1.1. Contact organisation
Statistical Office of the Slovak Republic
1.2. Contact organisation unit
Cross-sectional Statistics Department
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Statistical Office of the Slovak Republic
Lamacska cesta 3/C
P. O. Box 67
840 00 Bratislava 4
Slovakia
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
31 October 2025
3.1. Data description
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government 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.
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 the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
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 units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is 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.
FM methodology is used for identifying R&D. R&D covers three activities: basic research, applied research and experimental development.
NSE and SSH are covered and separately available at the 1-digit level (6 main fields of science). From 2013 onwards, indicators (R&D personnel , R&D expenditure) are available at 2-digit level of FOS.
SEO are covered and available at chapter level of NABS.
3.3.2. Sector institutional coverage
| Government sector | Corresponds to Frascati manual. The FM methodology was implemented in the 1994 R&D questionnaire. From 2011 onwards, the classification of R&D organisations by sectors is based on the ESA2010 classification. The frame population comprises all institutional units of the government active in the reference period. It is derived from the Statistical business register by extraction of units classified by the national accounts (ESA 2010) to S.13 (general government) with the exclusion of those units included in the HES. |
|---|---|
| Hospitals and clinics | Medical institutions complete the questionnaire only when performing R&D activities - tasks within the framework of a particular research programme. From reference year 2009 onwards, university hospitals are classified in the HES. |
| Inclusion of units that primarily do not belong to GOV and the borderline cases. |
Not applicable. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Treatment in line with the FM §2.122. |
|---|---|
| External R&D personnel | Treatment in line with the Frascati Manual §5.20-5.24. |
| Clinical trials: compliance with the recommendations in FM §2.61. | Not applicable. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Available. |
|---|---|
| Payments to rest of the world by sector - availability | Not available. |
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. Data collection is only from R&D performers belonging to the business and government sector. |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Extramural R&D expenditures are surveyed in a separate module. A clear explanation for respondents is included in the survey questionnaire. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Not applicable |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
|---|---|
| Source of funds | Source of funds follows the Frascati Manual methodology. From 2006 onwards, full breakdown according to FM. Since 2018, data on internal/external funds and transfer/exchange funds are collected. |
| Type of R&D | All 3 types of R&D available, basic research, applied research and experimental development. |
| Type of costs | From 1996 onwards, the breakdown by type of costs is available. Since 2018, more detailed breakdown of capital and current expenditure according to FM2015 is available. |
| Defence R&D - method for obtaining data on R&D expenditure | Only defence related R&D expenditure performed by the civil sector is surveyed. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total number of persons during the calendar year. |
|---|---|
| Function | The classification into the three categories of personnel is by ISCO-08 classification. |
| Qualification | Questions on personnel by qualification were introduced in the 1994 questionnaire. Qualification at level ISCED-5B (First-stage tertiary education - practical) was included in the secondary education. From 2006 onwards, qualification at level ISCED 5B (ISCED 2011 554) is separately available. From 2016, qualification structure is available for employees only. |
| Age | Available for researchers; for researchers - employees from 2003 to 2015, for internal researchers from 2016. |
| Citizenship | Available for researchers; for researchers - employees from 2003 to 2015, for internal researchers from 2016. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
|---|---|
| Function | The classification into the three categories of personnel is by ISCO-08 classification. |
| Qualification | Questions on personnel by qualification were introduced in the 1994 questionnaire. Qualification at level ISCED-5B (First-stage tertiary education - practical) was included in the secondary education. From 2006 onwards, qualification at level ISCED 5B (ISCED 2011 554) is separately available. From 2016, qualification structure available for employees only. |
| Age | Not surveyed. |
| Citizenship | Not surveyed. |
3.4.2.3. FTE calculation
FTE is provided by units in the R&D questionnaire, it is calculated according to the formula:
Sum of work-hours in R&D activities over the current year divided by 2000
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
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 Government 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.
| 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 | The target population is in line with the Frascati Manual recommendations. It includes: - governmental institutions, research centres - institutions of the Slovak Academy of Science - libraries |
|
| Estimation of the target population size | 94 |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | The frame population comprises all institutional units of the government active in the reference period. It is derived from the Statistical business register by extraction of units classified by the national accounts (ESA) to S.13 (general government) with the exclusion of those units included in the Higher education sector.. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Inclusion of enterprises from the source of the Statistical Office of the Slovak Republic: - having R&D as principal activity (NACE 72) regardless of size - having principal activity NACE=6411, 65 regardless of size - filled in the R&D questionnaire in one of the previous 3 years - answered "yes" to the question on engagement in in-house R&D activities in the previous CIS survey - answered "yes" to the question on R&D performance in the SBS survey in previous year Inclusion of enterprises from the source owned by the Ministry of Education, Research, Development and Youth of the SR: Inclusion of enterprises from the source owned by the Financial Administration of the Slovak Republic Inclusion of enterprises from media sources that were mentioned as performing R&D |
| Inclusion of units that primarily do not belong to the frame population | No. |
| Systematic exclusion of units from the process of updating the target population | There is no unit systematically excluded in the frame population. |
| Estimation of the frame population | 8251 |
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.
Reference period is calendar year 2023.
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 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 | There is not R&D specific statistical legislation. Production of R&D statistics is governed by the general national statistical legislation. Act No. 540/2001 Coll. on State Statistics, as amended. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Act No. 540/2001 Coll. on State Statistics, as amended; Program of State Statistical Surveys, published for three years in the Collection of Laws of the SR. |
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:
- Confidentiality protection required by law: Act No. 540/2001 Coll. on State Statistics, as amended.
Internal Directive on the Protection of Confidential Statistical Data (regulates the method of management and implementation of activities related to ensuring the protection of confidential statistical data in the Statistical Office of the Slovak Republic).
- Confidentiality commitments of survey staff: The survey staff signed the confidentiality commitment and confidential treatment of individual data of enterprises.
7.2. Confidentiality - data treatment
Confidential data are protected according to the CR (EC) No 322/97 and according to the national Act No 540/2001 on State Statistics, as amended.
The internal methodological instruction of the Statistical Office of the Slovak Republic regulates specific methods and values of parameters used in the protection of confidential statistical data of surveys and data sets specified in the Directive on the protection of confidential statistical data.
Identifying confidential cells in aggregated data: minimum frequency rule (n=3) together with the k % dominance rule.
8.1. Release calendar
The Catalog of Publications is publicly available on the website of the Statistical Office of the Slovak Republic and it contains basic information on the issued titles, issue dates, periodicity and language version.
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
At national level: Release calendar of publications
8.3. Release policy - user access
Information on all new released publications is available on the website of the Statistical Office of the Slovak Republic. The release policy determines the availability of statistical data to all users at the same time.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
At national level the frequency of R&D data dissemination is yearly for provisional and final data:
On-line database of the Statistical Office of the Slovak Republic
The way to R&D data on this address: DATAcube. - Access to database DATAcube. - Multi-domain statistics - Science, technology and innovation - Research and Development
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | N | |
| 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 | Yearbook of Science and Technology of the SR 2024 Statistical Yearbook of the SR 2024 – chapter on S&T&I |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y | Slovak Republic in Figures 2024 - chapter on R&D Women and Men in Slovakia 2024 - chapter on R&D Statistical yearbook of the capital of the SR Bratislava 2024 - chapter on R&D Regional capital city Košice 2023 - chapter on R&D |
1) Y – Yes, N - No
10.3. Dissemination format - online database
On-line database of the Statistical Office of the Slovak Republic
The way to R&D data on this address: DATAcube. - Access to database DATAcube. - Multi-domain statistics - Science, technology and innovation - Research and Development
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 |
Micro-data are provided only for scientific purposes according to the stated rules. Conditions for granting access to confidential statistical data for scientific purposes are provided on the website of the Statistical Office of the Slovak Republic. |
|---|---|
| Access cost policy | Payment required. |
| Micro-data anonymisation rules | Anonymized micro-data are provided to outside users for scientific purposes. Users (researchers) have to sign an agreement with the Statistical Office of the Slovak Republic that includes also data protection items. |
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 | Statistics: Science, technology and innovation |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Meta-information is available in on-line publication and on-line database, which includes description of indicators, definitions, survey methodology etc.
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.) | Metadata on the statistical web-site and in publications, methodological explanation in the questionnaire. |
|---|---|
| Requests on further clarification, most problematic issues | There are several requests from data users for further clarification, especially regarding the breakdown of the data. |
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).
At national level, Statistical Office of the SR has established the system of quality management. Quality manual contains description of system of quality management and fulfillment of requirements of standard ISO 9001.
The application of the Quality manual in practice ensures that all activities with impact on the quality of statistical products are planned, managed, examined, evaluated and meet the requirements accepted in the customer order.
Quality manual is available only in Slovak.
The basis of the whole system of quality management is the European Statistics Code of Practice.
11.2. Quality management - assessment
The overall quality of the GOV R&D statistical outputs is very good. The survey methodology follows the Frascati manual recommendations and the national and international requirements. The R&D statistics complies with 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 R&D survey coverage, reference period, data collection, checking and data processing follow the Eurostat methodology and recommendations for production of the common R&D statistics of the EU member states. Results of the survey for the country total and by regions as well were transmitted to Eurostat. Transmission of R&D data to Eurostat was realised in the SDMX format via eDAMIS - the safe, secure procedure.
Main strengths of the survey:
- R&D survey is an annual exhaustive survey
- The survey methodology complies with the Frascati Manual methodology and the Eurostat/OECD harmonised R&D data collection
- All mandatory and most of optional indicators were introduced to the R&D survey
- Enterprises are contacted to consult errors and missing variables in all necessary cases
- The item-non response is equal to zero
Main activities undertaken to assure high quality of GOV R&D statistics:
- Increase the response rate by several reminders
- Communications with respondents
- Use of best practices, quality guidelines, quality management activities used in the Statistical Office of the Slovak Republic according to ISO 9001.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | The European Commission (DG RTD, DG ENTR) | Data used in publications and further development |
| 1 | Eurostat | Data used for dissemination in Eurostat on-line database and publications, preparation of EP and Council report etc. |
| 1 | OECD, UN | Data used in databases, publications and international comparisons |
| 1 | Ministry of Economy; Ministry of Finance; Ministry of Education, Research, Development and Youth; Other Ministries; Government Office of the Slovak Republic; National Bank of Slovakia | Data used for policy making in the field of Science, Technology and Innovation, further for sectoral comparisons and international comparisons |
| 1 | Statistical Office of the Slovak Republic | Data used for storing in the database, publishing in national publications and on the web site |
| 2 | Slovak Chamber of Commerce and Industry (SCCI), Association of Industrial Research and Development Organisations | Data used for analysis and comparisons |
| 2 | Regional Chambers of SCCI | Data used for sectoral and regional comparisons |
| 3 | Press with economic content | Data used for general public |
| 4 | Slovak Academy of Science, Research institutes, Higher education institutes, Researchers and students | Data used for analysis and training |
| 5 | Enterprises | Data used for analysis and preparation of the enterprise strategy and development |
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 | The Statistical Office of the Slovak Republic carried out an overall user satisfaction survey in 2024, where also R&D statistics was included; Slovak Statistics Satisfaction Survey The satisfaction survey is only available in Slovak. |
|---|---|
| User satisfaction survey specific for R&D statistics | No, the survey covered several statistical areas, where products of R&D statistics were included together with innovation, energy and environment statistics in one category. |
| Short description of the feedback received | Average rate of user satisfaction with products of these statistics was 68,8 %. No specific feedback for these statistics was in 2024. |
12.3. Completeness
All mandatory datasets were transmitted.
12.3.1. Data completeness - rate
not available
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 of 30 July 2020. 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 | not applicable |
| Obligatory data on R&D expenditure | not applicable |
| Optional data on R&D expenditure | not applicable |
| Obligatory data on R&D personnel | not applicable |
| Optional data on R&D personnel | Part of optional data on R&D personnel is available only for internal personnel (researchers). Breakdown by qualification contain only data on internal R&D personnel, the qualification of external R&D personnel is not surveyed. Age group and citizenship data are only on internal researchers, as external researchers are not surveyed in these classifications. |
| Regional data on R&D expenditure and R&D personnel | not applicable |
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-1994 | Annual | Extension of the structure of sources. 2000: introduction of funds from HE institutions, 2004: source from abroad divided to public and private, 2006: full breakdown of source from abroad according to the FM, 2018: extension of the survey from own sources in the GOV and PNP sectors. |
2000, 2004, 2006, 2018 | To be able to provide data in ESTAT/OECD questionnaires. | |
| Type of R&D | Y-1994 | Annual | ||||
| Type of costs | Y-1994 | Annual | 1996: Detailed breakdown of capital and current expenditure, 2016: Current R&D expenditure includes also costs related to external R&D personnel, 2018: Scholarship of PhD students is surveyed separately. |
1996, 2016, 2018 | To fulfill EU requirements. | |
| Socioeconomic objective | Y-1997 | Annual | ||||
| Region | Y-1996 | Annual | ||||
| FORD | Y-1996 | Annual | Introduction of 2-digit level of FOS | 2009 | To be able provide data for users. | |
| Type of institution | Y-2015 | 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-2002 | Annual | ||||
| Function | Y-1994 | Annual | ||||
| Qualification | Y-1994 | Annual | Separation of the education level ISCED 5B (ISCED 2011 554) for employees only. | 2006 | To be able provide data for users. | |
| Age | Y-2003 | Annual | Extension for internal personnel, before for employees only. | 2016 | ||
| Citizenship | Y-2003 | Annual | Extension for internal personnel, before for employees only. | 2016 | ||
| Region | Y-1996 | Annual | ||||
| FORD | Y-1996 | Annual | Introduction of 2-digit level of FOS | 2009 | To be able provide data for users. | |
| Type of institution | N |
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-1994 | Annual | ||||
| Function | Y-1994 | Annual | ||||
| Qualification | Y-1994 | Annual | Separation of the education level ISCED 5B (ISCED 2011 554) for employees only. | 2006 | To be able provide data for users. | |
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-1996 | Annual | ||||
| FORD | Y-1996 | Annual | Introduction of 2- digit level of FOS. | 2009 | To be able provide data for users. | |
| Type of institution | N |
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 |
|---|---|---|---|---|---|
| R&D Expenditure in selected areas of R&D | Y-2006 | Annual | Information and communication technologies, of which software; biotechnology; nanotechnologies and nanomaterials | Total R&D Expenditure and R&D Expenditure from government sources | |
| Extramural R&D expenditure | Y-2010 | Annual | domestic; abroad | ||
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 |
|---|---|---|
| Yes - only employees - researchers and estimate for internal researchers | HC, FTE | Annual |
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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- 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 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
Not applicable. Census survey.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
Not applicable
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | |
| Government | |
| Higher education | |
| Private non-profit | |
| Rest of the world | |
| Total |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
Not applicable
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | |
| Technicians | ||
| other support staff | ||
| Qualification | ISCED 8 | |
| ISCED 5-7 | ||
| ISCED 4 and below |
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.
- Description/assessment of coverage errors : No coverage errors known.
- Measures taken to reduce their effect: No need of such measures.
- Share of PNP (if PNP is included in GOV): Not applicable.
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.
- Description/assessment of measurement errors: No errors known. The software for data collection and processing of data contains also checks to eliminate errors and logical inconsistencies. All errors and logical inconsistencies are consulted with respondents and corrected sequentially.
- Measures taken to reduce their effect: The R&D questionnaire contains detailed methodological explanation for filling in the particular modules. The regional department of the statistical office collects the questionnaires. Responsible persons of the regional office are instructed regularly about the improvements of the survey and necessary steps during the collection procedure.
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) |
|---|---|---|
| 93 | 94 | 0,01 |
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 | Census survey. Data collection was made by the web questionnaire. Data entry is realised by the software ISIS - ZBER that is own software of the Statistical Office of the Slovak Republic. It is a database oriented program product that provides in the integrated form: · record keeping and evaluation of reporting duty · first data recording by the keyboard · data editing on the base of the defined checks · enables manual and automation data updating (auto-corrections) · protocols about its activity. The central database stores the data in the form of source micro-data (ISIS – ZBD). The software for data processing includes the variable (HODPOV) defined for response and non-response classification. |
|---|---|
| Estimates of data entry errors | Data entry errors were corrected. Their number is not available. |
| Variables for which coding was performed | Coding was not performed because the online survey contains drop- down lists of codebooks with descriptions for field of science, country of citizenship and final production of R&D. |
| Estimates of coding errors | Online census survey. Number of coding errors is not available. Coding errors were corrected immediately. |
| Editing process and method | Online census survey. Editing is carried out using the software that contains controls for identification of errors. All inconsistencies are corrected during the processing procedure. All missing information is amended and item non-response is equal to zero in the case of responded questionnaires. All missing information is amended and item non-response is equal to zero in the case of responded questionnaires. |
| Procedure used to correct errors | Online census survey. In the case of occurred errors detected by the software, respondents were re-contacted to discuss the errors and correct them. |
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)
- End of reference period: 31 December 2023
- Date of first release of national data: 10 October 2024
- Lag (days): 285
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 26 June 2025
- Lag (days): 544
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
No divergences from FM, from international classification, no divergence in survey coverage.
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, 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 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 | No | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | No | |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Socioeconomic objectives 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 8 (mainly sub-chapter 8.5). | No | |
| Survey questionnaire / data collection form | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No | |
| Cooperation with respondents | FM2015 Chapter (mainly sub-chapter 8.5). | No | |
| Data processing methods | FM2015 Chapter 8 (mainly sub-chapters 8.5-8.6). | No | |
| Treatment of non-response | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.7). | Not applicable. | Census survey. |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No |
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 1994 | 1994, 1997, 2016, 2018 | 1994: Frascati definitions were adopted for the national R&D surveys. 1997: Change in the methodology of institutional classification by sectors resulted in the reclassification of some institutions from the Government sector to the Business enterprise sector. 2016: Inclusion of external personnel. 2018: Methodological change in content of the indicator R&D employees in head counts; it includes total number of R&D employees during the reference year, before number of R&D employees at the end of the reference year (as of December 31). |
| Function | From 1994 | ||
| Qualification | From 1994 | 2006, 2016 | Qualification at level ISCED 5B separately available, before 2006 included in the secondary education. 2016: for employees only. |
| R&D personnel (FTE) | From 1994 | 1994, 1997, 2016 | 1994: Frascati definitions were adopted for the national R&D surveys. |
| Function | From 1994 | ||
| Qualification | From 1994 | 2006, 2016 | Qualification at level ISCED 5B separately available, before 2006 included in the secondary education. 2016: for employees only. |
| R&D expenditure | From 1994 | 1994, 1997 | 1994: Frascati definitions were adopted for the national R&D surveys. 1997: Change in the methodology of institutional classification by sectors resulted in the reclassification of some institutions from the Government sector to the Business enterprise sector. |
| Source of funds | From 1994 | 2006 | Introduction of detailed breakdown of R&D expenditure from source abroad according to the FM. |
| Type of costs | From 1994 | ||
| Type of R&D | From 1994 | ||
| Other | From 1994 | 2013 | Regional breakdown by NUTS2 is according to local units. |
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
The data are produced in the 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.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Classification ESA2010 is used in R&D statistics for definition of sectors. GOV sector includes institutional units classified in S.13 with exclusion of those units included in the HES.
R&D survey data are regularly used in SNA calculations.
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 – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers(in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 228045 | 4275,3 | 3282,0 |
| Final data (delivered T+18) | 228045 | 4275,3 | 3282,0 |
| 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) | Explanations of consistency issues, if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | R&D labour costs in Euro / FTEs of internal personnel = 28941,0 Euro | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Other current costs for external R&D personnel in Euro / FTEs of external personnel = 17167,4 Euro |
(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 separately available. | |
| Data collection costs | Not separately available. | |
| Other costs | Not separately available. | |
| Total costs | Not separately available. |
1) 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) | 93 | Number of responding enterprises with R&D activity only. |
| Average Time required to complete the questionnaire in hours (T)1) | 6,99 | Based on the response to a direct question in the questionnaire. |
| Average hourly cost (in national currency) of a respondent (C) | 11,9 | Based on average labour costs in whole economy. |
| Total cost | 7735,83 | R x T x C |
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: Annual survey on research and development (In Slovak: Ročný výkaz o výskume a vývoji)
Type of survey: Census
Variables the survey contributes to: All R&D variables and breakdowns requested by the Commission Implementing Regulation (EU) 2020/1197 and almost all optional variables.
18.1.2. Sample/census survey information
| Sampling unit | R&D performers in the Government sector |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable. |
| Stratification variable classes | Not applicable. |
| Population size | 94 |
| Planned sample size | Census |
| Sample selection mechanism (for sample surveys only) | Not applicable. |
| Survey frame | National Business Register. |
| Sample design | Not applicable, census. |
| Sample size | Not applicable, census. |
| Survey frame quality | Very good |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | No administrative data collection is carried out. |
|---|---|
| Description of collected data / statistics | Not applicable. |
| Reference period, in relation to the variables the administrative source contributes to | Not applicable. |
| Variables the administrative source contributes to | Not applicable. |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | The only source for compilation of data transmitted to Eurostat is the R&D survey of the Statistical Office of the Slovak Republic. The R&D questionnaire is collected from research centres, individual R&D units of governmental authorities. |
|---|---|
| Description of collected information | Information filled in the questionnaire by individual R&D units include number of R&D personnel (researchers, technicians and equivalent staff, supporting staff) by qualification and sex in HC and FTE. Researchers in HC also by age and citizenship. Information from individual units concern also intramural expenditure on R&D, by type of R&D, source of funds, fields of science, socio-economic objectives of R&D performed. |
| Data collection method | The R&D survey of the Statistical Office of the Slovak Republic (SOSR) is an online survey. The statistical system of the Slovak Republic is decentralised, the Regional Department is charged to collect the questionnaires. The Cross-sectional Statistics Department, responsible for R&D statistics of the SOSR provides the methodology and organises seminars for training of the regional staff. Monitoring of non-response is made by the regional staff during the collection period. The integrated statistical information system used for data collection contains also function for generation of reminders for statistical units. Reminders are sent twice to alert them to meet the survey deadline. Collection and checking of data is made by the regional staff, all further treatments are taken over by the R&D statistics staff of the SOSR. Administrative data sources are not used. |
| Time-use surveys for the calculation of R&D coefficients | |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Online survey |
| Incentives used for increasing response | No |
| Follow-up of non-respondents | email, telephone |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 0,99 |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | 2023_SK_RD_questionnaire_VV6-01_EN.pdf |
| R&D national questionnaire and explanatory notes in the national language: | 2023_SK_RD_questionnaire_VV6-01_SK.pdf |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | see Annexes |
Annexes:
2023_SK_RD_questionnaire_in_English
2023_SK_RD_questionnaire_in_Slovak
18.4. Data validation
Data validation is embedded in the integrated statistical information system (ISIS) of the Statistical Office of the Slovak Republic.
When collecting data, the following checks are distinguished:
- formal checks carried out automatically in the data collection process
- informal checks to check the complexity and relationships between variables. In terms of severity of errors, a distinction is made between:
-
- I - Informative errors - provide additional information that is necessary for the process of data checking and correction. These are a preventive check for completed or incomplete data, e.g. number format not followed, number of spaces exceeded, mandatory item not filled in. Responding unit can submit the eForm with informative errors.
- Z - Serious errors - refer to specific errors that must be corrected or justified by the reporting unit, e.g. an invalid entry in a drop-down list discovery variable, an incorrect checksum. Responding unit will be able to submit the questionnaire even with these errors, but will be contacted by the Statistical Office of the Slovak Republic and asked to correct them.
- B - Blocking errors - prevent the questionnaire from being submitted. These are errors such as incorrect data format, e.g. a text string instead of a number. Only after the errors have been corrected will it be possible to click on the Submit button.
Controls and algorithms for creation of outputs, which ensure their required quality, are also defined in the ISIS system.
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 | R&D survey is an annual survey. Data are produced in the same way in the odd and even years. |
|---|---|
| Data compilation method - Preliminary data | R&D survey is an annual survey. Data are produced in the same way in the odd and even years. |
18.5.3. Measurement issues
| Method of derivation of regional data | Until 2012, units in all sectors were classified by region based on their main location (according to the location of the company or institution). From 2013 onwards, the regional breakdown is based on local units. The new method did not cause any significant changes in the data. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Coefficients are not used. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation and VAT are excluded from R&D expenditure. |
18.5.4. Weighting and estimation methods
| Description of weighting method | No weighting or estimation methods were used. Information collected by the R&D statistical survey from R&D performing units is considered final. |
|---|---|
| Description of the estimation method | Not applicable. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No commens.
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government 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 Government 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.
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 the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
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.
31 October 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
Not requested. R&D statistics cover national and regional data.
Reference period is calendar year 2023.
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:
- 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.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- 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.
At national level the frequency of R&D data dissemination is yearly for provisional and final data:
On-line database of the Statistical Office of the Slovak Republic
The way to R&D data on this address: DATAcube. - Access to database DATAcube. - Multi-domain statistics - Science, technology and innovation - Research and Development
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.
