EAGER: Unified categories for describing and quantifying scientific research

该项目开发了最先进的机器学习方法来描述和量化科学研究。所采取的特殊方法是开发新的主题模型，可以在各种文本数据源中学习基础研究类别，包括NSF和NIH资助奖，科学出版物和美国专利。重要的创新是技术的构建，以允许来自领域专家和最终用户的反馈。该方法具有潜在的变革性，因为它可以通过提高不同数据源中主题的质量和实用性来克服当前主题建模方法的一些已知限制。这个基于网络的工具显示和操纵学习的主题，以便用户可以应用它来创建科学资助，研究和生产的全面概述，包括对以下问题的回答：-NSF，NIH和其他机构资助了哪些类型的科学？哪些类型的科学是由受资助的研究人员产生的？美国专利中描述了哪些类型的科学？该工具还为用户提供有关科学科学以及资助与科学成就之间关系的更复杂问题的答案，跟踪趋势，描述资助计划，并确定资助重叠（跨机构，甚至机构内）。智力价值：拟议的研究在几个方面推进了跟踪科学研究的技术和方法。首先，它推动了无监督统计主题模型的发展，以分类，描述和衡量科学研究。其次，它解决了这类应用程序的主题建模的已知问题，例如提高所有主题的一致性，并使主题超越不同类型的文档集（赠款，出版物，专利）。由于能够利用赠款、出版物和专利的统一主题，用户可以更直接地衡量资助的影响。第三，研究开发了评估框架，将重点从机器学习指标转移到领域专家和最终用户的需求。更广泛的影响：这项工作有一系列更广泛的影响。它为资助机构工作人员、研究人员、感兴趣的公众、政府机构、媒体和其他利益攸关方创建了有用的数据。基于网络的工具允许用户创建定制的数据集，适合他们的特定需求。这些数据集使用户能够回答一系列科学政策问题的科学。在这项工作中创造的知识支持了星星度量等倡议，以记录科学研究投资的价值。

This project develops state-of-the-art machine learning methods to describe and quantify scientific research. The particular approach taken is to develop new topic models that can learn underlying research categories across a wide variety of text data sources including NSF and NIH grant awards, scientific publications, and US patents. The important innovation is the building of the technology to permit feedback from domain experts and end users. The approach is potentially transformative in that it can potentially overcomes some of the known limitations of current topic modeling approaches by improving the quality and utility of topics across diverse data sources. The web-based tool displays and manipulates learned topics so that users can apply it to create comprehensive overviews of scientific funding, research, and production, including the answers to questions like:- What types of science are funded by NSF, NIH and other agencies?- What types of science are produced by funded investigators?- What types of science are described in US patents?The tool also provide users with answers to more complex questions about the science of science and the relationship between funding and scientific achievements, tracking trends, describing funding programs, and identifying funding overlap (across agencies, or even within agencies). Intellectual Merit: The proposed research advances techniques and methods for tracking scientificresearch in several ways. First, it advances the development of unsupervised statistical topic models to categorize, describe, and measure scientific research. Second, it addresses known problems with topic modeling for this type of application, such as improving the coherence of all topics, and making topics transcend different types of document collections (grants, publications, patents). Users can more directly measure impacts of funding as a result of being able to make use of unified topics from grants, publications, and patents. Third, the research develops evaluation frameworks that shift the focus from machine learning metrics to the needs of domain experts and end users. Broader Impacts: This work has an array of broader impacts. It creates useful data for funding agency staff, researchers, interested public, government bodies, media and other stakeholders. The web based tool allows users to create custom-based data sets, tailored to their particular needs. Such data sets allow users to answer an array of science of science policy questions. The knowledge created in this work supports initiatives such as STAR METRICS to document the value of investments in scientific research.