EAGER: Open Science Data in Semantic Web Research

EAGER：语义网研究中的开放科学数据

• 批准号: 2032628
• 负责人: Pascal Hitzler
• 金额: $ 29.97万
• 依托单位: Kansas State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2032628&HistoricalAwards=false
• 关键词: EAGER; Open; Science; Data; Semantic

Establishing Open Science as the default practice in the academic community is critical. Reporting a research result is more than just the manuscript. It is the actual process undertaken, the data (and metadata), the implemented software (and environment), and more. It is also important to ask questions pertaining to transparency and reproducibility, such as those regarding their reviews, publishing metadata, and, in particular, Where are the data and software that underlie their reported results? By making the connections between these artifacts through Open Science practices, it democratizes research by lowering the barrier of entry to understanding (and replicating) cutting edge research, while simultaneously accelerating novel research by reducing the need to re-implement software or re-collect data. Connecting these components and data in a useful and understandable way is a core mission of Semantic Web research. The PIs propose to leverage the leadership and reputation of the Semantic Web journal by IOS Press to demonstrate that the open sharing of data underlying publications can be done without significant overhead and provides significant added value. The PIs will, for this journal, require authors to sustainably publish the data and software underlying their results, include software and data provision as part of the peer review assessment, and publish rich metadata for the supplementary data and software. The PIs will develop technology in support of open science and will work with the community to refine the software and policies that enhance open science for the Semantic Web journal. The approach can serve as a model for other journal editors who seek a blueprint for adopting similar Open Science Data practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

将开放科学确立为学术界的默认实践至关重要。报告一项研究成果不仅仅是一份手稿。它是实际进行的过程、数据（和元数据）、实现的软件（和环境）等等。同样重要的是，要提出有关透明度和可重复性的问题，例如有关其审查、发布元数据的问题，特别是，作为其报告结果基础的数据和软件在哪里？通过开放科学实践在这些人工制品之间建立联系，它通过降低理解（和复制）前沿研究的进入门槛来使研究民主化，同时通过减少重新实现软件或重新收集数据的需要来加速新的研究。以一种有用和可理解的方式连接这些组件和数据是语义Web研究的核心使命。PI建议利用IOS出版社的语义网期刊的领导地位和声誉，以证明公开共享出版物背后的数据可以在没有重大开销的情况下完成，并提供显着的附加值。PI将要求作者可持续地发布其结果背后的数据和软件，将软件和数据提供作为同行评审评估的一部分，并为补充数据和软件发布丰富的元数据。 PI将开发支持开放科学的技术，并将与社区合作，完善软件和政策，增强语义网期刊的开放科学。该方法可以作为其他期刊编辑的榜样，他们寻求采用类似的开放科学数据实践的蓝图。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Open Science data and the Semantic Web journal

开放科学数据和语义网络期刊

• DOI: 10.3233/sw-210427
• 发表时间: 2021
• 期刊: Semantic Web
• 影响因子: 3
• 作者: Hitzler, Pascal;Janowicz, Krzysztof;Shimizu, Cogan;Zhou, Lu;Eells, Andrew
• 通讯作者: Eells, Andrew

Aligning Patterns to the Wikibase Model

将模式与 Wikibase 模型对齐

• 发表时间: 2021
• 期刊: 2021.
• 作者: Eels, Andrew;Shimizu, Cogan;Zhou, Lu;Hitzler, Pascal;Gonzalez, Seila;Rehberger, Dean
• 通讯作者: Rehberger, Dean