1.1. Contact organisation
Turkish Statistical Institute (TurkStat)
1.2. Contact organisation unit
Sectoral Statistics Department, Science and Technology Statistics Group
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Devlet Mah.Necatibey Cad. No:114 06420 Çankaya/ANKARA
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Not required.
26 August 2025
2.1. Metadata last certified
26 August 2025
2.2. Metadata last posted
26 August 2025
2.3. Metadata last update
26 August 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.
3.3.2. Sector institutional coverage
| Tertiary education institution | All public and foundation universities included. |
|---|---|
| University and colleges: core of the sector | Universities and colleges form the main/core of the higher education R&D sector. |
| University hospitals and clinics | University hospitals and clinics included. |
| Inclusion of units that primarily do not belong to HES and the borderline cases |
Units administered by higher education institutions are included. |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No deviations |
|---|---|
| External R&D personnel | No external R&D personnel counted. |
| Clinical trials: compliance with the recommendations in the Frascati Manual §2.61. | Mostly from administrative sources; project expenditure collected via survey; compiled per FM §2.61. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Data are collected via the survey questionnaire according to the sector breakdowns recommended by the Frascati Manual. |
|---|---|
| Payments to rest of the world by sector - availability | N/a |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Data on the breakdown between intramural and extramural R&D expenditure are collected via the survey questionnaire. |
| Difficulties to distinguish intramural from extramural R&D expenditure | - |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Data are collected on a calendar year basis. |
|---|---|
| Source of funds | Data on sources of R&D funding are collected via the survey questionnaire, in line with Frascati Manual §4.104–4.108, Table 4.3. |
| Type of R&D | Data on the type of R&D are collected via the survey questionnaire in accordance with Frascati Manual §2.5. |
| Type of costs | Data on R&D costs are collected via the survey questionnaire in accordance with Frascati Manual §4.2. |
| Defence R&D - method for obtaining data on R&D expenditure | Data on R&D expenditure for defence are collected via the survey questionnaire in line with the methodology; no such detail is available from administrative sources. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Data are collected on a calendar year basis. |
|---|---|
| Function | Data are collected for the reference year in accordance with the Eurostat requirements. |
| Qualification | Data are collected for the reference year in accordance with the Eurostat requirements. |
| Age | Data are collected for the reference year in accordance with the Eurostat requirements. |
| Citizenship | Not available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Data are collected on a calendar year basis. |
|---|---|
| Function | Data are collected for the reference year in accordance with the Eurostat requirements. |
| Qualification | Data are collected for the reference year in accordance with the Eurostat requirements. |
| Age | Data are collected for the reference year in accordance with the Eurostat requirements. |
| Citizenship | N/a |
3.4.2.3. FTE calculation
Administrative records used for the calculation of researchers for the higher education sector and Time Use Survey results (2015 -2023) were revised.
Accordingly, head count and full-time equivalent figures by occupation have been updated for the years 2015-2023
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 | Census of all public and foundation universities. | All public and foundation universities (202) |
| Estimation of the target population size | All public and foundation universities (202) | All public and foundation universities (202) |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3 (Data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
The reference period for the collected data is the calendar year. All variables refer to the specified year, in line with reporting requirements.
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 | The R&D survey is conducted under the authority of the Turkish Statistical Institute (TÜİK) Law No. 5429 and other relevant legal provisions regulating official statistics and research activities in Turkey. The legal framework defines the obligations of surveyed units to provide data and the rights and responsibilities of both respondents and TÜİK. Relevant legal acts ensure the collection, processing, and confidentiality of R&D data in accordance with national standards. |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes, respondents are legally obliged to provide the requested data under the Turkish Statistical Institute (TÜİK) Law No. 5429 and related national legislation. |
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:
a) Confidentiality protection required by law: Confidentiality of the data provided by respondents is ensured and legally protected under the Turkish Statistical Institute (TÜİK) Law No. 5429, which guarantees that individual or unit-level information is not disclosed and is used solely for statistical purposes.
....
b) Confidentiality commitments of survey staff: All survey staff are legally bound and formally committed to maintain confidentiality of the data collected. They are trained and required to handle all individual or unit-level information in accordance with TÜİK Law No. 5429 and internal confidentiality protocols.
....
7.2. Confidentiality - data treatment
The information is collected to be used only for statistical work, privacy is guaranteed by Law No. 5429. It can not be used as evidence for emergence of any liability or investigation. This privacy is the legal responsibility of Turkish Statistical Institute.
8.1. Release calendar
From 1990 and onwards, R&D data are published annually.
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
It can be reached the contents of "Press Release" "Statistical Tables", "Databases", "Reports" and "Metadata" via National Statistical Release Calendar link after you choose the related topic in "Statistics" menu.
Moreover, it can be reached many information available in international or local level via "Regional Statistics", "Province Indicators", "International Selected Indicators" applications take place in "E-Services" menu using TurkStat Website.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Links | |
|---|---|---|
| Regular releases | Y | Regular releases on R&D Indicators |
| Ad-hoc releases |
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 | |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
R&D data collected by TÜİK are disseminated through the official online database of the Turkish Statistical Institute, available at Official Online Database of the Turkish Statistical Institute
The database provides access to detailed R&D statistics by sector, type of R&D, and other breakdowns in line with international standards. If available, information on the number of accesses to the dataset can be provided upon request. No alternative online database exists.
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 | Not available. |
|---|---|
| Access cost policy | Not available. |
| Micro-data anonymisation rules | Not available. |
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 | ||
| Data prepared for individual ad hoc requests | N | Online request form filled by users. | |
| Other | Y | It is prepared providing a protocol made with the related stakeholders. The confidentially is also saved in any case. It is not valid for the individual requests. |
1) Y – Yes, N - No
10.6. Documentation on methodology
It can be reach detailed information via Information on Statistical Methodology for R&D Indicators
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.) | R&D data is published on web with national metadata file. |
|---|---|
| Requests on further clarification, most problematic issues | All the required explanations are available on metadata file. |
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
During Frascati Manual revision process, TurkStat was in close cooperation with EU Member States and Eurostat.
The questionnaires sent by OECD and Eurostat were examined, the breakdowns that were not collecting were specified. Afterwards, the variables / breakdowns were included in the revised questionnaires.
Considering the updated manual and additional data requirements, data collection methods and reporting process were enhanced in a similar way to the other national statistical offices.
The survey has high response rates (2023: 100%) and the intensive check mechanism using for to guarantee a very high data quality,
As a result, quality of the R&D data is very good. The methodological measures taken are in compliance with the Frascati manual recommendations.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| Institutions | The Supreme Council for Science, Technology and Innovation Policies
|
Strategic goals, grant schemes, research project, government allocations for R&D activities |
| Institutions | OECD and Eurostat | International comparison. |
| Researchers and students | Researchers and students need statistics, analyses, ad hoc services, access to specific data | Statistics, analyses, access to specific data in Data Research Centre |
| Media | National and regional media | Press release results. |
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 | No |
|---|---|
| User satisfaction survey specific for R&D statistics | Not applicable |
| Short description of the feedback received | Not applicable |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Completeness of statistics is good.
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 | All mandatory data have been sent |
| Obligatory data on R&D expenditure | All mandatory data have been sent |
| Optional data on R&D expenditure | Most of the optional data have been sent |
| Obligatory data on R&D personnel | All mandatory data have been sent |
| Optional data on R&D personnel | Most of the optional data have been sent |
| Regional data on R&D expenditure and R&D personnel | All regional data have been sent |
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-1990 | |||||
| Type of R&D | Y-1990 | |||||
| Type of costs | Y-1990 | |||||
| Socioeconomic objective | No | |||||
| Region | Y-2010 (NUTS 1) Y-2015 (NUTS2) | |||||
| FORD | Y | |||||
| Type of institution | N |
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-1997 | Annual | None | None | None | |
| Function | Y-1996 | Annual | None | Adjustments have been made in order to ensure better alignment with Eurostat and OECD standards, including updates in classifications and breakdowns. | 2016 | Better comparability and policy needs |
| Qualification | Y-1990 | Annual | None | Adjustments have been made in order to ensure better alignment with Eurostat and OECD standards, including updates in classifications and breakdowns. | 2016 | Better comparability and policy needs |
| Age | Y-1990 | Annual | None | None | None | - |
| Citizenship | No data availble | - | - | - | - | - |
| Region | Y-2010 | Annual | None | - | 2016 | Better comparability and policy needs |
| FORD | Y-1990 | Annual | None | Adjustments have been made in order to ensure better alignment with Eurostat and OECD standards, including updates in classifications and breakdowns. | Better comparability and policy needs | |
| Type of institution | No data availble | - | - | - | - | - |
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-1990 | |||||
| Function | Y-1990 | |||||
| Qualification | Y-1990 | |||||
| Age | Y-1990 | |||||
| Citizenship | No | |||||
| Region | Y-2010 | |||||
| FORD | Y-1990 | |||||
| Type of institution | No |
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 |
|---|---|---|---|---|---|
| No details given. | No details given. | No details given. | No details given. | No details given. | No details given. |
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 |
|---|---|---|
| Not available. | Not available. | Not available. |
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
- 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 |
- '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.
- 'Good' = If at least one out of the three criteria described above is not fully met.
- 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.
- 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not be met.
- '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. Census survey.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Does not apply. |
| Government | Does not apply. |
| Higher education | Does not apply. |
| Private non-profit | Does not apply. |
| Rest of the world | Does not apply. |
| Total | Does not apply. |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Does not apply. |
| Technicians | Does not apply. | |
| Other support staff | Does not apply. | |
| Qualification | ISCED 8 | Does not apply. |
| ISCED 5-7 | Does not apply. | |
| ISCED 4 and below | Does not apply. |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
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.
a) Description/assessment of coverage errors: Census
....
b) Measures taken to reduce their effect:
....
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.
a) Description/assessment of measurement errors: No errors known.
....
b) Measures taken to reduce their effect:
....
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) |
|---|---|---|
| 202 | 202 | 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 |
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 | All of the respondents report via web questionnaire and the data is imported into a database. |
|---|---|
| Estimates of data entry errors | Data entries were primarily checked by regional directorates. Then, the examination and analysis of the data was completed at the center. The respondents were asked again about the suspicious data found and checked again.It was also compared with data from administrative records. |
| Variables for which coding was performed | The following variables had to be coded: Unit:
Individual staff member:
|
| Estimates of coding errors | There are no coding error estimates available. |
| Editing process and method | If any inconsistencies are detected due to plausibility checks, data are corrected either by contacting (telephone, e-mail) the unit for further inquiries or using other reliable sources of information. |
| Procedure used to correct errors | If any inconsistencies are detected due to plausibility checks, data are corrected either by contacting (telephone, e-mail) the unit for further inquiries or using other reliable sources of information. |
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)
a) End of reference period: 2023
b) Date of first release of national data: 06 November 2023
c) Lag (days): 311
14.1.2. Time lag - final result
a) End of reference period: 2023
b) Date of first release of national data: 06 November 2023
c) Lag (days): 540
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) | T + 10 | T + 18 |
| Actual date of transmission of the data (T+x months) | T + 12 | T + 12 |
| 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
Main concepts and definitions are used for the production of R&D statistics are given by the Frascati Manual. However, data on external R&D personnel and external R&D expenditure are not compiled.
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 | |
| Researcher | FM2015, § 5.35-5.39. | No | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| 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 | |
| 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 | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Survey questionnaire / data collection form | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Cooperation with respondents | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Coverage of external funds | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No | |
| Data processing methods | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Treatment of non-response | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No | |
| Method of deriving R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Quality of R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No | |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | No |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1) | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | 2015-2023 | 2015 | Methodology change (applied from 2015 onwards) |
| Function | 2015-2023 | 2015 | Methodology change (applied from 2015 onwards) |
| Qualification | 2015-2023 | 2015 | Methodology change (applied from 2015 onwards) |
| R&D personnel (FTE) | 2015-2023 | 2015 | Methodology change (applied from 2015 onwards) |
| Methodology change (applied from 2015 onwards) Function | |||
| Qualification | 2015-2023 | 2015 | |
| R&D expenditure | 2015-2023 | 2015 | Methodology change (applied from 2015 onwards): calculations are now based on improved sources and more reliable administrative records, and classifications were aligned with Eurostat and OECD standards to ensure international comparability. |
| Source of funds | 2015-2023 | 2015 | Methodology change (applied from 2015 onwards): calculations are now based on improved sources and more reliable administrative records, and classifications were aligned with Eurostat and OECD standards to ensure international comparability. |
| Type of costs | 2015-2023 | 2015 | Methodology change (applied from 2015 onwards): calculations are now based on improved sources and more reliable administrative records, and classifications were aligned with Eurostat and OECD standards to ensure international comparability. |
| Type of R&D | 2015-2023 | 2015 | Methodology change (applied from 2015 onwards): calculations are now based on improved sources and more reliable administrative records, and classifications were aligned with Eurostat and OECD standards to ensure international comparability. |
| Other | 2015-2023 | None | No breaks identified |
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
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
The same as in uneven years. Published and transmitted data are based on final collection; no estimation is applied in even years.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. 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 are produced according to System of National Accounts (SNA) and Frascati Manual 2015. Fallowing provisional data on R&D expenditure are provided to National Accounts Unit :
- Gross domestic expenditure on R&D by sector and type of cost
- Gross domestic expenditure on R&D by sector of performance and by source of funds
15.3.3. Coherence – Education statistics
Institutional arrangement: Most data are obtained from administrative sources, including the YÖK database, budgets and revolving funds, private university accounts; surveys are conducted for project and support R&D personnel.
Conceptual differences and comparison: Expenditure items, personnel categories, and estimation methods (time-use survey updated every five years) are specified.
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) | Not applicable due to the fact that preliminary data are considered final data. | Not applicable due to the fact that preliminary data are considered final data. | Not applicable due to the fact that preliminary data are considered final data. |
| Final data (delivered T+18) | |||
| Difference (of final data) | Not applicable | Not applicable | Not applicable |
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) | 682,654.75 TRY per FTE (62,408,297,230/ 91420.36). | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | No distinction between internal and external R&D personnel 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. | No work sub-contracted to third parties. |
| Data collection costs | Not separately available. | No work sub-contracted to third parties. |
| Other costs | Not separately available. | No work sub-contracted to third parties. |
| Total costs | Not separately available. | No work sub-contracted to third parties. |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs:
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 202 | |
| Average Time required to complete the questionnaire in hours (T)1) | Not available (N/A) | |
| Average hourly cost (in national currency) of a respondent (C) | Not available (N/A) | |
| Total cost | Not available (N/A) |
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.
R&D data for universities are compiled through a combination of survey and administrative sources. Project data and information on other R&D support personnel are collected via surveys. For all other variables, administrative records are used to estimate the R&D expenditures of universities. These administrative sources include budget and revolving fund accounts, financial statements, the Higher Education Council (YÖK) academic database, and the results of time-use surveys etc.
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 universities are compiled through a combination of survey and administrative sources. Project data and information on other R&D support personnel are collected via surveys. For all other variables, administrative records are used to estimate the R&D expenditures of universities. These administrative sources include budget and revolving fund accounts, financial statements, the Higher Education Council (YÖK) academic database, and the results of time-use surveys etc.
18.1.2. Sample/census survey information
| Sampling unit | All public and foundation (non-profit) universities |
|---|---|
| Stratification variables (if any - for sample surveys only) | Full coverage (census); no sampling applied. |
| Stratification variable classes | Census |
| Population size | 202 |
| Planned sample size | 202 |
| Sample selection mechanism (for sample surveys only) | Census |
| Survey frame | All public and foundation (non-profit) universities (202) |
| Sample design | Census |
| Sample size | 202 |
| Survey frame quality | - |
| Variables the survey contributes to | - |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Higher Education Council (YÖK): Provides the academic staff database. Ministry of Treasury and Finance: Provides budget and revolving fund accounts as well as financial statements of universities. Turkish Statistical Institute (TurkStat): Provides results of the Time Use Survey and project data and information on R&D support personnel through surveys |
|---|---|
| Description of collected data / statistics | The collected data cover project-based R&D activities, information on R&D support personnel, and universities’ R&D expenditures. Expenditure adn researchers data are derived from administrative sources such as budget and revolving fund accounts, balance sheets, and the academic staff database of the Higher Education Council (YÖK). In addition, results of time use surveys are employed to estimate the allocation of working time to R&D. |
| Reference period, in relation to the variables the administrative source contributes to | Reference period: Reference periods by data source and variable:
|
| Variables the administrative source contributes to | Budget & revolving fund accounts: Total university R&D expenditure (current and capital components, where separable) — used as primary input for expenditure estimates using Budget & revolving fund accounts . Expenditure breakdowns (using Budget & revolving fund accounts): personnel costs, operating costs, equipment and capital investment. Balance sheets and financial statements: The R&D expenditure of private universities is calculated using balance sheets and financial statements. Headcount and FTEs used in personnel cost and personnel-intensity calculations. YÖK academic database (personnel registry): Number of academic staff by rank (professor, associate prof., assistant prof., lecturer, research assistant, etc.). Time Use Survey (TurkStat): Average share of working time devoted to R&D vs. teaching/other activities (by occupation/education) — used to convert academic working time into R&D FTEs and to apportion salary costs to R&D. Survey for universities (project & R&D support personnel survey) : 1. complementary to administrative sources 2. Characteristics and counts of R&D support personnel ( technicians, lab staff, admin supporting R&D ). |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | The primary data collection for university R&D statistics is conducted by Turkish Statistical Institute (TurkStat) through dedicated R&D surveys. Project-level data and information on R&D support personnel are collected directly from all public and foundation universities (census/micro-level). Administrative sources are used to complement survey data: budget and revolving fund accounts, financial statements, and the Higher Education Council (YÖK) academic staff database. Additionally, results from the national Time Use Survey are employed to derive R&D coefficients for full-time equivalents (FTEs) and to apportion personnel costs to R&D activities. |
|---|---|
| Description of collected information | Data include university R&D projects, R&D support personnel, and R&D expenditures. Administrative sources (budget, balance sheets, YÖK database) provide expenditure and staff data. Time Use Survey results are used to estimate R&D FTEs and allocate personnel costs. |
| Data collection method | Data are collected through surveys of all universities, supplemented with administrative records and Time Use Survey results to estimate R&D FTEs. |
| Time-use surveys for the calculation of R&D coefficients | National Time Use Survey results are used to estimate the share of working time devoted to R&D, which is then applied to calculate R&D full-time equivalents (FTEs) and apportion personnel costs. |
| Realised sample size (per stratum) | Census |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Web based on-line survey and administrative records |
| Incentives used for increasing response | Survey response rates are supported through follow-up reminders, direct contacts with universities, regional directorate manuals, explanatory notes in the questionnaire, the “ALO 124” help line, and in accordance with the legal framework of the Turkish Statistical Law (No. 5429). |
| Follow-up of non-respondents | There were no non-respondents; all universities provided the requested data. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Not applicable, as all universities responded and no proxy interviews were needed. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 100%, as all universities provided the requested data. |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Not applicable, as all universities responded and no non-response occurred. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | - |
| R&D national questionnaire and explanatory notes in the national language: | Research and Development Activities Survey, 2023 Higher Education R&D Activities Survey, 2023 |
| Other relevant documentation of national methodology in English: | Turkish Statistical Institute (TurkStat) R&D Survey Methodology Manual; Turkish Statistical Law (No. 5429) |
| Other relevant documentation of national methodology in the national language: | - |
18.4. Data validation
R&D data is checked for consistency and compared with previously calculated data before publication. Suspected errors are questioned and reported to the authorities.
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 | Final R&D statistics are compiled directly from actual survey responses and administrative sources for all universities; no estimation or imputation is applied. |
|---|---|
| Data compilation method - Preliminary data | No preliminary data |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | No such coefficients are used. |
|---|---|
| Revision policy for the coefficients | Not available |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not available |
18.5.4. Measurement issues
| Method of derivation of regional data | Regional R&D data are directly obtained by aggregating university-level data according to the location of each institution; no estimation or allocation is needed. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable, as all R&D expenditures are directly reported and no coefficients are needed for estimation. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | N/a |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | GUF R&D expenditures are derived from central government budget data, and R&D shares are estimated from these totals. |
18.5.5. Weighting and estimation methods
| Description of weighting method | Not applicable, as all universities are included and no weighting is required.
|
|---|---|
| Description of the estimation method | Not applicable, as all R&D data are directly reported and no estimation is performed. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
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.
26 August 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. R&D statistics cover national and regional data.
The reference period for the collected data is the calendar year. All variables refer to the specified year, in line with reporting requirements.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
- 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.
