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

机器学习和人工智能领域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，越来越多的人认识到开放数据对于可重复性和科学透明度的重要性，正在传统上不会产生大量公开数据集的领域取得进展。出版商和资助者以及其他利益攸关方的数据共享要求也造成了压力，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，为了最好地利用现有数据，并促进创建有用的未来数据集，健壮、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。开放源码开发模式为标准的创建和适应过程提供了巨大的潜在好处。特别是，标准的制定和调整可以利用对管理开放源码软件的开发至关重要的长期社会技术进程，并允许将社区的广泛意见纳入这些标准的制定过程中。这次讲习班的目的是在广泛的研究领域建立跨学科的联系，从而为交流知识和创造关于将开放源码模式应用于数据和元数据标准的新的和广泛有用的知识提供肥沃的土壤。此外，将产生的综合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，以最好地利用支持广泛社会目标的开源生产和治理原则。通过遵守正式描述的开源标准(例如，通过实施标准规范的模式，和/或通过实施自动标准验证)，在定义数据和元数据标准时也可以采用自动化测试和持续集成等过程，这些过程在开放源代码软件的开发中一直很重要。同样，开源治理为一系列利益相关者提供了在标准开发中的话语权，潜在地支持了在自上而下的标准开发模型中不会考虑的用例和关注事项。另一方面，开源模式也带有需要考虑的独特风险。这次研讨会的目的是讨论标准开发的开放源码模式对某一领域的实践产生重大影响的例子。重要的是，研讨会还将讨论这种模式过去不起作用的案例，以及这种模式不适合的案例。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.