EAGER: SciDatBench: Principles and Prototypes of Science Data Benchmarks

对大型科学数据集的分析需要在数据分析方法和用于分析的信息技术硬件和软件方面进行新的研究。这个项目正在调查和设计一套新的科学数据基准，称为SciDatBench。它建立了一个重要的和有代表性的大科学数据集的新集合，以及最佳实践分析所需的机器学习算法的典型软件实现。SciDatBench集合附有文档，允许它用于研究人员在快速发展的大数据分析技术的培训。该项目有可能影响广泛的科学学科，最终包括材料科学、环境科学、包括流行病学在内的生命科学、核聚变、粒子物理、天文学、地震和地球科学，每个领域都有一个以上的代表性问题。SciDatBench生成大数据分析基准的特定实例，并建立一个可持续的过程来维护和增强它们。该集合包括独立示例和端到端示例，这些示例需要在许多科学实验的分析中看到的多个组件。SciDatBench隶属于一个经过批准的科学数据工作组，其非常成功的MLPerf活动有80个组织成员，他们正在研究行业机器学习基准。正如NSF的使命所述，SciDatBench中最先进的例子正在为促进国家健康、繁荣和福利的科学发现的进步做出贡献。该项目积极让代表性不足的社区参与其活动。SciDatBench支持比较研究，并确定未来网络基础设施的需求，以支持科学数据分析。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

Analysis of large scientific data sets requires new research in both the data analysis methods and the information technology hardware and software to use in the analysis. This project is investigating and prototyping a new set of science data benchmarks, dubbed SciDatBench. It establishes a new collection of important and representative big scientific datasets together with typical software implementations of the machine learning algorithms that are needed for best practice analysis. The SciDatBench collection is accompanied by documentation allowing it to be used in the training of researchers in the rapidly evolving Big Data analysis techniques. The project has a potential to impact a broad range of scientific disciplines including eventually material sciences, environmental sciences, life sciences including epidemiology, fusion, particle physics, astronomy, earthquake, and earth sciences, with more than one representative problem from each of these domains.SciDatBench generates particular instances of big data analysis benchmarks and establishes a sustainable process for maintaining and enhancing them. This collection includes both standalone examples and end-to-end examples needing multiple components that are seen in the analysis of many science experiments. SciDatBench is affiliated as an approved Science Data working group with the very successful MLPerf activity with 80 organizational members looking at Industry machine learning benchmarks. The state-of-the-art examples in SciDatBench are contributing to progress in scientific discovery that advances the national health, prosperity, and welfare, as stated by NSF's mission. The project is proactively involving under-represented communities in its activities. SciDatBench supports comparative studies and identifies requirements for future cyberinfrastructure to support scientific data analysis.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.