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BD Spokes: SPOKE: WEST: Accelerating and Catalyzing Reproducibility in Scientific Computation and Data Synthesis

BD Spokes：SPOKE：WEST：加速和促进科学计算和数据合成的可重复性

基本信息

批准号：
1636796
负责人：
C Michael Barton
金额：
$ 101.46万
依托单位：
Arizona State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2021-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1636796&HistoricalAwards=false
关键词：
BD Spokes SPOKE WEST Accelerating

项目摘要

Norms of transparency and knowledge sharing in science encourage new research to build on prior discoveries, leading to rapid innovation and significant societal returns. This project establishes a new activity to help make scientific computation and data science more transparent and accessible. Computation has evolved from tools for assisting scientific research to digital laboratories where fundamental scientific discoveries take place. This is increasingly so for social and ecological sciences, which are combining big data with computational models to better understand the social and earth systems whose complex dynamics underlie many of the grand challenges faced by humanity today. Organized as a Spoke in the National Science Foundation's Big Data Innovation Hub and Spoke network, this project establishes a next generation, online Computational Model Library (CMLX). In the CMLX, software code for computational models used in social and ecological sciences is published and freely accessible to the scientific community and general public, and linked with an online database of scientific papers reporting on associated model-based science. In addition to the code library, the CMLX is developing an online repository to archive end-to-end workflows of model-based science, where the entire research process can be followed and reproduced, from data synthesis, to modeling, analysis, and visualization. A regional Working Group - including software developers, data providers, and user communities - will provide advice and expertise, as well as help establish community-wide standards for transparency and accessibility in scientific computation. The CMLX is enabling transparent access to scientific procedures, models, and data that can be used to assess the consequences of alternative scenarios, policies, and assumptions for more sustainable management of complexly coupled human and earth systems.

科学领域的透明度和知识共享规范鼓励新的研究建立在先前的发现之上，从而导致快速创新和显著的社会回报。该项目建立了一个新的活动，以帮助使科学计算和数据科学更加透明和可访问。计算已经从辅助科学研究的工具发展到基础科学发现发生的数字实验室。社会科学和生态科学越来越多地将大数据与计算模型相结合，以更好地了解社会和地球系统，其复杂的动态是当今人类面临的许多重大挑战的基础。作为美国国家科学基金会大数据创新中心和辐条网络的辐条，该项目建立了下一代在线计算模型库（CMLX）。在CMLX中，发布了用于社会和生态科学的计算模型的软件代码，科学界和公众可以免费获取，并与报告相关模型科学的科学论文在线数据库相链接。除了代码库之外，CMLX还在开发一个在线存储库，以归档基于模型的科学的端到端工作流程，从数据合成到建模，分析和可视化，整个研究过程都可以跟踪和再现。一个包括软件开发人员、数据提供者和用户社区在内的区域工作组将提供建议和专业知识，并帮助建立科学计算透明度和可访问性的社区标准。CMLX使科学程序、模型和数据的透明访问成为可能，这些程序、模型和数据可用于评估替代方案、政策和假设的后果，从而对复杂耦合的人类和地球系统进行更可持续的管理。