则,旨在最大程度地提高研究数据的价值和实用性,并强调使其他人可以发现和重用数字对象的重要性。如何将这些原理不仅适用于数据,而且还适用于消耗和产生这些原理的工作流程和协议的问题仍在争论中,并提出了许多挑战。在本文中,我们描述了一种将FAIR原理同时应用于科学工作流以及相关数据的双重方法。我们在PREDICT工作流程(一种被广泛引用的药物再利用工作流程)的情况下应用和评估我们的方法。这包括对涉及的数据集进行公平化,以及应用语义技术来表示和存储有关通用协议,具体工作流程指令及其执行跟踪的详细版本的数据。我们提出了一种语义模型来满足这些特定要求,并通过回答能力问题对其进行了评估。该语义模型由许多现有本体(包括Workflow4ever,PROV,EDAM和BPMN)中的类和关系组成。这样,我们就可以制定和回答新的能力问题。我们的评估表明,我们的FAIRified OpenPREDICT工作流程现在在很大程度上遵守FAIR原则,并且能够回答我们的新能力问题的实用性和实用性。具体的工作流程说明及其执行跟踪。我们提出了一种语义模型来满足这些特定要求,并通过回答能力问题对其进行了评估。该语义模型由许多现有本体(包括Workflow4ever,PROV,EDAM和BPMN)中的类和关系组成。这样,我们就可以制定和回答新的能力问题。我们的评估表明,我们的FAIRified OpenPREDICT工作流程现在在很大程度上遵守FAIR原则,并且能够回答我们的新能力问题的实用性和实用性。具体的工作流程说明及其执行跟踪。我们提出了一种语义模型来满足这些特定要求,并通过回答能力问题对其进行了评估。该语义模型由许多现有本体(包括Workflow4ever,PROV,EDAM和BPMN)中的类和关系组成。这样,我们就可以制定和回答新的能力问题。我们的评估表明,我们的FAIRified OpenPREDICT工作流程现在在很大程度上遵守FAIR原则,并且能够回答我们的新能力问题的实用性和实用性。该语义模型由许多现有本体(包括Workflow4ever,PROV,EDAM和BPMN)中的类和关系组成。这样,我们就可以制定和回答新的能力问题。我们的评估表明,我们的FAIRified OpenPREDICT工作流程现在在很大程度上遵守FAIR原则,并且能够回答我们的新能力问题的实用性和实用性。该语义模型由许多现有本体(包括Workflow4ever,PROV,EDAM和BPMN)中的类和关系组成。这样,我们就可以制定和回答新的能力问题。我们的评估表明,我们的FAIRified OpenPREDICT工作流程现在在很大程度上遵守FAIR原则,并且能够回答我们的新能力问题的实用性和实用性。
更新日期:2020-09-21
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