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Academic papers on economics
On the Problems and Countermeasures of Sci-tech Statistics in Colleges and Universities
Abstract: The statistical work of science and technology in colleges and universities is a part of scientific research management. Accurate statistical data of science and technology will provide important reference for the state and schools to make scientific and technological plans and decisions. However, there are still many problems in the scientific and technological statistics of colleges and universities in China. This paper describes several key problems existing in the actual operation of scientific and technological statistics in colleges and universities: the workload statistics of the personnel involved in the project are seriously distorted; The calculation of matching funds is too complicated; The requirements for the classification of national economic industries and socio-economic goals are too detailed. This paper puts forward corresponding countermeasures and suggestions for discussion with colleagues.
Keywords:: Discussion on Key Issues of Sci-tech Statistics in Colleges and Universities
cite
Science and technology statistics in colleges and universities are composed of humanities and social sciences and science, engineering, agriculture and medical science and technology statistics. Statistical data are reported in the form of annual report. The contents of the annual report include [1]: scientific and technological personnel, scientific and technological developers, scientific and technological activity funds, scientific and technological institutions, scientific and technological projects, research achievements, academic exchanges, technology transfer and intellectual property rights. The scientific and technological statistical indicators and the scientific and technological statistical system of the Ministry of Education are guidelines and tools to carry out scientific and technological statistical work, but there are still many problems in the process of implementing scientific and technological indicators and operating scientific and technological statistical system.
1. Several key problems in the process of science and technology statistics
The workload statistics of project participants in 1. 1 (that is, the number of project participants in social science annual report 2 [2] and science and technology annual report 4- 1 and 4-2 [3]) are seriously distorted.
The workload of staff engaged in the project refers to the sum of the number of full-time staff (including foreign experts and visiting scholars outside the higher education system) who undertake project research tasks and work for more than 1 month this year and the number of full-time staff. Full-time staff refers to full-time scientific and technical personnel, and part-time staff refers to personnel engaged in project research in teacher positions, teaching assistant positions and administrative positions. Part-time employees are converted into full-time employees based on one year's working time 10 months.
The workload data of the personnel participating in the project comes from the working hours of various professional titles in the research project and the number of graduate students participating in the project. The data needs to be specific to how much everyone involved in the scientific research project has done in this project, which is equivalent to full-time work. All researchers are required to register and record the working hours of a scientific research project every day until the end of the year, and then convert them into full-time workload. It can be seen that this statistical data needs a wide range of data sources and detailed data. However, in the actual operation process, the personnel involved in the project did not make a detailed record of the working hours invested in the specific project. Therefore, there is no accurate source basis for the workload data of project input personnel. In the current statistical operation process, this data is subjectively developed by statisticians according to the logical range of the data, imagining the data out of thin air, then piecing the data together and adjusting it to the specified range of the data, and it is completed? What is the workload of the people involved in the project? Statistical work. Such statistical data is meaningless, which will not only increase the useless workload of statisticians, but also help statisticians to think negatively about the significance of statistical work. The untruthfulness of such statistical data is also an important factor that statistical work has been neglected for many years.
The calculation of matching funds for 1.2 is too complicated to guarantee the accuracy of the data.
In the annual report of science and technology in colleges and universities? Science and technology project situation table? (i.e. annual report of science and technology st4- 1 and st4-2), and the matching funds shall be classified according to the project type (973, 863, National Natural Science Foundation of China, etc.). ), but in the annual report? Table of funds for scientific and technological activities? (that is, the annual report of science and technology st2), the support funds need to be classified according to the source of the project (that is, the funds for scientific and technological activities of the school in that year are supporting the national science and technology plan projects). If only a few projects have matching funds, the workload will not be too great. For units with large matching funds, the calculation of this fund is too complicated. At present, there are two situations in the financial filing of matching funds in colleges and universities: in the first case, the matching funds and the funds issued by the project are made into a fund account at the time of financial filing. In this case, where are the matching funds? Science and technology project situation table? (that is, the annual report of science and technology st4- 1 table and st4-2 table) can accurately reflect, but in? Table of funds for scientific and technological activities? (that is, the st2 table of the annual report of science and technology) cannot be correctly reflected. In the second case, since the finance has been put on record, the matching funds will be set up independently. In this case, can the matching funds be set in? Science and technology project situation table? And then what? Table of funds for scientific and technological activities? However, it takes complicated calculation to get accurate data, which is very complicated and requires statisticians to have considerable computer data processing ability. Because at present, the science and technology statistics system of the Ministry of Education is not equipped in the science and technology project calculation database? Matching funds? This indicator, so the science and technology statistics system of the Ministry of Education will not display the summary amount of supporting funds when summarizing and calculating science and technology projects. Calculation? Table of funds for scientific and technological activities? The matching funds in the system need statisticians to establish their own programs for data processing. Such statistical work has added a huge workload to statisticians. In actual statistical work, there are few statisticians with computer programming ability. In order to display the data conveniently, statisticians will allocate the matching funds for the whole year subjectively, and the accuracy of the data cannot be guaranteed.
1.3 in the annual report? Classification and code of national economy industry? The requirements are too detailed and the data is inaccurate.
In the annual report (that is, st4 table), one of the statistical indicators of science and technology projects is? Classification and code of national economy industry? (that is, L 17), the classification code to be selected for statistics is 3-digit code. This index requires each project (project) to select an appropriate classification code from 396 classification code tables [3] according to the national economic industry classification and code GB/T 4754-2002. The classification code is not specifically set and classified in the contract issued by the project (from national project to independent project). When doing annual report statistics, statisticians are required to classify each item in such a detailed way. If there are 2000 projects under study, statisticians need to make 2000*396=792000 choices. Assuming that the time to make a selection is 10 second, how to complete the selection in project statistics? Classification and code of national economy industry? The work takes 2200 hours. Facts show that this statistical classification index is too detailed and unrealistic. In practice, the result can only be that statisticians make a rough choice according to the subject to which the project belongs, and then fill in the same subject by copying? Classification and code of national economy industry? (that is, L 17) column.
1.4 in the annual report? Classification and code of socio-economic goals? The requirements are too detailed and the data is inaccurate.
In the annual report (that is, st4 table), one of the statistical indicators of science and technology projects is? Classification and code of socio-economic goals? (that is, L 18), the classification code to be selected for statistics is 4-digit code. This index requires each project (project) to select an appropriate classification code from 98 classification code tables [3] according to the standard of "Classification and Code of Social and Economic Goals" GB/T 24450-2009, and compare it with? Classification and code of national economy industry? Same,? Classification and code of socio-economic goals? There is no specific setting and classification in the contract issued by the project (from national project to self-designed project). Choice? Classification and code of socio-economic goals? The workload is also very heavy. According to the statistical calculation of 2000 projects under research, the selected number should be 2000*98= 196000. If the selection time is 10 second, complete the selection in the project statistics? Classification and code of socio-economic goals? The work takes 544 hours. Facts show that this statistical index is too demanding and unrealistic. In practice, the result can only be that statisticians make a rough selection according to the undertaker of the project, and then fill in the same undertaker project by copying? Classification and code of socio-economic goals? (that is, L 18) column.
2. In view of several key problems existing in sci-tech statistics, the corresponding countermeasures are put forward.
2. 1 The workload data of personnel invested in the project has no accurate basis, and the statistical data is too false. It is suggested to cancel the statistics of project input workload in the annual report.
2.2 Regarding the classification and summary of matching funds, it is suggested to add? Matching funds? Further improve the summary calculation program in the science and technology statistics system of the Ministry of Education, so that the matching funds can be summarized separately according to the project source and project type.
2.3 for？ National economic industry classification code? And then what? Socio-economic target classification code? It is suggested that only major categories should be included in the annual report statistics. ? National economic industry classification code? Select a 2-digit code. Socio-economic target classification code? Also choose a 2-digit code, which can not reduce the workload of statisticians, and more importantly, ensure the accuracy of the data.
3. Some suggestions on doing a good job in scientific and technological statistics in colleges and universities
3. 1 Timely revision of statistical indicators
The statistical work of colleges and universities in China started with 1985 [4] and has a statistical history of 27 years. Although the statistical system and level are constantly improving, China's scientific and technological progress has developed by leaps and bounds. All kinds of scientific research projects and investment funds have advanced by leaps and bounds, and the statistical data of science and technology have soared, but all statistical indicators have remained basically unchanged for more than 20 years. As a result, some indicators and statistical methods in current science and technology statistics can't keep up with the changes of current science and technology development. For example, in the past, when a person worked on a project, the actual scientific research workload could basically be accurately counted by estimation. But now one person is working on two projects at the same time, ranging from 10, and the number of participants in a project is generally 3 to 30, so it is extremely difficult to count the workload. It is no longer feasible to calculate the scientific research workload with the current statistical system and means. According to the actual situation, it is very necessary to revise the statistical indicators in time.
3.2 increase investment in scientific and technological statistics research topics in colleges and universities.
Every year, the statistical work of science and technology in colleges and universities is a boring and arduous work. Because the work of science and technology statistics seems simple, it is actually very difficult, and the investment in special research topics is very small, which leads to the frequent replacement of statisticians who actually engage in science and technology statistics. In order to synchronize science and technology statistics with science and technology progress, stabilize the team of science and technology statisticians and improve the science and technology content of science and technology statistics [5], it is very necessary to increase investment in science and technology statistics research topics.
3.3 Strengthen the application of scientific and technological statistical data in colleges and universities
The annual report of science and technology statistics in colleges and universities is a data achievement that needs scientific research statisticians to spend months of hard work. However, most school leaders do not pay much attention to the data in this report. One of the reasons is that the data in the annual report is inconsistent with the statistical caliber and indicators of the data actually needed by universities for scientific and technological decision-making. Another reason is that the higher authorities did not evaluate the data in the annual report of science and technology statistics of colleges and universities. Only by improving the application value of data can university leaders pay more attention to the statistical work of science and technology and make the statistical work better and more practical.
Fund Project: Guangxi Education Department Fund Project? Research on pre-data processing based on the annual report of scientific and technological statistics in colleges and universities? (2009 1 1MS2 1).
References:
[1] Wu Liping, some thoughts on scientific statistics in colleges and universities [J] Journal of Chongqing University of Posts and Telecommunications, No.6, 2006, 986-987.
[2] People's Republic of China (PRC) Ministry of Education National Science and Technology Statistics Annual Report of Colleges and Universities (Humanities and Social Sciences), 2010.
[3] Composition of scientific statistics in colleges and universities (science, engineering, agriculture and medicine), Science and Technology Department of the Ministry of Education, 2006+0438+0.
[4] Liu Renyi, Reflections on the Authenticity and Accuracy of Science and Technology Statistics in Colleges and Universities [J], statistics and decision, 2006 1 1 (below) 65-66.
[5] Xu Fang, Strengthening the innovation of scientific research statistics in universities and improving the level of scientific research management [J], Science and Technology Management Research, No.9, 2008,151.
About the author:
Long (1965.9-), female, Han nationality, Shaodong, Hunan, is a senior engineer in the Science and Technology Department of Guangxi University, engaged in scientific research management and research.
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