评估环境科学中的语义互操作性:各种方法和语义人工制品。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Cristina Di Muri, Martina Pulieri, Davide Raho, Alexandra N Muresan, Andrea Tarallo, Jessica Titocci, Enrica Nestola, Alberto Basset, Sabrina Mazzoni, Ilaria Rosati
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引用次数: 0

摘要

只有通过采用富含语义人工制品的机器可操作(元)数据标准,才能确保数字研究产品的集成和再利用。本研究汇编了环境科学领域的 540 个语义人工制品,以便:i. 检查其在科学领域和主题中的覆盖范围;ii. 评估其公平合理性的关键方面;iii. 评估管理和治理方面的问题。分析表明,大多数语义人工制品涉及陆地生物圈领域,其中一小部分不符合 FAIR 原则。例如,5.5%的语义人工制品未在语义目录中提供,8%的人工制品未使用标准模型语言和格式构建,24.6%的人工制品在发布时未获得使用许可,22.4%的人工制品未提供版本信息,或在提供这些人工制品的目录中存在不同版本。这项调查讨论了常见的语义实践,概述了现有的差距,并提出了潜在的解决方案,以应对一些原本旨在保证语义互操作性的资源所面临的语义互操作性挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing semantic interoperability in environmental sciences: variety of approaches and semantic artefacts.

The integration and reuse of digital research products can be only ensured through the adoption of machine-actionable (meta)data standards enriched with semantic artefacts. This study compiles 540 semantic artefacts in environmental sciences to: i. examine their coverage in scientific domains and topics; ii. assess key aspects of their FAIRness; and iii. evaluate management and governance concerns. The analyses showed that the majority of semantic artefacts concern the terrestrial biosphere domain, and that a small portion of the total failed to meet the FAIR principles. For example, 5.5% of semantic artefacts were not available in semantic catalogues, 8% were not built with standard model languages and formats, 24.6% were published without usage licences and 22.4% without version information or with divergent versions across catalogues in which they were available. This investigation discusses common semantic practices, outlines existing gaps and suggests potential solutions to address semantic interoperability challenges in some of the resources originally designed to guarantee it.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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