Research and development (R&D) (rd)

1.1. Contact organisation

Institute of Statistics (INSTAT) (Albania)

1.2. Contact organisation unit

Unit for R&D, Innovation, Information Technology Statistics

1.3. Contact name

Olsa Ulqinaku

1.4. Contact person function

Head of Unit

1.5. Contact mail address

National Institute of Statistics, Vllazen Huta street

1.6. Contact email address

oulqinaku@instat.gov.al

1.7. Contact phone number

+355 (4) 2233356 / 2233358

1.8. Contact fax number

+355 (4) 2233356 / 2233358

10 March 2026

2.1. Metadata last certified

10 March 2026

2.2. Metadata last posted

10 March 2026

2.3. Metadata last update

10 March 2026

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

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

The guiding document to preparing the quality reports is the European Statistical System (ESS)  for Quality and Metadata Reports — re-edition 2021.

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.

3.1. Data description

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

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

The guiding document to preparing the quality reports is the European Statistical System (ESS)  for Quality and Metadata Reports — re-edition 2021.

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.

3.2. Classification system

• The distribution of principal economic activity and by industry orientation are based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
• The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
• The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
• The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD).
• The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011

3.3. Coverage - sector

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

3.3.1. General coverage

Definition of R&D

R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.

R&D Survey covers a sample of enterprises with 10+ employees, in all the territory of Albania.

 

R&D definition used identical to the FM definition.

3.3.2. Sector institutional coverage

 

Business enterprise sector (BES)	Private enterprises with 10+ employees that are potential R&D performers.  All NACE classes (B - U) and size classes are included.
Hospitals and clinics	Private hospitals are included, but practically irrelevant for R&D.
Inclusion of units that primarily do not belong to BES and the borderline cases.	not applicable

3.3.3. R&D variable coverage

R&D administration and other support activities	Personnel working exclusively for R&D administration and their costs are included in labour costs and R&D personnel. General support activities can be included in "other current costs" as part of the "overhead costs".
External R&D personnel	Enterprises are asked to include their external R&D personnel in R&D personnel and simultaneously report their costs as "other current costs".
Clinical trials: compliance with the recommendations in FM §2.61.	not applicable

3.3.4. International R&D transactions

Receipts from rest of the world by sector - availability	Not available
Payments to rest of the world by sector - availability	Not available
Intramural R&D expenditure in foreign-controlled enterprises – coverage	Not available

 

3.3.5. Extramural R&D expenditures

According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit enterprise) is not included in intramural R&D performance totals (FM, §4.12).

Data collection on extramural R&D expenditure (Yes/No)	No
Method for separating extramural R&D expenditure from intramural R&D expenditure
Difficulties to distinguish intramural from extramural R&D expenditure

3.4. Statistical concepts and definitions

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

3.4.1. R&D expenditure

Coverage of years	Every two years an R&D survey is conducted.
Source of funds	Breakdown available by the different funding sectors
Type of R&D	Three types of RD following FM
Type of costs	3 types of costs are distinguished: Labour costs, other current costs, capital expenditure
Economic activity of the unit	Classification by main economic activity; information from SBS of the same calendar year is used
Economic activity of industry served (for enterprises in ISIC/NACE 72)	Not applicable
Product field	NOt applicable
Defence R&D - method for obtaining data on R&D expenditure	Enterprises are asked to classify their R&D activities into the different NABS objectives.

3.4.2. R&D personnel

See below.

3.4.2.1. R&D personnel – Head Counts (HC)

Coverage of years	Every 2 years
Function	Personnel is broken down by all three types of function. Distinction between "researchers" and "technicians" is sometimes difficult for enterprises as firms do not use those terminologies primarily adapted for academia. Detailed FM definitions of the three types of functions are provided to the respondents.
Qualification	All personnel attributed to the functional categories “researchers” and “technicians” are classified by formal qualification (in terms of the Frascati categories, in full conformity with ISCED-11). Distinction can be made between ISCED levels 8, 7, 6, 5 and 4 and below: PhD, master study, bachelor or short study, post-secondary college, master craftman's diploma, school leaving examination in a higher technical or vocational school (e.g. BHS, HTL, HAK), school leaving examination in an academic secondary school (e.g. AHS, BMS, apprenticeship), other education. For the category “other supporting staff”, no information on formal qualification is available; R&D personnel of this category is attributed to the qualification category “other qualifications" (ISCED 4 and below).
Age	Not available
Citizenship	Not available

3.4.2.2. R&D personnel – Full Time Equivalent (FTE)

Coverage of years	Every two years an R&D survey is conducted.
Function	Personnel is broken down by all three types of function. Distinction between "researchers" and "technicians" is sometimes difficult for enterprises as firms do not use those terminologies primarily adapted for academia. Detailed FM definitions of the three types of functions are provided to the respondents. FTEs are reported directly by the enterprises.
Qualification	All personnel attributed to the functional categories “researchers” and “technicians” are classified by formal qualification (in terms of the Frascati categories, in full conformity with ISCED-11). Distinction can be made between ISCED levels 8, 7, 6, 5 and 4 and below: PhD, master study, bachelor or short study, post-secondary college, master craftman's diploma, school leaving examination in a higher technical or vocational school (e.g. BHS, HTL, HAK), school leaving examination in an academic secondary school (e.g. AHS, BMS, apprenticeship), other edcuation. For the category “other supporting staff”, no information on formal qualification is available; R&D personnel of this category is attributed to the qualification category “other qualifications" (ISCED 4 and below).
Age	Not available.
Citizenship	Not available.

3.4.2.3. FTE calculation

 

FTE are directly asked in the questionnaire. Plausibility checks are in place to avoid that the number of FTE in a category are higher than the headcounts. It is also checked if the FTE in a category are at least 10% of the headcounts. If not, the number of headcounts is reduced, e.g. 5 headcounts together must have at least 0.5 FTE.

3.5. Statistical unit

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

Responding unit and observation unit in the R&D survey in the BES is the legal unit. The statistical unit is the statistical enterprise. 

 

Data collected for 2023 and 2024 will be produced and published on 2026.

3.6. Statistical population

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

3.6.1. National target population

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

Data for 2023 and 2024 will be available in December 2026.

	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
Estimation of the target population size
Size cut-off point
Size classes covered (and if different for some industries/services)
NACE/ISIC classes covered

3.6.2. Frame population – Description

The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population.

 

Data for 2023 and 2024 will be available in December 2026.

Method used to define the frame population
Methods and data sources used for identifying a unit as known or supposed R&D performer
Inclusion of units that primarily do not belong to the frame population
Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D
Number of “new”1) R&D enterprises that have been identified and included in the target population
Systematic exclusion of units from the process of updating the target population
Estimation of the frame population

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

3.7. Reference area

Not requested. R&D statistics cover national and regional data.

3.8. Coverage - Time

Not requested, see concept 12.3.3. (data availability).

3.9. Base period

The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.

R&D expenditure is published in the following units:  Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).

R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.

Data available on RD expenditure and Personnel for reference period 2021 and 2022

6.1. Institutional Mandate - legal acts and other agreements

See below.

6.1.1. European legislation

Legal acts / agreements:

Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. Regulation No 2020/1197 sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.

The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.

6.1.2. National legislation

At national level:

• Law no. 17/2018 “On Official Statistics”
• Official Statistics Programme 2017-2021 and Official Statistics Programme 2022–2026

At European level:

• Regulation (EU) 2019/2152 of the European Parliament and of the Council of 27 November 2019 on European business statistics
• Frascati Manual, 2015

Existence of R&D specific statistical legislation	No
Are respondents obliged by the national law to provide raw and administrative data:	No

6.1.3. Standards and manuals

• Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
• EBS Methodological Manual on R&D Statistics

6.2. Institutional Mandate - data sharing

Not requested.

7.1. Confidentiality - policy

A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.

At the level of the ESS,  the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009). 

At national level:

• Confidentiality protection required by law: Yes
• Confidentiality commitments of survey staff: oath of Office.

7.2. Confidentiality - data treatment

Albanian Institute of Statistics protects and does not disseminate data it has obtained or it has access to, which enable the direct or indirect identification of the statistical units. Albania Institute of Statistics takes all appropriate preventive measures so as to render impossible the identification of individual statistical units by technical or other means that might reasonably be used by a third party. Statistical data that could potentially enable the identification of the statistical unit are disseminated by Albania Institute of Statistics if and only if:

• The data have been treated, as it is specifically set out in the Regulation Regulation (EC) No 223/2009 on European statistics, in such a way that their dissemination does not prejudice statistical confidentiality or
• The statistical unit has given its consent, without any reservations, for the disclosure of data.

The confidential data that are transmitted to Albania Institute of Statistics are used exclusively for statistical purposes and the only persons who have the right to have access to these data are the personnel engaged in this task. Issues referring to the observance of statistical confidentiality are examined by the staff working in Albania Institute of Statistics. The responsibilities of this staff are to recommend on: which detailed level the statistical data can be disseminated, so as the identification, either directly or indirectly, of the surveyed statistical unit is not possible; the anonymization criteria for the microdata provided to users; the access granting to researchers on confidential data for scientific purposes.

8.1. Release calendar

The National Institute of Statistics (INSTAT) publishes on the official webpage the calendar of all statistics produced by INSTAT and other agencies of the national statistical system.

Link below:

Publications Calendar

8.2. Release calendar access

At Eurostat level this is: Release calendar - Eurostat (europa.eu)

8.3. Release policy - user access

In accordance with Article 34 of Law no. 17/2018 "On Official Statistics", are distributed so that all users have immediate and equal rights, all available media formats, INSTAT and agencies are used, having distribution responsibilities in the program, requiring meet any requirement of any organization or individual for unpublished data or specific analyzes. Channels from which users can get the results are as follows:

1. Website - online release;
2. Written requests (by mail or email);

Data request, session available for external users.

At INSTAT the frequency of R&D data dissemination is every two years for final data.

10.1. Dissemination format - News release

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

10.1.1. Availability of the releases

	Availability (Y/N)1)	Links
Regular releases	Y	Main R&D Indicators
Ad-hoc releases	N

1) Y - Yes, N – No

10.2. Dissemination format - Publications

See below.

10.2.1. Availability of means of dissemination

Means of dissemination	Availability (Y/N)1)	Links
General publication/article	Y	Main R&D Indicators
Specific paper publication (e.g. sectoral provided to enterprises)	N

1) Y – Yes, N - No

10.3. Dissemination format - online database

See below

10.3.1. Data tables - consultations

Not requested.

10.4. Dissemination format - microdata access

No micro data produces

10.4.1. Provisions affecting the access

Access rights to micro-data	Not available
Access cost policy
Micro-data anonymisation rules

10.5. Dissemination format - other

See below.

10.5.1. Metadata - consultations

Not requested.

10.5.2. Availability of other dissemination means

Dissemination means	Availability (Y/N)1)	Micro-data / Aggregate figures	Comments
Internet: main results available on the national statistical authority’s website	Y	Aggregate
Data prepared for individual ad hoc requests	N
Other

1) Y – Yes, N - No

10.6. Documentation on methodology

See below

10.6.1. Metadata completeness - rate

Not requested.

10.7. Quality management - documentation

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

10.7.1. Documentation and users’ requests

Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.)	Tables and Graphs
Requests on further clarification, most problematic issues

INSTAT is committed to ensure the highest quality with respect to the compilation of statistical information. In accordance with the Statistics Law No 17/2018 "On Official Statistics", INSTAT use statistical methods and processes in compliance with internationally recognized scientific principles and standards conduct ongoing analyses of the statistics with a view to quality improvements and ensure that statistics are as up-to-date. In performing its tasks it follows the general principles of quality management from the European Statistics Code of Practice. INSTAT declares that it takes into account the following principles: impartiality, quality of processes and products, user orientation, employee orientation, effectiveness of statistical processes, reducing the workload for respondents.

11.1. Quality assurance

INSTAT is committed to ensure the highest quality with respect to the compilation of statistical information. In accordance with the Statistics Law No 17/2018 "On Official Statistics", INSTAT use statistical methods and processes in compliance with internationally recognized scientific principles and standards conduct ongoing analyses of the statistics with a view to quality improvements and ensure that statistics are as up-to-date. In performing its tasks it follows the general principles of quality management from the European Statistics Code of Practice. INSTAT declares that it takes into account the following principles: impartiality, quality of processes and products, user orientation, employee orientation, effectiveness of statistical processes, reducing the workload for respondents.

11.2. Quality management - assessment

RD statistics involve identifying the Expenditure and Personnel in 4 sectors of performance of RD activity.

Timeliness: Data for 2023 and 2024 will be available on December 2026. This also due to the fact that many enterprise themselves do not yet have the requested information available at the time of the R&D survey.

See below

12.1. Relevance - User Needs

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

12.1.1. Needs at national level

Users’ class1)	Description of users	Users’ needs
Data for 2023 and 2024 will be available in December 2026.

1)      

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
User satisfaction survey specific for R&D statistics	No specific user satisfaction survey for R&D statistics is undertaken.
Short description of the feedback received

12.3. Completeness

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

12.3.1. Data completeness - rate

Only aggregated data on Expenditure and Personnel infolved in RD activity were produced and transmitted to Eurostat.

12.3.2. Completeness - overview

Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197.

 

	Reasons for missing cells
Preliminary variables	No preliminary data produced
Obligatory data on R&D expenditure
Optional data on R&D expenditure
Obligatory data on R&D personnel
Optional data on R&D personnel
Regional data on R&D expenditure and R&D personnel

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	Yes	2 years
Type of R&D	Yes	2 years
Type of costs	Yes	2 Years
Socioeconomic objective	No
Region	No
FORD	No
Type of institution	Yes	2 Years

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	No	2 year
Function	No	2 year
Qualification	Yes	2 year
Age	No	2 year
Citizenship	No	2 year
Region	No	2 year
FORD	No	2 year
Type of institution	Yes	2 year
Economic activity	Yes	2 year
Product field	No
Employment size class	Yes

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	No
Function	No
Qualification	No
Age	No
Citizenship	No
Region	No
FORD	No
Type of institution	No
Economic activity	No
Product field	No
Employment size class	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

1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional'), if R&D data for BES are collected for additional breakdowns or/and at more detailed level than requested.

2) Y-start year

12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)

Cross-classification	Unit	Frequency
Not available

See below

13.1. Accuracy - overall

Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).

Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:

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	Data for 2023 and 2024 will be available in December 2026.
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

Data for 2023 and 2024 will be available in December 2026.

Indicators	5 (Very Good)1)	4 (Good)2)	3 (Satisfactory)3)	2 (Poor)4)	1 (Very poor)5)
Total intramural R&D expenditure
Total R&D personnel in FTE
Researchers in FTE

1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (BES R&D). Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.  

2) 'Good' = If at least one out of the three criteria described above is not fully met.

3) 'Satisfactory' = If the average rate of response is lower than 60% even by meeting the two remaining criteria.

4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not met.

5) 'Very Poor' = If all the three criteria are not met.

13.2. Sampling error

That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.

13.2.1. Sampling error - indicators

See below. 

13.2.1.1. Variance Estimation Method

Does not apply as a sample survey among all R&D performing units is carried out.

 

13.2.1.2. Confidence interval for key variables by NACE

Data for 2023 and 2024 will be available in December 2026.

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

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

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

13.2.1.3. Confidence interval for key variables by Size Class

Data for 2023 and 2024 will be available in December 2026.

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

13.3. Non-sampling error

Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.

13.3.1. Coverage error

Coverage errors (or frame errors) are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.

• Description/assessment of coverage errors: Data for 2023 and 2024 will be available in December 2026.
• Measures taken to reduce their effect: No measures

13.3.1.1. Over-coverage - rate

Not requested.

13.3.1.2. Common units - proportion

Not requested.

13.3.1.3. Frame misclassification rate

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

Data for 2023 and 2024 will be available in December 2026.

By size class for the Industry Sector (NACE Rev. 2: 01-03, 05-09, 10-33, 35, 36-39, 41-43)	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250-and more employees and self-employed persons	TOTAL
Number or surveyed enterprises in the stratum (according to frame)
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)
Misclassification rate
By size class for the Services Sector (NACE Rev 2.: 45-47, 49-53, 55-56, 58-63, 64-66 68, 69-75, 77-82, 84, 85, 86-88, 90-93, 94-96, 97-98, 99)	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250-and more employees and self-employed persons	TOTAL
Number or surveyed enterprises in the stratum (according to frame)
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)
Misclassification rate

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: Data for 2023 and 2024 will be available in December 2026.
• Measures taken to reduce their effect: No measures.

13.3.3. Non response error

Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.

There are two elements of non-response:

• Unit non-response, which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
• Item non-response, which occurs when data only on some, but not all survey variables are collected on a designated population unit.

The extent of response (and accordingly of non response) is also measured with response rates.

13.3.3.1. Unit non-response - rate

The main interest is to judge if the response from the target population was satisfying by computing the weighted and un-weighted response rate.

Definition:

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

Definition:

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

13.3.3.1.1. Unit non-response rates by Size Class

Data for 2023 and 2024 will be available in December 2026.

	0-9 employees and self-employed persons (optional)	10-49 employees and self-employed persons	50-249 employees and self-employed persons	250-and more employees and self-employed persons	TOTAL
Number of units with a response in the realised sample
Total number of units in the sample
Unit Non-response rate (un-weighted)
Unit Non-response rate (weighted)

13.3.3.1.2. Unit non-response rates by NACE

Data for 2023 and 2024 will be available in December 2026.

	Industry1)	Services2)	TOTAL
Number of units with a response in the realised sample
Total number of units in the sample
Unit Non-response rate (un-weighted)
Unit Non-response rate (weighted)

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

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

13.3.3.1.3. Recalls/Reminders description

Not applicable

13.3.3.1.4. Unit non-response survey

Data for 2023 and 2024 will be available in December 2026.

Conduction of a non-response survey
Selection of the sample of non-respondents
Data collection method employed
Response rate of this type of survey
The main reasons of non-response identified

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

Data for 2023 and 2024 will be available in December 2026.

	R&D Expenditure	R&D Personnel (FTE)	Researchers (FTE)
Item non-response rate (un-weighted) (%)
Imputation (Y/N)
If imputed, describe method used, mentioning which auxiliary information or stratification is used

13.3.3.3. Magnitude of errors due to non-response

Data for 2023 and 2024 will be available in December 2026.

	Magnitude of error (%) due to non-response
Total intramural R&D expenditure
Total R&D personnel in FTE
Researchers in FTE

 

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 for 2023 and 2024 will be available in December 2026.

Data entry method applied
Estimates of data entry errors
Variables for which coding was performed
Estimates of coding errors
Editing process and method
Procedure used to correct errors

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.

Data released in December 2024 with reference years 2021 and 2022 was collected in the first quarter of 2023.

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: 2022
• Date of first release of national data:  6 December 2024
• Lag (days): 340

14.1.2. Time lag - final result

Data for 2023 and 2024 will be available in December 2026.

• End of reference period: 
• Date of first release of national data:
• Lag (days):

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

Data for 2023 and 2024 will be available in December 2026.

	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)
Delay (days)
Reasoning for delay

15.1. Comparability - geographical

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

15.1.1. Asymmetry for mirror flow statistics - coefficient

Not requested.

15.1.2. General issues of comparability

No issues of comparability known.

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).
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).
Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel	FM2015, §5.25
Intramural R&D expenditure	FM2015 Chapter 4 (mainly sub-chapter 4.2).
Special treatment for NACE 72 enterprises	FM2015, § 7.59.
Statistical unit	FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).
Target population	FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).
Identification of not known R&D performing or supposed to perform R&D enterprises	FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).
Sector coverage	FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics).
NACE coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18
Enterprise size coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18
Reference period for the main data	Reg. 2020/1197 : Annex 1, Table 18
Reference period for all data	Reg. 2020/1197 : Annex 1, Table 18

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.

Data for 2023 and 2024 will be available in December 2026.

Methodological issues	Reference to recommendations	Deviation from recommendations	Comments on national treatment / treatment deviations from recommendations
Data collection preparation activities	FM2015 Chapter 7 (mainly sub-chapter 7.7).
Data collection method	FM2015 Chapter 7 (mainly sub-chapter 7.7).
Cooperation with respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).
Follow-up of non-respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).
Data processing methods	FM2015 Chapter 6 (mainly sub-chapter 6.7).
Treatment of non-response	FM2015 Chapter 6 (mainly sub-chapter 6.7).
Data weighting	FM2015 Chapter 7 (mainly sub-chapter 7.7).
Variance estimation	FM2015 Chapter 6 (mainly sub-chapter 6.9).
Data compilation of final and preliminary data	Reg. 2020/1197 : Annex 1, Table 18
Survey type	FM2015 Chapter 6 (mainly sub-chapter 6.4).
Sample design	FM2015 Chapter 6 (mainly sub-chapter 6.4).
Survey questionnaire	FM2015 Chapter 6 (mainly sub-chapter 6.4).

15.2. Comparability - over time

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

15.2.1. Length of comparable time series

See below.

15.2.2. Breaks in time series

The first data collection available is for 2022 referenbce year. Data for 2023 and 2024 will be available in December 2026.

	Length of comparable time series	Break years1	Nature of the breaks
R&D personnel (HC)
Function
Qualification
R&D personnel (FTE)
Function
Qualification
R&D expenditure
Source of funds
Type of costs
Type of R&D
Other

1)       Breaks years are years for which data are not fully comparable to the previous period.

15.2.3. Collection of data in the even years

Data are collected at the end of every even year corresponding to the last reference year. The collection takes place in December of the final reference year and the results are published in the following even year. For example, the data for the reference years 2023 and 2024 were collected in December 2024 and will be published in December 2026.

15.3. Coherence - cross domain

This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics.  Intramural R & D expenditure (code 230101 in the Commission Implementing Regulation (EU) 2020/1197) and R & D personnel (code 230201) are surveyed also in foreign-controlled EU enterprises statistics (inward FATS).

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

15.3.1. Coherence - sub annual and annual statistics

Not requested.

15.3.2. Coherence - National Accounts

Micro-data from the R&D survey of all sectors of performance are made available to National Accounts statistics.

15.3.3. National Coherence Assessments

Variable name	R&D Statistics - Variable Value	Other national statistics - Variable value	Other national statistics - Source	Difference in values (of R&D statistics)	Explanation of / comments on difference
Not available

15.4. Coherence - internal

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

15.4.1. Comparison between preliminary and final data

This part compares key R&D variables as preliminary and final data.

The first data collection available is for 2022 referenbce year. Data for 2023 and 2024 will be available in December 2026.

	Total R&D expenditure (in 1000 of national currency)	Total R&D personnel (in FTEs)	Total number of researchers (in FTEs)
Preliminary data (delivered at T+10)
Final data (delivered T+18)
Difference (of final data)

 

Comments : No preliminary data produced

....

15.4.2. Consistency between R&D personnel and expenditure

Data for 2023 and 2024 will be available in December 2026.

	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)
Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2)

(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

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

The first data collection available is for 2022 referenbce year. Data for 2023 and 2024 will be available in December 2026.

	Value	Computation method
Number of Respondents (R)
Average Time required to complete the questionnaire in hours (T)1
Average hourly cost (in national currency) of a respondent (C)
Total cost

1)        T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘re-contact time’)

17.1. Data revision - policy

Not requested.

17.2. Data revision - practice

Not requested.

17.2.1. Data revision - average size

Not requested.

18.1. Source data

Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. This section collects information on the type of data collection instruments used as well as methodological information for each data collection instrument. Depending on the type of data collection instrument used, only the sections corresponding to that data collection instrument are filled in.

18.1.1. Data source – general information

Data for the BES and PNP sectors are collected through a survey based on a sample of units. Meanwhile, data for the GOV and HEI sectors are collected through official letters sent to the respective institutions.

18.1.2. Sample/census survey information

The first data collection available is for 2022 referenbce year. Data for 2023 and 2024 will be available in December 2026.

Sampling unit
Stratification variables (if any - for sample surveys only)
Stratification variable classes
Population size
Planned sample size
Sample selection mechanism (for sample surveys only)
Survey frame
Sample design
Sample size
Survey frame quality
Variables the survey contributes to

18.1.3. Information on collection of administrative data or of pre-compiled statistics

The first data collection available is for 2022 referenbce year. Data for 2023 and 2024 will be available in December 2026.

Source
Description of collected data / statistics
Reference period, in relation to the variables the administrative source contributes to
Variables the administrative source contributes to

18.2. Frequency of data collection

See 12.3.3.

18.3. Data collection

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

18.3.1. Data collection overview

The first data collection available is for 2022 referenbce year. Data for 2023 and 2024 will be available in December 2026.

Realised sample size (per stratum)
Mode of data collection
Incentives used for increasing response
Follow-up of non-respondents
Replacement of non-respondents (e.g. if proxy interviewing is employed)
Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility)
Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods)

18.3.2. Questionnaire and other documents

The first data collection available is for 2022 referenbce year. Data for 2023 and 2024 will be available in December 2026.

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

18.4. Data validation

Data provided by INSTAT are checked for consistency and plausibility, and compared with previously provided data before being imported in the internal production database. 

18.5. Data compilation

See below.

18.5.1. Imputation - rate

Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100 / (Total number of possible records for x)

18.5.1.1. Imputation rate by Size class

The first data collection available is for 2022 referenbce year. Data for 2023 and 2024 will be available in December 2026.

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

18.5.1.2. Imputation rate by NACE

The first data collection available is for 2022 referenbce year. Data for 2023 and 2024 will be available in December 2026.

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

 

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

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

18.5.2. Data compilation methods

Data for 2023 and 2024 will be available in December 2026.

Data compilation method - Final data
Data compilation method - Preliminary data

18.5.3. Measurement issues

Method of derivation of regional data	Albania is NUTS1
Coefficients used for estimation of the R&D share of more general expenditure items
Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures

18.5.4. Weighting and estimation methods

Data for 2023 and 2024 will be available in December 2026.

Weight calculation method
Data source used for deriving population totals (universe description)
Variables used for weighting
Calibration method and the software used
Estimation

18.6. Adjustment

Not requested.

18.6.1. Seasonal adjustment

Not requested.

No comments.

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

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

The guiding document to preparing the quality reports is the European Statistical System (ESS)  for Quality and Metadata Reports — re-edition 2021.

Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.

10 March 2026

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

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

Responding unit and observation unit in the R&D survey in the BES is the legal unit. The statistical unit is the statistical enterprise. 

 

Data collected for 2023 and 2024 will be produced and published on 2026.

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

Not requested. R&D statistics cover national and regional data.

Data available on RD expenditure and Personnel for reference period 2021 and 2022

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.

At INSTAT the frequency of R&D data dissemination is every two years for 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.

Data released in December 2024 with reference years 2021 and 2022 was collected in the first quarter of 2023.

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

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