Research and development (R&D) (rd)

1.1. Contact organisation

General Directorate of Education and Science Statistics
(DGEEC - Direção Geral de Estatísticas da Educação e Ciência)

1.2. Contact organisation unit

Directorate for Science and Technology Statistics and Information Society Services
(DSECTSI - Direção de Serviços de Estatística da Ciência e Tecnologia e da Sociedade de Informação)

1.3. Contact name

Restricted from publication

1.4. Contact person function

Restricted from publication

1.5. Contact mail address

Av. 24 de Julho, 134
1399-054 Lisboa, PORTUGAL

1.6. Contact email address

Restricted from publication

1.7. Contact phone number

Restricted from publication

1.8. Contact fax number

Not required.

2.1. Metadata last certified

2 October 2025

2.2. Metadata last posted

2 October 2025

2.3. Metadata last update

2 October 2025

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. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU)No 2012/995 concerning the production and development of Community statistics on science and technology.

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.

3.3.2. Sector institutional coverage

Business enterprise sector (BES)	All enterprises known or supposed to perform R&D activities, including occasional and continuous R&D performers, in all branches of activity and size classes.
Hospitals and clinics	Private hospitals and clinics are included in BES, public hospitals and medical centers are all included in the Government sector, including university hospitals and clinics.
Inclusion of units that primarily do not belong to BES and the borderline cases.	BES also comprise some borderline institutions (private non-profit institutions and private associations) serving industries.

3.3.3. R&D variable coverage

R&D administration and other support activities	In accordance with FM (2015: 2.122), R&D management and other R&D support activities are considered and reported as part of the unit R&D activities.
External R&D personnel	In accordance with FM (2015); covered.
Clinical trials: compliance with the recommendations in FM §2.61.	In accordance with FM (2015: 2.61), Clinic trials are mentioned as an example of R&D activities in the R&D survey. If applicable, the R&D related to clinical trials is to be reported among all the R&D performed by the company. It is not requested detailed information whether it was included in the response or not.

3.3.4. International R&D transactions

Receipts from rest of the world by sector - availability	Statistics on international R&D transactions regarding receipts from the rest of the world are available, according to FM (2015: table 4.3). Data by sources of funds for R&D, namely funding from the “Rest of the world”, are classified as follows: European Union (EU), Foreign enterprises in the same group, Other foreign enterprises, Foreign governments (GOV), Foreign higher education institutions, Foreign private non-profit institutions (PNP), Other international organizations. These data are displayed as aggregated figures, as “Foreign Funds”.
Payments to rest of the world by sector - availability	Statistics on international R&D transactions regarding payments to the rest of the world are available. To what concerns the distribution by providers and recipients of extramural funds (for R&D exchange and transfer), R&D data coverage regarding the rest of the world is classified as follows: Foreign enterprises in the same group, Other foreign enterprises, Foreign private non-profit institutions, technological centers or interface institutions with enterprises, Foreign higher education institutions, Foreign governments (GOV), Other foreign organizations.
Intramural R&D expenditure in foreign-controlled enterprises – coverage	It is possible to distinguish between foreign-controlled and domestic enterprises (the survey includes a question about the distribution, in percentage, of the company capital according to its origin: National public, National private and Foreign capital). However, data on foreign-affiliates is 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)	Yes.
Method for separating extramural R&D expenditure from intramural R&D expenditure	In order to separate extramural R&D expenditure from intramural R&D expenditure, there are two distinct sections in the form: Section IV: only for ‘Intramural R&D Expenditure’, and Section V: only for ‘Extramural R&D Expenditure’. Both concepts are also explained in annexes (see point 10.6.), explanatory notes, and also in the FAQ that are provided with the survey (in the online platform). The extramural R&D expenditures are compiled separating the transfer funds for R&D from exchange funds for R&D, and both are disaggregated by type of institution (enterprises in the same group Other enterprises, GOV, HES, PNP institutions; technological centers/interface institutions related to enterprises, and other institutions) and geographic location (Portugal or rest of the world).
Difficulties to distinguish intramural from extramural R&D expenditure	Some units still have difficulty in distinguishing both concepts, in particular distinguish acquiring R&D services within the scope of their own R&D activities from contracting R&D services from third parties. Having this in mind, the definition of both concepts is provided in the form of explanatory notes and annexes (see point 10.6.). Furthermore, during the data collection, DGEEC clarifies any doubt on this matter, whenever requested. Moreover, all along the validation process, the values reported for intramural and extramural expenses are compared and, in case of suspicion of duplicated information or of misunderstanding, the company is contacted (email or telephone) for clarification and, if necessary, for data corrections.

3.4. Statistical concepts and definitions

See below.

3.4.1. R&D expenditure

Coverage of years	Calendar year.
Source of funds	In accordance with FM (2015).
Type of R&D	In accordance with FM (2015).
Type of costs	In accordance with FM (2015).
Economic activity of the unit	Main economic activity of the company conducting the R&D activity.
Economic activity of industry served (for enterprises in ISIC/NACE 72)	These companies are surveyed the same way as the others, through the same form.
Product field	In accordance with FM (2015).
Defence R&D - method for obtaining data on R&D expenditure	Defence R&D expenditure is estimated based on the socioeconomic objective (SEO) "Defence". In Portuguese R&D form, the company must report and distribute the percentage of their R&D activities according to their socioeconomic objective(s) (distribution of 100% in as many SEO applicable).

3.4.2. R&D personnel

See below.

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

Coverage of years	Total number of persons employed during the calendar year.
Function	R&D personnel is reported by the company, in Section III of the form, as aggregated data (percentage of time dedicated to R&D in the reference year breakdown by gender, qualification, function and internal/external personnel) and in individual form (not mandatory in BES) for the tertiary graduated personnel in R&D there is a question about the main R&D function performed in the reference year. In the validation process, the data from Section III is compared with the information of the individual forms, with data from previous years and the company is contacted for clarification and, if necessary, for data corrections.
Qualification	R&D personnel is reported by the company, in Section III of the form, as aggregated data (percentage of time dedicated to R&D in the reference year breakdown by gender, qualification, function and internal/external personnel) and in individual form (not mandatory in BES) for the tertiary graduated personnel in R&D there is a question about the highest degree obtain until the end of the reference year. In the validation process, the data from Section III is compared with the information of the individual forms, with data from previous years and the company is contacted for clarification and, if necessary, for data corrections.
Age	In the individual form for the tertiary graduated personnel in R&D, the date of birth is requested (optional). Because, neither the individual form nor the question is mandatory, breakdown by this variable may be underestimated.
Citizenship	In the individual form for the tertiary graduated personnel in R&D, country of nationality is requested (optional). Because, neither the individual form nor the question are mandatory, breakdown by this variable may be underestimated.

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

Coverage of years	(See procedure for HC).
Function	(See procedure for HC).
Qualification	(See procedure for HC).
Age	(See procedure for HC).
Citizenship	(See procedure for HC).

3.4.2.3. FTE calculation

FTE is reported by the company as aggregated data (percentage dedicated to R&D in the reference year by gender, qualification, function and internal/external personnel). After, in the validation process, the data is compared with the information of the individual forms (in BES the individual form it’s not mandatory and it’s only for tertiary graduated personnel in R&D, as mentioned before), with data from previous years and the company is questioned if needed.

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.

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.

 

	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	Target population are enterprises known or supposed to perform R&D. No exclusion is made with respect to size or industry.
Estimation of the target population size	12000
Size cut-off point	No.
Size classes covered (and if different for some industries/services)	All.
NACE/ISIC classes covered	All.

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.

 

Method used to define the frame population	Includes all enterprises listed in the official Business Register – as defined by the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The frame population is the same as the target population. This register is regularly updated.
Methods and data sources used for identifying a unit as known or supposed R&D performer	National register of R&D enterprises is updated by a range of administrative information that allow to identify these enterprises. Some of the information sources are: previous R&D surveys (Directorate General of Education and Science Statistics); applications for R&D projects financed by national entities (Science and Technology Foundation and other entities from Ministry of Science, Technology and Higher Education; Ministry of Economy); E.U. funded R&D programs and other international funded programs, like Eureka and Iberoeka programs; Applications for tax R&D incentives – SIFIDE – (Agência Nacional de Inovação - entity linked to Portuguese Ministry of Science, Technology and Higher Education and Portuguese Ministry of Economy, that it is essentially dedicated to the promotion of innovation and technological development with a view to facilitating closer ties between research activities and the Portuguese business sector.) Community Innovation Survey – enterprises that declare to perform R&D activities (Directorate General of Education and Science Statistics of Portuguese Ministry of Science, Technology and Higher Education and Statistics Portugal); National office register – enterprises whose main or secondary activity is NACE 72 (research and development activities) and enterprises that declare to spend some investments for R&D - National accounts (Statistics Portugal); Enterprises founded in technological parks (websites).
Inclusion of units that primarily do not belong to the frame population	See point 3.3.2.
Frequency and the methods applied for inclusion R&D performers not known and not supposed to perform R&D	Yearly.
Number of “new”1) R&D enterprises that have been identified and included in the target population	About 2000
Systematic exclusion of units from the process of updating the target population	Enterprises that answer to the survey in the three years before without R&D activities, unless they appear in one or more of the above-mentioned sources of information for the reference period.
Estimation of the frame population	Unknown.

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.

Calendar year.

6.1. Institutional Mandate - legal acts and other agreements

See below.

6.1.1. European legislation

Legal acts / agreements:

Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. 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

Existence of R&D specific statistical legislation	Production of national R&D statistics is governed by the general national statistical legislation (Law Nr. 22/2008 of May 13th).
Are respondents obliged by the national law to provide raw and administrative data:	Yes: Law Nr. 22/2008 of May 13th (Law for the National Statistical System).

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:

a)       Confidentiality protection required by law:

DGEEC proceeds according to the National Statistical System (Law Nr. 22/2008 of May 13th) that regulates statistical confidentiality (Article 6).

b)       Confidentiality commitments of survey staff:

The staff working directly with statistical production have to sign commitment document, which ensures the acknowledge of the confidentiality issues and data protection law. 

7.2. Confidentiality - data treatment

The rule applied for defining cells with direct disclosure risk (primary confidentiality) is 2 firms or less in a cell is considered confidential (Rule of Three).When necessary, it is applied the rule of the secondary confidentiality, if then disclosure is possible by subtraction.

8.1. Release calendar

The calendars of statistical operations and statistical publications are publicly available (in January) on the DGEEC's website.

8.2. Release calendar access

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

At national level this is: Release calendar Estatistica

8.3. Release policy - user access

R&D data, by sectors of performance, is available to all users on DGEEC’s website.

When data/publications are released on DGEEC website, the units surveyed in the reference year, regardless of the type of response (performing R&D or not), are also informed by email.

The Chief of Staff of the Minister of Science, Technology and Higher Education, 24 hours before the scheduled of release, can receive information under embargo, as it is publicly described in the European Statistics Code of Practice and in accordance with point 15, chapter B, of Statistics Portugal (INE).

At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.

At national level it is also yearly for provisional and final data.

See below.

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	N
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	Content: National R&D Statistical Yearbook of BES (2023). Format: online Links: General publications
Specific paper publication (e.g. sectoral provided to enterprises)	Y	Content: Highlights of provisional data on R&D Expenditure and R&D Personnel (includes BES) Provisional data Highlights of final data on R&D Expenditure and R&D Personnel (includes BES): Final data Publication on the Main R&D Indicators for the BES in 2023: Format: online: Publication main R&D indicators

1) Y – Yes, N - No

10.3. Dissemination format - online database

A list of the R&D companies is provided on DGEEC’s website, which contains the name and general contacts of each company that in the reference year performed R&D and authorized this disclosure.

List R&D companies

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 micro-data	Under the Cooperation Protocol between Statistics Portugal (INE), DGEEC and the Foundation for Science and Technology (FCT), R&D microdata can be acceded only by researchers and for scientific purposes. DGEEC is the entity that certifies researchers for the use of microdata, and only after that process of certification, INE provide access to it.
Access cost policy	Free of charge.
Micro-data anonymisation rules	Applied by Statistics Portugal (INE).

10.5. Dissemination format - other

See below.

10.5.1. Metadata - consultations

Not requested.

10.5.2. Availability of other dissemination means

Dissemination means	Availability (Y/N)1)	Micro-data / Aggregate figures	Comments
Internet: main results available on the national statistical authority’s website	Y	Aggregate figures	The National R&D Statistical Yearbook for the BES sector contains the main indicators and variables collected in the R&D BES Survey, and it’s available online, in XLS/ODS format, to all users. Statistical Yearbook
Data prepared for individual ad hoc requests	Y	Microdata and Aggregate figures	Microdata confidentiality is guaranteed by law and anonymization rules are in place. Other statistical demands for specific data that are not available on DGEEC’s website, can be provided under specific request.
Other	N

1) Y – Yes, N - No

10.6. Documentation on methodology

The BES R&D data dissemination files provide methodological information and the questionnaire. The publication of main R&D statistics and indicators(online) gives information to users about methodological procedures, data collection methods and list of variables; presents a definition of conceptual framework; provides a copy of R&D questionnaire and offers a range of tables. When it is necessary, some of the tables are available with the respective foot notes. For further explanations, users can request telephone or e-mail assistance.

List of national descriptive text and/or references to methodological documents available to the R&D statistical process:

• IPCTN form – BES available: BES Survey
• IPCTN Methodological Document (DGEEC, INE); available: Methodological documents and at INE’s website: INE's Documentacao Metodologica
• Validation Handbook (internal working document / DGEEC),

R&D form (IPCTN), for 2023 reference year, had 5 annexes which provide useful information for a correct filling of the questionnaire:

• Annexe I – Concepts and examples of R&D activities (and also on intramural and extramural expenditures),
• Annexe II – Human resources performing R&D activities,
• Annexe III – Fields of R&D Classification (FORD, 2015),
• Annexe IV – SEO according to Eurostat (NABS, 2007),
• Annexe V – National priorities according to R&D National Strategy for an Intelligent Specialisation (ENEI 2030),
• Annexe VI – List of products.

The survey’s Methodological Document provides all information related to this national statistical operation. In general, it describes the needs, the goals and the funding of this statistical operation; it also provides a general characterisation of the statistical operation (type, source of information used, periodicity, geographic scope, breaks in time series, information available to users and its dissemination means) and contains methodological information on target population, data collecting, data treatment, data dissemination, and more. It’s the official methodological document approved by INE for registering this statistical operation in the NSS.

Data validation rules are defined in the Validation Handbook.

Rules for questionnaire non-response cases were defined in the Handbook (including uncompleted questionnaires). All these documents are updated on a yearly basis, for each statistical operation.

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.)	Along with each publication, it is always provided detailed information on data collection and metadata. FAQ are also frequently updated and displayed online regarding the R&D survey.
Requests on further clarification, most problematic issues	No.

11.1. Quality assurance

At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation). The Code of Practice available at Code of Practice

DGEEC as the institution responsible for the production of official statistics, must respect and be governed by national and international statistical quality standards, in accordance with article 7 of Law Nr. 22/2008 of May 13, and by the European Statistics Code of Practice. In the case of the R&D statistics, DGEEC also follows Eurostat's and OECD methodological recommendations. Several data validation processes take place before the R&D indicators are released. These validation procedures have been updated over the years that, complemented with the use of new database management tools, have resulted in more efficient and effective data management. The major improvements were obtained through the use of technology and information systems, through a wider range of administrative databases, through more efficient communication between organizations, but also due to the expertise staff involved in these statistical operations, who greatly contributed to the quality of the survey responses. The activities carried out by the technicians are highly complex and require in-depth knowledge of the concepts and methodologies inherent to this survey, in the field of statistics, as well as high computer skills, especially in programs that allow data management and calculation - for example, SQL, SAS and SPSS. In addition, communication skills and contact with respondents are also privileged, both in the validation phase and in the phase of obtaining responses, which is why it is necessary to guarantee continuous training for all those who contribute to this census. Training has been given to technicians in these areas, as well as complementing training in areas related to database management and making information available (data scientist, PowerBI, etc.). Internal manuals are updated annually, including a validation handbook, and a detail description of the entire final validation process, which takes place in SQL. A thorough validation procedure of data collected is carried out, consisting in finding and correcting internal inconsistencies among the sections and questions in survey and by comparing with data from previous years or with other data sources. All efforts are made to reduce errors, to identify and correct them. Assistance/helpdesk is provided to respondents during data collection. During the validation process, respondents are contacted for further clarifications or for correcting errors. For all questionnaires, the following procedures are carried out: The electronic form includes validations that allow checking the consistency of the information (it may be necessary to contact the company, if incoherence is found). Inconsistencies of the "Error" type must be resolved before registration is accepted. Once the data registration is concluded, the information is analysed and processed, namely the analysis of the consistency of the year's data, comparison of values with the previous year for quality control. Some data is confronted with information from other administrative sources considered relevant. DGEEC had also implemented a Quality Management System for improving the quality of its services by monitoring and measuring its services, processes and procedures. As part of this system, procedures are included in matters such as document management, resource management, human resources skills improvement on statistical production. The Quality Manual, as well as the commitment to quality could be also noticed in DGEEC’s annual programs that are publicly available on its website.

11.2. Quality management - assessment

The Portuguese R&D survey (IPCTN) is part of the National Statistical System, and DGEEC, as responsible for these official statistics, is legally obliged to respect the national and international statistical quality standards (Law for the National Statistical System, European Statistics Code of Practice).

The IPCTN survey has been implemented for almost 4 decades, with continuous adaptation, improvement, and ongoing monitoring to assure that the survey is according to FM.

The quality is considered very good, mainly because of the intensive follow-up activities with the respondents, the administrative databases that complement the survey, which contributes to the data validation, as well as the high response rates over the years.

12.1. Relevance - User Needs

Please see sub-concept 12.1.1. in the long metadata file. 

12.1.1. Needs at national level

Users’ class1)	Description of users	Users’ needs
1- Institutions	European Commission	Data on national R&D expenditure and R&D human resources for policy-making.
1- Institutions	OECD	OECD R&D Questionnaires; Biotech and Nanotech Questionnaire
1- Institutions	RICYT - Red Iberoamericana e Interamericana de Indicadores de Ciencia y Tecnología	Data on national R&D expenditure and R&D human resources for policymaking.
1- Institutions	Portuguese Ministries	Data on national R&D expenditure and R&D human resources for policymaking.
1- Institutions	Statistics Portugal	Structural R&D indicators, statistical yearbooks and other statistical outputs
3 - Media	National Media	Main R&D data are available on DGEE’s website
4 - Researchers and students	Researchers and students	Individual ad hoc requests
6 - Other	Other institutions	Individual ad hoc requests

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	User satisfaction survey is sent to all individual ad hoc requests.
User satisfaction survey specific for R&D statistics	No specific survey satisfaction for R&D statistics, but in the R&D survey the units can give feedback in the additional field that is provided in the survey.
Short description of the feedback received	So far, this information has not been regularly treated, thus a short description of the feedback received can’t be provided.

12.3. Completeness

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

12.3.1. Data completeness - rate

For BES, completeness rate is 100% for mandatory data and 97% for total data required.

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	Not applicable
Obligatory data on R&D expenditure	Not applicable
Optional data on R&D expenditure	Not applicable
Obligatory data on R&D personnel	Not applicable
Optional data on R&D personnel	Not applicable
Regional data on R&D expenditure and R&D personnel	Not applicable

12.3.3. Data availability

See below.

12.3.3.1. Data availability - R&D Expenditure

	Availability1)	Frequency of data collection	Gap years – years with missing data	Changes - Description	Changes - Year of introduction	Changes - Reasons
Source of funds	Y - 1982	1982-2007(Every two years); 2008 onwards (Yearly)
Type of R&D	Y - 1984	1984-2007(Every two years); 2008 onwards (Yearly)
Type of costs	Y - 1982	1982-2007(Every two years); 2008 onwards (Yearly)
Socioeconomic objective	Y - 2001	2001-2007(Every two years); 2008 onwards (Yearly)
Region	Y - 1995	1995-2007(Every two years); 2008 onwards (Yearly)		Data compiled at regional level according to NUTS2002.	2003	NUTS 2002 was introduced for compiling data at regional level.
FORD	Y - 2005	2005-2007(Every two years); 2008 onwards (Yearly)
Type of institution	Y - 1995	1995-2007(Every two years); 2008 onwards (Yearly)
Type of institution	Y - 2008	2008 onwards (Yearly)

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 - 1995	1995-2007 (Every two years);2008 onwards (Yearly)		Extend the variables collection	2001	Data on age variable available only for tertiary education graduated personnel. Until 2001, this data was only available for researchers.
Function	Y - 1982	1982-2007 (Every two years); 2008 onwards (Yearly)	1994	R&D personnel classification by occupation revised. Researcher concept was enlarged Revision of the occupation categories for R&D personnel based on ISCO classification.	1992; 1995; 2013	1992: R&D personnel classification by occupation was revised for the 1995R&D survey and retrospectively back to1992. From 1992onwards, data for RSE and technicians are not comparable with those for previous years. 1995: Researcher concept started to include all university diplomates (ISCED level 5B included) performing R&D activities. 2013: Occupation categories break down for R&D personnel: researcher, technical and other support staff were defined based on the main functions performed by each individual within its R&D activities rather than on the academic qualification level. This modification was made in accordance with the criteria established on ISCO classification)
Qualification	Y - 1995	1995-2007 (Every two years); 2008 onwards (Yearly)
Age	Y - 1995	1995-2007 (Every two years); 2008 onwards (Yearly)		Extend the variables collection	2013	Data on age variable available only for tertiary education graduated personnel. Until 2013, these data were only available for researchers.
Citizenship	Y - 1995	1995-2007 (Every two years); 2008 onwards (Yearly)		Extend the variables collection	2013	Data on age variable available only for tertiary education graduated personnel. Until 2013, these data were only available for researchers.
Region	Y - 1995	1995-2007 (Every two years); 2008 onwards (Yearly)		Data compiled at regional level according to NUTS2002.	2003	NUTS 2002 was introduced for compiling data at regional level.
FORD	Y - 1995	1995-2007 (Every two years); 2008 onwards (Yearly)		Changes in classifications by field of science.	2007; 2016	2007: it was adopted the revised classification of fields of science and technology (FOS2007). The revision was only at 2 -digit level, which considered emerging and interdisciplinary fields. 2016 onwards: it was adopted the FORD classification (2015).
Type of institution	Y - 1997	1997-2007 (Every two years); 2008 onwards (Yearly)
Economic activity	Y - 1995	1995-2007 (Every two years); 2008 onwards (Yearly)
Employment size class	Y - 1995	1995-2007 (Every two years); 2008 onwards (Yearly)

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	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)
Function	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)
Qualification	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)
Age	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)
Citizenship	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)
Region	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)
FORD	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)
Type of institution	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)
Economic activity	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)
Employment size class	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)	Same for HC (see above)

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
Not applicable.

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
Yes	FTE and HC	Every year

13.1. Accuracy - overall

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

 

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

1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.

2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:

a) Coverage errors,

b) Measurement errors,

c) Non response errors and

d) Processing errors.

 

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

13.1.1. 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	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Total R&D personnel in FTE	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Researchers in FTE	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

1)  Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.

2)  The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.

13.1.2. Assessment of the accuracy with regard to the main indicators

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

1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys (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

Not applicable; Census survey.

13.2.1.2. Confidence interval for key variables by NACE

	Industry sector1)	Services sector2)	TOTAL
R&D expenditure	Not applicable	Not applicable	Not applicable
R&D personnel (FTE)	Not applicable	Not applicable	Not applicable

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

	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	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
R&D personnel (FTE)	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

13.3. Non-sampling error

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

13.3.1. Coverage error

Coverage errors (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.

 

a)       Description/assessment of coverage errors:

Not applicable.

b)       Measures taken to reduce their effect:

Not applicable.

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.

 

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)	Not applicable	Not applicable.	Not applicable	Not applicable	Not applicable
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Misclassification rate	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
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)	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Number of surveyed enterprises that have changed stratum (after inspection of their characteristics)	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable
Misclassification rate	Not applicable	Not applicable	Not applicable	Not applicable	Not applicable

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:

Not applicable.

b)      Measures taken to reduce their effect:

Not applicable.

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

	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	4000	4130	2441	649	11220
Total number of units in the sample	4591	4433	2556	675	12363 (including 108 with unknown size class without R&D activity)
Unit Non-response rate (un-weighted)	13%	7%	4%	4%	9%
Unit Non-response rate (weighted)	Does not apply. Census survey.	Does not apply. Census survey.	Does not apply. Census survey.	Does not apply. Census survey.	Does not apply. Census survey

13.3.3.1.2. Unit non-response rates by NACE

	Industry1)	Services2)	TOTAL
Number of units with a response in the realised sample	4187	7033	11220
Total number of units in the sample	4455	7908	12363
Unit Non-response rate (un-weighted)	6%	11%	9%
Unit Non-response rate (weighted)	Does not apply. Census survey.	Does not apply. Census survey.	Does not apply. Census survey.

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

Four official reminding letters are sent for non-respondents. Furthermore, telephone and email contacts were made in order to follow-up the non-respondent units.

13.3.3.1.4. Unit non-response survey

Conduction of a non-response survey	No.
Selection of the sample of non-respondents	Not applicable.
Data collection method employed	Not applicable.
Response rate of this type of survey	Not applicable.
The main reasons of non-response identified	Companies do not carry out R&D activities, lengthy questionnaire and difficulty of compiling the R&D information.

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%
Imputation (Y/N)	N	N	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

	Magnitude of error (%) due to non-response
Total intramural R&D expenditure	Not applicable
Total R&D personnel in FTE	Not applicable
Researchers in FTE	Not applicable

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	Responses through an online electronic questionnaire. The data is saved directly into SQL Databases.
Estimates of data entry errors	Not available.
Variables for which coding was performed	Most variables have an underlying code, excepting the open-ended questions such as R&D expenditure or the number of R&D personnel. Variables coded: NACE, dimension, region (NUTS), country, FORD, SEO, type of costs, source of funds, ISCED, type of R&D, products, sex, etc.
Estimates of coding errors	Not available.
Editing process and method	Automatic validation procedures: the online platform for data collection had validated units’ responses while filling in the questionnaire, minimizing the item non-response and ensuring logical coherence of the information provided. For the remaining responses, when necessary, DGEEC requests clarifications to the units. A macro level validation is also considered. During validation procedure, the data reported by the units were compared with those given in previous years; also, key variables were contrasted with data available in other information sources. Data was processed using platform functionalities (ASP.net 2.0 or 3.0 - Active Server Pages – technologies to interact with Microsoft SQL Server) and other standard software applications, such as Microsoft Office ACCESS.
Procedure used to correct errors	In case of data discrepancies or wrong information, the survey staff contacts the company for corrections. Reopening the questionnaire, in order to the company make their corrections, is an option, the corrections can be made DGEEC according to the company information. The Unit has always access to the final response: accessing the survey with their credentials, where they can see the information, and also download a PDF with the response.

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: 31 December 2023

b) Date of first release of national data: 20 December 2024

c) Lag (days): 355

14.1.2. Time lag - final result

a) End of reference period: 31 december 2023

b) Date of first release of national data: 20 December 2024

c) Lag (days): 355

14.2. Punctuality

Punctuality refers to the time lag between the release date of data and the target date on which they were scheduled for release as announced officially.

14.2.1. Punctuality - delivery and publication

Punctuality of time schedule of data release = (Actual date of the data release) - (Scheduled date of the data release).

14.2.1.1. Deadline and date of data transmission

	Transmission of provisional data	Transmission of final data
Legally defined deadline of data transmission (T+_ months)	10	18
Actual date of transmission of the data (T+x months)	10	15
Delay (days)	0	0
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

The R&D statistics are according to FM (2015) and harmonized with OECD’s criteria, therefore international comparability is ensured.

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
Special treatment for NACE 72 enterprises	FM2015, § 7.59.	No
Statistical unit	FM2015 Chapter 7 (mainly paragraphs 7.3 and 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No
Target population	FM2015 Chapter 7 (mainly sub-chapter 7.7 in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No
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).	No
Sector coverage	FM2015 Chapter 3 (mainly sub-chapter 3.5) in combination with Eurostat's EBS Methodological Manual on R&D Statistics).	No
NACE coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No
Enterprise size coverage and breakdown	Reg. 2020/1197 : Annex 1, Table 18	No
Reference period for the main data	Reg. 2020/1197 : Annex 1, Table 18	No
Reference period for all data	Reg. 2020/1197 : Annex 1, Table 18	No

15.1.4. Deviations from recommendations

The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (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 preparation activities	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No
Data collection method	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No
Cooperation with respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No
Follow-up of non-respondents	FM2015 Chapter 7 (mainly sub-chapter 7.7).	No
Data processing methods	FM2015 Chapter 6 (mainly sub-chapter 6.7).	No
Treatment of non-response	FM2015 Chapter 6 (mainly sub-chapter 6.7).	No
Data weighting	FM2015 Chapter 7 (mainly sub-chapter 7.7).	Not applicable
Variance estimation	FM2015 Chapter 6 (mainly sub-chapter 6.9).	Not applicable
Data compilation of final and preliminary data	Reg. 2020/1197 : Annex 1, Table 18	No
Survey type	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No
Sample design	FM2015 Chapter 6 (mainly sub-chapter 6.4).	Not applicable
Survey questionnaire	FM2015 Chapter 6 (mainly sub-chapter 6.4).	No

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

	Length of comparable time series	Break years1	Nature of the breaks
R&D personnel (HC)	1982-2012 2013 onwards	2013	2013: Reallocation to the BES of units previously classified in the PNP Sector (according to the criteria of the FM (2002)).
Function	1982-1992 1995-2012 2013 onwards	1995; 2013	1995: Following a general revision of the existing methodology undertaken in 1997 (and the data revised back to 1995): the concept of Researcher was enlarged to include all university graduates (including ISCED level 5B) developing R&D activities. 2013: Revision of the occupation categories of R&D personnel (researcher, technical and other supporting staff) took place in accordance with the criteria based on ISCO classification of occupations; therefore, occupations were defined according to the main functions performed by each individual within its R&D activities, rather than exclusively defined by the level of academic qualification.
Qualification	1995 onwards
R&D personnel (FTE)	Same as for HC (see above)	Same as for HC (see above)	Same as for HC (see above)
Function	Same as for HC (see above)	Same as for HC (see above)	Same as for HC (see above)
Qualification	Same as for HC (see above)	Same as for HC (see above)	Same as for HC (see above)
R&D expenditure	1982-2012 2013 onwards	2013	2013: Reallocation to the BES of units previously classified in the PNP Sector (according to the criteria of the FM (2002)).
Source of funds
Type of costs
Type of R&D
Other	1995-2007 2008 onwards	2008	2008: 1995: NACE revision to ISIC Rev. 4/NACE Rev. 2.

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

15.2.3. Collection of data in the even years

The survey is annual since 2008; data have been produced in the same way in odd and 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.  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

Not available.

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
R&D data collected by DGEEC is used by Statistics Portugal (INE) for the inward FATS data.
For CIS, in common years with R&D statistics, comparisons of R&D expenditures are carried out.

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.

	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)	2 833 961,9	41 245,0	28 362,8
Final data (delivered T+18)	2 843 724,3	41 578,8	28 340,8
Difference (of final data)	9 762,4	333,8	-22

Comments : No 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)	Internal R&D Personnel expenditure / Internal R&D Personnel FTE = 39975€ per year
Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2)	External R&D Personnel expenditure / External R&D Personnel FTE = 33648€ per year

(1)    Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).

(2)    Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).

The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.

16.1. Costs summary

	Costs for the statistical authority (in national currency)	Cost for the NSI in time use / person / day
Staff costs	Not separately available	Not separately available
Data collection costs	Not separately available	Not separately available
Other costs	Not separately available	Not separately available
Total costs	Not separately available	Not separately available

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)	11220 (of 12363 surveyed companies)	Number of completed and submitted answers
Average Time required to complete the questionnaire in hours (T)1	Total enterprises with submitted responses: 1h12m Enterprises that performed R&D activities: 2h14m Enterprises without R&D activities (intramural or extramural): 9m Enterprises with only extramural R&D activities: 14m.	In the section ‘Additional information’ there is a question about time spent on completing the questionnaire/survey.   Based on this report, we calculate the average time breakdown by sector and situation according to R&D activities.
Average hourly cost (in national currency) of a respondent (C)	Not available
Total cost	Not available

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

17.1. Data revision - policy

Not requested.

17.2. Data revision - practice

Not requested.

17.2.1. Data revision - average size

Not requested.

18.1. Source data

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

18.1.1. Data source – general information

National Scientific and Technological Potential Survey (IPCTN, Portuguese acronym) – Business Enterprise Sector is a census survey.

There is no combination of sample survey and census data or of dedicated R&D and other survey(s).

The variables the survey contributes to are R&D expenditures by: type of expenditure; sources of funds; SEO; FORD; NABS; Region (NUTS II); type of R&D.

The survey was is launched in March 2023 (T+3). Data collection period was extended until November of 2023 the same year. Along with data collection is also implemented the data validation process, which involve contacts with the enterprises (if needed).

In the last two months of the year the final validation, in SQL, is implemented to produce the final results.

18.1.2. Sample/census survey information

Sampling unit	Enterprise
Stratification variables (if any - for sample surveys only)	Not applicable
Stratification variable classes	Not applicable
Population size	12363
Planned sample size	Not applicable.
Sample selection mechanism (for sample surveys only)	Not applicable.
Survey frame	The definition of the frame population is based on the following sources of information: previous R&D surveys (Directorate General of Education and Science Statistics); applications for R&D projects financed by national entities (Science and Technology Foundation and other entities from Ministry of Science, Technology and Higher Education; Ministry of Economy); E.U. funded R&D programs and other international funded programs, like Eureka and Iberoeka programs; applications for tax R&D incentives – SIFIDE –(Agência Nacional de Inovação - entity linked to Portuguese Ministry of and Portuguese Ministry of Economy, that it is essentially dedicated to the promotion of innovation and technological development with a view to facilitating closer ties between research activities and the Portuguese business sector.) ; Community Innovation Survey –enterprises that declare to perform R&D activities (Directorate General of Education and Science Statistics and Statistics Portugal); National office register – enterprises whose main or secondary activity is NACE 72(research and development activities) and enterprises that declare to spend some investments for R&D - National accounts (Statistics Portugal); Enterprises founded in technological parks(websites).
Sample design	Not applicable
Sample size	Not applicable
Survey frame quality	Extent of double entries of enterprises or extent of entries which correspond to institutions outside the reference population are not significant.
Variables the survey contributes to	R&D expenditures by: type of expenditure; sources of funds; SEO; FORD; NABS; product field; Region (NUTS); type of R&D R&D personnel by: function; qualification; sex; FORD; Region (NUTS II); group age; nationality.

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

Source	Statistics Portugal (INE).
Description of collected data / statistics	Number of employees and turnover.
Reference period, in relation to the variables the administrative source contributes to	Reference year.
Variables the administrative source contributes to	HC; FTE; qualification; intramural expenditures, number of employed persons.

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

Realised sample size (per stratum)	11220
Mode of data collection	Electronic survey on an online platform: Electronic Survey
Incentives used for increasing response	Official-letters are sent, explaining the importance of the survey, its uses and to which organizations the data is provided. In addition, contacts by telephone and email (reminders, or other type of notification) in order to increase the response rate.
Follow-up of non-respondents	Four letters of formal notice are sent: the first one is when the statistical operation is launched and, at the most, 3 reminders can be sent for non-responders. Furthermore, telephone and email contacts were made in order to follow-up the non-respondent units.
Replacement of non-respondents (e.g. if proxy interviewing is employed)	Not applicable.
Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility)	The response rate was 90,8% in 2023.
Non-response analysis (if applicable -- also see section 18.5.4 Data compilation - Weighting and Estimation methods)	Not applicable

18.3.2. Questionnaire and other documents

Annex	Name of the file
R&D national questionnaire and explanatory notes in English:	ipctn23e_Form_EN
R&D national questionnaire and explanatory notes in the national language:	ipctn23e_Form_PT
Other relevant documentation of national methodology in English:	Not available
Other relevant documentation of national methodology in the national language:	IPCTN Methodological Document (DGEEC, INE); available: Methodological document Documentacao Metodologica

18.4. Data validation

A thorough validation procedure of data collected is carried out, consisting in finding and correcting internal inconsistencies among the sections and questions in survey and by comparing with data from previous years or with other data sources. All efforts are made to reduce errors or at least to identify and correct them. It's provided assistance to respondents during data collection. During the validation process, entities are contacted for further clarifications or for correcting errors. For all questionnaires, the following procedures are carried out: The electronic form includes validations that allow checking the consistency of the information (it may be necessary to contact the company, if incoherence is found). Inconsistencies of the "Error" type must be resolved before registration is accepted. Once the data registration is concluded, the information is analysed and processed, namely the analysis of the consistency of the year's data, and comparison of values with the previous year for quality control.

Some data are confronted with information from other sources considered relevant.

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

Size class	R&D Expenditure		R&D personnel (FTE)
	Unweighted	Weighted	Unweighted	Weighted
0-9 employees and self-employed persons (optional)	Not applicable	Not applicable	Not applicable	Not applicable
10-49 employees and self-employed persons	Not applicable	Not applicable	Not applicable	Not applicable
50-249 employees and self-employed persons	Not applicable	Not applicable	Not applicable	Not applicable
250-and more employees and self-employed persons	Not applicable	Not applicable	Not applicable	Not applicable
TOTAL	Not applicable	Not applicable	Not applicable	Not applicable

18.5.1.2. Imputation rate by NACE

NACE	R&D Expenditure		R&D personnel (FTE)
	Unweighted	Weighted	Unweighted	Weighted
Industry1)	Not applicable	Not applicable	Not applicable	Not applicable
Services2)	Not applicable	Not applicable	Not applicable	Not applicable
TOTAL	Not applicable	Not applicable	Not applicable	Not applicable

 

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 compilation method - Final data

Final data between surveyed years are estimated based in simple averages (for all odd years between 1982 and 1990 and for all even years from 1995 to 2007). The data for 1993 and 1994 were estimated by applying the annual average growth rate for the period 1992-1995 for GERD. By sector of performance, the value was distributed in terms of their weight (%) in GERD as it was observed in 1992. From the reference year 2008 the production of data is annual.

Data compilation method - Preliminary data

Preliminary data is transmitted within 10 months of the end of the calendar year of the reference period (T+10). Data compilation methods of preliminary data are equal to the compilation methods of final data: it is always real data extracted at moment. The difference between preliminary and final data relays on the ongoing process of validation and on the response rate. At this stage, the answers are still being analyzed and contacts within the person responsible for the answer are being sent out to ensure the transmission of lacking information or the correction of incoherent data. Some other enterprises are still answering the survey (until the end of the year/period reference).

18.5.3. Measurement issues

Method of derivation of regional data	Since 2007, the region in BES takes account of the municipality where the mostly R&D expenditure of the company is carried out, according to the levels given by the Nomenclature of Territorial Units for Statistics (NUTS).
Coefficients used for estimation of the R&D share of more general expenditure items	Not applicable
Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures	In accordance with FM (2015); the exclusion of VAT and provisions for depreciation are in the R&D form explanatory notes.

18.5.4. Weighting and estimation methods

Weight calculation method	Not applicable
Data source used for deriving population totals (universe description)	Not applicable
Variables used for weighting	Not applicable
Calibration method and the software used	Not applicable
Estimation	Not applicable

18.6. Adjustment

Not requested.

18.6.1. Seasonal adjustment

Not requested.

Brief historical notes on the IPCTN Survey (all sectors included):

o The IPCTN survey started to be administrated in 1982 (every two years; even years), however in 1994 was a gap year (missing data). For the years that the IPCTN survey was not administrated, data was based on simple averages between the surveyed years.

o For 1993 and 1994, the GERD was estimated by applying the annual average growth rate of the period 1992-1995. By sector of performance, the value was distributed in terms of their weight (%) in the GERD, as was observed in 1992.

o For the 1999 IPCTN survey, special attention was given to the public hospital and clinics: a special survey was launched based on R&D undertaken projects (project approach).

o From 2001 onwards: DGEEC started to undertake electronic Survey administration and also postal survey (when required by the units). In 1997 and1999: Floppy disk and postal survey. Until 1995: postal survey.

o From 2008 onwards, the IPCTN Survey started to be administrated yearly.

o In the 2009 IPCTN Survey, a new revision of the hospitals’ core model questionnaire was made, and, from 2011 onwards, hospitals and health facilities were surveyed along with other units (in the institutional sector).

o From 2008 to 2010, phases 1, 2 and 3 of clinical trials were surveyed in R&D core model questionnaire for public and university hospitals.

o In 2010, it was introduced a new section in the IPCTN Survey for the BE sector: Section VI – Biotechnology R&D activities.

IPCTN23 Form English Version
IPCTN23 Form Portuguese Version
R&D Survey’s Methodological Document (IPCTNe)

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. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU)No 2012/995 concerning the production and development of Community statistics on science and technology.

2 October 2025

See below.

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.

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.

Calendar year.

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

 

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

1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.

2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:

a) Coverage errors,

b) Measurement errors,

c) Non response errors and

d) Processing errors.

 

Model assumption errors should be treated under the heading of the respective error they are trying to reduce.

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

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

See below.

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

At Eurostat level the frequency of R&D data dissemination is yearly for provisional and final data.

At national level it is also 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.

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.