IEICE TRANSACTIONS on Information
Predicting Research Trends Identified by Research Histories via Breakthrough Researches
Summary :
Since it is difficult to understand or predict research trends, we proposed methodologies for understanding and predicting research trends in the sciences, focusing on the structures of grants in the Japan Society for the Promotion of Science (JSPS), a Japanese funding agency. Grant applications are suitable for predicting research trends because these are research plans for the future, different from papers, which report research outcomes in the past. We investigated research trends in science focusing on research histories identified in grant application data of JSPS. Then we proposed a model for predicting research trends, assuming that breakthrough research encourages researchers to change from their current research field to an entirely new research field. Using breakthrough research, we aim to obtain higher precision in the prediction results. In our experimental results, we found that research fields in Informatics correlate well with actual scientific research trends. We also demonstrated that our prediction models are effective in actively interacting research areas, which include Informatics and Social Sciences.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.2 pp.355-362
- Publication Date
- 2015/02/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2013EDP7435
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Nagayoshi YAMASHITA
Japan Society for the Promotion of Science
Masayuki NUMAO
Osaka University
Ryutaro ICHISE
Principles of Informatics Research Division, National Institute of Informatics
Keyword
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Nagayoshi YAMASHITA, Masayuki NUMAO, Ryutaro ICHISE, "Predicting Research Trends Identified by Research Histories via Breakthrough Researches" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 2, pp. 355-362, February 2015, doi: 10.1587/transinf.2013EDP7435.
Abstract: Since it is difficult to understand or predict research trends, we proposed methodologies for understanding and predicting research trends in the sciences, focusing on the structures of grants in the Japan Society for the Promotion of Science (JSPS), a Japanese funding agency. Grant applications are suitable for predicting research trends because these are research plans for the future, different from papers, which report research outcomes in the past. We investigated research trends in science focusing on research histories identified in grant application data of JSPS. Then we proposed a model for predicting research trends, assuming that breakthrough research encourages researchers to change from their current research field to an entirely new research field. Using breakthrough research, we aim to obtain higher precision in the prediction results. In our experimental results, we found that research fields in Informatics correlate well with actual scientific research trends. We also demonstrated that our prediction models are effective in actively interacting research areas, which include Informatics and Social Sciences.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2013EDP7435/_p
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@ARTICLE{e98-d_2_355,
author={Nagayoshi YAMASHITA, Masayuki NUMAO, Ryutaro ICHISE, },
journal={IEICE TRANSACTIONS on Information},
title={Predicting Research Trends Identified by Research Histories via Breakthrough Researches},
year={2015},
volume={E98-D},
number={2},
pages={355-362},
abstract={Since it is difficult to understand or predict research trends, we proposed methodologies for understanding and predicting research trends in the sciences, focusing on the structures of grants in the Japan Society for the Promotion of Science (JSPS), a Japanese funding agency. Grant applications are suitable for predicting research trends because these are research plans for the future, different from papers, which report research outcomes in the past. We investigated research trends in science focusing on research histories identified in grant application data of JSPS. Then we proposed a model for predicting research trends, assuming that breakthrough research encourages researchers to change from their current research field to an entirely new research field. Using breakthrough research, we aim to obtain higher precision in the prediction results. In our experimental results, we found that research fields in Informatics correlate well with actual scientific research trends. We also demonstrated that our prediction models are effective in actively interacting research areas, which include Informatics and Social Sciences.},
keywords={},
doi={10.1587/transinf.2013EDP7435},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Predicting Research Trends Identified by Research Histories via Breakthrough Researches
T2 - IEICE TRANSACTIONS on Information
SP - 355
EP - 362
AU - Nagayoshi YAMASHITA
AU - Masayuki NUMAO
AU - Ryutaro ICHISE
PY - 2015
DO - 10.1587/transinf.2013EDP7435
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2015
AB - Since it is difficult to understand or predict research trends, we proposed methodologies for understanding and predicting research trends in the sciences, focusing on the structures of grants in the Japan Society for the Promotion of Science (JSPS), a Japanese funding agency. Grant applications are suitable for predicting research trends because these are research plans for the future, different from papers, which report research outcomes in the past. We investigated research trends in science focusing on research histories identified in grant application data of JSPS. Then we proposed a model for predicting research trends, assuming that breakthrough research encourages researchers to change from their current research field to an entirely new research field. Using breakthrough research, we aim to obtain higher precision in the prediction results. In our experimental results, we found that research fields in Informatics correlate well with actual scientific research trends. We also demonstrated that our prediction models are effective in actively interacting research areas, which include Informatics and Social Sciences.
ER -
English
