Research and development (R&D) (rd)

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 Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise 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 business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).

The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).

The guiding document to preparing the quality reports is the European Statistical System (ESS)  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 distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
• 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

Please see the sub-concepts 3.3.1 to 3.3.5. in the full metadata view.

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

Business enterprise sector (BES)	BES covers all enterprises and organizations, as well as research and development units within the company (independent and also associated R&D working places). 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. BES sector covers S.11 and S.12 with the exclusion of those units included in the HES sector. All NACE codes (Sections A to U) are covered. NACE code of ENTR: Only the head LeU's identifiers on NACE activity, size and region are taken into account. There are possible multiple product fields within an enterprise and legal units too. In the specific module in the R&D questionnaire R&D expenditure is collected for up to 5 resulting R&D product fields. The region is determined at the enterprise level and does not cover multiple regions.
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 sector.
Inclusion of units that primarily do not belong to BES 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 FM §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.
Intramural R&D expenditure in foreign-controlled enterprises – coverage	Inward foreign affiliates covered. It is possible to distinguish between foreign-controlled and domestic enterprises in cooperation with the department responsible for Statistical Business Register.

3.3.5. Extramural R&D expenditures

According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) 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

Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.

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 basic structure of the breakdown by type of costs is available. Since 2018, more detailed breakdown of capital and current expenditure according to FM2015 is available.
Economic activity of the unit	Main economic activity of the institution conducting the R&D activity. For ENTR: Main activity of the head LeU is used.
Economic activity of industry served (for enterprises in ISIC/NACE 72)	From 1998 onwards, a specific question introduced to the R&D questionnaire for enterprises with NACE 73 (NACE Rev.2 72 from 2008 onwards) for providing the industry served. According to this information data for the whole unit was assigned to the given industry. Change was introduced in 2010 when a specific new module was added in the 2010 R&D questionnaire with the structure of R&D expenditure by product field.
Product field	Available from 2010 onwards. A specific new module was added in the 2010 R&D questionnaire with the structure of R&D expenditure by product field.
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. The qualification structure is only available for employees, in the case of working proprietors and unpaid family workers, it is estimated according to functional categories.
Age	Available for researchers; from 2003 to 2015 for researchers - employees, for internal researchers from 2016 onwards.
Citizenship	Available for researchers; from 2003 to 2015 for researchers - employees, for internal researchers from 2016 onwards.

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. The qualification structure is only available for employees, in the case of working proprietors and unpaid family workers, it is estimated according to functional categories.
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 for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.

Reporting unit in the R&D survey in the BES is the legal unit (LeU). From 2021 onwards, the statistical unit is the statistical enterprise.

Data are collected on all or most legal units (LeUs) in an enterprise. Collected variables from all LeUs belonging to the one statistical enterprise are considered additive variables and are aggregated at the enterprise level. Data are reporting at the enterprise level.

3.6. Statistical population

Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.

3.6.1. National target population

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 Business Enterprise Sector should consist of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. In practice however, countries in their R&D surveys might exclude some enterprises for which R&D activities are deemed to be non-existent or negligible, in order to limit the response burden or due to budgetary constraints.

 

	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 consist of all enterprises, having R&D as a principal activity and also of enterprises not having R&D as a principal activity, but their R&D potential represents, recalculated by the full time equivalent, at least one man-year.
Estimation of the target population size	815
Size cut-off point	No cut-off point, all size classes are covered.
Size classes covered (and if different for some industries/services)	All size classes are covered.
NACE/ISIC classes covered	All NACE classes are covered.

3.6.2. Frame population – Description

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.

Method used to define the frame population	The frame population includes all business enterprises active in the reference period. It is derived from the official Statistical business register by extraction of financial and non-financial corporations S.11 and S.12 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 Slovak Republic is: units that received any grant or incentive for R&D from the state budget units with certification of capability to perform R&D Inclusion of enterprises from the source owned by the Financial Administration of the Slovak Republic: taxpayers with deduction of expenditures (costs) for R&D 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.
Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D	Annual inclusion of the informative question on R&D performance to the SBS survey.
Number of “new”1) R&D enterprises that have been identified and included in the target population	Compared to 2022, the target population included 123 "new" R&D performers. However, some of them could have been surveyed in previous years.
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	349346

1)       i.e. enterprises previously not known or not supposed to perform R&D

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 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. Regulation No 2020/1197 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; National Statistical Legislation
Are respondents obliged by the national law to provide raw and administrative data:	Act No. 540/2001 Coll. on State Statistics, as amended; National Act on Statistics Program of State Statistical Surveys, published for three years in the Collection of Laws of the SR; National Statistical Programe

6.1.3. Standards and manuals

• Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
• EBS 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

At Eurostat level 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.

At Eurostat level the frequency of R&D data dissemination 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, Statistical Online Database
The way to R&D data on this address: DATAcube. - Access to database DATAcube. - Multi-domain statistics - Science, technology and innovation - Research and Development
Yearbook of Science and Technology of the SR 2024

10.1. Dissemination format - News release

Please see the sub-concepts 10.1 to 10.5 in the full metadata view.

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, Statistical Online Database
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 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

Please see the sub-concept 10.7.1 in the full metadata view.

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 business 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.
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 business 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

Please see the sub-concept 12.1.1 in the full metadata view.

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, used also in national accounts and FATS statistics
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 the user satisfaction survey in 2024, where also R&D statistics was included; 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

Please see the sub-concept 12.3.2 in the full metadata view.

12.3.1. Data completeness - rate

All mandatory datasets were transmitted.

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.

 

	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
Economic activity	Y-1996	Annual
Product field	N
Employment size class	Y-1997	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-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
Economic activity	Y-1994	Annual
Product field	N
Employment size class	Y-1997	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
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'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.

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:

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 (BES R&D). 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 key variables by NACE

Not applicable 

	Industry sector1)	Services sector2)	TOTAL
R&D expenditure
R&D personnel (FTE)

1)        Industry sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)

2)        Services sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66, 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)

13.2.1.3. Confidence interval for key variables by Size Class

Not applicable 

	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250- and more employees and self-employed persons	TOTAL
R&D expenditure
R&D personnel (FTE)

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 (or frame 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.

13.3.1.1. Over-coverage - rate

Not requested.

13.3.1.2. Common units - proportion

Not requested.

13.3.1.3. Frame misclassification rate

Misclassification rate measures the percentage of enterprises that changed stratum between the time the frame was last updated and the time the survey was carried out. It is defined as the number of enterprises that changed stratum divided by the number of enterprises which belong to the stratum, according to the frame. The rate can be estimated based on the characteristics of the surveyed enterprises.

By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250-and more employees and self-employed persons	TOTAL
Number or surveyed enterprises in the stratum (according to frame)	Not available	Not available	Not available	Not available
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)	Not available	Not available	Not available	Not available
Misclassification rate
By size class for the Services Sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250-and more employees and self-employed persons	TOTAL
Number or surveyed enterprises in the stratum (according to frame)	Not available	Not available	Not available	Not available
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)	Not available	Not available	Not available	Not available
Misclassification rate	Not available	Not available	Not available	Not available

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 satisfying by computing the weighted and un-weighted response rate.

Definition:

• Eligible are the sample units which indeed belong to the target population. Frame imperfections always leave the possibility that some sampled units may not belong to the target population. Moreover, when there is no contact with sample units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’

Definition

• 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 
• Weighted Unit Non- Response Rate = [1 - (Total weighted responding units) / (Total weighted number of eligible / unknown eligibility units in the sample)] * 100

13.3.3.1.1. Unit non-response rates by Size Class

	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250-and more employees and self-employed persons	TOTAL
Number of units with a response in the realised sample	215	196	170	95	676
Total number of units in the sample	310	222	182	101	815
Unit Non-response rate (un-weighted)	0,31	0,12	0,07	0,06	0,17
Unit Non-response rate (weighted)	Not applicable.	Not applicable.	Not applicable.	Not applicable.	Not applicable.

13.3.3.1.2. Unit non-response rates by NACE

	Industry1)	Services2)	TOTAL
Number of units with a response in the realised sample	296	380	676
Total number of units in the sample	324	491	815
Unit Non-response rate (un-weighted)	0,09	0,23	0,17
Unit Non-response rate (weighted)	Not applicable.	Not applicable.	Not applicable.

1)        Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)

2)        Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)

13.3.3.1.3. Recalls/Reminders description

Units that did not submit the completed questionnaires on time were reminded directly through the electronic system used for data collection, the Integrated Statistical Information System (ISIS). Two automatic reminders are built into ISIS. In addition, some units were reminded by phone. Information on the number of telephone recalls is not available.

13.3.3.1.4. Unit non-response survey

Conduction of a non-response survey	The non-response survey was not carried out.
Selection of the sample of non-respondents	Not applicable.
Data collection method employed	Not applicable.
Response rate of this type of survey	Not applicable.
The main reasons of non-response identified	The main reason is that addressed units did not perform R&D activities.

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
Imputation (Y/N)	N	N	N
If imputed, describe method used, mentioning which auxiliary information or stratification is used	Not applicable.	Not applicable.	Not applicable.

13.3.3.3. Magnitude of errors due to non-response

	Magnitude of error (%) due to non-response
Total intramural R&D expenditure	Not available.
Total R&D personnel in FTE	Not available.
Researchers in FTE	Not available.

 

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.
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

This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.

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 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 deviation
Researcher	FM2015, §5.35-5.39.	No deviation
Approach to obtaining Headcount (HC) data	FM2015, §5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation
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 deviation
Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel	FM2015, §5.25	No deviation
Intramural R&D expenditure	FM2015 Chapter 4 (mainly sub-chapter 4.2).	No deviation
Special treatment for NACE 72 enterprises	FM2015, § 7.59.	No deviation
Statistical unit	FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation
Target population	FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation
Identification of not known R&D performing or supposed to perform R&D enterprises	FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation
Sector coverage	FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No deviation
NACE coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No deviation
Enterprise size coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No deviation
Reference period for the main data	Reg. 2020/1197 : Annex 1, Table 18	No deviation
Reference period for all data	Reg. 2020/1197 : Annex 1, Table 18	No deviation

 

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 preparation activities	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No deviation
Data collection method	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No deviation
Cooperation with respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No deviation
Follow-up of non-respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No deviation
Data processing methods	FM2015 Chapter 6 (mainly sub-chapter 6.7).	No deviation
Treatment of non-response	FM2015 Chapter 6 (mainly sub-chapter 6.7).	No deviation
Data weighting	FM2015 Chapter 7 (mainly sub-chapter 7.7).		Census survey. No data weighting.
Variance estimation	FM2015 Chapter 6 (mainly sub-chapter 6.9).	Not applicable.	Census survey.
Data compilation of final and preliminary data	Reg. 2020/1197 : Annex 1, Table 18	No deviation
Survey type	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No deviation
Sample design	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No deviation	Census survey among all known or supposed R&D performing enterprises.
Survey questionnaire	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No deviation

 

15.2. Comparability - over time

For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.

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 and working proprietors and unpaid family workers. 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, in the case of working proprietors and unpaid family workers, it is estimated according to functional categories.
R&D personnel (FTE)	From 1994	1994, 1997, 2016	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 and working proprietors and unpaid family workers.
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, in the case of working proprietors and unpaid family workers, it is estimated according to functional categories.
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 is 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.  Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).

The Community innovation survey also collects the R&D expenditure of enterprises that form the coverage of the CIS survey.

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. BES sector includes institutional units classified in S.11 and S.12 with exclusion of those units included in the HES. R&D survey data are regularly used in SNA calculations.

15.3.3. National Coherence Assessments

Variable name	R&D Statistics - Variable Value	Other national statistics - Variable value	Other national statistics - Source	Difference in values (of R&D statistics)	Explanation of / comments on difference
Intramural R&D expenditure in 2023	713729 thous. Euro	582941 thous. Euro	CIS 2022	130788 (18%)	Differences are due to: different reference years (2023 vs. 2022), different survey method - census survey among all potential R&D performers (R&D survey 2023) vs. sample survey (CIS 2022).
Intramural R&D expenditure in 2023	379415 thous. Euro	379415 thous. Euro	IFATS in 2023	0	R&D data and FATS data are collected in different departments of the SOSR. Indicators for inward FATS are compiled from R&D survey data which ensures full coherence at the level of the Slovak Republic. Full coherence of data at NACE and size-class levels can be reached to the extent possible taking into account compilation processes between both domains. Consequently, some enterprise data undergo more extensive and longer validation and revision procedures, which may lead to temporary inconsistencies across statistical outputs.
R&D personnel in 2023	7071	7071	IFATS in 2023	0	R&D data and FATS data are collected in different departments of the SOSR. Indicators for inward FATS are compiled from R&D survey data which ensures full coherence at the level of the Slovak Republic. Full coherence of data at NACE and size-class levels can be reached to the extent possible taking into account compilation processes between both domains. Consequently, some enterprise data undergo more extensive and longer validation and revision procedures, which may lead to temporary inconsistencies across statistical outputs.

15.4. Coherence - internal

Please see the sub-concepts 15.4.1 and 15.4.2 in the full metadata view.

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 (in 1000 of national currency)	Total R&D personnel (in FTEs)	Total number of researchers (in FTEs)
Preliminary data (delivered at T+10)	713729	10070,9	6440,1
Final data (delivered T+18)	713729	10070,9	6440,1
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)	R&D labour costs in Euro / FTEs of internal personnel = 40115,6
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 = 57872,8

(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.

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)	676	Number of responding enterprises with R&D activity only.
Average Time required to complete the questionnaire in hours (T)1	4,16	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	33464,7	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	Enterprise
Stratification variables (if any - for sample surveys only)	Not applicable.
Stratification variable classes	Not applicable.
Population size	815
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	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

Please see the sub-concepts 18.3.1 and 18.3.2 in the full metadata view.

18.3.1. Data collection overview

Realised sample size (per stratum)	Census
Mode of data collection	Mode of the R&D survey data collection: 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 Statistical Office of the Slovak Republic (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.
Incentives used for increasing response	Incentives are not used for increasing response.
Follow-up of non-respondents	Reminders are sent twice to non-respondents to alert them to the deadline for completing the questionnaire.
Replacement of non-respondents (e.g. if proxy interviewing is employed)	Non-respondents are not replaced by proxy.
Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility)	0,83
Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods)	No

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:
Other relevant documentation of national methodology in the national language:

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:
1 - formal checks carried out automatically in the data collection process
2 - 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)

18.5.1.1. Imputation rate by Size class

 

Not applicable

Size class	R&D Expenditure		R&D personnel (FTE)
	Unweighted	Weighted	Unweighted	Weighted
0-9 employees and self-employed persons (optional)
10-49 employees and self-employed persons
50-249 employees and self-employed persons
250-and more employees and self-employed persons
TOTAL

18.5.1.2. Imputation rate by NACE

 

Not applicable

NACE	R&D Expenditure		R&D personnel (FTE)
	Unweighted	Weighted	Unweighted	Weighted
Industry1)
Services2)
TOTAL

 

1)       Industry (NACE Rev. 2: 01-03, 05-09,10-33,35,36-39,41-43)

2)       Services (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)

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

Weight calculation method	No weighting or estimation methods were used. Information collected by the R&D statistical survey from R&D performing units is considered final.
Data source used for deriving population totals (universe description)	Only data source is the R&D survey, data for population total are aggregated data from the survey.
Variables used for weighting	Not used.
Calibration method and the software used	Not used.
Estimation	Not applicable.

18.6. Adjustment

Not requested.

18.6.1. Seasonal adjustment

Not requested.

No comments.

Statistics on Business enterprise R&D (BERD) measure research and experimental development (R&D) performed in the business enterprise 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 business enterprise sector consists of all R&D performing enterprises (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector. The “enterprise” is defined in Council Regulation (EEC) No 1993/696 of 15 March 1993. The results are related to the population of all R&D performing enterprises classified in Sections A to U of the common statistical classification of economic activities as established by Regulation (EC) No 1893/2006 of the European Parliament and of the Council (NACE Rev.2).

The main concepts and definitions used for the production of R&D statistics are given by OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics, and by Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).

The guiding document to preparing the quality reports is the European Statistical System (ESS)  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

Please see the sub-concepts 3.4.1 and 3.4.2 in the full metadata view.

The statistical unit for BERD is the enterprise as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.

Reporting unit in the R&D survey in the BES is the legal unit (LeU). From 2021 onwards, the statistical unit is the statistical enterprise.

Data are collected on all or most legal units (LeUs) in an enterprise. Collected variables from all LeUs belonging to the one statistical enterprise are considered additive variables and are aggregated at the enterprise level. Data are reporting at the enterprise level.

Please see the sub-concepts 3.6.1 and 3.6.2 in the full metadata view.

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:

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.

At Eurostat level the frequency of R&D data dissemination 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, Statistical Online Database
The way to R&D data on this address: DATAcube. - Access to database DATAcube. - Multi-domain statistics - Science, technology and innovation - Research and Development
Yearbook of Science and Technology of the SR 2024

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.

This sub-concept refers to the geographical comparability of data among the 27 Member States and the EFTA and Candidate Countries.

For more information related to the break years and the nature of the breaks, see the following sub-concepts in the full metadata view.