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 of Science and Technology and Information Society Statistics
(DSECTSI - Direção de Serviços de Estatística da Ciência e Tecnologia e da Sociedade de Informação)
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Av. 24 de julho, 134
1399-054 Lisboa, PORTUGAL
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
31 October 2025
2.2. Metadata last posted
31 October 2025
2.3. Metadata last update
8 January 2026
3.1. Data description
Statistics on Government Budget Allocations for R&D (GBARD) measure government support to research and development (R&D) activities, and thereby provide information about the priority governments give to different public R&D funding activities. This type of funder-based approach for reporting R&D involves identifying all the budget items that may support R&D activities and measuring or estimating their R&D content.
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 (FM 2015, Chapter 12), 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).
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 (Commission Implementing Regulation (EU) 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 (europa.eu)).
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
Distribution by socioeconomic objectives (SEO) is based on the Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) at one digit level.
3.2.1. National classification
| National nomenclature of SEO used | NABS - Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (Eurostat, 2007), is used for national nomenclature of SEO. |
|---|---|
| Correspondence table with NABS | Not applicable |
3.2.2. NABS classification
| Deviations from NABS | No deviations |
|---|---|
| Problems in identifying / separating NABS chapters and sub chapters | Coefficients (from R&D survey) are used to report this data. |
| Ability to distribute Non-oriented research and General University Funds (GUF) by fields of R&D | “Non-oriented research” and “General University Funds (GUF)” are not available by fields of research and development (FORD). |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | According to Frascati Manual (FM, 2015). |
|---|---|
| Coverage of R&D or S&T in general | R&D is measured; S&T is also collected, but for internal use. |
| Fields of R&D (FORD) covered | According to FM (2015), all FORD classifications are covered. |
| Socioeconomic objective (SEO by NABS) | According to FM (2015) |
3.3.2. Definition and coverage of government
GBARD statistics are assumed to report detailed data on all the government's budget items that may support R&D activities and to measure or estimate their R&D content. For the purposes of GBARD, the Government sector comprises (a) the central (federal) government, (b) regional (state) government and (c) local (municipal) government subsectors (FM2015, Chapter 12).
| Levels of government | Definition | Included / Not included | Comments |
|---|---|---|---|
| Central (federal) government | Entities from Ministry of Science Technology and Higher Education (MCTES), Central Services of the Direct Administration and Indirect Administration Bodies, which coordinate and finance the National S&T and R&D System; State Laboratories, Foundation for Science and Technology (FCT) and Higher Education Institutions (Public Universities and Polytechnic Institutes), Other R&D implementing and/or funding bodies under the supervision of other Ministries that perform/finance R&D and S&T activities. |
||
| Regional (state) government | Entities from autonomous regions of Azores and Madeira, that finance R&D and S&T activities. | ||
| Local (municipal) government | Not included |
3.4. Statistical concepts and definitions
Not requested.
3.5. Statistical unit
Relevant government units within the scope of GBARD - ministries and other government units funding R&D.
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.
| Definition of the national target population | Ministries and other government units funding R&D. |
|---|---|
| Estimation of the target population size | Not applicable |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested. See point 5.
3.9. Base period
Not requested. 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.
Not requested.
a) Calendar year: Reference year (January to December)
b) Fiscal year: Not applicable
Start month:
End month:
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
Since the beginning of 2021, GBARD statistics are based on 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. GBARD statistics were based until the end of 2020 on the Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
6.1.2. National legislation
Although, there is not GBARD specific statistical legislation, the Protocol of delegation of statistical functions signed by DGEEC and Statistics Portugal is the legal instrument that gives to DGEEC the status of official statistical authority with obligations to comply with legal and regulatory provisions of the Portuguese National Statistical System (Law Nr. 22/2008 of May 13).
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
Confidentiality, being one of the process quality components, concerns the privacy of data providers (households, enterprises, administrations and other respondents), the confidentiality of the information they provide and the extent of its use for statistical purposes.
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.
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 acknowledgement of the confidentiality issues and data protection law.
7.2. Confidentiality - data treatment
Not applicable
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
link: Statistical Calendar
8.3. Release policy - user access
GBARD data is available to all users on the DGEEC’s website.
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).
Yearly
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Content, format, 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 | Content, format, links, ... |
|---|---|---|
| General publication/article (paper, online) |
N | |
| Specific paper publication (paper, online) |
Y | Publication with data on:
Metadata is available in this publication, regarding the procedures and calculations methods. Name of the publication: Government Budget Allocations for C&T and R&D | Dotações Orçamentais para C&T e I&D |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Along with the PDF publication, it is provided an Excel with all collected data, by institution (micro-data), for the reference year; general data (GBARD in million euros and in % of national budget).
link: Dissemination of R&D Data
10.3.1. Data tables - consultations
Not requested.
10.4. Dissemination format - microdata access
See below.
10.4.1. Provisions affecting the access
| Access rights to the information | Full access |
|---|---|
| Access cost policy | Free access |
| Micro-data anonymisation rules | Not applicable; According to national statistical law, for public institutions there is no need of anonymisation. |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1 | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | Aggregate data/Micro-data | Publication with long series and detailed data from 2002 onwards |
| Data prepared for individual ad hoc requests | N | Full data is available at DGEEC’s website; ad hoc requests are no applicable to GBARD statistics | |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
In 2018, DGEEC introduced a methodological revision of the calculation of Budgetary appropriations for R&D, which essentially consisted of the breakdown of appropriations from national State Budget funds. The new methodology was applied retrospectively to the series of all national GBARD data from 2002 onwards, and previous publications have been reviewed and republished on the DGEEC website. By applying the new methodology to data from the year 2002, and not just in the most recent year, it pretends to avoid a time series break, while still allowing for a consistent analysis of the evolution of national long-term S&T and R&D allocations from that year on, the longest period possible. The GBARD methodology is described in national publications.
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, etc.) | Online publication of GBARD data contains detailed budget data (micro-data) for each institution from 2002 onwards. The publication includes metadata (introduction and notes references), methods and calculation procedures are also available. |
|---|---|
| Request on further clarification | So far, no clarifications have been requested. |
| Measure to increase clarity | Since the 2020 reference year, we have changed the publication in order to make it more understandable, more transparent and more appealing. By focusing the publication on the data for the reference year, instead of presenting micro-data for all series (since 2002), makes it clearer for the user. In addition, for contextualization, we present two general figures: one with the evolution of the amounts over the last 20 years, and other with the proportion of these mounts in the total public budget, by year. All the formats (previous and presents) of these publications are the DGEEC’s website. |
| Impression of users on the clarity of the accompanying information to the data | We believe that the improvements made to the way this information is made available have been well received by users. |
11.1. Quality assurance
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 GBARD statistics, DGEEC also follows Eurostat's and OECD methodological recommendations.
Several data validation processes take place before the indicators are released. This 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.
A thorough validation procedure of data collected is carried out, consisting in finding and correcting inconsistencies 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.
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 programmes that are publicly available on its website.
11.2. Quality management - assessment
The methodology used by Portugal to produce GBARD data is based on official sources. The main source is the annual Government General Budget (information provided by the national Directorate-General for the Budget (DGO) and Institute of Education Financial Management (IGEFE)).
The GBARD data is also complemented by the information requested by DGEEC to other national entities responsible for financing S&T/R&D.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
|---|---|---|
| 1 - European level | EU Commission; Eurostat | Micro-data |
| 1 - National level | Portuguese Ministries | |
| 1 - International organisations | OECD; UN | |
| 3 - Media | National media | GBARD data displayed on DGEEC’s website. |
| 4 - Researchers and students | Researchers and students | |
| 6 - Other | Other institutions |
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 | On a regular basis, user satisfaction survey is sent to all individual ad hoc requests. Also, for the R&D survey (which is a source for GBARD) the units can give feedback in the additional field that is provided in the survey. |
|---|---|
| User satisfaction survey specific for GBARD statistics | There is no satisfaction survey for GBARD statistics users. |
| Short description of the feedback received | Not applicable |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
Not applicable
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.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells | |
|---|---|---|---|---|---|---|
| Provisional budget statistics1 | X | |||||
| Obligatory final budget statistics1 | X | |||||
| Optional final budget statistics2 | X | Portugal started to report NABS sub chapters level in GBARD 2023. |
1) Criteria: Obligatory data (provisional budget and final budget). Only 'Very Good' = 100% and 'Very Poor' <100% apply.
2) Criteria: Optional data (final budget). 'Very Good' = 100%; 'Good' = >75%;'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.
12.3.3. Data availability
See below.
12.3.3.1. Data availability – Provisional data
| Availability1 | Frequency of data collection | Gap years – years with missing data | Time of compilation (T+x)2 | Comments | |
|---|---|---|---|---|---|
| Total GBARD | Y, 2002 onwards | Yearly | T+6 | ||
| NABS Chapter level | Y, 2002 onwards | Yearly | T+12 | ||
| NABS Sub-chapter level | N | ||||
| Special categories - Biotech | N | ||||
| Special categories - Nanotech | N | ||||
| Special categories - Security | N |
1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.
2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled
12.3.3.2. Data availability – Final data
| Availability1 | Frequency of data collection | Gap years – years with missing data | Time of compilation (T+x)2 | Comments | |
|---|---|---|---|---|---|
| Total GBARD | Y, 2002 onwards | Yearly | T+6 | ||
| NABS Chapter level | Y, 2002 onwards | Yearly | T+12 | The difference between the provisional and final data is due to the information that is used for the distribution by NABS. In the provisional data the information from the R&D survey (distribution by SEO) of year T-1 is used, while in the final data is used the information from the R&D survey of year T-0. | |
| NABS Sub-chapter level | Y, 2023 onwards | Yearly | T+12 | Data provided using coefficients from R&D survey | |
| Special categories - Biotech | N | ||||
| Special categories - Nanotech | N | ||||
| Special categories - Security | N |
1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year.
2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled
12.3.3.3. Data availability – Other special categories
| Special categories | Stage1 | Availability1 | Frequency of data colletion | Gap years – years with missing data | Time of compilation (T+x)3 | Comments |
|---|---|---|---|---|---|---|
| Not applicable. |
1) Stage: P - provisional, F - final.
2) Availability of the data: No, data are not available, Y: Yes, data are available + start year.
3) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | |||
|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non response errors | |||
| - | - | - | - | - | - | - |
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 (5) In the event that errors of a particular type do not exist, is used the sign ‘-‘.
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 GBARD.
13.1.2. Assessment of the accuracy
| Indicators | 5 (Very Good)1 | 4 (Good)2 | 3 (Satisfactory)3 | 2 (Poor)4 | 1 (Very poor)5 |
|---|---|---|---|---|---|
| GBARD | x | ||||
| National public funding to transnationally coordinated R & D | x |
1) High level of coverage (At least all national or federal ministries and the ministries and agencies responsible for R&D funding at state or regional level). High rate of response (>90%) in data collection. All figures broken down by NABS.
2) If at least one out of the three criteria described above would not be fully met.
3) In the event that the rate of response would be lower than 80% even by meeting the two remaining criteria.
4) In the event that the average rate of response would be lower than 70% and at least one of the two remaining criteria would not be met.
5) If all the three criteria described above are not met.
13.2. Sampling error
Not requested.
13.2.1. Sampling error - indicators
Not requested.
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
- Description/assessment of coverage errors: Not applicable.
- Measures taken to reduce their effect: Not applicable.
13.3.1.1. Over-coverage - rate
Not applicable
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones. The survey questionnaire used for data collection may have led to the recording of wrong values.
- Description/assessment of measurement errors: Not applicable.
- Measures taken to reduce their effect: Not applicable.
13.3.3. Non response error
Non response errors: occur 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.
- Problems in obtaining data from targeted information providers: Not applicable.
- Measures taken to reduce their effect: Not applicable.
- Effect of non-response errors on the produced statistics: Not applicable.
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
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.
- Data processing and editing processes: Not applicable.
- Description of errors: Not applicable.
- Measures taken to reduce their effect: Not applicable.
13.3.5. Model assumption error
Model assumption errors occur when the assumptions made for the estimation of parameters, models, the testing of statistical hypotheses, etc., are violated. As a result, the quality of the resulting statistics is affected (e.g. degrees of confidence might be inflated).
Description/assessment: Not applicable.
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
Date of first release of national data: T-6 (the 2023 results were published at national level, in June of the reference year).
14.1.2. Time lag - final result
Date of first release of national data: T-6
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) | 6 | 12 |
| Actual date of transmission of the data (T+x months) | 6 | 12 |
| Delay (days) | 0 | 0 |
| Reasoning for delay | no delay | no delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Regulation No 2020/1197, Frascati manual and the EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts / issues.
| Concept / Issue | Reference to recommendations | Deviation from recommendations | National definition / Treatment / Deviations from recommendations |
|---|---|---|---|
| Research and development | FM2015 Chapter 2 (mainly paragraphs 2.3 and 2.4). | No deviation | |
| Coverage of levels of government | FM2015, §12.5 to 12.9 | No deviation | |
| Socioeconomic objectives coverage and breakdown | Reg. 2020/1197: Annex 1, Table 20 | No deviation | |
| Reference period | Reg. 2020/1197: Annex 1, Table 20 | No deviation |
15.1.3. Deviations from recommendations
GBARD encompass all spending allocations met from sources of government revenue foreseen within the budget, such as taxation. Spending allocations by extra-budgetary government entities are within the scope only to the extent that their funds are allocated through the budgetary process (FM2015 §12.9). The following table lists a number of key methodological issues, which may affect the international comparability of national GBARD statistics.
| Methodological issues | Reference to recommendations | Deviation from recommendations | National definition / Treatment / Deviations from recommendations |
|---|---|---|---|
| Definition of GBARD | FM § 12.9 | No deviation | |
| Stages of data collection | FM2015 §12.41 | No deviation | |
| Gross / net approach, net principle | FM2015 §12.20 and 12.21 | No deviation | |
| EU/other funds | Eurostat's EBS Methodological Manual on R&D Statistics | No deviation | |
| Types of expenditure | FM2015 §12.15 to 12.18 | No deviation | |
| Current and capital expenditure | FM §12.15 | No deviation | |
| Extra budgetary funds | FM §12.8, 12.20, 12.38 | No deviation | |
| Loans | FM §12.31, 12.32, 12.34 | No deviation | |
| Indirect funding, tax rebates, etc. | FM §12.31 - 12.38 | No deviation | |
| Treatment of multi-annual projects | FM2015 §12.44 | No deviation | |
| Treatment of GBARD going to R&D abroad | FM2015 §12.19 | No deviation | |
| Criterion for distribution by socioeconomic objective | FM2015 §12.50 to 12.71 | No deviation | |
| Method of identification of primary objective | Eurostat's EBS Methodological Manual on R&D Statistics, topic 2, statement B.6 | No deviation |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
|---|---|---|---|
| Provisional data | 2002 onwards | ||
| Final data | 2002 onwards |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.3. Coherence - cross domain
There might be some comparability problems because in Government-financed GERD it is possible that the R&D units include funds from Structural Programs financed by EU (Community funds).
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
Not requested.
15.4. Coherence - internal
This part compares GBARD statistics from the provisional and final budget for the reference year.
15.4.1. Comparison between provisional and final data according to NABS 2007
| R&D allocations in the provisional budget delivered at T+6 | R&D allocations in the final budget delivered at T+12 | Difference (of final data) | |
|---|---|---|---|
| Exploration and exploitation of the Earth | 8781,253 | 8034,352 | -746,9 |
| Environment | 33502,42 | 34355,65 | 853,2301 |
| Exploration and exploitation of space | 6502,348 | 6036,354 | -465,994 |
| Transport, telecommunication and other infrastructures | 22775,53 | 24823,39 | 2047,859 |
| Energy | 19680,52 | 20577,77 | 897,253 |
| Industrial production and technology | 42398,42 | 44458,17 | 2059,755 |
| Health | 73686,83 | 75528,03 | 1841,198 |
| Agriculture | 19423,84 | 19488,96 | 65,12114 |
| Education | 22639,95 | 21842,64 | -797,306 |
| Culture, recreation, religion and mass media | 12969,01 | 12719,53 | -249,483 |
| Political and social systems, structures and processes | 12264,55 | 12409,75 | 145,1964 |
| General advancement of knowledge: R&D financed from General University Funds (GUF) | 469844 | 469844 | 0 |
| General advancement of knowledge: R&D financed from other sources than GUF | 55752,78 | 50015,95 | -5736,83 |
| Defence | 2065,719 | 2152,624 | 86,9048 |
| TOTAL GBARD | 802287,2 | 802287,2 | 0 |
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) | % sub-contracted1) | |
|---|---|---|
| Staff costs | Not separately available | No work sub-contracted to third parties. |
| Data collection costs | Not separately available | No work sub-contracted to third parties. |
| Other costs | Not separately available | No work sub-contracted to third parties. |
| Total costs | Not separately available | No work sub-contracted to third parties. |
| Comments on costs | ||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | Not applicable | |
| Average Time required to complete the questionnaire in hours (T)1 | Not available | |
| 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 applicable
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
- Provisional data: State Budget (estimated initial budget) + R&D survey (T-1) + Administrative data of public institutions budget (higher education and other entities from Ministry of Science) + official-letter requesting the data to some units.
- Final data: Provisional data + R&D survey (T-0).
- General University Funds (GUF): Administrative data of higher education institutions public budget (provided by Instituto de Gestão Financeira da Educação, I.P. (IGEFE, acronym in Portuguese)).
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Provisional data | Final data | Comments | |
|---|---|---|---|
| Data collection method | Text analysis (Budget and other government documents). Direct contact with ministries or agencies | Same as provisional data | |
| Stage of data collection | Initial budget appropriations | The difference between the provisional and final data is due to the information that is used for the distribution by NABS. In the provisional data the information from the R&D survey (distribution by SEO) of year T-1 is used, while in the final data is used the information from the R&D survey of year T-0. | |
| Reporting units | Mainly funding/administering Institutions; but also performing institutions (in the case of the State Laboratories). | Same as provisional data | |
| Basic variable | Government R&D appropriations. | Same as provisional data | |
| Time of data collection (T+x)1) | T+6 | Same as provisional data | |
| Problems in the translation of budget items | N/A | ||
1) Time of data collection (T+x): T is assumed to represent the end of reference period. x expresses the number of months after (positive) or before (negative) T when data is collected.
18.3.2. General University Funds (GUF)
The General University Funds (GUF) are calculated through the amounts enrolled in the Portuguese state budget to institutions of higher education. Instituto de Gestão Financeira da Educação, I.P. (IGEFE, acronym in Portuguese) is the entity from the Ministry of Science, Technology and Higher Education, responsible for this information, which is provided to DGEEC.
18.3.3. Distribution by socioeconomic objectives (SEO)
| Level of distribution of budgetary items – institution or programme/project | National R&D Survey is the source used to the breakdown by NABS. This information is based on the distribution (%) by socio-economic objectives of the Government funds of GERD as reported by R&D units in each of the four sectors of performance (BES, GOV, HES, PNP). GUF is considered in Chapter 12 of NABS. |
|---|---|
| Criterion of distribution – purpose or content | |
| Method of identification of primary objectives | |
| Difficulties of distribution |
18.3.4. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| GBARD national questionnaire and explanatory notes in English: | No applicable |
| GBARD national questionnaire and explanatory notes in the national language: | No applicable |
| Other relevant documentation of national methodology in English: | No applicable |
| Other relevant documentation of national methodology in the national language: | No applicable |
18.4. Data validation
A thorough validation procedure of data collected is carried out, mainly analysing inconsistencies among data from previous years. During the validation process, entities are contacted for further clarifications or for correcting errors. It's provided assistance to respondents during data collection.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Not applicable
18.5.2. Data compilation methods
See below.
18.5.2.1. Identifying R&D
| Method(s) of separating R&D from non-R&D | Coefficients are applied. |
|---|---|
| Description of the use of the coefficient (if applicable) | A R&D coefficient is used for each type of institution (which can be between 1% and 100%), and according to it R&D outlays are calculated. |
| Coefficient estimation method |
|
| Frequency of updating of coefficients | Coefficients are updated yearly for the State Laboratories and for some entities from MCTES. |
18.5.2.2. General University Funds (GUF)
| Method(s) of separating R&D from non-R&D | Coefficients are applied. |
|---|---|
| Description of the use of the coefficient (if applicable) | The coefficients applied are based on the national R&D statistics, according to the average time spent in R&D activities by the human resources in higher education institutions. |
| Coefficient estimation method | GUF: R&D coefficient is 40% of the appropriations for the Universities and 30% for polytechnic institutes. |
| Frequency of updating of coefficients | Every two years |
18.5.2.3. Other issues
| Treatment of multi-annual programmes | Multi-annual programs are allocated to the year in which they are budgeted. |
|---|---|
| Possibility to classify budgetary items by COFOG functions | No possibility. |
| Possibility to classify budgetary items by other nomenclatures e.g. NACE | No possibility. |
| Method of estimation of future budgets | No future budgets are estimated. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on Government Budget Allocations for R&D (GBARD) measure government support to research and development (R&D) activities, and thereby provide information about the priority governments give to different public R&D funding activities. This type of funder-based approach for reporting R&D involves identifying all the budget items that may support R&D activities and measuring or estimating their R&D content.
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 (FM 2015, Chapter 12), 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).
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 (Commission Implementing Regulation (EU) 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 (europa.eu)).
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.
8 January 2026
Not requested.
Relevant government units within the scope of GBARD - ministries and other government units funding R&D.
See below.
Not requested.
a) Calendar year: Reference year (January to December)
b) Fiscal year: Not applicable
Start month:
End month:
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
- Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
- Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
-
- Coverage errors,
- Measurement errors,
- Non response errors and
- Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
Not requested.
See below.
- Provisional data: State Budget (estimated initial budget) + R&D survey (T-1) + Administrative data of public institutions budget (higher education and other entities from Ministry of Science) + official-letter requesting the data to some units.
- Final data: Provisional data + R&D survey (T-0).
- General University Funds (GUF): Administrative data of higher education institutions public budget (provided by Instituto de Gestão Financeira da Educação, I.P. (IGEFE, acronym in Portuguese)).
Yearly
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.
