DataDesc:为研究软件界面创建和共享技术元数据的框架。

IF 6.7 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Patterns Pub Date : 2024-10-01 eCollection Date: 2024-11-08 DOI:10.1016/j.patter.2024.101064
Patrick Kuckertz, Jan Göpfert, Oliver Karras, David Neuroth, Julian Schönau, Rodrigo Pueblas, Stephan Ferenz, Felix Engel, Noah Pflugradt, Jann M Weinand, Astrid Nieße, Sören Auer, Detlef Stolten
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引用次数: 0

摘要

研究软件的重复使用对于提高研究效率和学术交流至关重要。软件的应用使研究人员能够复制、验证和扩展研究结果。分析开放源代码有助于理解、比较和整合各种方法。然而,由于找不到相关软件或与现有研究流程不兼容,往往无法继续使用。这就导致了重复性的软件开发,阻碍了研究人员个人和整个研究团体的进步。本文介绍了 DataDesc(数据描述)框架--一种用机器可执行的元数据描述软件界面数据模型的方法。除了专门的元数据模式外,还介绍了一种交换格式和支持工具,以方便收集和自动发布软件文档。这种方法切实提高了研究软件的 FAIRness,即可查找性、可访问性、互操作性和可重用性,并有效促进了其对研究的影响。
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DataDesc: A framework for creating and sharing technical metadata for research software interfaces.

The reuse of research software is central to research efficiency and academic exchange. The application of software enables researchers to reproduce, validate, and expand upon study findings. The analysis of open-source code aids in the comprehension, comparison, and integration of approaches. Often, however, no further use occurs because relevant software cannot be found or is incompatible with existing research processes. This results in repetitive software development, which impedes the advancement of individual researchers and entire research communities. In this article, the DataDesc (Data Description) framework is presented-an approach to describing data models of software interfaces with machine-actionable metadata. In addition to a specialized metadata schema, an exchange format and support tools for easy collection and the automated publishing of software documentation are introduced. This approach practically increases the FAIRness, i.e., findability, accessibility, interoperability, and reusability, of research software as well as effectively promotes its impact on research.

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来源期刊
Patterns
Patterns Decision Sciences-Decision Sciences (all)
CiteScore
10.60
自引率
4.60%
发文量
153
审稿时长
19 weeks
期刊介绍:
期刊最新文献
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