EAGER: Improving scientific innovation by linking funding and scholarly literature

该项目确定了科学家和组织及其主题兴趣，从而能够跟踪过去的生产力和影响。通过将学术文献和拨款联系起来，这个项目创建了一个统一的数据集，涵盖了不同的科学学科和联邦拨款奖励类型。网络为缺乏研究和资助计划知识的科学家提供了公平的竞争环境。预计用户将花费更少的时间搜索文献，而花更多的时间评估重要性和影响。该项目合并了不同的出版物和赠款储存库，消除了歧义，丰富了有关科学家和组织的信息，并建立了一个基于网络的工具来帮助浏览这些信息。该项目通过对来自《联邦记者》的大约260万笔赠款以及来自微软学术图表(83M)、MEDLINE(25M)、PubMed开放获取子集(1M)、Arxiv(0.6M)和国家经济研究局[NBER](14K)的数百万篇文章的合并多源数据集进行建模，解决了其中的许多问题。该项目创建了一个基于网络的工具，可以生成与用户兴趣相关的出版物、拨款、科学家和组织的即时报告。统一的数据集和网络工具可能会彻底改变项目官员评估提案的方式，以及研究人员如何找到可资助的想法，使科学更快、更准确、更少偏见。

This project identifies scientists and organizations and their topical interests, enabling the tracking of past productivity and impact. By linking scholarly literature and grants, this project creates a unified dataset that captures diverse scientific disciplines and federal grant award types. A web-based levels the playing field for scientists lacking knowledge about research and funding programs. Users are expected to spend less time searching the literature and more time evaluating significance and impact. This project consolidates disparate repositories of publications and grants, disambiguates and enriches information about scientists and organizations, and builds a web-based tool to help navigate this information. This project solves many of these issues by modeling the relationship approximately 2.6 million grants from the Federal RePORTER, and a consolidated, multi-source dataset of millions of articles from Microsoft Academic Graph (83 M), MEDLINE (25 M), PubMed Open Access Subset (1 M), ArXiv (0.6 M), and the National Bureau of Economic Research [NBER] (14K). The project creates a web-based tool that generates instantaneous reports about publications, grants, scientists, and organizations related to users' interests. The unified dataset and web tool could revolutionize how Program Officers evaluate proposals and how researchers find fundable ideas, making science faster, more accurate, and less biased.