Safe Bets and Risky Propositions: Leveraging Rich Data to Understand Potential in Science Teams

安全赌注和冒险提议：利用丰富的数据了解科学团队的潜力

• 批准号: 1856090
• 负责人: Noshir Contractor
• 金额: $ 54.97万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1856090&HistoricalAwards=false
• 关键词: Safe; Bets; Risky; Propositions; Leveraging

Innovation in science and technology is often the province of teams. As knowledge becomes ever more specialized, teams can tackle complex problems requiring insight from multiple domains. However, teams made up of diverse experts often struggle to combine knowledge effectively. In fact, research shows that while promising in theory, in practice, diverse teams often fail to realize their potential. Diverse teams are crucial to society. We are on the cusp of major breakthroughs in fields like artificial intelligence and brain science that require diverse expertise. These breakthroughs require large public expenditures in teams of scientists working in these fields. How can we identify the ''safe bets'' and ''risky propositions''? Which mixes distinguish mediocre teams from the teams whose work will fuel decades of important discoveries? These are the questions we seek to answer. We develop methods for characterizing scientific expertise and markers of productive collaboration among scientists based on machine-learning analysis of the full text of the papers they have written. Previous work relies on structural markers, and has yet to peer into the ideas themselves. Applying recent developments in text analytics machine learning, the full text of publications and patents is used to characterize expertise and diversity in ways that otherwise would not be possible. These advances have scientific and practical value. Practically speaking, we need a better way to predict which teams are most likely to be productive, and invest in them. Beyond this application, being able to predict diverse teams most likely to succeed will help to address societal challenges involving teams in other contexts as well, such as healthcare, disaster response, cybersecurity, and the military.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.

科学和技术的创新往往是团队的领域。随着知识变得越来越专业化，团队可以解决需要多个领域洞察力的复杂问题。然而，由不同专家组成的团队往往很难有效地将联合收割机知识结合起来。事实上，研究表明，虽然在理论上很有希望，但在实践中，多元化的团队往往无法实现他们的潜力。多元化的团队对社会至关重要。我们正处于人工智能和脑科学等领域重大突破的风口浪尖，这些领域需要各种专业知识。这些突破需要在这些领域工作的科学家团队的大量公共支出。我们如何区分“安全赌注”和“风险提议”？哪些组合将平庸的团队与那些工作将推动数十年重要发现的团队区分开来？这些都是我们要回答的问题。我们基于对科学家撰写的论文全文的机器学习分析，开发了表征科学专业知识和科学家之间富有成效的合作标志的方法。以前的工作依赖于结构标记，还没有深入到思想本身。应用文本分析机器学习的最新发展，出版物和专利的全文被用来以其他方式描述专业知识和多样性。这些进展具有科学和实用价值。实际上，我们需要一种更好的方法来预测哪些团队最有可能产生生产力，并投资于他们。除此之外，能够预测最有可能成功的不同团队将有助于解决涉及其他环境中的团队的社会挑战，例如医疗保健，灾难响应，网络安全和军事。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。

项目成果

Diversity, networks, and innovation: A text analytic approach to measuring expertise diversity

• DOI: 10.1017/nws.2022.34
• 发表时间: 2022-12
• 期刊: Network Science
• 影响因子: 1.7
• 作者: Alina Lungeanu;Ryan Whalen;Y. J. Wu;Leslie A. DeChurch;N. Contractor

Patent Similarity Data and Innovation Metrics

专利相似性数据和创新指标

• DOI: 10.1111/jels.12261
• 发表时间: 2020
• 期刊: Journal of Empirical Legal Studies
• 影响因子: 1.7
• 作者: Whalen, Ryan;Lungeanu, Alina;DeChurch, Leslie;Contractor, Noshir
• 通讯作者: Contractor, Noshir