Collaborative Research: Elements: Shared Data-Delivery Infrastructure to Enable Discovery with Next Generation Dark Matter and Computational Astrophysics Experiments

现代实验室对每分钟流经我们星球的许多不同的银河信使提供了前所未有的灵敏度：宇宙射线，来自遥远星系的光，难以捉摸的中微子，可能还有暗物质。 将这些信息与模型和模拟数据相结合，可以深入了解我们的宇宙是如何开始和继续演化的-物体最初坍缩的规模，恒星和星系的发展以及我们自己星系内的动态。然而，这些数据通常无法访问：实验或社区的科学家们正在努力与他们用来访问数据的复杂定制程序进行斗争。 转换到标准格式通常不是一种选择：这些数据格式是为通常不包括跨实验综合或链接的要求而设计的。由于数据难以访问、理解和分析，初级科学家--更不用说公众--很难从数据中产生新的见解。 通过巧妙地重复使用和组合来自不同实验和模拟的数据，可以产生跨领域的调查，但很少进行。该项目使数据在合作内部和合作之间都可以访问，为在地球仪中搜索信号提供基础设施。 扩大现有的努力，以改善数据访问，使这一项目成为可能：yt是软件，提供统一的模拟数据访问; Kaitai是一种数据描述语言，使人们能够轻松访问任何数据格式; Rucio和其他工具提供了一个标准的接口，允许数据下载;和ServiceX可以识别，最终用户只需付出很少的努力就可以对数据进行子集和处理。科学家们已经建立了实验，提供了关于我们世界的大量信息。 技术描述个人数据传输基础设施（PONDD）解决了现有暗物质和天体物理实验的数据挑战，同时不需要更改现有数据格式。 这种对任何文件格式的非侵入性、无需更改的支持为我们提供了机会，使我们能够从暗物质搜索和天体物理模拟这两个已确定的用例扩展到许多其他依赖自定义文件格式的数据驱动科学领域。这项工作提供了一个基础设施，可以从多个来源以支持良好的格式（如Parquet）无缝交付数据。 为了成功地向最终用户提供交叉实验数据，我们将高能物理和更广泛的NSF社区正在进行的项目汇集在一起;虽然该项目将涉及软件产品的开发，（yt和开泰）它还将包括对网络基础设施现有投资的综合，以及改善其长期长期可持续性。该项目由计算机和信息科学与工程局高级基础设施办公室和物理学司支持。数学和物理科学。该奖项反映了NSF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。

Modern laboratories provide unprecedented sensitivity to the many different galactic-messengers that stream through our planet by the minute: cosmic rays, light from distant galaxies, elusive neutrinos, and possibly dark matter. Combining this information with models and data from simulations provides insight into how our universe began and continues to evolve -- the scales at which objects first collapsed, the development of stars and galaxies, and the dynamics within our own galaxy.However, this data is often inaccessible: scientists within an experiment or community struggle with the complex, custom-built programs they use to access the data. And switching to a standard format is usually not an option: these data formats are designed for requirements that often do not include cross-experiment synthesis or linking.Junior scientists - let alone the public - can struggle to generate new insights from the data because the data is difficult to access, understand and analyze. The cross-cutting inquiry that could arise from clever reuse and combination of data from different experiments and simulations is rarely conducted.This project makes data accessible both within and across collaborations, providing the infrastructure to search for signals in detectors across the globe. Extending existing efforts to improve data access makes this project possible: yt is software that provides uniform access to simulation data; Kaitai is a data-description language that enables easy access to any data format; Rucio and other tools provide a standard interface that allows data downloads; and ServiceX can identify, subset and process data with little effort from the end user.Scientists have built experiments that offer an incredible wealth of information about our world. This project works to make that information accessible to everyone.Technical DescriptionThe Personal Data-Delivery infrastructure (PONDD) addresses the data challenges of existing dark matter and astrophysics experiments while requiring no changes to existing data formats. This non-invasive, no-changes-necessary support for any file format provides opportunities to expand beyond our two identified use cases, dark matter searches and astrophysics simulations, into many other data-driven science domains that rely on custom file formats.This work delivers an infrastructure that seamlessly delivers data in a well-supported format (such as Parquet) from multiple sources. To successfully deliver cross-experiment data to end users, we bring together ongoing projects from High Energy Physics and the broader NSF community; while this project will involve development of software products (yt and Kaitai) it will also include synthesis of existing investments in cyberinfrastructure and efforts to improve their long-term sustainability.This project is supported by the Office of Advanced Infrastructure in the Directorate for Computer and Information Science and Engineering and the Division of Physics in the Directorate for Mathematical and Physical Sciences.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.