Collaborative Research: Workshop on Confidential Data Collection for Innovation Analysis in Organizations' to be held at Microsoft headquarters in September 2009 -- Redmond, WA.

协作研究：组织创新分析机密数据收集研讨会将于 2009 年 9 月在华盛顿州雷德蒙德的 Microsoft 总部举行。

• 批准号: 0943047
• 负责人: Nicholas Bloom
• 金额: $ 0.58万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0943047&HistoricalAwards=false
• 关键词: Collaborative; Research; Workshop; Confidential; Data

Understanding how organizations promote innovation is a key element of advancing the science of innovation, and hence the science of innovation policy. Data on innovation inputs, innovation processes, and innovation outputs are increasingly being captured and stored electronically. A number of fundamental bottlenecks to using these data to advance social science research exist due to unsolved issues of privacy, data integration, and data quality. The core scientific challenge is how to make such real-world, large-scale data available to researchers to nurture innovation and perform valid experimentation, while maintaining data privacy. Fortunately, computer scientists have been developing a variety of techniques and building new tools that manage large data sets in ways that can potentially help in supporting and measuring innovation activities.This workshop brings together social scientists, the users of data on innovation, together with computer scientists, the creators of new tools for collecting data while protecting privacy concerns. The workshop includes leading computer scientists with specialties in data management, data mining, security/privacy and social networks as well as social/organizational scientists, such as economists, sociologists, psychologists and anthropologists.The focus of the workshop is to identify emerging major challenges in thisinterdisciplinary area. Three different types of data critical to the study of innovation are the focus of study: third party data, such as U.S. Census data, patendt databases, NSF funding data or citation databases; detailed insider data such as internal communications, team video, or team documentation; and broader insider data such as cross-firm surveys.The broader impact of the workshop is substantial. First, an enhanced empirical basis for studying innovation is necessary to guide policy decisions. Second, the workshop participants represent a broad variety of disciplines, which creates a broader interdisciplinary network of study. Finally, the attendance of graduate students advances the goal of training a new cohort of researchers in the field.

理解组织如何促进创新是推进创新科学的关键要素，因此也是创新政策科学的关键要素。关于创新投入、创新过程和创新产出的数据越来越多地以电子方式获取和存储。由于未解决的隐私、数据集成和数据质量问题，使用这些数据推进社会科学研究存在许多根本瓶颈。核心的科学挑战是如何使这些真实世界的大规模数据可供研究人员使用，以促进创新和进行有效的实验，同时保持数据隐私。幸运的是，计算机科学家们一直在开发各种技术和构建新的工具，以管理大型数据集的方式，可以帮助支持和衡量创新活动。本次研讨会汇集了社会科学家，创新数据的用户，以及计算机科学家，收集数据的新工具的创造者，同时保护隐私问题。研讨会包括数据管理、数据挖掘、安全/隐私和社交网络方面的领先计算机科学家以及社会/组织科学家，如经济学家、社会学家、心理学家和人类学家。研讨会的重点是确定这一跨学科领域新出现的主要挑战。研究的重点是对创新研究至关重要的三种不同类型的数据：第三方数据，如美国人口普查数据、专利数据库、NSF资助数据或引用数据库;详细的内部数据，如内部通信、团队视频或团队文档;以及更广泛的内部数据，如跨公司调查。研讨会的广泛影响是巨大的。第一，必须加强研究创新的经验基础，以指导政策决定。其次，研讨会的参与者代表了广泛的学科，这创造了一个更广泛的跨学科研究网络。最后，研究生的出席推进了在该领域培养一批新的研究人员的目标。