1.1. Contact organisation
Statistisches Bundesamt
1.2. Contact organisation unit
Unit H24 - Research, Culture
1.3. Contact name
Restricted from publication
1.4. Contact person function
Restricted from publication
1.5. Contact mail address
Gustav-Stresemann-Ring 11
D-65180 Wiesbaden
Germany
1.6. Contact email address
Restricted from publication
1.7. Contact phone number
Restricted from publication
1.8. Contact fax number
Restricted from publication
4 November 2025
2.1. Metadata last certified
4 November 2025
2.2. Metadata last posted
4 November 2025
2.3. Metadata last update
4 November 2025
3.1. Data description
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
3.2. Classification system
- The local unit for the statistics are compiled at regional level according to NUTS 2 – Nomenclature of Territorial Units for Statistics;
- The distribution by socioeconomic objectives (SEO) are based on Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS);
- The fields of research and development are based on Classification and distribution by Fields of Research and Development (FORD);
- The R&D personnel and researchers by educational attainment are classified by the International Standard Classification of Education ISCED 2011.
3.3. Coverage - sector
See below.
3.3.1. General coverage
Definition of R&D
R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge - including knowledge of humankind, culture and society - and to devise new applications of available knowledge.
3.3.2. Sector institutional coverage
| Tertiary education institution | All public universities colleges and centres that have their R&D activities under the direct control of, or administered by tertiary education institution and there main focus on academic tasks. |
|---|---|
| University and colleges: core of the sector | Yes |
| University hospitals and clinics | Yes |
| Inclusion of units that primarily do not belong to HES and the borderline cases |
No |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Academic personnel which belongs to the administrative staff and administrative personnel of the teaching and research units is included. In the calculation process non-teaching and non-R&D activities of all personnel are eliminated. |
|---|---|
| External R&D personnel | Post-graduate students receiving funding are included as external personnel. |
| Clinical trials: compliance with the recommendations in the Frascati Manual §2.61. | Yes |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Yes |
|---|---|
| Payments to rest of the world by sector - availability | Not available. |
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 | Intramural and extramural expenditures are separately surveyed in the questionnaire. |
| Difficulties to distinguish intramural from extramural R&D expenditure |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | 1981-2023 |
|---|---|
| Source of funds | Funds from enterprises:
Funds from Government:
For the higher education sector, the breakdown by sources of funds is only available at total sector level, not at fields-of-science level. |
| Type of R&D | Not available. |
| Type of costs | No breakdown is available for the additional funds from the German Research Association (see details under Source of funds). |
| Defence R&D - method for obtaining data on R&D expenditure | Not available. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | 1981-2023 |
|---|---|
| Function |
|
| Qualification | Not available. |
| Age | Not available. |
| Citizenship | Not available. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | 1981-2023 |
|---|---|
| Function |
|
| Qualification | Not available. |
| Age | Not available. |
| Citizenship | Not available. |
3.4.2.3. FTE calculation
FTE is calculated by taking 100% of personnel working full-time and 50% (main occupation) respectively 20% (second vocational job) of personnel working part-time.
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the HES Sector should consist of all R&D performing institutional units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
|---|---|---|
| Definition of the national target population | The target population consists of all institutions of the higher education sector, including university hospitals and all research institutes, centres, experimental stations and clincs that have their R&D activities under the direct control of, or administered by, tertiary institutions. . Borderline institutes located at universities but independent in accounting are included in GOV. | |
| Estimation of the target population size | 428 |
3.7. Reference area
Not requested. R&D statistics cover national and regional data.
3.8. Coverage - Time
Not requested, see concept 12.3.3 (Data availability).
3.9. Base period
The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
R&D expenditure is published in the following units: Euro (MIO_EUR) and Euro per inhabitant (EUR_HAB); data are available in the following units: basic unit National currency (MIO_NAC); Purchasing Power Standard (MIO_PPS); Purchasing Power Standard at 2005 prices (MIO_PPS_KP05); Purchasing Power Standard per inhabitant at constant 2005 prices (PPS_HAB_KP05); Percentage of gross domestic product (PC_GDP); and Percentage of total R&D expenditure (PC_TOT - for the breakdown by source of funds).
R&D personnel data are published in full-time equivalent (FTE), in head count (HC), as a percentage of total employment and as a percentage of active population.
2023 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.
The Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020.
6.1.2. National legislation
| Existence of R&D specific statistical legislation | No |
|---|---|
| Are respondents obliged by the national law to provide raw and administrative data: | Yes |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
A property of data indicating the extent to which their unauthorised disclosure could be prejudicial or harmful to the interest of the source or other relevant parties.
At the level of the ESS, the EU regulation 223/2009 on European statistics defines confidential data as data which allows statistical units (respondents) to be identified, either directly - by formal identifiers such as respondents’ names, addresses, identification numbers - or indirectly - by using a combination of variables or characteristics such as age, gender, education - thereby disclosing individual information (see Article 2(1)(e) of regulation 223/2009).
At national level:
- Confidentiality protection required by law: Federal Statistics Act (BStatG).
- Confidentiality commitments of survey staff: Is ensured by an oath of office.
7.2. Confidentiality - data treatment
No data treatment necessary to ensure confidentiality.
8.1. Release calendar
Data are disseminated nationally but without a predefined release calendar.
8.2. Release calendar access
For Eurostat this is: Release calendar - Eurostat (europa.eu)
8.3. Release policy - user access
Publications/data releases are usually accompanied by a press release (accessible to the public).
The frequency of R&D data dissemination at Eurostat level is yearly for provisional and final data.
10.1. Dissemination format - News release
See below.
10.1.1. Availability of the releases
| Availability (Y/N)1 | Links | |
|---|---|---|
| Regular releases | Y | |
| 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 | N | |
| Specific paper publication (e.g. sectoral provided to enterprises) | N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Research and development database (Genesis online)
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 | No micro-data access. |
|---|---|
| Access cost policy | |
| Micro-data anonymisation rules |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1) | Micro-data / Aggregate figures | Comments |
|---|---|---|---|
| Internet: main results available on the national statistical authority’s website | Y | Some key breakdowns are available additionally to the GENESIS-online-database at the Website | |
| Data prepared for individual ad hoc requests | Y | No data treatment necessary to ensure confidentiality. | |
| Other | N | Data prepared for regular publications of other authorities, for example state statistical offices and federal and state ministries for education and research (see above) |
1) Y – Yes, N - No
10.6. Documentation on methodology
National quality reports (entire HES data collection)
Calculation of R&D coefficients
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.) | See above. |
|---|---|
| Requests on further clarification, most problematic issues | No available. |
11.1. Quality assurance
At Eurostat level, the common quality framework of the European Statistical System (ESS) is composed of the European Statistics Code of Practice, the Quality Assurance Framework of the ESS, and the general quality management principles (such as continuous interaction with users, continuous improvement, integration, and harmonisation).
11.2. Quality management - assessment
The overall assessment of the HES R&D methodology is good especially because of the mandatory character. Some weakness appear while not asking for R&D expenditure and personnel but instead working with R&D coefficients.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1) | Description of users | Users’ needs |
|---|---|---|
| 1 | Eurostat, European Commission | Data tabulation and publication, building EU aggregates; research policy assessment |
| 1 | Federal Ministry of Research, Technology and Space (BMFTR) | Research policy making and assessment, analysis and publications |
| 1 | OECD | Data tabulation, analysis and publication |
| 4 | Mainly economists | Analysis, policy assessment |
1) Users' class codification
1- Institutions:
• European level: Commission (DGs, Secretariat General), Council, European Parliament, ECB, other European agencies etc.
• in Member States, at the national or regional level: Ministries of Economy or Finance, other ministries (for sectoral comparisons), National Statistical Institutes and other statistical agencies (norms, training, etc.), and
• International organisations: OECD, UN, IMF, ILO, etc.
2- Social actors: Employers’ associations, trade unions, lobbies, among others, at the European, national or regional level.
3- Media: International or regional media – specialized or for the general public – interested both in figures and analyses or comments. The media are the main channels of statistics to the general public.
4- Researchers and students (Researchers and students need statistics, analyses, ad hoc services, access to specific data.)
5- Enterprises or businesses (Either for their own market analysis, their marketing strategy (large enterprises) or because they offer consultancy services)
6- Other (User class defined for national purposes, different from the previous classes.)
12.2. Relevance - User Satisfaction
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys.
12.2.1. National Surveys and feedback
| Conduction of a user satisfaction survey or any other type of monitoring user satisfaction | No user satisfaction survey has been conducted yet. |
|---|---|
| User satisfaction survey specific for R&D statistics | Not available |
| Short description of the feedback received | Not available |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
100 percent
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| Reasons for missing cells | |
|---|---|
| Preliminary variables | No missing cells. |
| Obligatory data on R&D expenditure | No missing cells. |
| Optional data on R&D expenditure | Lack of resources or no legal basis for collecting optional data. |
| Obligatory data on R&D personnel | No missing cells. |
| Optional data on R&D personnel | Lack of resources or no legal basis for collecting optional data. |
| Regional data on R&D expenditure and R&D personnel | No missing cells. |
12.3.3. Data availability
See below.
12.3.3.1. Data availability - R&D Expenditure
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Source of funds | Y-1981 | Yearly | ||||
| Type of R&D | N | |||||
| Type of costs | Y-1981 | Yearly | ||||
| Socioeconomic objective | N | |||||
| Region | Y-1995 | Yearly | ||||
| FORD | Y-1995 | Yearly | ||||
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.2. Data availability - R&D Personnel (HC)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-1995 | Yearly | ||||
| Function | Y-1995 | Yearly | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y-1995 | Yearly | ||||
| FORD | Y-1995 | Yearly | Changes in national classification. | 2015 | Adoption to national classification. | |
| Type of institution | N |
1) Y-start year, N – data not available
12.3.3.3. Data availability - R&D Personnel (FTE)
| Availability1) | Frequency of data collection | Gap years – years with missing data | Changes - Description | Changes - Year of introduction | Changes - Reasons | |
|---|---|---|---|---|---|---|
| Sex | Y-1995 |
Yearly |
||||
| Function | Y-1995 |
Yearly |
||||
| Qualification | N |
|
||||
| Age | N |
|
||||
| Citizenship | N |
|
||||
| Region | Y-1995 |
Yearly |
||||
| FORD | Y-1995 |
Yearly |
||||
| Type of institution | N |
|
1) Y-start year, N – data not available
12.3.3.4. Data availability - other
| Additional dimension/variable available at national level1) | Availability2) | Frequency of data collection | Breakdown variables | Combinations of breakdown variables | Level of detail |
|---|---|---|---|---|---|
| Researchers | Y-1995 | Yearly | |||
| University graduates | Y-1995 | Every two years |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
12.3.3.5. R&D personnel - Cross-classification by function and qualification (if available in FTE and HC)
| Cross-classification | Unit | Frequency |
|---|---|---|
| Not available | ||
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors1) | Non-sampling errors1) | Model-assumption Errors1) | Perceived direction of the error2) | ||||
|---|---|---|---|---|---|---|---|
| Coverage errors | Measurement errors | Processing errors | Non response errors | ||||
| Total intramural R&D expenditure | Not applicable |
2 |
4 |
1 |
3 |
|
+/- |
| Total R&D personnel in FTE | Not applicable |
2 |
4 |
1 |
3 |
|
+/- |
| Researchers in FTE | Not applicable |
2 |
4 |
1 |
3 |
|
+/- |
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (6). If errors of a particular type do not exist, the sign ‘:‘ is used.
2) The perceived direction of the ‘overall’ error using the signs “+” for over estimation, “-” for under estimation and “+/-” when assumption of the direction of the error cannot be made for R&D.
13.1.2. Assessment of the accuracy with regard to the main indicators
| Indicators | 5 (Very Good)1) |
4 (Good)2) |
3 (Satisfactory)3) |
2 (Poor)4) |
1 (Very poor)5) |
|---|---|---|---|---|---|
| Total intramural R&D expenditure | x |
||||
| Total R&D personnel in FTE | x |
||||
| Researchers in FTE | x |
1) 'Very Good' = High level of coverage (annual rate of substitution in the target population lower than 5%). High average rates of response (>80%) in census and sample surveys. Full data consistency with reference to totals and relationships between variables in the dataset sent to Eurostat.
2) 'Good' = If at least one out of the three criteria described above is not fully met.
3) 'Satisfactory' = If the average rate of response is lower than 60%, even by meeting the two remaining criteria.
4) 'Poor' = If the average rate of response is lower than 60% and at least one of the two remaining criteria is not be met.
5) 'Very Poor' = If all the three criteria are not met.
13.2. Sampling error
That part of the difference between a population value and an estimate thereof, derived from a random sample, which is due to the fact that only a subset of the population is enumerated.
13.2.1. Sampling error - indicators
See below.
13.2.1.1. Variance Estimation Method
Not applicable.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
|---|---|
| Business enterprise | Not applicable |
| Government | Not applicable |
| Higher education | Not applicable |
| Private non-profit | Not applicable |
| Rest of the world | Not applicable |
| Total | Not applicable |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
|---|---|---|
| Occupation | Researchers | Not applicable |
| Technicians | Not applicable | |
| Other support staff | Not applicable | |
| Qualification | ISCED 8 | Not applicable |
| ISCED 5-7 | Not applicable | |
| ISCED 4 and below | Not applicable |
13.3. Non-sampling error
Non-sampling errors occur in all phases of a survey. They add to the sampling errors (if present) and contribute to decreasing overall accuracy. It is important to assess their relative weight in the total error and devote appropriate resources for their control and assessment.
13.3.1. Coverage error
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors:
Complete coverage because all institutions of the higher education sector, including university hospitals and all research institutes, centres, experimental stations and clinics, that have their R&D activities under the direct control of, or administered by, tertiary institutions, are included in HES. Borderline institutes located at universities but independent in accounting are included in GOV.
b) Measures taken to reduce their effect:
Yearly assessment to update the official registers of the higher education sector.
13.3.1.1. Over-coverage - rate
Not requested.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (e.g. difficulty to distinguish intramural from extramural R&D Expenditure). The survey questionnaire used for data collection may have led to the recording of wrong values, or there may be respondent or interviewer bias.
a) Description/assessment of measurement errors:
Measurement errors can occur while using R&D coefficients.
b) Measures taken to reduce their effect:
Regular recalculation of the R&D coefficients every two years based on current personnel data.
13.3.3. Non response error
Non-response occurs when a survey failed to collect data on all survey variables from all the population units designated for data collection in a sample or complete enumeration.
There are two elements of non-response:
- Unit non-response which occurs when no data (or so little as to be unusable) are collected on a designated population unit.
- Item non-response which occurs when data only on some, but not all survey variables are collected on a designated population unit.
The extent of response (and accordingly of non response) is also measured with response rates.
13.3.3.1. Unit non-response - rate
The main interest is to judge if the response from the target population was satisfactory by computing the un-weighted response rate.
Definition: Eligible are the survey units which indeed belong to the target population. Frame imperfections always leave the possibility that some units may not belong to the target population. Moreover, when there is no contact with certain units and no other way to establish their eligibility they are characterised as ‘unknown eligibility units’.
Un-weighted Unit Non- Response Rate = [1 - (Number of units with a response) / (Total number of eligible and unknown eligibility units in the survey)] * 100
13.3.3.1.1. Un-weighted unit non-response rate
| Number of units with a response in the survey | Total number of units in the survey | Unit non-response rate (Un-weighted) |
|---|---|---|
| 428 | 428 | 0 (mandatory survey) |
13.3.3.2. Item non-response - rate
Definition:
Un-weighted Item Non-Response Rate (%) = [1-(Number of units with a response for the item) / (Total number of eligible , for the item, units in the sample)] * 100
13.3.3.2.1. Un-weighted item non-response rate
| R&D Expenditure | R&D Personnel (FTE) | Researchers (FTE) | |
|---|---|---|---|
| Item non-response rate (un-weighted) (%) | No information available. | No information available. | No information available. |
| Comments | Close to zero due to mandatory survey. | Close to zero due to mandatory survey. | Close to zero due to mandatory survey. |
13.3.4. Processing error
Between data collection and the beginning of statistical analysis, data must undergo a certain processing: coding, data entry, data editing, imputation, etc. Errors introduced at these stages are called processing errors. Data editing identifies inconsistencies or errors in the data.
13.3.4.1. Identification of the main processing errors
| Data entry method applied | Possible errors are checked by comparing the results with results from previous surveys and calling back the respondent units. |
|---|---|
| Estimates of data entry errors | Data from institutions are rejected if not correct. The information provider has to deliver new data. |
| Variables for which coding was performed | See above. |
| Estimates of coding errors | See above. |
| Editing process and method | See above. |
| Procedure used to correct errors | Data from institutions are rejected if not correct. Contact and questions to the institution until correct data is provided. |
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: 7. March 2025
c) Lag (days): 430
14.1.2. Time lag - final result
a) End of reference period: 31. December 2023
b) Date of first release of national data: 1. September 2025
c) Lag (days): 610
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 | 18 |
| Delay (days) | 0 | 0 |
| Reasoning for delay |
15.1. Comparability - geographical
See below.
15.1.1. Asymmetry for mirror flow statistics - coefficient
Not requested.
15.1.2. General issues of comparability
No general issues.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual (FM) and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
|---|---|---|---|
| R&D personnel | FM2015 Chapter 5 (mainly sub-chapter 5.2). | No deviation | |
| Researcher | FM2015, § 5.35-5.39. | No deviation | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No deviation | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly sub-chapter 4.2). | No deviation | |
| Statistical unit | FM2015 §3.70 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No deviation | Coherent with FM2015 § 9.24 |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | Yes | Sports science" is included in "humanities" and "pharmacy" is included in "natural sciences". Until 2014: Humanities generally include educational sciences, linguistics, psychology. Since 2014: Engineering and technology generally include computer information science. |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No deviation |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual (FM), where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Reference to recommendations | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
|---|---|---|---|
| Data collection method | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Questionaire |
| Survey questionnaire / data collection form | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Online form |
| Cooperation with respondents | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Deadline extension is possible |
| Coverage of external funds | FM2015 Chapter 9 (mainly sub-chapter 9.4). | No deviation | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | FM2015 Chapter 9 (mainly sub-chapter 9.4). | Yes | No distiction is made, definition in Germany differs ("basic funds") |
| Data processing methods | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Administrative data in combination with R&D coefficients |
| Treatment of non-response | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Imputation |
| Variance estimation | FM2015 Chapter 6 (mainly sub-chapter 6.9). | No deviation | Not required due to full survey |
| Method of deriving R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | R&D coefficients on basis of time use survey 2016/2017, recalculation every 2 years on basis of personnel data |
| Quality of R&D coefficients | FM2015 Chapter 9 (mainly sub-chapter 9.5). | No deviation | Last update R&D coefficients: 2022 |
| Data compilation of final and preliminary data | Reg. 2020/1197: Annex 1, Table 18 | 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 | |
|---|---|---|---|
| R&D personnel (HC) | 2016-2023 | 2016, 1995, 1991 | 2016: Revised methodology for R&D coefficients on basis of time use survey in 2016/2017 -> increase of R&D coefficients 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. 1991: Graduate students conducting research and receiving grants for that purpose were included for the first time among higher education researchers (close to 10000 FTE). The relevant grants paid to them in 1991 were included in higher education R&D expenditure (HERD), so total grants paid to higher education in 1991 were well above the earlier figures, because only grants paid directly by government had been included up till then; the outcome was an increase in total government funding for R&D performed by higher education in that year. 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. |
| Function | 2016-2023 | Breakdown: Researchers, Technicans, other supporting staff. (According to the employment status or professional position of the higher education institution at which persons are employed.) |
|
| Qualification | Not available (optional) | ||
| R&D personnel (FTE) | 2016-2023 | 2016, 2006, 1995, 1991 | 2016: Revised methodology for R&D coefficients on basis of time use survey in 2016/2017 -> increase of R&D coefficients. 2006: Modification of the calculation method for R&D personnel (FTE). 1995: The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. 1991: Graduate students conducting research and receiving grants for that purpose were included for the first time among higher education researchers (close to 10000 FTE). The relevant grants paid to them in 1991 were included in higher education R&D expenditure (HERD), so total grants paid to higher education in 1991 were well above the earlier figures, because only grants paid directly by government had been included up till then; the outcome was an increase in total government funding for R&D performed by higher education in that year. 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. |
| Function | 2016-2023 | Breakdown: Researchers, Technicans, other supporting staff. (According to the employment status or professional position of the higher education institution at which persons are employed.) |
|
| Qualification | Not available (optional) | ||
| R&D expenditure | 2016-2023 | 2016, 1995, 1991 | 2016: Revised methodology for R&D coefficients on basis of time use survey in 2016/2017 -> increase of R&D coefficients. 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. 1991: Graduate students conducting research and receiving grants for that purpose were included for the first time among higher education researchers (close to 10000 FTE). The relevant grants paid to them in 1991 were included in higher education R&D expenditure (HERD), so total grants paid to higher education in 1991 were well above the earlier figures, because only grants paid directly by government had been included up till then; the outcome was an increase in total government funding for R&D performed by higher education in that year. 1995:The method of evaluating R&D resources in the higher education sector changed and was applied retroactively to 1981 to make the 1981 to 1994 data compatible with the more recent data. |
| Source of funds | 1995-2023 | Downwards adjustment of government funding to higher education. | |
| Type of costs | 1995-2023 | Breakdown: Current Costs (Labour Costs, other current costs), Capital Expenditures. |
|
| Type of R&D | Not available |
||
| Other | Not available |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
Are the data produced in the same way in the odd and even years? If no, please explain the main differences.
15.3. Coherence - cross domain
This part deals with any national coherence assessments which may have been undertaken. It reports results for variables which are the same or relevant to R&D statistics, from other national surveys and / or administrative sources and explains and comments on their degree of agreement with R&D statistics. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual (FM) regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
No HES sector in National accounts.
15.3.3. Coherence – Education statistics
No information available.
15.4. Coherence - internal
See below.
15.4.1. Comparison between preliminary and final data
This part compares key R&D variables as preliminary and final data.
| Total R&D expenditure – HERD (in 1000 of national currency) | Total R&D personnel (in FTEs) | Total number of researchers (in FTEs) | |
|---|---|---|---|
| Preliminary data (delivered at T+10) | 22395000 |
161900 |
125100 |
| Final data (delivered T+18) | 23026413 |
157813 |
121503 |
| Difference (of final data) | 631413 |
-4087 |
-3597 |
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) | FTE: 149296 (in Thousand) Labour Costs: 13364828 (in Thousand) |
|
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | We have no information on the costs of external R&D personnel (Post-graduates) in HES |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | Cost for the NSI in time use/person/day | |
|---|---|---|
| Staff costs | Not available | |
| Data collection costs | Not available | |
| Other costs | Not available | |
| Total costs | Not available |
The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies.
Comments on costs:
....
16.2. Components of burden and description of how these estimates were reached
| Value | Computation method | |
|---|---|---|
| Number of Respondents (R) | 428 | |
| 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 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
- Administrative statistics provided by the bodies of tertiary education sector.
- R&D coefficients based on an initial survey in 2017 with continuous updating every two years.
- Data on postgraduate students receiving funding and on scholarships and fellowships for postgraduates are collected separately by the Federal Statistical Office every two years.
18.1.2. Sample/census survey information
| Sampling unit | No sample survey |
|---|---|
| Stratification variables (if any - for sample surveys only) | |
| Stratification variable classes | |
| Population size | |
| Planned sample size | |
| Sample selection mechanism (for sample surveys only) | |
| Survey frame | |
| Sample design | |
| Sample size | |
| Survey frame quality | |
| Variables the survey contributes to |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Administrative statistics provided by the bodies of tertiary education sector. |
|---|---|
| Description of collected data / statistics | HES personnel and expenditures data without distinction as to whether they concern research or teaching. To make this distinction R&D coefficients were separately calculated and updated regularly |
| Reference period, in relation to the variables the administrative source contributes to | 2023 |
| Variables the administrative source contributes to | See above |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Microdata of every institution is requested |
|---|---|
| Description of collected information | All mandatory variables are collected. |
| Data collection method | The administrative and questioned data is collected online by statistical offices of the federal states.The questionnaire is improved regularly. The federal states deliver their collected data to the federal statistical office. |
| Time-use surveys for the calculation of R&D coefficients | Last survey in 2016 |
| Realised sample size (per stratum) | Not relevant |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Online |
| Incentives used for increasing response | No incentives, mandatory survey |
| Follow-up of non-respondents | Personal contact |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | Imputation |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | see 13.3.3 |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | Not available |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
|---|---|
| R&D national questionnaire and explanatory notes in English: | Not available |
| R&D national questionnaire and explanatory notes in the national language: | Expenditures Personnel |
| Other relevant documentation of national methodology in English: | Not available |
| Other relevant documentation of national methodology in the national language: | See 10.6 and 10.7.1 |
18.4. Data validation
Data validation carried out by the Statistical Offices of the federal states at micro level.
Data validation at macro level carried out by the Federal statistical office.
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)
The imputation rate is nearly zero due to the mandatory survey.
18.5.2. Data compilation methods
| Data compilation method - Final data | Update of the preliminary data with later incoming reports of the institutions surveyed. |
|---|---|
| Data compilation method - Preliminary data | Missing values are estimated on the basis of previous cycles. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | The coefficients are based on a empirical and normative design. It includes data and assumptions about the activities of personnel in the Higher education sector. Since repoting year 1995 the Federal Statistical Office distinguishes between "research funded by basic resources" and "research funded by external resources" in regard to R&D expenditure and R&D personnel. External funds and personnel financed by external funds are entirely classified as R&D. R&D expenditures and R&D personnel funded by basic resources are estimated by appling R&D coefficients. The R&D coefficients and the basic assumptions of the method for deriving the coefficients have been reviewed in years 2016 and 2017. There are different R&D coefficients and methods for the different types of universities and collegues within the higher education sector. Universities, collegues of education and collegues of theology: Form reporting year 2016, the R&D coefficients are subjected directly to time use for R&D. The Federal Statistical Office surveyed the scientific staff for its time use for R&D in the winter semester 2016/2017. The survey was voluntary and the data were wighted by the official figures of personnel in HES to ensure representativeness. R&D coefficients for universities are adjusted regularly on basis of Personnel Statistcs of Higher Education Sector. |
|---|---|
| Revision policy for the coefficients | The coefficients are revised every two years with the updated structure of scientific personnel. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | The last update of the coefficients was for the reported year 2022.The aggregation level for other higher education institutions than university is very general. This could affect the quality of the data. |
18.5.4. Measurement issues
| Method of derivation of regional data | Information of local units from student statistics (Studierendenstatistik) were used to deviate expenditures. |
|---|---|
| Coefficients used for estimation of the R&D share of more general expenditure items | R&D coefficients are used. The coefficients are based on a empirical and normative design. It includes data and assumptions about the activities of personnel in the Higher education sector. Since repoting year 1995 the Federal Statistical Office distinguishes between "research funded by basic resources" and "research funded by external resources" in regard to R&D expenditure and R&D personnel. External funds and personnel financed by external funds are entirely classified as R&D. R&D expenditures and R&D prsonnel funded by basic resources are estimated by appling R&D coefficients. The R&D coefficients and the basic assumptions of the method for deriving the coefficients have been reviewed in years 2016 and 2017. There are different R&D coefficients and methods for the different types of universities and collegues within the higher education sector. Universities, collegues of education and collegues of theology: Form reporting year 2016, the R&D coefficients are subjected directly to time use for R&D. The Federal Statistical Office surveyed the scientific staff for its time use for R&D in the winter semester 2016/2017. The survey was voluntary and the data were wighted by the official figures of personnel in HES to ensure representativeness. R&D coefficients for universities are adjusted regularly on basis of Personnel Statistcs of Higher Education Sector. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | VAT excluded |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | Calculation ist not possible. |
18.5.5. Weighting and estimation methods
| Description of weighting method | Not relevant |
|---|---|
| Description of the estimation method | Not relevant |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics (EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
The guiding document to preparing the quality reports is the European Statistical System (ESS) Handbook for Quality and Metadata Reports — re-edition 2021.
Since the beginning of 2021, the collection of R&D statistics is based on Commission Implementing Regulation (EU) No 2020/1197 of 30 July 2020. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail.
4 November 2025
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
See below.
Not requested. R&D statistics cover national and regional data.
2023 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.
