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“Science and Technology Indicators” is a basic resource for understanding Japanese science and technology activities based on objective, quantitative data. It classifies science and technology activities into five categories, R&D Expenditure; R&D Personnel; Higher Education; The Output of R&D; and Science, Technology, and Innovation. The multiple relevant indicators (approximately 150 indicators) show the state of Japanese science and technology activities. “Japanese Science and Technology Indicators 2014” adds a new indicator that utilizes the results of the Japanese National Innovation Survey in time-series comparison. In indicators related to human resources development, data representation has been improved to more clearly show the status of women and international students. Additionally, seven column-style articles use indicators to focus on timely issues and specific themes from today’s society.

Using “Japanese Science and Technology Indicators 2014” to look at conditions in Japan, total Japanese R&D expenditure has changed little since 2009. The percentage of researchers who are female is especially small in the business enterprise sector. The number of female students enrolling in Japanese higher education institutions is rising. Japan ranked number one in the world in share of patents (patent families) during the 2000s. Japan’s competitive superiority of its high-technology industries is falling, but the competitiveness in medium high-technology industries is maintained high level.

NISTEP created a database on resource allocation (budget on science and technology) and important activities (thousands articles from white paper on science and technology) in FY2011-12, in which data was collected in the past decades. The database aims at organizing and overviewing data on science, technology and innovation (STI). In the study, NISTEP held a workshop with policy makers and policy researchers on STI policy to discuss issues on organization and utilization of policy data, including the NISTEP’s database. We gained some insights from the discussion such as the necessity for future policy formulation to research policy context in the past, data sets composed both of published fact data, data explanation from the standpoint of policy makers. As another theme, we surveyed existing databases on STI policy, such as government’s budget on science and technology and governmental activities, both within and outside Japan. We put some databases together in the report, from the Japan’s ministries, the international organizations (OECD, EU), the US institutes (OSTP, AAAS), the UK institutes and so on.

The next became clear as the result of Statistical Analysis of Science and Technology Interest Level, Nobel Prize Award Interest Level, and The Science and Technology Contribution Expect Degree to The Japanese Economic Maintenance or Improvement in Global Competitiveness from the data of Research material No.211 “The Change of the Public Attitudes to Science and Technology”.
For Improvement policies such as Science and Technology Interest Level, Information dissemination which led the Internet about basic research is needed. For Improvement policies such as Nobel Prize Award Interest Level, the construction of structure which makes information spread more widely in order to be dependent on a respondent attribute from subjectivity, for example, a museum, a substantial science café are needed. The Science and Technology Expect Degree to Economic Competitiveness is greatly dependent on subjectivity from a respondent attribute. For the improvement, the measure which responds to the science and technology subject according to the right time is needed.
Moreover, Regardless of the existence of the Nobel Prize Award Interest Level, the Science and Technology Expect Degree to Economic Competitiveness falled during about 2 months, and it will go up after that. It seems that immediately after the Nobel Prize Award recognition of the importance of basic science increases, at the same time, recognition that the pile over the long period of basic research leads to social utilization will increase by the news about years of struggles or difficulties about research activities, etc.

In the case of postdocs in the “narrow sense” at research institutions, their future career paths are uncertain, and growing older while repeatedly renewing terms of unstable employment is becoming a problem. In this study, in order to clarify this situation and its causes, we use individual data_ from the “Survey on postdoctoral fellows regarding employment and moving-out situations – FY 2009 -,” Knowledge Infrastructure Policy Division, Science and Technology Policy Bureau, MEXT, to analyze transitions to permanent employment (full-time, unlimited term). Postdocs are most commonly ages 30-34. Their average number of years since completing a doctorate is 4-5. Transition to permanent employment can take as long as 5-7 years. Clearly, it is often the case that the transition is made after the training period of the postdoc, at the turning point of the end of that term. However, the average transition rate of 6.3% is markedly lower than the transition rate of general college graduates from non-regular to regular employment. The transition rates of women, those employed in science and medicine, and those employed using competitive funds in particular are significantly lower. Based on these circumstances, the necessity of providing support through the turning point that is the fifth year to enable transition to stable employment is indicated. In the future, it will be necessary to also consider the transition to permanent employment of research assistant, who appointed to fixed terms, which is not captured in this data.

For comprehensively understanding the status and trends of R&D activities in a country or a region, various data on research input and output should be collected, processed, and organized. Specifically, data analysis of research output data obtained from bibliographic databases at the organizational or departmental level (micro-data analysis) is necessarily accompanied with accurate identification of author-affiliated organizations and departments which generally have numerous name variations.

In order to help micro-data analysis conducted by researchers and policy-makers, NISTEP has carried out a project “Development of data infrastructure on R&D activities in universities and public organizations” since FY2011. Through this project, it prepares and publishes an organization name dictionary playing a central role in identification and some lists of name variations in databases for universities and public organizations in Japan. This report outlines the project, with some results of analysis on name variations of author-affiliated organizations. Finally, it discusses importance of standardization of organization name description.

An Overview of Disease Prediction, Prevention, Diagnosis, and Treatment Technologies for the Realization of a Healthy and Active Aging Society
– A Study on Lifestyle-related Disease (Type 2 Diabetes) –

This survey was conducted as part of the Science and Technology Scenario Planning with a vision for the future. In order to achieve a healthy and active aging society in Japan, the socioeconomic problems related to type 2 diabetes must be addressed. To achieve this, the prediction, prevention, diagnosis of type 2 diabetes and the technology related to its treatment were schematized.

First, based on a literature review and a debate at the Expert Workshop, technologies to control type 2 diabetes were organized from the viewpoints of disease stage, prediction, prevention, diagnosis, and treatment; these were compiled in a technology map. Next, based on the technology map, 11 technical scenarios that anticipate changes in technology related to drugs, medical equipment, and regenerative medicine up to approximately 2030, were created. Relative comparisons of the impact from the viewpoints of scope of technical application, timing of technical achievement, timing of social implementation, and size of industrial and medical spillover were performed between these technical scenarios. The results revealed that scenarios on predictive diagnostic markers, imaging diagnosis, and regenerative medicine had a stronger impact than other scenarios.

Furthermore, challenges for the progress of practical applications and research development incorporating technical maps and technical scenarios were investigated.

Finally, 11 future challenges that may become particularly important for Japan were identified.


Technology Foresight for Scenario Planning
-A study on Lifestyle-related Disease (Type 2 Diabetes)-

The Science and Technology Foresight Center, National Institute of Science and Technology Policy has implemented Scenario Planning that can accommodate political, social, and economic needs as part of the 10 th Science and Technology Foresight being held at the center from 2013 through 2015. In this survey, a Delphi study on type 2 diabetes was conducted as part of scenario planning. In Japan, type 2 diabetes is becoming a public health problem with a major socioeconomic impact, which must be addressed to achieve a healthy and active aging society. To date, technology maps and scenarios have been created at the Science and Technology Foresight Center in order to schematize technology related to type 2 diabetes. In order to revise these scenarios, experts at the Japan Diabetes Society and the Science and Technology Foresight Center completed repeated questionnaire surveys regarding the technologies considered important to effectively address the health problems related to type 2 diabetes.

This investigation aims to clarify the content of the innovation indicators contained in The Global Innovation Index (“GII”) report drafted by INSEAD, with the aim of providing reference information for the selection of indicators to measure the state of innovation in Japan.

I have carried out an analysis from the perspective of investigating what types of innovation indicators have been used in the GII, how the selected innovation indicators have changed each year, and how Japan’s global innovation index ranking has changed in accordance with the selected indices.

The types of indicators used in GII were as follows. In the 2008-09 GII, soft data accounted for 46%, hard data accounted for 40% and index data accounted for 14%. By the time of the 2013 GII, there had been significant changes in the indicators used, and hard data had increased to 71%, soft data accounted for just 6% and index data accounted for 23%.

Japan’s ranking in the GII was 9th in the 2008-09 GII, but in the 2013 GII its ranking had fallen to 22nd. When we look at Japan’s ranking based on innovation input and innovation output, its ranking did not change significantly in terms of innovation input, but in terms of innovation output its ranking fell significantly.

Japan’s ranking in “7. Creative outputs”, which is one of the sub-categories of innovation output, fell sharply from the 2009-2010 GII to the 2011 GII, and this can be considered to have been a result of changes in the indicators used.