TY - JOUR
T1 - 基础研究资助导向识别及演化分析:以 NSF 为例
AU - Wei, Huanan
AU - Lei, Ming
AU - Wang, Xuefeng
AU - Yu, Yin
N1 - Publisher Copyright:
© 2023 Data Analysis and Knowledge Discovery. All rights reserved.
PY - 2023/5
Y1 - 2023/5
N2 - [Objective] This paper identifies and analyzes the funding orientation of basic research projects funded in the United States, aiming to provide suggestions for improving the funding layout of science funds in China. [Methods] Based on the literature review, we established a feature system for identifying funding orientation from four dimensions: basic information, collaborative characteristics, project characteristics, and output characteristics. Then, we constructed a recognition model with the help of machine learning. Finally, we conducted the corresponding evolution analysis. [Results] The SVM model with an RBF kernel had a better identification effect. The case analysis of synthetic biology showed that the NSF balanced“free exploration”and“demand-oriented”. The basic research of“free exploration”was consistent throughout. In contrast, the basic research of“demand-oriented”was relatively scarce in the early stages, gradually increasing with the development of the field. Changes in the two funding orientations are closely related to the development stage of the discipline and the national strategic policies. [Limitations] We only chose one field for case analysis, which lacked representativeness. We only included NSF project data and did not include NIH, FDA, and other data, so the comprehensiveness of the data source needs to be strengthened. [Conclusions] This study is a valuable exploration of identifying basic research funding orientation. By identifying and analyzing the funding orientation of NSF projects in synthetic biology, this study can provide suggestions for the funding layout of NSFC in China and promote the coordinated development of basic research in China.
AB - [Objective] This paper identifies and analyzes the funding orientation of basic research projects funded in the United States, aiming to provide suggestions for improving the funding layout of science funds in China. [Methods] Based on the literature review, we established a feature system for identifying funding orientation from four dimensions: basic information, collaborative characteristics, project characteristics, and output characteristics. Then, we constructed a recognition model with the help of machine learning. Finally, we conducted the corresponding evolution analysis. [Results] The SVM model with an RBF kernel had a better identification effect. The case analysis of synthetic biology showed that the NSF balanced“free exploration”and“demand-oriented”. The basic research of“free exploration”was consistent throughout. In contrast, the basic research of“demand-oriented”was relatively scarce in the early stages, gradually increasing with the development of the field. Changes in the two funding orientations are closely related to the development stage of the discipline and the national strategic policies. [Limitations] We only chose one field for case analysis, which lacked representativeness. We only included NSF project data and did not include NIH, FDA, and other data, so the comprehensiveness of the data source needs to be strengthened. [Conclusions] This study is a valuable exploration of identifying basic research funding orientation. By identifying and analyzing the funding orientation of NSF projects in synthetic biology, this study can provide suggestions for the funding layout of NSFC in China and promote the coordinated development of basic research in China.
KW - Basic Research
KW - Funding-Oriented Identification
KW - Machine Learning
KW - NSF
KW - Synthetic Biology
UR - http://www.scopus.com/inward/record.url?scp=85169687795&partnerID=8YFLogxK
U2 - 10.11925/infotech.2096-3467.2022.0627
DO - 10.11925/infotech.2096-3467.2022.0627
M3 - 文章
AN - SCOPUS:85169687795
SN - 2096-3467
VL - 7
SP - 10
EP - 20
JO - Data Analysis and Knowledge Discovery
JF - Data Analysis and Knowledge Discovery
IS - 5
ER -