Please wait a minute...
Data Analysis and Knowledge Discovery
Current Issue | Archive | Adv Search |
Identification and evolution of funding-oriented for basic research: Case study of NSF
Wei Huanan, Lei Ming, Wang Xuefeng, Yu Yin
(School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China)
Download:
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper identifies and analyzes the funding-oriented of basic research projects funded by the United States, and provides suggestions for improving the funding layout of science funds in China. [Methods] On the basis of literature review, the feature of funding-oriented recognition was determined from four dimensions of basic information, cooperation characteristics, project characteristics and output characteristics, and the recognition model was constructed by machine learning model. Then, the corresponding evolution analysis is carried out. [Results] SVM model recognition with RBF kernel is better. The Synthetic biology case studies show the following four characteristics. NSF gives consideration to both "free exploration" and "demand-oriented". The basic research of "free exploration" runs through the whole process. The basic research of "demand-oriented" is less in the early stage and gradually increases with the development of the field. The change and development of the two kinds of funding-oriented are closely related to the development stage of the discipline and the national strategy and policy. [Limitations] Selecting only one case for analysis is slightly underrepresented; It was only represented by 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 useful exploration of the identification of basic research funding-oriented. By identifying and analyzing the funding-oriented 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.

Key words basic research      funding-oriented identification      machine learning      synthetic biology      NSF      
Published: 09 November 2022
ZTFLH:  TP393,G250  

Cite this article:

Wei Huanan, Lei Ming, Wang Xuefeng, Yu Yin. Identification and evolution of funding-oriented for basic research: Case study of NSF . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022-0627     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y0/V/I/1

[1] Nie Hui, Wu Xiaoyan. Detecting Depression Factors with Gradient Boosting Tree and Explainable Machine Learning Model SHAP[J]. 数据分析与知识发现, 2024, 8(3): 41-52.
[2] Zhang Yunqiu, Huang Qifei, Zhu Xiang. Predicting Drug-Target Relationship Based on Relation Fusion and Bidirectional Mass Diffusion Model[J]. 数据分析与知识发现, 2024, 8(2): 155-167.
[3] Liu Zhifeng, Wang Jimin. Review of Interpretable Machine Learning for Information Resource Management[J]. 数据分析与知识发现, 2024, 8(1): 16-29.
[4] Liu Tianchang, Wang Lei, Zhu Qinghua. Predicting User Churn of Smart Home-based Care Services Based on SHAP Interpretation[J]. 数据分析与知识发现, 2024, 8(1): 40-54.
[5] Xu Chen, Zhang Wei. Detecting Crowdfunding Frauds Based on Textual and Imbalanced Data[J]. 数据分析与知识发现, 2023, 7(9): 125-135.
[6] Wei Huanan, Lei Ming, Wang Xuefeng, Yu Yin. Analyzing Evolution of Basic Research Funding Orientation: Case Study of NSF[J]. 数据分析与知识发现, 2023, 7(5): 10-20.
[7] Lin Weizhen, Liu Hongwei, Chen Yanjun, Wen Zhanming, Yi Minqi. Customer Satisfaction Modelling for Healthcare Wearable Devices Through Online Reviews[J]. 数据分析与知识发现, 2023, 7(5): 145-154.
[8] Jiang Linfu, Yuan Zhenming, Zhang Xingwei, Jiang Huaqiang, Sun Xiaoyan. Ten-Year Prediction of Coronary Heart Disease Based on PCHD-TabNet[J]. 数据分析与知识发现, 2023, 7(5): 133-144.
[9] Lv Qi, Shangguan Yanhong, Zhang Lin, Huang Ying. Interdisciplinary Measurement Based on Automatic Classification of Text Content[J]. 数据分析与知识发现, 2023, 7(4): 56-67.
[10] Qu Zongxi, Sha Yongzhong, Li Yutong. Predicting Major Infectious Diseases Based on Grey Wolf Optimization and Multi-machine Learning: Case Study of COVID-19[J]. 数据分析与知识发现, 2022, 6(8): 122-133.
[11] Zhao Yang, Yan Zhouzhou, Shen Qiqi, Li Zhonghang. Evaluating Privacy Policy for Mobile Health APPs with Machine Learning[J]. 数据分析与知识发现, 2022, 6(5): 112-126.
[12] Wang Lu, Le Xiaoqiu. Research Progress on Citation Analysis of Scientific Papers[J]. 数据分析与知识发现, 2022, 6(4): 1-15.
[13] Wang Ruojia, Yan Chengxi, Guo Fengying, Wang Jimin. Predicting Churners of Online Health Communities Based on the User Persona[J]. 数据分析与知识发现, 2022, 6(2/3): 80-92.
[14] Wu Jinhong, Mu Keliang. Automatic Identifying Abnormal Behaviors of International Journals[J]. 数据分析与知识发现, 2022, 6(2/3): 385-395.
[15] Hu Yamin, Wu Xiaoyan, Chen Fang. Review of Technology Term Recognition Studies Based on Machine Learning[J]. 数据分析与知识发现, 2022, 6(2/3): 7-17.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn