Please wait a minute...
Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (2/3): 60-67    DOI: 10.11925/infotech.2096-3467.2019.0677
Current Issue | Archive | Adv Search |
Detecting Funding Topics Evolutions with Visualization
Chen Ting1,2,3,Wang Haiming3(),Wang Xiaomei3
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China
2Department of Library, Information and Archives Management, University of Chinese Academy of Sciences,Beijing 100190, China
3Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
Download: PDF(2878 KB)   HTML ( 15
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This study tries to detect funding topics and their evolution based on data from NASA’s Small Business Innovation Research Program.[Methods] First, we created funding maps with two-time windows for topics of funding applications. Then, we identified areas with higher number of topics in the map. Finally, we determined the trends by comparing the changes of hotspots from the two maps.[Results] The proposed method identified the disappeared, continuous and emerging funding topics from the maps.[Limitations] The algorithm parameters and results need to be adjusted and evaluated manually.[Conclusions] The proposed method could effectively detect funding toipics and their evolution, which helps scientific management and policy decision making.

Key wordsVisualization      Kernel Density      Funding Focus      Evolution     
Received: 14 June 2019      Published: 26 April 2020
ZTFLH:  P315 G35  
Corresponding Authors: Haiming Wang     E-mail: wanghaiming@casisd.cn

Cite this article:

Chen Ting,Wang Haiming,Wang Xiaomei. Detecting Funding Topics Evolutions with Visualization. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 60-67.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0677     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I2/3/60

Procedures for the Discovery of Funding Hotspots
Visual Map of NASA SBIR Awards
Visual Map and the Changes in the Funding Hotspots of NASA SBIR Awards
2000-2008财年 2009-2017财年
簇号 项目数量 研究内容 簇号 项目数量 研究内容
1-1 24 数据挖掘、决策系统软件工具 2-1 105 用于航空航天系统的软件工具开发(人机交互系统、可视化分析工具、无人机系统命令与控制,空管系统规划、运行和控制,决策支持系统等)
1-2 25 用于航空航天系统的软件工具开发(人机交互系统、可视化分析工具、航空管理系统等)
1-3 74 纳米材料、复合材料等多种材料及其航空航天应用 2-2 31 纳米材料、复合材料等多种材料及其航空航天应用
1-5 67 航空器空气动力学算法、建模和仿真工具(声学、气动弹性力学、CFD工具、旋翼机建模、燃烧等) 2-4 31 航空器空气动力学算法、建模和仿真工具(声学、气动弹性、湍流等)
Continuous Funding Hotspot
簇号 项目数量 研究内容
1-4 31 基于MEMS的空间望远镜可变形镜面技术
1-6 58 基于HgCdTe、AlGaN、GaAs等材料的探测及成像半导体光电器件
1-7 29 高性能锂离子电池及其纳米复合电极、电解液材料开发
1-9 28 用于航天器各系统的监视及检测技术
Withered Funding Hotspots (FY 2000 to 2008)
簇号 项目数量 研究内容
2-3 86 用于空间极端环境的电子器件
2-6 71 用于采样返回等太阳系探测任务的新型推进器技术
2-7 56 大型太阳能阵列部署结构
Emerging Funding Hotspots (FY 2009 to 2017)
[1] 王小梅, 李国鹏, 陈挺 . 中国与世界主要科技强国的科学资助分析:基于科学结构图谱2010-2015[J]. 中国科学基金, 2018,32(4):424-433.
[1] ( Wang Xiaomei, Li Guopeng, Chen Ting . Scientific Funding of Scientific Prowess Nations: Analysis Based on the Science Map 2010-2015[J]. Bulletin of National Natural Science Foundation of China, 2018,32(4):424-433.)
[2] 杜建, 唐小利 . 美国国立卫生研究院与中国国家自然科学基金资助艾滋病细分领域分析[J]. 中国预防医学杂志, 2012,13(9):718-722.
[2] ( Du Jian, Tang Xiaoli . Analysis of the AIDS Segment by the National Institutes of Health and the National Natural Science Foundation of China[J]. Chinese Preventive Medicine, 2012,13(9):718-722.)
[3] 申艳军, 杨更社, 唐丽云 , 等. 2006-2015年“寒区岩土力学与工程”领域国家自然科学基金资助情况统计及发展趋势浅析[J]. 冰川冻土, 2015,37(5):1294-1303.
[3] ( Shen Yanjun, Yang Gengshe, Tang Liyun , et al. Development Trend and Statistics of the Field of Geotechnical Mechanics and Engineering in Cold Regions Funded by NSFC During 2006-2015[J]. Journal of Glaciology and Geocryology, 2015,37(5):1294-1303.)
[4] 邓方, 宋苏, 刘克 , 等. 国家自然科学基金自动化领域数据分析与研究热点变化[J]. 自动化学报, 2018,44(2):377-384.
[4] ( Deng Fang, Song Su, Liu Ke , et al. Data and Research Hotspot Analyses of National Natural Science Foundation of China in Automation Field[J]. Acta Automatica Sinica, 2018,44(2):377-384.)
[5] Park J, Blume-Kohout M, Krestel R , et al. Analyzing NIH Funding Patterns over Time with Statistical Text Analysis [C]// Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016.
[6] Pellicano E, Dinsmore A, Charman T . What Should Autism Research Focus upon? Community Views and Priorities from the United Kingdom[J]. Autism, 2014,18(7):756-770.
[7] Schwedt T J, Shapiro R E . Funding of Research on Headache Disorders by the National Institutes of Health[J]. Headache, 2009,49(2):162-169.
[8] 陈挺, 李国鹏, 王小梅 . 基于t-SNE降维的科学基金资助项目可视化方法研究[J]. 数据分析与知识发现, 2018,2(8):1-9.
[8] ( Chen Ting, Li Guopeng, Wang Xiaomei . Visualizing Appropriation of Research Funding with t-SNE Algorithm[J]. Data Analysis and Knowledge Discovery, 2018,2(8):1-9. )
[9] van der Maaten L, Hinton G . Visualizing Data Using t-SNE[J]. Journal of Machine Learning Research, 2008,9:2579-2605.
[10] Zhong G Q, Cheriet M . Large Margin Low Rank Tensor Analysis[J]. Neural Computation, 2014,26(4):761-780.
[11] Li W T, Cerise J E, Yang Y N , et al. Application of t-SNE to Human Genetic Data[J]. Journal of Bioinformatics and Computational Biology, 2017,15(4):1750017.
[12] Pezzotti N, Lelieveldt B P F, van der Maaten L , et al. Approximated and User Steerable tSNE for Progressive Visual Analytics[J]. IEEE Transactions on Visualization and Computer Graphics, 2017,23(7):1739-1752.
[13] Liu S S, Bremer P T, Thiagarajan J J , et al. Visual Exploration of Semantic Relationships in Neural Word Embeddings[J]. IEEE Transactions on Visualization and Computer Graphics, 2018,24(1):553-562.
[14] Xu X Y, Yan Z, Xu S L . Estimating Wind Speed Probability Distribution by Diffusion-Based Kernel Density Method[J]. Electric Power Systems Research, 2015,121:28-37.
[15] Xia Z X, Yan J . Kernel Density Estimation of Traffic Accidents in a Network Space[J]. Computers, Environment and Urban Systems, 2008,32(5):396-406.
[16] Anderson T K . Kernel Density Estimation and K-means Clustering to Profile Road Accident Hotspots[J]. Accident Analysis and Prevention, 2009,41(3):359-364.
[17] Dehnad K . Density Estimation for Statistics and Data Analysis[J]. Technometrics, 1987,29(4):495-495.
[1] Xuhui Li,Tao Yu,Ting Li,Yiwen Li,Jinguang Gu. An Evolutionary Schema for Metadata Description[J]. 数据分析与知识发现, 2020, 4(1): 76-88.
[2] Jianhua Hou,Pan Liu. Measuring Tech-Entropy of System Evolution: An Empirical Study of Patents[J]. 数据分析与知识发现, 2019, 3(8): 21-29.
[3] Peng Guan,Yuefen Wang,Zhu Fu. Analyzing Topic Semantic Evolution with LDA: Case Study of Lithium Ion Batteries[J]. 数据分析与知识发现, 2019, 3(7): 61-72.
[4] Haici Yang,Jun Wang. Visualizing Knowledge Graph of Academic Inheritance in Song Dynasty[J]. 数据分析与知识发现, 2019, 3(6): 109-116.
[5] Yanan Yang,Wenhui Zhao,Jian Zhang,Shen Tan,Beibei Zhang. Visualizing Policy Texts Based on Multi-View Collaboration[J]. 数据分析与知识发现, 2019, 3(6): 30-41.
[6] Jianhua Liu,Zhixiong Zhang,Qin Zhang. Revealing Sci-Tech Policy Evolution with Entity Relationship[J]. 数据分析与知识发现, 2019, 3(5): 57-67.
[7] Guang Zhu,Hu Liu,Xinmeng Du. Health APPs and Privacy Concerns: A Three-Entities Game-theoretic Approach[J]. 数据分析与知识发现, 2019, 3(5): 93-106.
[8] Jiang Wu,Guanjun Liu,Xian Hu. An Overview of Online Medical and Health Research: Hot Topics, Theme Evolution and Research Content[J]. 数据分析与知识发现, 2019, 3(4): 2-12.
[9] Lu An,Yanping Liang. Selection of Users’ Behaviors Towards Different Topics of Microblog on Public Health Emergencies[J]. 数据分析与知识发现, 2019, 3(4): 33-41.
[10] Lin Wang,Ke Wang,Jiang Wu. Public Opinion Propagation and Evolution of Public Health Emergencies in Social Media Era: A Case Study of 2018 Vaccine Event[J]. 数据分析与知识发现, 2019, 3(4): 42-52.
[11] Xiang Li,Xiaodong Qian. Research on Impact of Commodity Online Evaluation for Consumption Convergence[J]. 数据分析与知识发现, 2019, 3(3): 102-111.
[12] Zhiqiang Wu,Zhongming Zhu,Wei Liu,Sili Wang. Research and Practice on the Extension of Knowledge Analysis and Visualization Function in CSpace[J]. 数据分析与知识发现, 2019, 3(3): 112-119.
[13] Peiyao Zhang,Dongsu Liu. Topic Evolutionary Analysis of Short Text Based on Word Vector and BTM[J]. 数据分析与知识发现, 2019, 3(3): 95-101.
[14] Hongqinling Wang,Zhichao Ba,Gang Li. Conversational Topic Intensity Calculation and Evolution Analysis of WeChat Group[J]. 数据分析与知识发现, 2019, 3(2): 33-42.
[15] Gang Li,Sijing Chen,Jin Mao,Yansong Gu. Spatio-Temporal Comparison of Microblog Trending Topics on Natural Disasters[J]. 数据分析与知识发现, 2019, 3(11): 1-15.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938   E-mail:jishu@mail.las.ac.cn