1.1. Contact organisation
Statistical Office of the Republic of Slovenia (SURS)
1.2. Contact organisation unit
Social Services Statistics Section
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Litostrojska cesta 54, 1000 Ljubljana, Slovenia
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
31 July 2025
2.1. Metadata last certified
31 July 2025
2.2. Metadata last posted
31 July 2025
2.3. Metadata last update
31 July 2025
See below.
3.1. Data description
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris, 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) 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 distribution of principal economic activity and by product field is based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- 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 based on Classification and distribution by Fields of Research and Development (FORD);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Private non-profit sector | Units are classified into the Private Non-Profit (PNP) sector based on administrative data on institutional sectors from the Slovenian Business Register. |
|---|---|
| Inclusion of units that primarily do not belong to PNP and the borderline cases | No reclassification or adjustments are made to the sector of performance data obtained from the administrative source. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Units are instructed to include R&D administration and support activities only if they are directly related to R&D projects and meet the criteria for R&D as defined in the Frascati Manual. |
|---|---|
| External R&D personnel | External R&D personnel must be reported separately from internal staff, and only if they are directly involved in the execution of R&D activities. |
| Clinical trials: compliance with the recommendations in Frascati Manual §2.61. | Clinical trials are included when they meet the definition of R&D. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Receipts and payments from abroad (i.e. Rest of the World) by sector are available. |
|---|---|
| Payments to rest of the world by sector - availability | Payments to abroad by sector are available. Extramural R&D expenditures are inquired about and can be distinguished for institutions abroad by the following categories: to foreign affiliates, to other enterprises, to foreign universities and research institutions of HES, to other PNP research organisations, to international organisations and to others. |
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 |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Data on intramural and extramural R&D expenditure is collected separately. |
| Difficulties to distinguish intramural from extramural R&D expenditure | All efforts are made to distinguish between intramural and extramural R&D expenditure; however, there remains a possibility of some double-counting and misreporting. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
|---|---|
| Source of funds | R&D expenditures are classified by source of funds into five sectors: business enterprise, government, higher education, private non-profit and abroad. All these categories are broken down into sub-categories. |
| Type of R&D | All three types of R&D (basic research, applied research and experimental development) are included. |
| Type of costs | In line with FM 2015 four types of costs are distinguished: labour costs, other current costs and capital expenditures (land and buildings, instruments and equipments, capitalised computer software and other intellectual property products). All these categories are broken down into sub-categories. |
| Defence R&D - method for obtaining data on R&D expenditure | It is covered for all sectors of performance. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Calendar year. |
|---|---|
| Function | Data on R&D personnel by occupation (group) (researcher, technicians and equivalent staff, other supporting staff) in head counts (HC) and sex are available. |
| Qualification | Data on R&D personnel by qualification (level) (doctoral or equivalent, short cycle tertiary, bachelor, master or equivalent, other level of education) in head counts (HC) and sex are available for all occupation groups. |
| Age | Data on R&D personnel by age (group) in head counts (HC) are available only for researchers. |
| Citizenship | Data on R&D personnel by citizenship (group) in head counts (HC) are available only for researchers. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
|---|---|
| Function | Data on R&D personnel by occupation (group) (researcher, technicians and equivalent staff, other supporting staff) in full time equivalents (FTE) and sex are available. |
| Qualification | Data on R&D personnel by qualification in full time equivalents (FTE) are not available. |
| Age | Data on R&D personnel by age (group) in full time equivalents (FTE) are not available. |
| Citizenship | Data on R&D personnel by citizenship (group) in full time equivalents (FTE) are not available. |
3.4.2.3. FTE calculation
The following formula is used for calculating the FTE:
FTE = N × T × M
Where:
N = number of persons
T = proportion of time spent on R&D
M = proportion of the year worked (months/12)
The following are examples used for calculating Full-Time Equivalents (FTE):
-
1 researcher working full-time (100%) in R&D all year:
1 × 1 × 1 = 1.0 FTE -
3 researchers working half-time (50%) in R&D all year:
3 × 0.5 × 1 = 1.5 FTE -
2 researchers working 20% of their time in R&D throughout the year:
2 × 0.2 × 1 = 0.4 FTE -
1 researcher working full-time (100%) for half the year:
1 × 1 × (6/12) = 0.5 FTE -
2 researchers working 25% of their time in R&D for 8 months:
2 × 0.25 × (8/12) = 0.33 FTE
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 PNP Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | All firms known or supposed to perform R&D, i.e. potential R&D performers, are surveyed. | Does not apply |
| Estimation of the target population size | 122 |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested. See concept 12.3.2. (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.
For this report, data from the calendar year 2023 was used as the reference period.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
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 no legislation specific to R&D statistics. However, the collection of R&D data is conducted under the National Statistical Act, which provides the legal basis for all official statistical surveys. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Respondents are obliged under the National Statistics Act to complete and submit statistical questionnaires, including those related to R&D. Administrative data is typically not collected directly from respondents, but rather obtained from relevant public records and institutions. |
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
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
a) Confidentiality protection required by law: The National Statistics Act requires that all data collected for statistical purposes be strictly confidential and used exclusively for statistical analysis. The Act prohibits the disclosure of individual data and ensures that no information can be traced back to a specific reporting unit.
b) Confidentiality commitments of survey staff: All staff involved in statistical data collection, processing, and dissemination are legally bound by confidentiality obligations under the National Statistics Act.
7.2. Confidentiality - data treatment
All R&D data collected are treated as confidential and are used only for statistical purposes. By following general statistical confidentiality framework used by the Statistical Office of the Republic of Slovenia (SURS), statistics can be published only in aggregate form, i.e. in a manner that does not allow recognition of the unit to which data relate. Identifiable information can be published only exceptionally in cases where the unit gives its written consent to publish it. More information on statistical confidentiality is available on on the official website under the section Statistical confidentiality.
8.1. Release calendar
Release calendar is publicly accessible.
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
All data and metadata information are published and are accessible to users through various channels; among them is the most important website. The information on the website is available free of charge and is also available at English, for the widest possible use that is actively promoted.
The release policy determines the dissemination 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.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | Y |
R&D data are disseminated annually through one preliminary and one final release. The preliminary and final release are published on the official website of the statistical office. The final data are also made available in the SI-STAT Database. The same data that are published nationally are also transmitted to Eurostat, ensuring full consistency. Press releases (in the form of article) YES (Language: ENG, SI) |
| 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 | Final data are released together with a short accompanying text on the official website of the Statistical Office. |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
The disseminated data are available in the official database of the Statistical Office, namely the SI-STAT Database. The data are accessible in both Slovenian and English.
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As the Eurostat receives no R&D micro level data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the microdata.
10.4.1. Provisions affecting the access
| Access rights to the micro-data | The statistical office enables researchers to access data for the purpose of research. Access to micro-level data must be requested; more information is available on the official website. |
|---|---|
| Access cost policy | Free of charge. |
| Micro-data anonymisation rules | Micro-data are anonymised to ensure that individual respondents cannot be identified. All direct and indirect identifiers are removed or masked before data access is granted, in compliance with the National Statistics Act requirements. |
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 | Data on R&D are published in the First Release and at a more detailed level in the SiStat database. |
| Data prepared for individual ad hoc requests | Y | Aggregate figures | |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Methodological materials are available on the official website under the section Questionnaires, Methodological Explanations, Quality Reports.
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.) | With each annual release of final data, the methodological explanations and the quality report are published to ensure transparency and data reliability. |
|---|---|
| Requests on further clarification, most problematic issues | To date, no significant requests for further clarification or issues have been reported. |
See below.
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
11.2. Quality management - assessment
Overall quality of R&D statistics is good.
The coverage of reporting units is full. R&D statistics domain follow general quality management activities used in SURS (including the European Statistics Code of Practice and the Fundamental Principles of Official Statistics). Detailed methodological instructions for completing questionnaire are available. Data validation is performed in collaboration with reporting units. Discussion of results, substantive matters and key issues are points on the agenda or the subjects under discussion of the Research and Development, and Technology Statistics Advisory Committee.
However, there are still some aspects to be improved at R&D statistics. Despite the available methodological guidelines and instructions reporting units still have some problems with recognizing their R&D performance, understanding the R&D definitions, identifying an capturing the real/proper R&D content of activities and corresponding items. Most of the reporting units do not have records tailored to survey reporting, so they often make use of estimates without considering the substantive relevance between the items.
In the R&D statistics domain’s quality assurance activities are guaranteed through:
- clear and well-structured survey questionnaire with detailed methodological instructions for its completion;
- single point for communicating with business entities regarding the submission of data (i.e. Contact Center);
- good competences of Call Center staff and personnel responsible for data editing (training before the questionnaires are sent to the reporting units);
- good cooperation with the reporting units during data collection phase;
- computer control programs for input data;
- feedback from key reporting units and data users;
continuous updating and improvement of methodological instructions in the light of past experience.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | OECD | Working Party of National Experts on Science and Technology Indicators (NESTI) - data request for OECD Key Biotech Indicators (KBI) and the OECD Key Nanotech Indicators (KNI) |
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 | SURS measured general user satisfaction in 2024. Overall satisfaction with SURS’s products and services reached an average score of 8.4, trust in the institution 8.8, and trust in the data 8.7 (on a scale from 1 – disagree completely to 10 – agree completely). |
|---|---|
| User satisfaction survey specific for R&D statistics | It is not. |
| Short description of the feedback received | R&D statistics falls within the scope of the Statistical Advisory Committee on Research and Development Activities and Technologies. More information on the operation of the Committe is available on the following website LINK (in Slovene only). |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
100%
12.3.2. Data availability
Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D): 1.08 %
12.3.2.1. Incorporation of PNP sector in another sector
| Incorporation of PNP in another sector | PNP units are treated as a separate sector and are not incorporated into other sectors. |
|---|---|
| Reasons for not producing separate R&D statistics for the PNP sector | Does not apply. |
| Share of PNP expenditure in the total expenditure of the other sector | Does not apply. |
| Share of PNP R&D Personnel in the respective figure of the other sector | Does not apply. |
12.3.2.2. Non-collection of R&D data for the PNP sector
| Reasons for not compiling R&D statistics for the PNP sector | Does not apply. |
|---|---|
| PNP R&D expenditure/ GERD*100) | Does not apply. |
| Share of PNP R&D Personnel in the respective figure of the total national economy | Does not apply. |
12.3.2.3. Data availability on more detail level
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| Does not apply. | |||||
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
12.3.2.4. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| R&D personnel by occupation, qualification and sex | HC | Annually |
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.
13.2. 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
Confidence interval for Total R&D expenditure: Does not apply, as the survey is conducted as a census covering all R&D-performing units.
Confidence interval for Total R&D personnel (FTE): Does not apply.
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
a) Extent of non-sampling errors: Non-samplinh errors were not observed during the data collection process.
b) Measures taken to reduce the extent of non-sampling errors: /
c) Methods used in order to correct/adjust for such errors: /
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.
13.3.1.1. Over-coverage - rate
Not applicable. The survey covers all known or presumed R&D-performing units. The number of unidentified R&D performers, along with their associated R&D expenditure and personnel, is considered negligible.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
No errors known.
13.3.3. Non response error
Not requested.
13.3.3.1. Unit non-response - rate
Not requested.
13.3.3.2. Item non-response - rate
Not requested.
13.3.4. Processing error
Not requested.
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
a) End of reference period: 31 December 2023
b) Date of first release of national data: 5 November 2024
c) Lag (days): T+10
14.1.2. Time lag - final result
a) End of reference period: 31 December 2023
b) Date of first release of national data: 5 March 2025
c) Lag (days): T+14
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 | 14 |
| Actual date of transmission of the data (T+x months) | 10 | 10 |
| 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 issues known. National R&D statistics is produced in line with the Frascati methodology.
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) paragraphs and the EBS Methodological Manual on R&D Statistics 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 | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | no | |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | no | |
| Intramural R&D expenditure | FM2015,Chapter 4 (mainly sub-chapter 4.2). | no | |
| Statistical unit | FM2015, § 10.40-10.42 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | no | |
| Target population | FM2015, § 10.40-10.42 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | no | |
| Sector coverage | FM2015, § 10.2-10.8 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | no | |
| Reference period for the main data | Reg. 2020/1197: Annex 1, Table 18 | no | |
| Reference period for all data | 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, 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 10 (mainly sub-chapter 10.6). | no | |
| Survey questionnaire / data collection form | FM2015 Chapter 10 (mainly sub-chapter 10.6). | no | |
| Cooperation with respondents | FM2015 Chapter 10 (mainly sub-chapter 10.6). | no | |
| Data processing methods | FM2015 Chapter 10 (mainly sub-chapter 10.6). | no | |
| Treatment of non-response | FM2015 Chapter 10 (mainly sub-chapter 10.6). | not applicable | |
| 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) | |
2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
| Function | 2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| Qualification | 2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| R&D personnel (FTE) | 2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
|
| Function | |
2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
| Qualification | |
2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
| R&D expenditure | |
2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
| Source of funds | |
2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
| Type of costs | |
2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
| Type of R&D | |
2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
| Other | 2011, 2017 | With the data for 2011 new administrative sources were introduced in the survey on R&D, which enabled additional identification of (potential) R&D performers and thus improved sample unit coverage. At the same time, weighting adjustment for non-response was also introduced in the survey on R&D. By both reasons values of R&D activity in all sectors of performance increased. With data for 2017 some methodological changes were introduced in the R&D survey as a result of the Frascati methodology harmonisation and at the same time statistical data protection was also introduced to the R&D survey. Following methodological changes were introduced:
|
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 production process is consistent across survey years.
15.3. Coherence - cross domain
See below.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not available.
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 PNP R&D expenditure (in 1000 of national currency) | Total PNP R&D personnel (in FTEs) | Total number of PNP researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 14,691 | 176.7 | 127.6 |
| Final data (delivered T+18) | 14,691 | 176.7 | 127.6 |
| Difference (of final data) | 0 | 0 | 0 |
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) | Not available | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | Not available |
(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. | Not separately available. |
| Data collection costs | Not separately available. | Not separately available. |
| Other costs | Not separately available. | Not separately available. |
| Total costs | Not separately available. | 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: Expenditure data are not disaggregated by sector of performance. The R&D survey is fully implemented by the Statistical Office of the Republic of Slovenia.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 122 | Number of all sampled units in the PNP sector (unweighted) |
| Average Time required to complete the questionnaire in hours (T)1) | ||
| Average hourly cost (in national currency) of a respondent (C) | ||
| Total cost |
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
R&D data for the Private Non-Profit (PNP) sector are collected through the national census survey Research and development activity by performers (eng.) - (Razvojno raziskovalna dejavnost pri izvajalcih - the offical name in Slovenian language).
The survey is conducted annually and uses a single questionnaire to collect R&D data across all sectors, including PNP. The survey does not rely on administrative data for the collection of R&D expenditure or personnel data—only direct reporting from units is used. Participation in the survey is mandatory for all reporting units, as required by the national statistical legislation. Units are formally invited in writing to participate and are required to complete the questionnaire independently—no interviewers are involved. Data are submitted via an online questionnaire, which includes detailed instructions and guidance for proper completion.
For additional support, a call center is available to assist with methodological questions, and technical assistance is offered to resolve any platform-related issues. If units do not submit their data on time, they receive two automated reminders, followed by direct contact from the call center.
As part of the quality assurance process, submitted data are thoroughly checked for anomalies and inconsistencies. Units are recontacted if validation checks indicate potential issues. This verification process plays a key role in ensuring the accuracy and reliability of the collected R&D data.
18.1.2. Sample/census survey information
| Sampling unit | Does not apply. |
|---|---|
| Stratification variables (if any - for sample surveys only) | Does not apply. |
| Stratification variable classes | Does not apply. |
| Population size | Does not apply. |
| Planned sample size | Does not apply. |
| Sample selection mechanism (for sample surveys only) | Does not apply. |
| Survey frame | Does not apply. |
| Sample design | Does not apply. |
| Sample size | Does not apply. |
| Survey frame quality | Does not apply. |
| Variables the survey contributes to | Does not apply. |
R&D is a census survey.
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Does not apply. |
|---|---|
| Description of collected data / statistics | Does not apply. |
| Reference period, in relation to the variables the administrative source contributes to | Does not apply. |
| Variables the administrative source contributes to | Does not apply. |
18.2. Frequency of data collection
See 12.3.2.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | |
|---|---|
| Description of collected information | Microdata are collected from reporting units through a questionnaire. |
| Data collection method | Census survey |
| Time-use surveys for the calculation of R&D coefficients | |
| Realised sample size (per stratum) | The total sample included 2452 reporting units, of which 122 units belonged to the PNP sector. |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | WEB questionnaire — no interviewers are involved, however a call center is available to assist with methodological questions. |
| Incentives used for increasing response | If units do not submit their data on time, they receive two automated reminders. |
| Follow-up of non-respondents | If units do not submit their data on time, they receive two automated reminders. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Does not apply. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | Not applicable. No interviews were conducted, as data were collected through a web-based questionnaire. |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Participation in the survey is mandatory for all reporting units; therefore, non-response analysis is not conducted. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | The methodological explanations - Research and development activity by performers, Slovenia, 2023 |
| R&D national questionnaire and explanatory notes in the national language: | Vprašalnik za statistično raziskovanje Raziskovalno-razvojna dejavnost pri izvajalcih, 2023 Metodološko pojasnilo - Raziskovalno-razvojna dejavnost pri izvajalcih, 2023 |
| Other relevant documentation of national methodology in English: | Does not apply. |
| Other relevant documentation of national methodology in the national language: | Does not apply. |
Annexes:
Vprašalnik za statistično raziskovanje Raziskovalno-razvojna dejavnost pri izvajalcih, 2023
Metodološko pojasnilo - Raziskovalno-razvojna dejavnost pri izvajalcih, 2023
The methodological explanations - Research and development activity by performers, Slovenia, 2023
18.4. Data validation
In cases of anomalies or inconsistencies, reporting units are contacted for verification. Other validation activities include comparisons with previous survey cycles and investigation of statistical inconsistencies.
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)
The dataset does not include any imputed values.
18.5.2. Data compilation methods
| Data compilation method - Final data | Does not apply. |
|---|---|
| Data compilation method - Preliminary data | Does not apply. |
The survey is carried out on an annual basis; therefore, data are not subject to compilation.
18.5.3. Measurement issues
| Method of derivation of regional data | Information on the region is derived from the Business Register based on the registered address of each reporting unit. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Does not apply. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Depreciation is excluded from R&D expenditures. |
18.5.4. Weighting and estimation methods
| Description of weighting method | Weighting adjustment for non-response takes into account also units that do not answer the questionnaire (i.e. do not fill in it) when calculating statistics on R&D. Corresponding weights are calculated stratum by stratum given the activity and the enterprise size (considering the number of persons employed). Difference between provisional and final data arises mostly due to the additionally received data after the (formal) end of data collection. |
|---|---|
| Description of the estimation method | Does not apply. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
The data are not subject to seasonal adjustment.
No comments.
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris, 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) 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 July 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.
For this report, data from the calendar year 2023 was used as the reference period.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects). Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
