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EAGER: Improving scientific innovation by linking funding and scholarly literature

EAGER：通过将资金与学术文献联系起来提高科学创新

基本信息

批准号：
1646763
负责人：
Daniel Acuna
金额：
$ 16.87万
依托单位：
Syracuse University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1646763&HistoricalAwards=false
关键词：
EAGER Improving scientific innovation linking

项目摘要

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.

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。通过链接学术文献和赠款，该项目创建了一个统一的数据集，捕捉不同的科学学科和联邦补助金奖励类型。基于网络的平台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，更多的时间评估意义和影响。该项目整合了不同的出版物和赠款库，消除了科学家和组织的歧义并丰富了有关信息，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RePORTER的约260万赠款以及来自Microsoft Academic Graph（83 M），MEDLINE（25 M），PubMed Open Access Subset（1 M），ArXiv（0.6 M）和国家经济研究局[NBER]（14 K）的数百万篇文章的综合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、赠款、科学家和组织的即时报告。统一的数据集和网络工具可以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。