1.1. Contact organisation
Directorate General 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 Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and 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) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The distribution of principal economic activity and by product field is based on Statistical classification of economic activities in the European Community (NACE Rev. 2);
- The local units for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) is based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development based on Classification and distribution by Fields of Research and Development (FORD);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Private non-profit sector | PNP sector includes the units, established in Portugal, classified by the Portuguese System of National Accounts (SNA), in the reference year, as non-profit institutes serving households, excluding those contained within the HES. |
|---|---|
| Inclusion of units that primarily do not belong to PNP and the borderline cases | Not applicable |
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 Frascati Manual §2.61. | Clinic trials are mentioned as an example of R&D activities (according to FM 2015: 2.61), 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 unit. It is not requested detailed information whether they were 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 transaction 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”, were classified as follows:
These data are displayed as aggregated figures, as “Foreign Funds”. |
|---|---|
| Payments to rest of the world by sector - availability | Statistics on international R&D transaction regarding payments to the rest of the world are available. To what concerns the distribution by providers and recipients of extramural funds (for exchange R&D and transfer R&D), R&D data coverage regarding the rest of the world
|
3.3.5. Extramural R&D expenditures
According to the Frascati Manual (FM), expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) is not included in intramural R&D performance totals (FM, §4.12).
| Data collection on extramural R&D expenditure (Yes/No) | Yes |
|---|---|
| Method for separating extramural R&D expenditure from intramural R&D expenditure | To separate extramural R&D expenditure from intramural R&D expenditure, there are two distinct sections in the form: the section IV - only for ‘Intramural R&D Expenditure’; and the section V - only for ‘Extramural R&D Expenditure’. Both concepts are also explained in the annexes (see point 10.6.), explanatory notes, and 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 (GOV, HES, PNP institutions; enterprises, technological centres/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. The main difficulty encountered with the coverage of extramural funds was specifically related with the fact that, for the purchase of R&D, some respondents still struggled to distinguish intramural from extramural R&D expenditure, namely distinguishing acquiring R&D services within the scope of their own R&D activities and contracting R&D services from third parties. Having this in mind, the definition of both concepts is provided in the 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 units are 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) |
| Defence R&D - method for obtaining data on R&D expenditure | Defense R&D expenditure is estimated based on the socio-economic objective (SEO) "Defence". In Portuguese R&D form, the unit must report and distribute the percentage of their R&D activities according to their socio-economic 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 | Calendar year |
|---|---|
| Function | The R&D form is composed by two main modules: the first module collets information on intramural and extramural R&D expenditures (type of intramural R&D costs, sources of funds, type of R&D, SEO, etc.). The second module is an individual form which collects detailed information about each person with tertiary level of education (=> ISCED 5) performing R&D activities in the unit, namely the percentage of time spent in R&D. In this individual form, there is a question about the main R&D function performed in the reference year. For R&D personnel with no tertiary education (< ISCED 5), the function is reported in the first module, in a table (section III of the form), breakdown by sex, HC and FTE. |
| Qualification | In the individual form (explained above), the qualification is requested (mandatory). The respondent should report the highest degree obtain until the end of the reference year. |
| Age | In the individual form (explained above), the date of birth is requested (optional). Because, it is only requested for personnel with ISCED => level 5, and it is not a mandatory question, the breakdown by this variable may be underestimated. |
| Citizenship | In the individual form (explained above), country of nationality is requested (optional). Because, it is only requested for personnel with ISCED => level 5, and it is not a mandatory question, the breakdown by this variable may be underestimated. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar year |
|---|---|
| Function | Same procedure for HC (see above) |
| Qualification | Same procedure for HC (see above) |
| Age | Same procedure for HC (see above) |
| Citizenship | Same procedure for HC (see above) |
3.4.2.3. FTE calculation
For R&D personnel with tertiary level of education (=> ISCED 5), the FTE calculation is based on the information provided in the individual form on the percentage of time dedicated to R&D activities.
Although a person may perform R&D in more than one unit, due to the fact that there is an individual form, it is possible to identify if the same person is performing R&D in other units. If they exceeded 100%, the percentage is adjusted accordingly to the number of times that the person is identify and according to the percentage of time in R&D activities that was reported in each unit, assuring that no one is counted for more than 1.0 FTE.
For R&D personnel without tertiary education (< ISCED 5), the FTE is reported by the respondent unit as aggregated data (by gender and function) and, after the validation process (where the data is compared with previous years and/or questioned if needed), considered as such.
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the PNP Sector should consist of all R&D performing units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | Units classified by the Portuguese SNA as non-profit institutes serving households (Institutional Sector: 1500000), established in Portugal, having scientific and technological potential for performing R&D activities (only potential R&D performers, regular and occasional R&D performers), excluding those contained within the HE Sector. It is considered as potential R&D performers the units that have receive public R&D funding for these activities, either in the form of R&D subsidies, research grants, other forms of hiring researchers and other fundings; and/or private R&D funding in the form of payment of R&D contract services or other. The Directory of Institutions of the National Scientific and Technological System is updated considering the information reported in previous R&D surveys, and administrative data from other sources (see point 18.1.3). |
|
| Estimation of the target population size | Not applicable |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested. See concept 12.3.2. (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.
Reference year.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Legal acts / agreements:
Since the beginning of 2021, the collection of R&D statistics is based on the Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. The transmission of R&D data is mandatory for Member States and EEA countries.
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. |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D
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 must sign commitment document, which ensures the acknowledge of the confidentiality issues and data protection law.
7.2. Confidentiality - data treatment
R&D data is checked in order to assure confidentiality. Monitoring and control of the data is made before it is published, assuring no confidential information is released. If applicable, checking any cell with less than 3 population units, and properly modifying the table to avoid also secondary disclosure is applied.
8.1. Release calendar
The calendars of statistical operations and statistical publications are publicly available (in January) on the DGEEC's website: Calendar of statistical publications
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
National: National Release Calendar
8.3. Release policy - user access
R&D data, by sectors of performance, is available to all users on the DGEEC’s website.
When data/publications are released in DGEEC website, the units surveyed in the reference year, regardless 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).
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1) | Links | |
|---|---|---|
| Regular releases | 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 for the PNP. Link: National R&D Statistical Yearbook |
| Specific paper publication (e.g. sectoral provided to enterprises) | Y |
|
1) Y – Yes, N - No
10.3. Dissemination format - online database
A list of the R&D units is provided in DGEEC’s website, which contains the Unit’s name, general contacts, and the FORD of each unit that in the reference year performed R&D.
Link: List of R&D Units
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
As Eurostat receives no R&D micro-data from the reporting countries, users should contact directly the respective national statistical institute (NSI) for access to the micro-data.
10.4.1. Provisions affecting the access
| Access rights to the micro-data | 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 the 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 PNP sector contains the main indicators and variables collected in the IPCTN Survey, and it’s available online to all users in XLS/ODS format. National Statistical Yearbook |
| Data prepared for individual ad hoc requests | Y | Micro-data and Aggregate figures | Other statistical demands for specific data, that are not available on DGEEC’s website, can be provided under specific request. Micro-data confidentiality is guaranteed by law and anonymization rules are in place. |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
List of national descriptive text and/or references to methodological documents available to the R&D statistical process:
- IPCTN form – Institutional Sector (includes GOV, HE and PNP sectors); available: Survey Institutional Sectors
- IPCTN Methodological Document (DGEEC, INE); available: Estatistica Dgeec Document and at INE’s website: Documentacao Metodologica
- Validation Handbook (internal working document / DGEEC),
- Handbook for questionnaire non- response cases (internal working document / DGEEC).
IPCTN form, 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
- Annexe II - Intramural and extramural R&D expenses
- Annexe III – Human resources performing R&D activities,
- 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 – Fields of R&D Classification (FORD, 2015)
The IPCTN 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 (included uncompleted questionnaires), as well as for units that had submitted their questionnaire as non-R&D performer although being funded by the Foundation for Science and Technology (see point 18.1.3.).
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
See below.
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 detail 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 requests on further clarification on this subject have been received. |
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 European Code of Practice
DGEEC, as the institution responsible for producing 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.
Major improvements were obtained using 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 detailed 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, 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 entity, 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 analyzed 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 administrative sources considered relevant. DGEEC had also implemented a Quality Management System for improving the quality by measuring its services, processes and procedures. As part of this system, procedures are included in matters such as document management, resource management, and 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 guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
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 European Code of Practice
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 for the data validation, as well as the high response rates over the years.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 |
Description of users |
Users’ needs |
|---|---|---|
| 1- Institutions |
European Commission |
Eurostat R&D questionnaires. |
| 1- Institutions |
OECD |
OECD R&D Questionnaires; Biotech and Nanotech Questionnaire. |
| 1- Institutions |
RICYT – Red Iberoamericana de Indicadores de Ciencia y Tecnología |
Questionnaire “El Estado de la Ciencia. Principales Indicadores de Ciencia y Tecnología Iberoamericanos / Interamericanos. |
| 1- Institutions |
Portuguese Ministries |
Data on national R&D expenditure and R&D human resources for policy-making. |
| 1- Institutions |
Statistical Portugal (INE) |
Structural R&D indicators, statistical yearbooks and other statistical outputs. |
| 3- Media |
National Media |
Main R&D data displayed on DGEE’s website. |
| 4- Researchers and students |
Researchers and students |
Ad hoc requests |
| 6-Other |
Other institutions |
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 | A 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
See below.
12.3.1. Data completeness - rate
See below.
12.3.2. Data availability
Share of PNP R&D expenditure in GERD (Gross Domestic Expenditure on R&D):
12.3.2.1. Incorporation of PNP sector in another sector
| Incorporation of PNP in another sector | Not applicable |
|---|---|
| Reasons for not producing separate R&D statistics for the PNP sector | Not applicable |
| Share of PNP expenditure in the total expenditure of the other sector | Not applicable |
| Share of PNP R&D Personnel in the respective figure of the other sector | Not applicable |
12.3.2.2. Non-collection of R&D data for the PNP sector
| Reasons for not compiling R&D statistics for the PNP sector | Not applicable; PNP sector is covered in the Portuguese R&D statistics. |
|---|---|
| PNP R&D expenditure/ GERD*100) | Less than 5% |
| Share of PNP R&D Personnel in the respective figure of the total national economy | Not available |
12.3.2.3. Data availability on more detail level
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| No details given. | No details given. | No details given. | No details given. | No details given. | No details given. |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
12.3.2.4. 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.
13.2. 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
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
Coefficient of variation for Total R&D expenditure:
Coefficient of variation for Total 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.
a) Extent of non-sampling errors:
b) Measures taken to reduce the extent of non-sampling errors:
c) Methods used in order to correct/adjust for such errors:
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Not requested.
13.3.3. Non response error
Not requested.
13.3.3.1. Unit non-response - rate
Not requested.
13.3.3.2. Item non-response - rate
Not requested.
13.3.4. Processing error
Not requested.
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: 23 July 2024
c) Lag (days): 205
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 | Not applicable | Not applicable |
Comparability aims at measuring the impact of differences in applied statistical concepts and definitions on the comparison of statistics between geographical areas, non-geographical domains or overtime. It is the extent to which differences between statistics are attributed to differences between the true values of statistical characteristics.
The factors that may cause two statistical figures to lose comparability are attributes of the surveys that produce them. These attributes may be grouped into two major categories: (a) concepts of the survey and (b) measurement / estimation methodology.
The two following sections present lists of key attributes. Information on some of the attributes will have already been reported in previous sections of this report but they are repeated here for completeness of the lists.
The coherence of statistics is their adequacy to be reliably combined in different ways and for various uses. It is, however, generally easier to show cases of incoherence than to prove coherence.
When originating from a single source, statistics are coherent in that elementary concepts can be combined reliably in more complex ways. When originating from different sources, and in particular from statistical surveys of different frequencies, statistics are coherent insofar as they are based on common definitions, classifications and methodological standards. The messages that statistics convey to users will then clearly relate to each other or at least will not contradict each other. The coherence between statistics is orientated towards the comparison of different statistics, which are generally produced in different ways and for different primary uses.
The definition of coherence: The extent to which the statistical characteristics confirm with those in other statistics such that the statistics can be expected to be used together in conjunction with, or as an alternative to.
15.1. Comparability - geographical
See below.
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. The R&D survey methodology is applied in same procedures in all national territory (no differences between mainland and islands)
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) paragraphs and the EBS Methodological Manual on R&D Statistics 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 | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No | |
| Approach to obtaining FTE data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Intramural R&D expenditure | FM2015,Chapter 4 (mainly sub-chapter 4.2). | No | |
| Statistical unit | FM2015, § 10.40-10.42 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015, § 10.40-10.42 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015, § 10.2-10.8 ((in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | 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, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No | |
| Survey questionnaire / data collection form | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No | |
| Cooperation with respondents | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No | |
| Data processing methods | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No | |
| Treatment of non-response | FM2015 Chapter 10 (mainly sub-chapter 10.6). | No | |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | No |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1) | Nature of the breaks | |
|---|---|---|---|
| R&D personnel (HC) | 1982-1992 1995-2012 2013 onwards |
1995 2013 |
1995: Units previously classified in the PNP Sector were reallocated to the HE and BE sectors. 2013: Reallocation to the HE sector 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 the revision of the existing methodology undertaken in 1997 (data revised back to 1995), the concept of Researcher was enlarged to include all university graduates performing R&D activities (including ISCED level 5B). 2013: Revision of the occupation categories of R&D personnel (researcher, technitians 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 | 1982 onwards |
|
|
| R&D personnel (FTE) | 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) |
| Qualification | Same for HC (see above) |
Same for HC (see above) |
Same for HC (see above) |
| R&D expenditure | 1982-1992 1995-2012 2013 onwards |
1995 2013 |
1995: Units previously classified in the PNP Sector were reallocated to the HE and BE sectors. 2013: Reallocation to the HES of units previously classified in the PNP Sector (according to the criteria of the FM (2002)). |
| Source of funds | 1982-2013 2014 onwards |
2013 |
2013: Reallocation to the HES of units previously classified in the PNP Sector (according to the criteria of the FM (2002)). |
| Type of costs | 1982-2015 2016 onwards |
2016 |
Labour costs and Other current costs were identified as breaks in time series due to the redeployment to Other current costs of the external R&D personnel costs. Nevertheless, these breaks in time series don’t affect the annual comparability of the total national R&D expenditure. |
| 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
The survey is annual since 2008; data have been produced in the same way in odd and even years.
15.3. Coherence - cross domain
See below.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not applicable
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total PNP R&D expenditure (in 1000 of national currency) | Total PNP R&D personnel (in FTEs) | Total number of PNP researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 118 734,9 |
1 710,9 |
1 167,8 |
| Final data (delivered T+18) | 115 832,5 |
1 748,4 |
1 174,9 |
| Difference (of final data) | - 2 902,4 |
37,4 |
7,1 |
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 = 36 123 € per year |
Grant holders paid by the unit or host institution are included in the internal R&D personnel, according to the European Business Statistics Methodological Manual for R&D statistics (2023 edition) (see 2.4.5. topic 5) |
| 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 = 19 994 € 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 |
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs:
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 149 (of 151 surveyed units) |
Number of completed and submitted answers |
| Average Time required to complete the questionnaire in hours (T)1) |
|
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) – Institutional Sectors (includes GOV, HE and PNP sectors) 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 is launched in March (T+15). Data collection period is extended until November of the same year. Along with data collection is also implemented the data validation processes, which involve contacts with the units (if needed).
In the last two months of the year the final validation, in SQL, is implemented to produce final results.
18.1.2. Sample/census survey information
| Sampling unit | Not applicable |
|---|---|
| Stratification variables (if any - for sample surveys only) | Not applicable |
| Stratification variable classes | Not applicable |
| Population size | Not applicable |
| Planned sample size | Not applicable |
| Sample selection mechanism (for sample surveys only) | Not applicable |
| Survey frame | Not applicable |
| Sample design | Not applicable |
| Sample size | Not applicable |
| Survey frame quality | Not applicable |
| Variables the survey contributes to | Not applicable |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Directorate General of Education and Science Statistics (DGEEC):
|
|---|---|
| Description of collected data / statistics | Observatory of Scientific and Teaching Employment (OECD; link: OECD Observatory of Scientific and Teaching Employment) includes:
RENATES: data on doctoral theses in progress, doctorates completed in Portugal, equivalences awarded by Portuguese universities, records of doctoral degrees communicated by the Directorate-General for Higher Education (DGES) and master's degrees completed. FCT: data on R&D units and institutions receiving public R&D funds (Pluriannual R&D funding program); data on the individuals that are directly financed by FCT with R&D grants;
|
| 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 |
18.2. Frequency of data collection
See 12.3.2.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | The survey is addressed to the director of the unit or to the respondent of the last R&D survey (if applicable). |
|---|---|
| Description of collected information | Information collected with references to the R&D variables requested by EU Regulation 2020/1197, namely:
|
| Data collection method | The IPCTN Survey is part of the national official statistics and the response is mandatory. It is an online survey, where each unit has their own credentials for the platform. Some questions are pre-filled, namely general information about the unit (e.g., name of the Unit, address, etc.), as well as information that was reported in other administrative sources and can be use in the R&D survey, in the individual form (FCT and OECD (see point 18.1.3)). The units can update and/or correct the information that was pre-filled, if necessary. For the launch of the survey it is sent, by email, a formal notice to each unit, explaining the importance of the data, the deadline for the response, the link to the FAQs, the direct contact (email and phone) of the staff that is responsible to monitor the unit's response, if they have any doubts filling the questionnaire. Each staff is responsible for a group of units, that includes tracking the responses, to clarify unit’s questions, and validate their responses. |
| Time-use surveys for the calculation of R&D coefficients | Not applicable |
| Realised sample size (per stratum) | Not applicable |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Electronic survey on an online platform: Electronic Survey on Online Platform |
| 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) | 98,7 % |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Not applicable |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | ipctn23i_Formulario_ENG |
| R&D national questionnaire and explanatory notes in the national language: | ipctn23i_Formulario |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | IPCTN23i_DMET_vfinal (IPCTN Survey’s Methodological Document) |
18.4. Data validation
A thorough validation procedure of data collected is carried out (see also point 11.1), that is implemented along with the data collection, and increased when then response rate is high (in the first months the main efforts are conducted to obtain the maximum number of responses, for the provisional results).
In general, the validation process can be described in three main levels:
- 1st: Validation of the online platform, when the units are filling the questionnaire. The survey’s electronic template form cross-checks the information consistency: "Error" type inconsistencies are checked out otherwise the platform don’t allow the questionnaire submission.
- 2nd: Considering the guidelines of the Validation Handbook, for the data collection, a validation procedure of the information gathered is carried out, regarding:
Coherence - analysis of data inconsistencies among the sections and the questions of the units’ questionnaire
Comparison - analysis of the data, compared with responses from with previous years (if available).
Cross-check - analysis of the data, compared with other sources: administrative databases (DGEEC’s or other entities)
- 3rd: parallel with the previous phases, DGEEC calculates estimations on Current and Capital expenditures on R&D activities in the reference year. This information is provided to the Units during the validation process, in order to compare it to what was reported in the survey.
Calculation on personnel costs, is made by considering the FTE and the career of R&D personnel that was reported by the units. Considering the information provided in the individual form (=> ISCED 5), as well in the section of the form for the personnel without tertiary education (< ISCED 5), and having the National Pay Scale System for Public Administration as the starting point, it is estimated an average salary for each person reported by the unit. The aggregate amount of these salaries is compared with the personnel expenditure reported by the Unit, compared also with the response of the previous year (when available), and analysed by the IPCTN survey staff.
Calculation on average salaries, considering only the report expenditure by the units are made, in order to validate the amount that was reported. if the average salary is too low or too high, units are contacted to provide clarifications.
In GOV sector, calculations of Capital cost are made only for the Hospitals, by considering the FTE of the R&D personnel, the HC of the human resources and the expenditures Hospitals.
During all validation process, if inconsistencies, or major variations, are detected, units are contacted for further clarification and, if needed, for correcting errors.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Imputation is the method of creating plausible (but artificial) substitute values for all those missing.
Definition:
Imputation rate (for the variable x) % = (Number of imputed records for the variable x) * 100/ (Total number of possible records for x)
18.5.2. Data compilation methods
| Data compilation method - Final data | Not applicable |
|---|---|
| Data compilation method - Preliminary data | Not applicable |
18.5.3. Measurement issues
| Method of derivation of regional data | According to the levels given by the Nomenclature of Territorial Units for Statistics (NUTS) and the location of the R&D Unit (obtained by the District provided in the form). |
|---|---|
| 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 is in the R&D form explanatory notes. |
18.5.4. Weighting and estimation methods
| Description of weighting method | Not applicable |
|---|---|
| Description of the estimation method | Not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Brief historical notes on the IPCTN Survey (all sectors included):
- 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 were based in simple averages between the surveyed years.
- For 1993 and 1994, the GERD were 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 it was observed in 1992.
- 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).
- From 2001 onwards: DGEEC has started to undertake electronic Survey administration and also postal survey (when required by the units). In 1997 and 1999: Floppy disk and postal survey. Until 1995: postal survey.
- From 2008 onwards, the IPCTN Survey started to be administrated yearly.
- In the 2009 IPCTN Survey, it was made a new revision of the hospitals’ core model questionnaire, and, from 2011 onwards, hospitals and health facilities were surveyed along with other units (in the institutional sector).
- 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.
- From years 2009 to 2011, DGEEC had reviewed data according to the following: i) removed all master students doing thesis from R&D personnel data that had been added to specific institutions (universities, colleges and polytechnic institutes) in the years 2009, 2010 and 2011 (that were estimated by DGEEC); ii) added postgraduate students at the PhD level with an individual R&D grant, financed by FCT, that weren’t included in the R&D personnel data of IPCTN surveys from 2009 to 2011.
- In 2010, it was introduced a new section in the IPCTN Survey for the BE sector: Section VI – Biotechnology R&D activities.
- In 2012, it was introduced the Section VI – Biotechnology R&D activities in the IPCTN Survey for the PNP, GOV and HE sectors. These data collection was interrupted from 2015 to 2017, and it was put back on in the 2018 survey.
Statistics on Private non-profit R&D (PNPRD) measure research and experimental development (R&D) performed in the private non-profit 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 private non-profit sector should consist of all R&D performing units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and 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) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
2 October 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993.
See below.
Not requested. R&D statistics cover national and regional data.
Reference year.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
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.
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
Timeliness and punctuality refer to time and dates, but in a different manner: the timeliness of statistics reflects the length of time between their availability and the event or phenomenon they describe. Punctuality refers to the time lag between the release date of the data and the target date on which they should have been delivered, with reference to dates announced in the official release calendar.
See below.
See below.
