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For any question on data and metadata, please contact: Eurostat user support |
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1.1. Contact organisation | Czech Statistical Office |
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1.2. Contact organisation unit | Society Development Statistics Department |
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1.5. Contact mail address | Na padesatem 81 100 82 Praha 10 Czech Republic |
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2.1. Metadata last certified | 29/12/2023 | ||
2.2. Metadata last posted | 29/12/2023 | ||
2.3. Metadata last update | 29/12/2023 |
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3.1. Data description | ||||||||||||
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics). Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Statistics on science, technology and innovation were collected until the end of 2020 based on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology. |
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3.2. Classification system | ||||||||||||
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3.2.1. Additional classifications | ||||||||||||
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3.3. Coverage - sector | ||||||||||||
See below. |
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3.3.1. General coverage | ||||||||||||
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3.3.2. Sector institutional coverage | ||||||||||||
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3.3.3. R&D variable coverage | ||||||||||||
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3.3.4. International R&D transactions | ||||||||||||
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3.3.5. Extramural R&D expenditures | ||||||||||||
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
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3.4. Statistical concepts and definitions | ||||||||||||
See below. |
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3.4.1. R&D expenditure | ||||||||||||
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3.4.2. R&D personnel | ||||||||||||
See below. |
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3.4.2.1. R&D personnel – Head Counts (HC) | ||||||||||||
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3.4.2.2. R&D personnel – Full Time Equivalent (FTE) | ||||||||||||
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3.4.2.3. FTE calculation | ||||||||||||
Number of R&D personnel (FTE) is filled by respondent. In the explanatory notes there are examples how FTEs can be derived from headcounts. But respondents usually don´t have records of FTE of their R&D personnel, so they use qualified guess. |
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3.4.2.4. R&D personnel - Cross-classification by function and qualification | ||||||||||||
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3.5. Statistical unit | ||||||||||||
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993. R&D workplace - In PNP sector institution performing R&D usually has 1 R&D workplace. |
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3.6. Statistical population | ||||||||||||
See below. |
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3.6.1. National target population | ||||||||||||
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units. The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the PNP Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
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3.7. Reference area | ||||||||||||
Not requested. R&D statistics cover national and regional data. |
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3.8. Coverage - Time | ||||||||||||
Not requested. See point 3.4. |
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3.9. Base period | ||||||||||||
Not requested. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat. |
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R&D personnel - HC, FTE R&D expenditure - thousand CZK |
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2021 - calendar year |
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6.1. Institutional Mandate - legal acts and other agreements | ||||||||||||||
See below. |
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6.1.1. European legislation | ||||||||||||||
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6.1.2. National legislation | ||||||||||||||
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6.1.3. Standards and manuals | ||||||||||||||
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development |
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6.2. Institutional Mandate - data sharing | ||||||||||||||
Not requested. |
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7.1. Confidentiality - policy | |||
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes. A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
a) Confidentiality protection required by law: Yes, derived from Act No 89/1995 Sb on the State Statistical Service.
b) Confidentiality commitments of survey staff: Yes, derived from Act No 89/1995 Sb on the State Statistical Service. |
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7.2. Confidentiality - data treatment | |||
Protection of confidential data – Data are considered confidential if data is individual or data of one enterprise is highly dominant. |
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8.1. Release calendar | |||
R&D data in Czechia are realesed in October (T+10). At about the same time, data are provided to Eurostat. |
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8.2. Release calendar access | |||
No release calendar for R&D data. |
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8.3. Release policy - user access | |||
Press conference in October. On this occasion data are released for public (tables on website). |
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Every year (press conference in October, publication in January) |
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10.1. Dissemination format - News release | ||||||||||||||||
See below. |
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10.1.1. Availability of the releases | ||||||||||||||||
1) Y - Yes, N – No |
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10.2. Dissemination format - Publications | ||||||||||||||||
See below. |
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10.2.1. Availability of means of dissemination | ||||||||||||||||
1) Y – Yes, N - No |
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10.3. Dissemination format - online database | ||||||||||||||||
Czech statistical office public database: https://vdb.czso.cz/vdbvo2/faces/en/index.jsf?page=statistiky#katalog=30851 |
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10.3.1. Data tables - consultations | ||||||||||||||||
Not requested. |
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10.4. Dissemination format - microdata access | ||||||||||||||||
See below. |
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10.4.1. Provisions affecting the access | ||||||||||||||||
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10.5. Dissemination format - other | ||||||||||||||||
See below. |
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10.5.1. Metadata - consultations | ||||||||||||||||
Not requested. |
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10.5.2. Availability of other dissemination means | ||||||||||||||||
1) Y – Yes, N - No |
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10.6. Documentation on methodology | ||||||||||||||||
Detailed metadata are on our website and in publication, but only in Czech language. |
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10.6.1. Metadata completeness - rate | ||||||||||||||||
Not requested. |
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10.7. Quality management - documentation | ||||||||||||||||
See below. |
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10.7.1. Information and clarity | ||||||||||||||||
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11.1. Quality assurance | |||
The crucial point at the beggining of survey is identification of all enterprises known or supposed to perform R&D. Czech Statistical Office use a lot of available sources for this purpose. R&D questionnaire is sent to statistical units in January and deadline for returning the questionnaire to Czech Statistical Office is in March. Many units ask for postponement. R&D questionnaire contains many checks. If there are any errors or incosistencies (e.g. big difference in year-on-year data) qualified staff of Czech Statistical Office contact respondent for clarification or correction of data. Staff of Czech Statistical Office is also ready for throughout the whole course of R&D statistical survey to help respondents if they need any assistance. Because of postponement by many enterprises and checking all data in quite comprehensive R&D questionnaire R&D survey usually ends at the beginning of September. After end of the survey (quetionnaire received after this date are not used), Czech Statistical Office processes the data. R&D data are usually published at the end of October. |
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11.2. Quality management - assessment | |||
R&D questionnaire contains detailed explanatory notes that helps respondents. If respondents are still uncertain with some items in questionnaire they contact qualified staff of Czech Statiscal Office for assistance. Due to the relatively large number of statisical units in R&D survey, high response rate, control mechanisms and careful work of qualified staff, the overall quality of the R&D survey is good. |
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12.1. Relevance - User Needs | ||||||||||||||||||||||||||||||||||||
See below. |
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12.1.1. Needs at national level | ||||||||||||||||||||||||||||||||||||
1) Users' class codification 1- Institutions: 2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level. 3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public. 4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.) 5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services) 6- Other (User class defined for national purposes, different from the previous classes.) |
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12.2. Relevance - User Satisfaction | ||||||||||||||||||||||||||||||||||||
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys. |
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12.2.1. National Surveys and feedback | ||||||||||||||||||||||||||||||||||||
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12.3. Completeness | ||||||||||||||||||||||||||||||||||||
See below. |
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12.3.1. Data completeness - rate | ||||||||||||||||||||||||||||||||||||
100 % |
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12.3.2. Data availability | ||||||||||||||||||||||||||||||||||||
Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D): 0.26% |
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12.3.2.1. Incorporation PNP sector in another sector | ||||||||||||||||||||||||||||||||||||
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12.3.2.2. Non-collection of R&D data for the PNP sector | ||||||||||||||||||||||||||||||||||||
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12.3.2.3. Data availability on more detail level | ||||||||||||||||||||||||||||||||||||
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'). 2) Y-start year |
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13.1. Accuracy - overall | |||
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted: 1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated. 2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise: a) Coverage errors, b) Measurement errors, c) Non response errors and d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce. |
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13.2. Sampling error | |||
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated. |
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13.2.1. Sampling error - indicators | |||
The main indicator used to measure sampling errors is the coefficient of variation (CV). Coefficient of variation for Total R&D expenditure : Coefficient of variation for Total R&D personnel (FTE) : |
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13.3. Non-sampling error | |||
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
a) Extent of non-sampling errors: Extent of errors is not measured. Data entry errors – 20 % is very rough estimate. Errors more often occur in newly surveyed units, which are not familiar with R&D indicators.
b) Measures taken to reduce the extent of non-sampling errors: There are many types of errors in data provided by respondents. For example: data not provided in correct unit of measure; questionnaire is not completely filled in; big year-to-year differences in data; FTE not correspond to labour cost; other current cost are missing; from administrative source we know that unit have R&D national or EU grant but it is not filled correctly in R&D expenditure by source of funds.
c) Methods used in order to correct / adjust for such errors: Continuously we try to improve explanatory notes in R&D questionnaire. If there are errors in data we often contact respondents by telephone. It helps reduce number of errors in next year of survey (it reduces probability that the respondent will make the same error again).
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13.3.1. Coverage error | |||
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling. |
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13.3.1.1. Over-coverage - rate | |||
Not requested. |
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13.3.1.2. Common units - proportion | |||
Not requested. |
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13.3.2. Measurement error | |||
Not requested. |
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13.3.3. Non response error | |||
Not requested. |
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13.3.3.1. Unit non-response - rate | |||
Not requested. |
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13.3.3.2. Item non-response - rate | |||
Not requested. |
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13.3.4. Processing error | |||
Not requested. |
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13.3.5. Model assumption error | |||
Not requested. |
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14.1. Timeliness | |||||||||||||||
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar. |
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14.1.1. Time lag - first result | |||||||||||||||
Time lag between the end of reference period and the release date of the results:
a) End of reference period: 31.12.2021 b) Date of first release of national data: 25.10.2022 c) Lag (days): 298 days |
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14.1.2. Time lag - final result | |||||||||||||||
a) End of reference period: 31.12.2021 b) Date of first release of national data: 25.10.2022 c) Lag (days): 298 days |
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14.2. Punctuality | |||||||||||||||
Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially. |
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14.2.1. Punctuality - delivery and publication | |||||||||||||||
Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release) |
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14.2.1.1. Deadline and date of data transmission | |||||||||||||||
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15.1. Comparability - geographical | ||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.1.1. Asymmetry for mirror flow statistics - coefficient | ||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.1.2. General issues of comparability | ||||||||||||||||||||||||||||||||||||||||||||||||
One big issue - external R&D personnel. We are afraid that comparability of this indicator among states is low. |
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15.1.3. Survey Concepts Issues | ||||||||||||||||||||||||||||||||||||||||||||||||
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual paragraphs and the EBS Methodological Manual on R&D Statistics with recommendations about these concepts / issues.
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15.1.4. Deviations from recommendations | ||||||||||||||||||||||||||||||||||||||||||||||||
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
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15.2. Comparability - over time | ||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.1. Length of comparable time series | ||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.2. Breaks in time series | ||||||||||||||||||||||||||||||||||||||||||||||||
1) Breaks years are years for which data are not fully comparable to the previous period. |
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15.2.3. Collection of data in the even years | ||||||||||||||||||||||||||||||||||||||||||||||||
Annual R&D quaestionnaire. Data are produced same way in the odd and even years. |
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15.3. Coherence - cross domain | ||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.3.1. Coherence - sub annual and annual statistics | ||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.3.2. Coherence - National Accounts | ||||||||||||||||||||||||||||||||||||||||||||||||
We try to be coherent with SNA. R&D data are used for the SNA calculation. |
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15.4. Coherence - internal | ||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.4.1. Comparison between preliminary and final data | ||||||||||||||||||||||||||||||||||||||||||||||||
This part compares key R&D variables as preliminary and final data.
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15.4.2. Consistency between R&D personnel and expenditure | ||||||||||||||||||||||||||||||||||||||||||||||||
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.). (2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel). |
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The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible. |
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16.1. Costs summary | |||||||||||||||||||||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies. |
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16.2. Components of burden and description of how these estimates were reached | |||||||||||||||||||||
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’) |
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17.1. Data revision - policy | |||
Not requested. |
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17.2. Data revision - practice | |||
Not requested. |
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17.2.1. Data revision - average size | |||
Not requested. |
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18.1. Source data | ||||||||||||||||||||||||||||||||||||||||||||
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in. |
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18.1.1. Data source – general information | ||||||||||||||||||||||||||||||||||||||||||||
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18.1.2. Sample/census survey information | ||||||||||||||||||||||||||||||||||||||||||||
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18.1.3. Information on collection of administrative data or of pre-compiled statistics | ||||||||||||||||||||||||||||||||||||||||||||
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18.2. Frequency of data collection | ||||||||||||||||||||||||||||||||||||||||||||
Annualy. |
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18.3. Data collection | ||||||||||||||||||||||||||||||||||||||||||||
See below. |
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18.3.1. Data collection overview | ||||||||||||||||||||||||||||||||||||||||||||
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18.3.2. Questionnaire and other documents | ||||||||||||||||||||||||||||||||||||||||||||
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18.4. Data validation | ||||||||||||||||||||||||||||||||||||||||||||
A lot of checks set in the questionnaire. Checking data with some administrative data. All incostitencies in submitted data are verified by qualified staff of Czech Statistical Office. |
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18.5. Data compilation | ||||||||||||||||||||||||||||||||||||||||||||
See below. |
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18.5.1. Imputation - rate | ||||||||||||||||||||||||||||||||||||||||||||
2.0% (2 out of 99 institutions) |
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18.5.2. Data compilation methods | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.3. Measurement issues | ||||||||||||||||||||||||||||||||||||||||||||
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18.5.4. Weighting and estimation methods | ||||||||||||||||||||||||||||||||||||||||||||
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18.6. Adjustment | ||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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18.6.1. Seasonal adjustment | ||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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R&D questionnaire in Czech language (BES, PNP) |