1.1. Contact organisation
Czech Statistical Office
1.2. Contact organisation unit
Society Development Statistics Department
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Na padesatem 81
100 82 Praha 10
Czech Republic
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
19 September 2025
2.1. Metadata last certified
19 September 2025
2.2. Metadata last posted
19 September 2025
2.3. Metadata last update
19 September 2025
3.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional 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 Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
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.
3.2. Classification system
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011.
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Tertiary education institution | All public universities and private universities supposed to perform R&D. |
|---|---|
| University and colleges: core of the sector | |
| University hospitals and clinics | All university hospitals are included in HES according to Frascati Manual guidelines. |
| Inclusion of units that primarily do not belong to HES and the borderline cases |
No such units known. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No deviations from FM. |
|---|---|
| External R&D personnel | External R&D personnel are persons working on special agreement to complete a job. Renumeration is included in other current costs. External R&D personnel (HC) are not included in Total R&D personnel (HC), due to the high risk of double counting. External R&D personnel are very often internal R&D personnel of other R&D unit. External R&D personnel (FTE) are included in Total R&D personnel (FTE). |
| Clinical trials: compliance with the recommendations in the Frascati Manual §2.61. | Corresponds to Frascati Manual. Clinical trials in phase 1, 2 and 3 are included. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | In the question on funding of R&D the following categories can be distinguished: - foreign enterprises, by EU, by international organisations, other foreign sources. |
|---|---|
| Payments to rest of the world by sector - availability | Table 496 of R&D questionnaire. Extramural R&D expenditures are surveyed only for R&D performing units. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), 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).
| Data collection on extramural R&D expenditure (Yes/No) | YES. Only extramural R&D expenditure of R&D performing units. |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Extramural R&D expenditure are asked in separate table 496 in R&D questionnaire. |
| Difficulties to distinguish intramural from extramural R&D expenditure | For distinguishing extramural R&D expenditure from intramural we have separate table 496 in R&D questionnaire. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
|---|---|
| Source of funds | Table 128b in R&D questionnaire. Data for transfer/exchange funds are not collected. Variables in 128b: Business Enterprise sector, public funds, funds of HES, private non-profit sector and sources from abroad: business enterprises, funds of the European Union and European Commission, other public sources (NATO, OECD, UNO and others), other sources. |
| Type of R&D | Table 129 in R&D questionnaire (3 types: basic research, applied research, experimental development) |
| Type of costs | Table 127 in R&D questionnaire (current costs are divided into labour costs of employees, labour costs of persons with short term contracts and other current costs; capital expenditure are divided into land and buildings, instruments and equipment, intangible fixed assets). |
| Defence R&D - method for obtaining data on R&D expenditure | Not specified. For Defence R&D is considered R&D of units in NACE 254, 304, 84. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | End of the year |
|---|---|
| Function | Table 125 in R&D questionnaire. |
| Qualification | not surveyed (surveyed not annualy but every 5 years) - last time surveyd in 2020 for researchers in HC. In R&D questionnaire were used these 4 categories of ISCED 11: 8, 7, 6, 5 and below. |
| Age | not surveyed (surveyed not annualy but every 5 years) - last time surveyd in 2020 for researchers+technicians together in HC. In R&D questionnaire were used these 6 categories: 24 and less, 25-34, 35-44, 45-54, 55-64, 65 and more. |
| Citizenship | Table 429 in R&D questionnaire |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | calendar year |
|---|---|
| Function | Table 125 in R&D questionnaire |
| Qualification | not surveyed - actually surveyed only for HC (every five year) |
| Age | not surveyed - actually surveyed only for HC (every five year) |
| Citizenship | not surveyed - actually surveyed only for HC (annually) |
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.
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
3.6. Statistical population
See below.
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 HES Sector should consist of all R&D performing institutional 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.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | All HES institutions known or supposed to perform R&D. Data for institutions are gathered by R&D workplaces with continuous or occasional R&D activities regardless of their size and economic activity (NACE). | |
| Estimation of the target population size | 65 institutions |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3 (Data availability).
3.9. Base period
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.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
2023 - calendar year
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. 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. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Act No 89/1995 Sb on the State Statistical Service |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
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.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
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.
7.2. Confidentiality - data treatment
Protection of confidential data – Data are considered confidential if data is individual or data of one institution is highly dominant.
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.
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
Press conference in October. On this occasion data are released for public (tables on website).
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Links | |
|---|---|---|
| Regular releases | Y | Regular national releases on R&D data |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Links |
|---|---|---|
| General publication/article | Y | General Publication Articles on R&D data |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Czech Statistical Office public database: Czech Statistical Office Public database
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to the micro-data | Microdata available only for scientific purposes. |
|---|---|
| Access cost policy | |
| Micro-data anonymisation rules | Microdata are anonymised (randomly generated code for R&D performing units). Microdata are accesible only in „Safe Center“ of Czech Statistical Office. |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1) | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | Aggregate figures | R&D Indicators Data base Higher Education and Government R&D |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | |
| Other |
1) Y – Yes, N - No
10.6. Documentation on methodology
Detailed metadata are on Czech Statistical Office website and in publication, but only in Czech language.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Detailed methodology is on Czech Statistical Office website and in publication. |
|---|---|
| Requests on further clarification, most problematic issues | Questions on differences between FTE and HC measurements, differences between researchers and technicians |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
11.2. Quality management - assessment
Relevance is the degree to which statistics meet current and potential users’ needs. It includes the production of all needed statistics and the extent to which concepts used (definitions, classifications etc.) reflect user needs. The aim is to describe the extent to which the statistics are useful to, and used by, the broadest array of users. For this purpose, statisticians need to compile information, firstly about their users (who they are, how many they are, how important is each one of them), secondly on their needs, and finally to assess how far these needs are met.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | Eurostat, European Commission, OECD, Government of the Czech Republic (R&D Council), Ministry of Industry and Business, Ministry of Education. | Data for analyses, decisions on policy issues, publishing etc. |
| 3 | Media | Interested in data for news articles and analytical purposes. |
| 4 | Researchers and students (universities, Academy of Science etc.) | Data for analytical purposes. |
| 5 | Enterprises or businesses |
1) Users' class codification
1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.
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.)
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | No user satisfaction survey for R&D survey was undertaken. |
|---|---|
| User satisfaction survey specific for R&D statistics | |
| Short description of the feedback received |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
approx. 95 %
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | No missing cells – All required preliminary variables delivered. |
| Obligatory data on R&D expenditure | No missing cells – All required obligatory data delivered. |
| Optional data on R&D expenditure | No missing cells – All required optional data delivered. |
| Obligatory data on R&D personnel | No missing cells – All required obligatory data delivered. |
| Optional data on R&D personnel | Not all optional data (R&D personnel by qualification, R&D personnel by age) were surveyed. |
| Regional data on R&D expenditure and R&D personnel | No missing cells – All required regional data delivered. |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y-1995 | Annual | ||||
| Type of R&D | Y-1995 | Annual | ||||
| Type of costs | Y-1995 | Annual | ||||
| Socioeconomic objective | Y-1998. N-2007 | |||||
| Region | Y-2001 | Annual | ||||
| FORD | Y-1995 | Annual | ||||
| Type of institution | Y-1995 | Annual |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-1999 | Annual | ||||
| Function | Y-1995 | Annual | ||||
| Qualification | Y-1995 | R&D personnel: 1995-2008 (annualy), 2011, 2015 Researchers: 2011, 2015, 2020 |
||||
| Age | Y-2011 | Researchers: 2011, 2015, 2020 | ||||
| Citizenship | Y-2011 | 2011, 2015- (from 2015 surveyed annualy) | ||||
| Region | Y-2001 | Annual | ||||
| FORD | Y-1995 | Annual (but not surveyed every year) | ||||
| Type of institution | Y-1995 | Annual |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-2000 | Annual | ||||
| Function | Y-1995 | Annual | ||||
| Qualification | Y-1995, N-2016 | R&D personnel: 1995-2008 (annualy), 2011, 2015 Researchers: 2011, 2015 |
||||
| Age | Y-2011, N-2016 | Researchers: 2011, 2015 | ||||
| Citizenship | N | |||||
| Region | Y-2001 | Annual | ||||
| FORD | Y-1995 | Annual - not surveyd in R&D questionnaire | ||||
| Type of institution | Y-1995 | Annual |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| Extramural R&D expenditure | Y-2008 | Annual | domestic / abroad | In year 2023: 5 different types of institutions from which R&D was purchased | |
| Revenue from sales of R&D services | Y-2013 | Annual | domestic / abroad | In year 2023: 6 different types of institutions to which R&D was saled | |
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
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| occupation (researchers) & qualification (table 126b in R&D questionnaire – 4 levels of education) | HC | Every 5 years (2020, next will be 2025) |
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.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | - | - | - | - | - | - | No error known. |
| Total R&D personnel in FTE | - | - | - | - | - | - | No error known. |
| Researchers in FTE | - | - | - | - | - | - | No error known. |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | x | ||||
| Total R&D personnel in FTE | x | ||||
| Researchers in FTE | x |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not be met.
5) 'Very Poor' = If all the three criteria are not met.
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.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
Does not apply as a census survey among all R&D performing units is carried out.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Does not apply. |
| Government | Does not apply. |
| Higher education | Does not apply. |
| Private non-profit | Does not apply. |
| Rest of the world | Does not apply. |
| Total | Does not apply. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Does not apply. |
| Technicians | Does not apply. | |
| Other support staff | Does not apply. | |
| Qualification | ISCED 8 | Does not apply. |
| ISCED 5-7 | Does not apply. | |
| ISCED 4 and below | Does not apply. |
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.
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.
a) Description/assessment of coverage errors:
No coverage errors
b) Measures taken to reduce their effect:
....
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement 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 costs; other current costs 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.
b) Measures taken to reduce their effect:
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).
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
| 222 | 222 | 0 % |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item Non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 0 % | 0 % | 0 % |
| Comments |
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | We used two types of electronic questionnaires (web questionnaire end electronic pdf). Data errors are recognized by checks. |
|---|---|
| Estimates of data entry errors | Does not apply. 20 % is very rough estimate. |
| Variables for which coding was performed | No coding is used. |
| Estimates of coding errors | Does not apply. |
| Editing process and method | Does not apply. |
| Procedure used to correct errors | Contacts with respondents (telephone, email) were realized in the cases of errors. Errors are then corrected by respondent themselves or by employees of statistical office. |
13.3.5. Model assumption error
Not requested.
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.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
a) End of reference period: 31 December 2023
b) Date of first release of national data: 23 October 2024
c) Lag (days): 297 days
14.1.2. Time lag - final result
a) End of reference period: 31 December 2023
b) Date of first release of national data: 23 October 2024
c) Lag (days): 297 days
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.
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)
14.2.1.1. Deadline and date of data transmission
| Transmission of provisional data | Transmission of final data | |
|---|---|---|
| Legally defined deadline of data transmission (T+_ months) | 10 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
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.
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 (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | NO | |
| Researcher | FM2015, § 5.35-5.39. | NO | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | NO | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | NO | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | YES | Reporting data for Total R&D personnel (HC) = Internal R&D personnel (HC). External R&D personnel (HC) are not included in Total R&D personnel (HC), due to the high risk of double counting. External R&D personnel are very often internal R&D personnel of other R&D unit. |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | NO | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | NO | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | NO | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | NO | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | NO | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | NO | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | NO | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | NO | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | NO |
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 (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 9 (mainly sub-chapter 9.5). | NO | Annual R&D questionnaire (census). |
| Survey questionnaire / data collection form | FM2015 Chapter 9 (mainly sub-chapter 9.5). | NO | R&D questionnaire (online, electronic pdf, in exceptional cases paper questionnaire). |
| Cooperation with respondents | FM2015 Chapter 9 (mainly sub-chapter 9.5). | NO | Three times written urgent calls to non-respondents. Important R&D performers that didn´t respond are contacted by phone. |
| Coverage of external funds | FM2015 Chapter 9 (mainly sub-chapter 9.4). | NO | not specified in R&D questionnaire |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | FM2015 Chapter 9 (mainly sub-chapter 9.4). | NO | not specified in R&D questionnaire |
| Data processing methods | FM2015 Chapter 9 (mainly sub-chapter 9.5). | NO | |
| Treatment of non-response | FM2015 Chapter 9 (mainly sub-chapter 9.5). | NO | The imputation has been applied to treat non-response for units that did not provide questionnaires in the given term. The imputation is used only for units, which filled R&D questionnaire in the previous year. |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | NO | census survey |
| Method of deriving R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | NO | no R&D coefficient used |
| Quality of R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | NO | no R&D coefficient used |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | NO | census survey (no differencies between preliminary and final data) |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1) | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | From 1995 | ||
| Function | From 1995 | ||
| Qualification | Not surveyed annualy | ||
| R&D personnel (FTE) | From 2005 | In 2005 there was a change in methodology for the collect of R&D personnel data in FTE. Data are provided in FTE by the reporting units, and based on new, more precise guidelines. | |
| Function | From 2005 | In 2005 there was a change in methodology for the collect of R&D personnel data in FTE. Data are provided in FTE by the reporting units, and based on new, more precise guidelines. | |
| Qualification | Not surveyed annualy | ||
| R&D expenditure | From 1995 | ||
| Source of funds | From 1995 | ||
| Type of costs | From 1995 | ||
| Type of R&D | From 1995 | ||
| Other |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
Annual R&D quaestionnaire. Data are produced same way in the odd and even years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual (FM) regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
We try to be coherent with SNA. R&D data are used for the SNA calculation.
15.3.3. Coherence – Education statistics
There are no national R&D data that could be compared with HES data from R&D survey.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 26 202 652 | 19 885 | 13 798 |
| Final data (delivered T+18) | 26 202 652 | 19 885 | 13 798 |
| Difference (of final data) | 0 | 0 | 0 |
Comments:
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 701 706 CZK | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 598 993 CZK |
(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).
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.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use/person/day | |
|---|---|---|
| Staff costs | Not available. | Not available. |
| Data collection costs | Not available. | Not available. |
| Other costs | Not available. | Not available. |
| Total costs | Not available. | Not available. |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs:
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 222 | |
| Average Time required to complete the questionnaire in hours (T)1) | 3,25 | estimation |
| Average hourly cost (in national currency) of a respondent (C) | 180,13 | |
| Total cost | 129 964 CZK |
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’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
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. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
18.1.1. Data source – general information
Survey name: The Annual Questionnaire on Research and Development - the mutation (a)
Type of survey: Census among enterprises known or supposed to performed R&D.
Combination of sample survey and census data: NO (only census)
18.1.2. Sample/census survey information
| Sampling unit | R&D workplace (in HES usually faculty) |
|---|---|
| Stratification variables (if any - for sample surveys only) | Does not apply. |
| Stratification variable classes | Does not apply. |
| Population size | 222 |
| Planned sample size | No planned sample size. All potential R&D performers are surveyed. |
| Sample selection mechanism (for sample surveys only) | Does not apply (census) |
| Survey frame | List of R&D units is prepared every year before start of R&D survey. |
| Sample design | Does not apply (census) |
| Sample size | Does not apply (census) |
| Survey frame quality | Good. But there is no availabale official list of R&D units. We must create it ourselves every year from available information sources. In case of some private universities, it's difficult to find out in advance, if they perform any R&D. |
| Variables the survey contributes to | List of variables that are available: Section 125: Number of employees R&D at 31 December by sex and FTE by sex by occupation (researchers, technicians/equivalent staff and other supporting staff). Section 336: Persons with short-term contracts for R&D: number of persons by sex and number of hours devoted R&D by sex by occupation (researchers, technicians/equivalent staff and other supporting staff). Section 429: Researchers (HC) by citizenship Section 127: Expenditure on R&D by type of costs (current costs are divided into labour costs of employees, labour costs of persons with short term contracts and other current costs; capital expenditure are divided into land and buildings, instruments and equipment, intangible fixed assets). Section 128b: Expenditure on R&D by source of funds: Business Enterprise sector, public funds, funds of HES, private non-profit sector and sources from abroad: business enterprises, funds of the European Union and European Commission, other public sources (NATO, OECD, UNO and others), other sources. Section 129: Expenditure on R&D by type of R&D (basic research, applied research and experimental development). Section 427: Expenditure on R&D in selected areas of R&D: total and from national public funds (Information and communication technologies, Software, Biotechnology, Nanotechnologies and nanomaterials). Section 496: Extramural expenditure on R&D by sector - national or foreign entities (BES, GOV, HES, PNP) Section 497: Revenues from the sales of R&D services - from national or foreign entities (BES, GOV, HES, PNP) |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Grants from national public funds - information system of R&D Council. |
|---|---|
| Description of collected data / statistics | Data form this source are used for control purposes only. |
| Reference period, in relation to the variables the administrative source contributes to | year 2023 |
| Variables the administrative source contributes to |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | R&D indicators are surveyed by the questionnaire: “The Annual Questionnaire on Research and Development"; the mutation (b) which is allocated for GOV and HES [VTR 5-01 (B)]. |
|---|---|
| Description of collected information | Information filled in the questionnaire from individual R&D workplace includes number of R&D personnel, intramural and extramural expenditure on R&D. Questionnaire contains variables requested by EU Regulation No 2020/1197. |
| Data collection method | Census survey. Data collected by R&D questionnaire (online, electronic pdf, in exceptional cases paper questionnaire). |
| Time-use surveys for the calculation of R&D coefficients | Does not apply. |
| Realised sample size (per stratum) | No sample size. All potential R&D performers are surveyed. |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | R&D questionnaire (online, electronic pdf, in exceptional cases paper questionnaire) |
| Incentives used for increasing response | Mandatory survey. No incentives used. |
| Follow-up of non-respondents | Reminders by email, some units by telephone. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Imputation used for non-respondents units, which filled R&D questionnaire in the previous year. (it doesn't occur in year 2023, because response rate in HES was 100 %) |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 100 % |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | No non-response survey is carried out. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | not available |
| R&D national questionnaire and explanatory notes in the national language: | Roční výkaz o výzkumu a vývoji za rok 2023 pro vládní a vysokoškolský sektor VTR 5-01 (b) |
| Other relevant documentation of national methodology in English: | not available |
| Other relevant documentation of national methodology in the national language: | Ukazatele výzkumu a vývoje za rok 2023 (publication) - available on CZSO website |
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.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)
18.5.2. Data compilation methods
| Data compilation method - Final data | Does not apply. Data transmitted on time. |
|---|---|
| Data compilation method - Preliminary data | Does not apply. Data transmitted on time. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | Does not apply |
|---|---|
| Revision policy for the coefficients | Does not apply |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Does not apply |
18.5.4. Measurement issues
| Method of derivation of regional data | The questionnaire for R&D survey in the Czech Republic contains except the identification number (the Business Register of the CZSO) also information about location of the R&D workplace of Research and Development, which is filled in quationnaire by respondent. Regional data based on the regions of R&D workplaces are available since the year 2001. Due to information about address of R&D workplace we can publish data by NUTS 4 (if they are not confidential). |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Does not apply. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation and VAT are excluded from R&D expenditure |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | Not separated |
18.5.5. Weighting and estimation methods
| Description of weighting method | Does not apply. |
|---|---|
| Description of the estimation method | Does not apply. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education 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 higher education sector should consist of all R&D performing institutional 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 Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
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.
19 September 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
See below.
Not requested. R&D statistics cover national and regional data.
2023 - calendar year
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.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
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.
See below.
See below.
