1.1. Contact organisation
National Institute of Statistics - Romania
1.2. Contact organisation unit
Department of Short Term Economic Indicators Statistics
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
16 Libertatii Bvd., Bucharest 5, ROMANIA, Postal code 050706
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 October 2025
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
31 October 2025
3.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011.
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
R&D definition used is in line with the Frascati Manual (FM).
Research and development is defined as any systematic and creative activity initiated to increase the volume of knowledge, including knowledge about man, culture and society and the use of this knowledge for new applications.
The research-development activity includes the technological design.
Does not include: market research activities, industrial and agricultural micro-production (except execution activities, prototypes, experimental installations, pilot stations), production and related activities, education and training activities, information services, general collection data, testing and standardization, patenting and licensing work, feasibility studies, specialized medical services, regular software development, industrial innovation (other than research and development), policy studies (application of research results -development to evaluate government policies).
3.3.2. Sector institutional coverage
| Tertiary education institution | Included |
|---|---|
| University and colleges: core of the sector | Included |
| University hospitals and clinics | Only teaching/training clinics |
| Inclusion of units that primarily do not belong to HES and the borderline cases |
Included |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No deviations from FM; personnel is not included but expenditure is included. |
|---|---|
| External R&D personnel | R&D The post-graduate students working in R&D, for whom corresponding R&D expenditures are fully included in R&D personnel. Starting with the reference year 2018, we introduced in the national questionnaire indicators related to external R&D personnel. |
| Clinical trials: compliance with the recommendations in the Frascati Manual §2.61. | Are included. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Available |
|---|---|
| Payments to rest of the world by sector - availability | Available |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Starting with 2018, we included in questionnaire a specific questions for intramural and extramural current costs related R&D personnel. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Difficulties to distinguish and understand for respondents the new indicators for External R&D personnel expenditure. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year. |
|---|---|
| Source of funds | In line with FM. |
| Type of R&D | In line with FM. |
| Type of costs | In line with FM, not detailed breakdown of costs. |
| Defence R&D - method for obtaining data on R&D expenditure | Data is obtained in the survey questionnaire. Data for Defense makes reference only to the expenditure for civilian purpose. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Data refer to end of period. |
|---|---|
| Function | Data compatible with ISCO-08. |
| Qualification | Not difficulties. |
| Age | Not difficulties. |
| Citizenship | We assimilate the citizenship with the origin country. In 2011, not included. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year. |
|---|---|
| Function | Data compatible with ISCO-08. |
| Qualification | Not difficulties. |
| Age | Not difficulties. |
| Citizenship | We assimilate the citizenship with the origin country. In 2011, not included. |
3.4.2.3. FTE calculation
The respondent unit calculates the hours worked in research projects for R&D personnel and also for post -graduate students and computes in full time equivalent.
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the HES Sector should consist of all R&D performing institutional units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | All Higher Education Institutes and University Clinics. | |
| Estimation of the target population size | Administrative sources for HES sector of performance units involved in R&D projects. Also, all units that stated in the last survey in the filter question intention to carry out CD activity in the reference year. |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested.
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
Reference period is the calendar previous year - 2023 reference year.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Yes. National research, development and innovation strategy 2014-2020 - National Strategy for R&I Modification and completion of the National Strategy for research, development and innovation 2014 - 2020, approved by GD 929/2014 - Modifications to National Strategy for R&I National Education Law – National Education Law Government Ordinance 57/2002 on scientific research and technological development - Government Ordinance on R&D Law 319/2003 on the Statute of research and development staff - National Law 319/2003 on R&d Staff Evaluation and classification in order to certify the institutions from the national research-development system - National Law regarding R&D Institutes Government Ordinance 41/2015 amending and supplementing Government Ordinance no. 57/2002 on scientific research and technological development – Government Ordinance 41/2015 Law 206/2004 on good conduct in scientific research, technological development and innovation – National Law 206/2004 Law on the organization and functioning of official statistics in Romania no. 226/2009 - National Statistical Law Nr 226/2009 Government Decision no. 586/2020 on the approval of the National Annual Statistical Program 2020 - National Annual Statistical Programme This right derives from Law 206/2004 on good conduct in scientific research, technological development and innovation – National Law Nr 206/2004 NIS President Order no 530/31 July 2001 Law 677/2001 - National Law Nr 677/2001 National Law 544/2001 - National Law Nr 544/2001 |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law:
No deviations from secure procedure.
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of such data and repealing Directive 95/46 / EC (General Regulation on data protection) - National Confidentiality Rules
Law no. 190 of 18 July 2018 on measures to implement Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of these data and repealing Directive 95/46 / EC (General Data Protection Regulation) - General Data Protection Regulation
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
LAW no. 363 of December 28, 2018 on protection natural persons regarding the processing of personal data by the competent authorities for the purpose of preventing, detecting, investigating, prosecuting and combating crime or the execution of punishments, educational and security measures, and regarding the free movement of such data
Law no. 102/2005 on the establishment, organization and functioning of the National Authority for the Supervision of Personal Data Processing, with subsequent amendments and completions.
- Confidentiality commitments of survey staff:
A confidentiality certificate agreement is signed upon employment, where the official terms of confidentiality are established.
7.2. Confidentiality - data treatment
Primary confidentiality:
- The rule of three (all cells with 3 and less units);
- The rule of dominance unit.
Secondary confidentiality:
- Disclosure by subtraction (differencing).
8.1. Release calendar
On the NIS website there are two calendars one for the press releases and the other for publications; both of them are accessible to the general public.
The final data are target to be published in press release and also in national publication to 11 months after the end of the reference year (in November).
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
For NIS Romania this is:
- Publication Catalogue - for publications
- Press Releases - for press release
8.3. Release policy - user access
The NIS has on the web page a section “Calendar of press releases”, with links to the monthly lists of publications planned for the current year. Each monthly list is sorted by date of publication and contains a brief description of the statistics to be provided. The monthly calendar is established for the following year in December of the previous year. NIS publishes annually on the site the calendar of press releases, calendar based on the terms of the Annual National Statistical Program and contains: title of the press release, reference period, date of issue. The monthly calendar is established and posted on the NIS website from December of the previous year. The calendar of press releases on the NIS website covers only the statistics published by the NIS
In the event of a change in the broadcast date, this is announced 24 hours before the calendar date, specifying the new broadcast date.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
The frequency of R&D data dissemination at the national data is annual.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Links | |
|---|---|---|
| Regular releases | Yes | The NIS press releases are sent directly by the NIS Press Office to the accredited media and ministries and are posted online at 9:00 a.m. in the dedicated section. Also, on the site there is the archive of these press releases, structured on statistical topics. |
| Ad-hoc releases | No |
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 | Yes | Web-site of Romanian National Institute of Statistics: National Statistical Institute's Website |
| Specific paper publication (e.g. sectoral provided to enterprises) | Yes | "Research and development activity in 2023" R&D Activity in 2023 |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Data for higher education (HES) sector of performance are available in database TEMPO ONLINE: Online Statistical Database
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to the micro-data | NIS does not have a "Safe center" for access to microdata. Due to the confidential nature of microdata, direct access to anonymized data is offered only for scientific research projects according to European and national legislation in the field, through an access contract. Legal framework The current European and national legal framework enables access to anonymised microdata available only for scientific purposes. The access is, in principle, limited to universities, research institutes, national statistical institutes, central banks within the EU and euro area countries, as well as to the European Central Bank. Individuals cannot be granted direct access to microdata. |
|---|---|
| Access cost policy | No |
| Micro-data anonymisation rules | Not applicable. |
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 | Yes | ||
| Data prepared for individual ad hoc requests | Yes | ||
| Other | No |
1) Y – Yes, N - No
10.6. Documentation on methodology
Detail information about R&D national survey for HES sector of performance applied are methodological notes, metadata and quality report.
Data are accompanied of metadata describing the indicators and the calculation thereof.
To all other questions regarding the methodology or the manner of designing the tables and the data we respond whenever necessary.
In the TEMPO online database, each indicator is accompanied by the related metadata.
10.6.1. Metadata completeness - rate
Not requested.
10.7. Quality management - documentation
See below.
10.7.1. Documentation and users’ requests
| Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.) | Metadata, graphs, methodological notes and quality report. |
|---|---|
| Requests on further clarification, most problematic issues | Number of R&D researchers in FTE. Further clarifications were not needed. To all other questions regarding the methodology or the manner of designing the tables and the data we respond whenever necessary. |
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).
The quality quantifies how well the statistics are fit for their purpose. The criteria to judge statistical quality correspond to: relevance, accuracy and reliability, timeliness and punctuality, accessibility and clarity, comparability, coherence and cost and burden. The aim is to reduce the errors of coverage, the non-response, the measurements errors, the processing errors.
The legal acts and other document related quality assurance are: Legislation concerning quality assurance, Task Forces or Working Groups, Law No. 226/2009 on the organisation and functioning of official statistics in Romania, Internal procedures, European Statistics Code of Practice, Quality Guidelines for Romanian Official Statistics.
Statistical practices used to compile national R&D data for government sector of performance are in compliance with Frascati Manual recommendations.
11.2. Quality management - assessment
The methodology was improved through the identification of universities, other tertiary education units and university clinics.
The R&D survey for HES sector of performance is conducted to provide knowledge about R&D indicators (mandatory and optional) and to allow comparisons with other European countries.
At every R&D survey for HES sector of performance, before the finalization of the national questionnaire, the main national users and the representatives of regional NIS departments are invited to a discussion, by Romanian NIS, to express their opinions and suggestions about the final national questionnaire (clarity, difficulties, understanding and perception of the questions and about other national statistical needs).
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | European Commission | Data used for the European R&D statistics and its further development. |
| 1 | Governmental departments: Parliament, Presidency, Ministry of National Education, Authorities for Regional Development. | Data used for R&D national and regional strategy and policy, publications and training. |
| 1 | OECD | Data used for international comparability. |
| 2 | Scientific institutes and universities | Data used for analyses. |
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 | NIS Romania uses a general user satisfaction survey addressed to a selection of users of all statistical domains. Last one survey in 2024. |
|---|---|
| User satisfaction survey specific for R&D statistics | We have comments received from the large categories of users, but we have not a satisfaction survey for all the users. |
| Short description of the feedback received | We have not specific requests on behalf of users. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Data completeness of final mandatory data are very good and good. National questionnaire survey for HES sector of performance included also mandatory and optional R&D indicators.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | not applicable |
| Obligatory data on R&D expenditure | not applicable |
| Optional data on R&D expenditure | not applicable |
| Obligatory data on R&D personnel | not applicable |
| Optional data on R&D personnel | not applicable |
| Regional data on R&D expenditure and R&D personnel | not applicable |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y-1999 | annual | 1993,1994 only current expenditure | introduced total expenditure | ||
| Type of R&D | Y-1995 | annual | ||||
| Type of costs | Y-1995 | annual | ||||
| Socioeconomic objective | Y-1995 | annual | ||||
| Region | Y-2000 | annual | ||||
| FORD | Y-1999 | annual | ||||
| Type of institution | Y- 2018 | annual |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-1999 | annual | ||||
| Function | Y-1993 | annual | ||||
| Qualification | Y-1993 | annual | ||||
| Age | Y-1993 | annual | 1993-2002 | new breakdown of the ages corresponding to Frascati Manual:up to 25, 25-34, 35-44,45-54, 55-64, 65 and more | ||
| Citizenship | Y-2004 | annual | ||||
| Region | Y-2000 | annual | ||||
| FORD | Y-1999 | annual | ||||
| Type of institution | Y-2018 | annual |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-1999 | annual | ||||
| Function | Y-1993 | annual | ||||
| Qualification | Y-1993 | annual | ||||
| Age | Y-1993 | annual | ||||
| Citizenship | Y-2004 | annual | ||||
| Region | Y-2000 | annual | ||||
| FORD | Y-1999 | annual | ||||
| Type of institution | Y-2018 | annual |
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| Number of scientific meetings organised at national level with international participation | 2000-2010 | annual | |||
| Training courses of R&D personnel | 2000-2010 | annual | |||
| Publications papers by scientific programs according with NABS clasiffications | 2000-2010 | annual | |||
| Number of R&D projects by NABS programs and by sources of funds | 2000-2010 | annual | |||
| Breakdown R&D expenditure by type of funds | 2018 | annual | |||
| Breakdown R&D personnel by status employment | 2018 | annua |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Yes | HC | annual |
| Yes | FTE | annual |
| Yes | both | annual |
| Yes | expenditure | annual |
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors,
- 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 | 5 | 4 | 5 | 4 | 4 | - | + |
| Total R&D personnel in FTE | 5 | 4 | 5 | 4 | 4 | - | + |
| Researchers in FTE | 5 | 4 | 5 | 4 | 4 | - | + |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | X | ||||
| Total R&D personnel in FTE | X | ||||
| Researchers in FTE | X |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not be met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
Not applicable.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not applicable |
| Government | Not applicable |
| Higher education | Not applicable |
| Private non-profit | Not applicable |
| Rest of the world | Not applicable |
| Total | Not applicable |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not applicable |
| Technicians | Not applicable | |
| Other support staff | Not applicable | |
| Qualification | ISCED 8 | Not applicable |
| ISCED 5-7 | Not applicable | |
| ISCED 4 and below | Not applicable |
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 error: We analyze the nomenclature specific to the units in the HES performance sector in addition to BR, the operating R&D national laws for this sector, the administrative sources and we find out if there are units that do not belong to HES.
- Measures taken to reduce their effect: For the units described in point a) we take the decision to move to another performance sector if necessary.
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: Comparing at national level the measurement values of the R&D indicators with the values of the previous reference year or data series.
- Measures taken to reduce their effect: If necessary, in order to correct the true value, we re-contact the unit / statistical territorial department.
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) |
|---|---|---|
| 142 | 151 | 6.0 |
13.3.3.2. Item non-response - rate
Definition: Un-weighted Item Non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | 0 | 0 | 0 |
| Comments | The response rate is calculated for the units that declared R&D activity in the reference year. | The response rate is calculated for the units that declared R&D activity in the reference year. | The response rate is calculated for the units that declared R&D activity in the reference year. |
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 entry method used was the following: data keying and responses through electronic online questionnaire. |
|---|---|
| Estimates of data entry errors | 0.1% |
| Variables for which coding was performed | The variables for which coding was performed have been: field of science, country of origin of R&D employees. |
| Estimates of coding errors | 0.1% |
| Editing process and method | The editing method is a combination of automated and manual methods. We apply a value range checked for every variable and compared with data from previous survey. |
| Procedure used to correct errors | The procedures of correcting errors identified by editing: re-contacting units to find out the correct values. |
13.3.5. Model assumption error
Not requested.
14.1. Timeliness
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
14.1.1. Time lag - first result
Time lag between the end of reference period and the release date of the results:
Indicator: (Release date of provisional/ first results) - (Date of reference for the data)
- End of reference period: 31 December 2023.
- Date of first release of national data: 15 November 2024.
- Lag (days): 319.
14.1.2. Time lag - final result
- End of reference period: 31 December 2023.
- Date of first release of national data: 29 November 2024.
- Lag (days): 333.
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) | ||
| 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
Previous 1993 R&D data could not be recomputed according with Frascati Manual due to the inclusion of other activities that did not belonged to Frascati Manual.
Since 1993 R&D data are in concordance to international classifications and respect recommendations of Frascati Manual except the following:
- military defense R&D ( defense R&D data include only civil defense R&D);
- R&D data for sector of performance abroad.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation | |
| Researcher | FM2015, § 5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Major fields of science and technology 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 9 (mainly sub-chapter 9.5). | No deviation | |
| Survey questionnaire / data collection form | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | |
| Cooperation with respondents | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | |
| Coverage of external funds | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No deviation | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No deviation | |
| Data processing methods | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | |
| Treatment of non-response | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | |
| Method of deriving R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | |
| Quality of R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | |
| 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) | None | ||
| Function | None | ||
| Qualification | 2003 | 1993-2003 first stage tertiary education theoretical (ISCED-5A) and practical (ISCED-5B) were surveyed together; since 2003 we have comparable data as they were surveyed separately. | |
| R&D personnel (FTE) | None | ||
| Function | None | ||
| Qualification | None | ||
| R&D expenditure | None | ||
| Source of funds | 1993, 1994 | during 1993 - 1994 we have data breakdown only by sources of funds for the current costs. | |
| Type of costs | 1993, 1994 | there are included only current costs and not sub-total capital expenditures. | |
| Type of R&D | 1993, 1994 | we have only total expenditures and not breakdown by sectors of performance there are included only current costs and not sub-total capital expenditures. | |
| Other | 1993, 1994 | first year for total intramural expenditures by main field of science. |
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
Yes.
The same as in the odd years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual (FM) regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
R&D statistics for HES sector of performance are compiled in according with institutional HES sector as defined based on the System of National Account (SNA).
15.3.3. Coherence – Education statistics
Not applicable.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 915021 | 8839 | 6475 |
| Final data (delivered T+18) | 915021 | 8839 | 6475 |
| Difference (of final data) | 0 | 0 | 0 |
Comments:
....
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration per year (cost in national currency) | Explanation of consistency issues if any | |
|---|---|---|
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | 51330 | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 59267 |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use/person/day | |
|---|---|---|
| Staff costs | not available separately | |
| Data collection costs | not available separately | |
| Other costs | not available separately | |
| Total costs | 160,774.45 |
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: Total costs is calculated for reference year 2024. Not available for reference year 2023.
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 81 | All respondents with R&D indicators |
| Average Time required to complete the questionnaire in hours (T)1) | 5.09 | Total number of hours (from questionnaire)/Number of all respondents with R&D indicators |
| Average hourly cost (in national currency) of a respondent (C) | Not available | Not available |
| Total cost | Not available | Not 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’)
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
The type of data collection is only census survey for government sector of performance, including specific chapter for data GBARD.
The main variables are:
- number of R&D employees in HC at 31 December and FTE aggregated by occupation, qualification, by sex, by citizen, status employment;
- researchers- by sex, age group, nationality, field of science, status employment;
- R&D expenditures- by type of costs, by sources of funds, by type of research, by NABS Programms, by sources and type of funds;
- R&D expenditures - payments received from abroad by type of funds institutions.
Data collection: March-April after reference year.
Data processing, validation, comparison: May-September after reference year.
Data dissemination (Press Release Communicate, Publication, Data base on line, Yearbook): November after reference year.
18.1.2. Sample/census survey information
| Sampling unit | Institutional unit. |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable |
| Stratification variable classes | Not applicable |
| Population size | Not applicable |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Very good |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Information are collected from R&D survey only and not from administrative data. |
|---|---|
| Description of collected data / statistics | Not applicable. |
| Reference period, in relation to the variables the administrative source contributes to | Reference year is previous year. |
| Variables the administrative source contributes to | Not applicable. |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Data is collected through national survey (R&D) addressed to HES sector of performance including universities, other units with tertiary education and R&D university clinics. |
|---|---|
| Description of collected information | All providers send the same information filled in the national R&D questionnaire concerning number of personnel and R&D expenditures. |
| Data collection method | Data collection is made by paper questionnaire or electronic online portal questionnaire. |
| Time-use surveys for the calculation of R&D coefficients | Not applicable |
| Realised sample size (per stratum) | Not applicable |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Postal surveys,online electronic questionnaire. |
| Incentives used for increasing response | Not applicable |
| Follow-up of non-respondents | 3 reminders |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 94.0% |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Not applicable |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available |
| R&D national questionnaire and explanatory notes in the national language: | CD-HES |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | Not available |
18.4. Data validation
The data are validated at NIS level, after ensuring the completeness of the results and checking all correlations between indicators.
The statistical data are compared for each type of indicator with the data of previous years and the errors that present suspicions are discussed and transmitted to the respondents and / or the territorial statistical departments.
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)
Not applicable.
No imputation rate.
18.5.2. Data compilation methods
| Data compilation method - Final data | National R&D survey for HES sector is carry out every year. |
|---|---|
| Data compilation method - Preliminary data | In accordance with the National Statistical Program approved by the Romanian Government and published in the Official Journal within 10 months of the reference period we provide data for the previous year. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | Not applicable |
|---|---|
| Revision policy for the coefficients | Not applicable |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not applicable |
18.5.4. Measurement issues
| Method of derivation of regional data | Each unit from sample has a specific code in order to have the regional identification. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | Exclusion of VAT and depreciation. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | Not applicable. |
18.5.5. Weighting and estimation methods
| Description of weighting method | Not applicable |
|---|---|
| Description of the estimation method | Not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
No comments.
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
31 October 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
See below.
Not requested.
Reference period is the calendar previous year - 2023 reference year.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- 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,
- 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.
The frequency of R&D data dissemination at the national data is annual.
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.
