ITR/AP: The Analytic Web

ITR/AP：分析网络

• 批准号: 0205575
• 负责人: Leon Osterweil
• 金额: $ 210万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205575&HistoricalAwards=false
• 关键词: ITR; AP; Analytic; Web

ABSTRACT0205575U of Mass, AmherstLeon J. OsterweilThe internet has changed forever the way science is done. Collaborations such as NSF-sponsoredGlobus, and GriPhyN are beginning to employ internet access to facilitate data sharing andexpedite collaboration among worldwide communities of geographically distributed researchers.This creates opportunities to do better science through the use of shared data and expertise, but italso creates risks arising from uncertainty about how internet-accessed data sets were producedand how they can be used. If scientists cannot determine how particular results were obtained,those results may be subsequently combined in ways that will lead to incorrect or misleadingconclusions, possibly with devastating effects.Our project will develop, demonstrate, and evaluate technologies to facilitate the building ofcommunity consensus by supporting definition, validation, repeatability, and adaptability ofscientific processes that integrate and combine web-based data sets and tools. Although there willalways be debate about the most appropriate models, methodologies and conclusions used inscientific inquiry, our work should help scientists focus on areas of disagreement and resolve thesedisagreements more quickly, perhaps through further experimentation and inquiry. Thus, our workwill also help policy makers in areas where scientific results are critical ingredients, such aseconomic policy, environmental management, and social initiatives..

【摘要】莱昂·j·奥斯特韦尔互联网永远地改变了科学研究的方式。诸如nsf赞助的globus和GriPhyN等合作项目正开始利用互联网访问来促进数据共享，并加快分布在世界各地的研究人员之间的合作。这为通过使用共享数据和专业知识来更好地开展科学创造了机会，但它也带来了风险，因为互联网访问的数据集是如何产生的，以及如何使用它们存在不确定性。如果科学家不能确定特定的结果是如何获得的，那么这些结果随后可能会以导致错误或误导性结论的方式组合在一起，可能会产生毁灭性的影响。我们的项目将开发、演示和评估技术，通过支持集成和组合基于web的数据集和工具的科学过程的定义、验证、可重复性和适应性，促进社区共识的建立。尽管对于在科学研究中使用的最合适的模型、方法和结论总是会有争论，但我们的工作应该帮助科学家关注分歧的领域，并更快地解决这些分歧，也许可以通过进一步的实验和研究。因此，我们的工作也将帮助决策者在科学成果至关重要的领域，如经济政策、环境管理和社会倡议。