See below.
1.1. Contact organisation
Statistical Office of the Republic of Slovenia (SURS)
1.2. Contact organisation unit
Demography and Social Statistics Division, 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, Slovenija
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
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 Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Government sector | In line with SNA standards and FM 2015 recommendations. |
|---|---|
| Hospitals and clinics | University hospitals and medical centres associated with higher education institutions are included in HES, other hospitals and medical centres are included in the GOV. |
| Inclusion of units that primarily do not belong to GOV and the borderline cases. |
In the GOV sector are not included units that primery not belong to it. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Management and administration support:
Library:
Computing departments:
|
|---|---|
| External R&D personnel | The coverage of external R&D personnel is in line with FM recommendations. This includes:
|
| Clinical trials: compliance with the recommendations in FM §2.61. | Clinical trials are treated as R&D in line with Frascati Manual 2015: Phases I, II, and III of clinical trials are classified as R&D, as they involve systematic investigation to gain new knowledge. Phase IV (post-marketing studies) is not included in R&D, except when the study is designed to generate further scientific or technological knowledge beyond routine monitoring. However, the current methodological guidelines for reporting units do not provide specific instructions regarding how to determine whether Phase IV trials qualify as R&D. This may lead to variability in how reporting units classify such activities. |
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. Data on extramural R&D expenditures are available and can be classified as follows: Domestic: small enterprises, medium-sized enterprises, large enterprises, public research institutions (excluding institutions of HES), public higher education institutions, others. Foreign (institutions abroad): foreign affiliates, other enterprises, foreign universities and research institutions of HES, other private non-profit research organisations, international organisations, others. |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | The questionnaire includes a separate question/table for extramural R&D expenditure, accompanied by additional clarifications for reporting units. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Some difficulties were identified, particularly in borderline cases. |
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 not covered. |
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 Government Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | The target population is composed of R&D performing units (known or assumed to perform R&D) classified as the general government (S.13) (with the exclusion of those units included in the HES). | |
| Estimation of the target population size | All regular and occasional R&D performers both are included. |
3.6.2. Frame population – Description
In ESS countries, the frame population for GOV R&D statistics is defined as the list of all the institutional units classified by the national accounts (ESA) as included in the General government (S.13), with the exclusion of those units included in the Higher education sector (HES).
| Method used to define the frame population | frame population Several sources are used for defining the frame population. |
|---|---|
| Methods and data sources used for identifying a unit as known or supposed R&D performer | Data sources used for identifying (known or supposed) R&D performers are: Statistical Business Register (Statistical Office of the Republic of Slovenia) - business entities, their subsidiaries and other divisions of business entities, registered for performing R&D activities (as covered by NACE Rev. 2 Division 72); List of recipients of state aid for investment in R&D (Ministry of Finance); List of business entities liable to general and regional tax incentives for R&D investments (Financial Administration of the Republic of Slovenia); The Community Innovation Survey (CIS) (Statistical Office of the Republic of Slovenia); Slovenian Current Research Information System (SICRIS) (Slovenian Research Agency); List of recipients of state aid for investment in R&D (Slovenian Research Agency). |
| Inclusion of units that primarily do not belong to the frame population | No |
| Systematic exclusion of units from the process of updating the target population | No |
| Estimation of the frame population | 74 units |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested, see concept 12.3.3 (Data availability).
3.9. Base period
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.
See below.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | There is 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 Statistics
6.2. Institutional Mandate - data sharing
Not requested.
See below.
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 collected R&D data 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 the official website under the section Statistical confidentiality.
See below.
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 in 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.
See below.
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 Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to the micro-data |
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.
See below.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1-International organizations | OECD | |
| 1-National level | Ministries, UMAR (The Institute of Macroeconomic Analysis 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 Republic of Slovenia (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 specific for R&D statistics. |
| 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 Statistical Advisory Committee (in Slovene only). |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
100%
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | All required data were submitted, and voluntary data were provided where available. |
| Obligatory data on R&D expenditure | All required data have been transmitted. |
| Optional data on R&D expenditure | Optional data have been transmitted where available. |
| Obligatory data on R&D personnel | All required data have been transmitted. |
| Optional data on R&D personnel | Optional data have been transmitted where available. |
| Regional data on R&D expenditure and R&D personnel | All required data have been transmitted. |
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 | Annual | ||||
| Type of R&D | Y | Annual | ||||
| Type of costs | Y | Annual | ||||
| Socioeconomic objective | N | Does not apply | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | N | Does not apply |
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 | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | Y | Annual | ||||
| Age | Y (only for researchers) | Annual | ||||
| Citizenship | Y (only for researchers) | Annual | ||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| Type of institution | N | Does not apply |
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 | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | N | Does not apply | ||||
| Age | N | Does not apply | ||||
| Citizenship | N | Does not apply | ||||
| Region | Y | Annual | ||||
| FORD | N | Does not apply | ||||
| Type of institution | N | Does not apply |
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 |
|---|---|---|---|---|---|
| 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
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Does not apply. | ||
See below.
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | : | : | |||||
| Total R&D personnel in FTE | : | : | |||||
| Researchers in FTE | : | : | |||||
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
Does not apply.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not available. |
| Government | Not available. |
| Higher education | Not available. |
| Private non-profit | Not available. |
| Rest of the world | Not available. |
| Total | Not available. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not available. |
| Technicians | Not available. | |
| other support staff | Not available. | |
| Qualification | ISCED 8 | Not available. |
| ISCED 5-7 | Not available. | |
| ISCED 4 and below | Not available. |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
- Description/assessment of coverage errors : The coverage errors were not detected.
- Measures taken to reduce their effect: Does not apply.
- Share of PNP (if PNP is included in GOV): Does not apply.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
- Description/assessment of measurement errors: Measurement errors may arise when questionnaires are completed by several persons or organisational units, by staff insufficiently familiar with R&D projects, or due to non-compliance with methodological instructions. Subjective assessments of R&D funds, which cannot always be derived directly from the reporting units’ records, can also lead to inconsistent or unreliable data.
- Measures taken to reduce their effect:
Data error detection controls focus on ensuring consistency of totals derived from different breakdowns. When significant intra-annual changes are identified, the reporting units are recontacted to provide additional explanatory information or to retransmit corrected data.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
| 73 | 73 | 0% |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item Non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible, for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | Item non-response is not measured. | Item non-response is not measured. | Item non-response is not measured. |
| Comments | Census survey. | Census survey. | Census survey. |
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 | Data collection is conducted through an online questionnaire deployed via eSTAT, the official web-based data collection platform of the Statistical Office used for all surveys. The questionnaire includes a comprehensive set of built-in validation and consistency checks designed to minimize measurement errors, prevent logical inconsistencies, and ensure completeness at the point of entry. This approach supports the collection of high-quality, reliable data from the outset. |
|---|---|
| Estimates of data entry errors | Does not apply. |
| Variables for which coding was performed | Does not apply. |
| Estimates of coding errors | Does not apply. |
| Editing process and method | Does not apply. |
| Procedure used to correct errors | Once the questionnaire is submitted, the data undergo additional automated validation procedures (referred to as logical controls). Any anomalies or inconsistencies identified during this phase are systematically verified through recontact with the reporting units. No further data editing is performed beyond these follow-ups, ensuring the integrity and transparency of the data collection process. |
13.3.5. Model assumption error
Not requested.
See below.
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: 5 November 2024
- Lag (days): T+10
14.1.2. Time lag - final result
- End of reference period: 31 December 2023
- Date of first release of national data: 5 March 2025
- 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 | 18 |
| Actual date of transmission of the data (T+x months) | 10 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
See below.
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 have been identified. National R&D statistics are produced in accordance with the Frascati Manual 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, 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 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 | |
| Statistical unit | FM2015, § 8.64-8.65 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015, § 8.63 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015, § 8.2-8.13 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Hospitals and clinics | FM2015, § 8.22 and 8.34 | No deviation | |
| Borderline research institutions | FM2015, § 8.14-8.23 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Fields of research & development coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No deviation | |
| Reference period | 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 method | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | |
| Survey questionnaire / data collection form | FM2015 Chapter 8 (mainly sub-chapter 8.5). | No deviation | |
| Cooperation with respondents | FM2015 Chapter (mainly sub-chapter 8.5). | No deviation | |
| Data processing methods | FM2015 Chapter 8 (mainly sub-chapters 8.5-8.6). | Does not apply. | |
| Treatment of non-response | FM2015 Chapter 8 (mainly sub-chapter 8.5). | Does not apply. | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.7). | Does not apply. | |
| Data compilation of final and preliminary data | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
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) | 1993-2021 | 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: the extended definition of the higher education sector (in addition to higher education institutions, higher vocational institutions are included) is applied, |
| Function | 1993-2021 | 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: the extended definition of the higher education sector (in addition to higher education institutions, higher vocational institutions are included) is applied, |
| Qualification | 1993-2021 | 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: the extended definition of the higher education sector (in addition to higher education institutions, higher vocational institutions are included) is applied, |
| R&D personnel (FTE) | 1993-2021 | 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: the extended definition of the higher education sector (in addition to higher education institutions, higher vocational institutions are included) is applied, |
| Function | 1993-2021 | 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: the extended definition of the higher education sector (in addition to higher education institutions, higher vocational institutions are included) is applied, |
| Qualification | 1993-2021 | 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: the extended definition of the higher education sector (in addition to higher education institutions, higher vocational institutions are included) is applied, |
| R&D expenditure | 1994-2021 | 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 | 1994-2021 | 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: the extended definition of the higher education sector (in addition to higher education institutions, higher vocational institutions are included) is applied, |
| Type of costs | 1994-2021 | 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: the extended definition of the higher education sector (in addition to higher education institutions, higher vocational institutions are included) is applied, |
| Type of R&D | 1994-2021 | 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: the extended definition of the higher education sector (in addition to higher education institutions, higher vocational institutions are included) is applied, |
| Other | Does not apply. | Does not apply. | Does not apply. |
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
Data are produced in the same way every year.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
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 R&D expenditure – GOVERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers(in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 223.387 | 3040 | 2246 |
| Final data (delivered T+18) | 223.387 | 3040 | 2246 |
| Difference (of final data) |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanations of consistency issues, if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 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) | ||
| Average Time required to complete the questionnaire in hours (T)1) | Not separately available. | |
| Average hourly cost (in national currency) of a respondent (C) | Not separately available. | |
| Total cost | Not separately available. |
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’)
See below.
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
See below.
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 Government Sector (GOV) are collected through the national survey Research and development activity by performers (eng.) - (the offical name in Slovenian language: Razvojno raziskovalna dejavnost pri izvajalcih). The sample is constructed using administrative records to identify eligible units and ensure their inclusion. Given this approach, it is assumed that all known R&D performing institutions are fully captured. In the unlikely event that a unit is omitted, its impact on total R&D expenditure and employment estimates would be minor and negligible.
The survey is conducted annually and uses a single questionnaire to collect R&D data across all sectors, including GOV. 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 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 | Units in the GOV sector. The GOV sector included 73 units (the number of valid responses obtained from the survey) |
|---|---|
| Stratification variables (if any - for sample surveys only) | Does not apply. |
| Stratification variable classes | |
| Population size | |
| Planned sample size | |
| Sample selection mechanism (for sample surveys only) | Does not apply. |
| Survey frame | Statistical Business Register (Statistical Office of the Republic of Slovenia) - business entities, their subsidiaries and other divisions of business entities, registered for performing R&D activities (as covered by NACE Rev. 2 Division 72), List of recipients of state aid for investment in R&D (Ministry of Finance), List of business entities liable to general and regional tax incentives for R&D investments (Financial Administration of the Republic of Slovenia), The Community Innovation Survey (CIS) (Statistical Office of the Republic of Slovenia), Slovenian Current Research Information System (SICRIS) (Slovenian Research Agency) |
| Sample design | |
| Sample size | |
| Survey frame quality | Coverage of the reference population is good. |
| Variables the survey contributes to | All data on employment and expenditure are collected directly through the survey; no administrative sources or pre-compiled statistics are used. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | All data on employment and expenditure are collected directly through the survey; no administrative sources or pre-compiled statistics are used. |
|---|---|
| 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 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Units in the GOV sector. |
|---|---|
| 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 GOV sector included 73 units (the number of valid responses obtained from the survey) |
| 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 automated reminders. |
| Follow-up of non-respondents | If units do not submit their data on time, they receive automated reminders. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 100%. |
| Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods) | Non-response analysis is not done. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Questionnaire (only in Slovene) Methodological explanation |
| R&D national questionnaire and explanatory notes in the national language: | Questionnaire (only in Slovene) Methodological explanation |
| 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:
The methodological explanations - Research and development activity by performers, Slovenia, 2023
Metodološko pojasnilo - Raziskovalno-razvojna dejavnost pri izvajalcih, 2023
Vprašalnik za statistično raziskovanje Raziskovalno-razvojna dejavnost pri izvajalcih, 2023
18.4. Data validation
Validation activities are: comparing the statistics with previous cycles, investigating inconsistencies in the statistics; performing micro data editing.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)
18.5.2. Data compilation methods
| Data compilation method - Final data | Does not apply. |
|---|---|
| Data compilation method - Preliminary data | Does not apply. |
18.5.3. Measurement issues
| Method of derivation of regional data | R&D performers are classified by statistical and cohesion region based on the address information (i.e., municipality) recorded in the Statistical Business Register. In cases where a reporting unit provides data for the entire enterprise, the address of the headquarters is generally used for deriving regional data. |
|---|---|
| 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
Not requested.
Statistics on Government R&D (GOVERD) measure research and experimental development (R&D) performed in the Government sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the Government sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
Main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and by the European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics).
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
31 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.
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:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
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.
