NSF Workshop: Towards an Open Source Model for Data and Metadata Standards

NSF 研讨会：迈向数据和元数据标准的开源模型

• 批准号: 2334483
• 负责人: Ariel Rokem
• 金额: $ 10万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2334483&HistoricalAwards=false
• 关键词: NSF; Workshop; Towards; Open; Source

Recent progress in machine learning and artificial intelligence promises to advance research and understanding across a wide range of fields and activities. In tandem, an increased awareness of the importance of open data for reproducibility and scientific transparency is making inroads in fields that have not traditionally produced large publicly available datasets. Data sharing requirements from publishers and funders, as well as from other stakeholders, have also created pressure to make datasets with research and/or public interest value available through digital repositories. However, to make the best use of existing data, and facilitate the creation of useful future datasets, robust, interoperable and usable standards need to evolve and adapt over time. The open-source development model offers significant potential benefits to the process of standard creation and adaptation. In particular, development and adaptation of standards can take advantage of long-standing socio-technical processes that have been key to managing the development of open-source software, and allow incorporating broad community input into the formulation of these standards. This workshop aims to create interdisciplinary connections across a wide range of research fields, thereby providing fertile ground for exchange of knowledge and the creation of new and broadly useful knowledge about the application of the open-source model to data and metadata standards. Furthermore, the synthesis that will be generated will be useful for policy makers and funders in determining worthwhile avenues for policy and funding investment to best make use of the open-source production and governance principles in support of broad societal goals.By adhering to open-source standards for formal descriptions (e.g., by implementing schemata for standard specification, and/or by implementing automated standard validation), processes such as automated testing and continuous integration, which have been important in the development of open-source software, can be adopted in defining data and metadata standards as well. Similarly, open-source governance provides a range of stakeholders a voice in the development of standards, potentially enabling use-cases and concerns that would not be taken into account in a top-down model of standards development. On the other hand, open-source models also carry unique risks that need to be taken into account. The goal of this workshop is to discuss examples where an open-source model for standards development has had significant impact on the practice within a field. Importantly, the workshop will also discuss cases where this model has not worked in the past, and cases where this model is not a good fit.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.

机器学习和人工智能的最新进展有望推动广泛领域和活动的研究和理解。与此同时，人们越来越认识到开放数据对于可重复性和科学透明度的重要性，正在向传统上不产生大型公开数据集的领域取得进展。来自出版商和资助者以及其他利益相关者的数据共享要求也产生了压力，要求通过数字存储库提供具有研究和/或公共利益价值的数据集。然而，为了充分利用现有数据并促进创建有用的未来数据集，稳健、可互操作和可用的标准需要随着时间的推移而发展和适应。开源开发模型为标准创建和适应过程提供了巨大的潜在好处。特别是，标准的开发和调整可以利用长期存在的社会技术流程，这些流程对于管理开源软件的开发至关重要，并允许将广泛的社区意见纳入这些标准的制定中。本次研讨会旨在建立跨广泛研究领域的跨学科联系，从而为知识交流和创建关于开源模型在数据和元数据标准中的应用的新的和广泛有用的知识提供肥沃的基础。此外，将生成的综合报告将有助于政策制定者和资助者确定有价值的政策和资金投资途径，以最好地利用开源生产和治理原则来支持广泛的社会目标。可以采用正式描述的源标准（例如，通过实施标准规范的模式，和/或通过实施自动化标准验证），可以采用自动化测试和持续集成等流程，这些流程在开源软件的开发中非常重要在定义中数据和元数据标准。同样，开源治理为一系列利益相关者提供了标准开发中的发言权，可能会实现自上而下的标准开发模型中不会考虑的用例和关注点。另一方面，开源模型也存在需要考虑的独特风险。本次研讨会的目标是讨论标准开发的开源模型对某个领域的实践产生重大影响的示例。重要的是，研讨会还将讨论该模型过去不起作用的案例，以及该模型不适合的案例。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和能力进行评估，被认为值得支持。更广泛的影响审查标准。