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A user-centric metadata model to foster sharing and reuse of multidisciplinary datasets in environmental and life sciences

机译:以用户为中心的元数据模型,用于促进环境和生命科学中多学科数据集的共享和重用

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The recent technological advancements and emergence of the open data in environmental and life sciences are opening new research opportunities while creating new challenges around data management. They make available an unprecedented amount of data that can be exploited for studying complex phenomena. However, new challenges related to data management need to be addressed to ensure effective data sharing, discovery and reuse, especially when dealing with interdisciplinary research contexts. These issues are magnified in interdisciplinary context, by the fact that each discipline has its practices, e.g., specific formats and metadata standards. Moreover, the majority of current data management practices do not consider semantic heterogeneity existing among disciplines. For this reason, we introduce a flexible metadata model that describes the datasets of various disciplines using a common paradigm based on the observation concept. It provides a key vision for articulating the user point of view and underlying scientific domains. In this study, we therefore decide to mainly reuse the SOSA lightweight ontology (Sensor, Observation, Sample, and Actuator) to efficiently leverage others existing ontologies to improve datasets discovery and reuse coming from Earth and life observation. The main benefit of the proposed metadata model is that it extends the technical description, usually provided by existing metadata models, with the observation context description enabling the need of a user viewpoint. Moreover, following the FAIR principles, the metadata model specifies the semantics of its elements using ontologies and vocabularies, and reuses as much as possible ontological and terminological existing resources. We show the benefit and applicability of the model through a case study we identified as representative after interviewing researchers in environmental and life sciences.
机译:最近的技术进步和环境和生命科学的开放数据的出现正在开辟新的研究机会,同时在数据管理周围创造了新的挑战。他们可以提供前所未有的数据,可以利用学习复杂现象。但是,需要解决与数据管理相关的新挑战,以确保有效的数据共享,发现和重用,特别是在处理跨学科研究环境时。这些问题在跨学科的背景下放大,因为每个学科都有其实践,例如特定的格式和元数据标准。此外,大多数当前数据管理实践不考虑在学科中存在的语义异质性。因此,我们介绍了一种灵活的元数据模型,描述了使用基于观察概念的公共范式来描述各种学科的数据集。它提供了一个关键愿景,用于阐明用户的观点和基础科学域。在这项研究中,我们决定主要重复使用SOSA轻量级本体(传感器,观察,样本和执行器),以有效利用其他现有的本体,以改善来自地球和寿命观察的数据集发现和重用。所提出的元数据模型的主要好处是它扩展了现有元数据模型通常提供的技术描述,观察语言描述能够实现用户视点。此外,在公平原则之后,元数据模型使用本体和词汇表指定其元素的语义,并尽可能多地重复使用本体的本体和术语现有资源。我们通过案例研究表明模型的利益和适用性,我们在环境和生命科学的研究人员面试后被确定为代表。

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