1.1. Contact organisation
Centraal Bureau voor de Statistiek (Statistics Netherlands)
1.2. Contact organisation unit
Statistiekproductie Bedrijfseconomische Statistieken
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
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
1 October 2021
2.2. Metadata last posted
1 October 2021
2.3. Metadata last update
1 October 2021
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 units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
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.
Statistics on science, technology and innovation were collected based on Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021.
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).
3.2.1. Additional classifications
| Additional classification used | Description |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | According to FM. |
| Fields of Research and Development (FORD) | Compiling the R&D statistics of the HES in the Netherlands, 8 major fields of science are distinguished. The corresponding fields of science of the Frascati Manual are mentioned between brackets. - Natuur (Natural sciences) - Techniek (Engineering and technology) - Gezondheid (Medical sciences) - Landbouw (Agricultural sciences) - Economie (Social sciences) - Rechten (Social sciences) - Gedrag en maatschappij (Social sciences) - Taal en cultuur (Humanities) |
| Socioeconomic objective (SEO) | According to FM. |
3.3.2. Sector institutional coverage
| Higher education sector | According to FM. |
| Tertiary education institution | According to FM. |
| University and colleges: core of the sector | Universities (WO) are included and also the universities of applied sciences (HBO). |
| University hospitals and clinics | Only teaching/training clinics. |
| HE Borderline institutions | Included if they are taken into account in the annual reports of the universities. |
| Inclusion of units that primary don't belong to HES | not applicable |
3.3.3. R&D variable coverage
| R&D administration and other support activities | |
| External R&D personnel | Research activities of post-graduate students on the university payroll are included. |
| Clinical trials |
3.3.4. International R&D transactions
| Receipts from Rest of the world by sector - availability | Yes, however other national governments and international organisations are not separate categories. |
| Payments to Rest of the world by sector - availability | Yes, however other national governments and international organisations are not separate categories. |
| R&D expenditure of foreign affiliates - coverage | No. |
3.3.5. Extramural R&D expenditures
According to the Frascati Manual, expenditure on extramural R&D (i.e. R&D performed outside the statistical unit) 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 | As reported by HES entities. |
| 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 | Calendar years. |
| Source of funds | For the HES there is information on the different sources of funds. The General University Fund (GUF) however is calculated as the balance of the intramural R&D expenditures minus all other sources of funds distinguished. |
| Type of R&D | According to FM. |
| Type of costs | According to FM. |
| 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 | Calender years. |
| Function |
R&D personnel can be divided into Researchers and Other staff. Technicians cannot be separated from other support staff.
|
| Qualification | not available |
| Age | not available |
| Citizenship | not available |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Calendar years. |
| Function |
R&D personnel can be divided into Researchers and Other staff. Technicians cannot be separated from other support staff.
|
| Qualification | not available |
| Age | not available |
| Citizenship | not available |
3.4.2.3. FTE calculation
Time of post-graduate students devoted to R&D is calculated in the same way as for other members of the university staff.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
3.5. Statistical unit
Both the reporting unit and the main statistical unit are the enterprise.
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.
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 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 Higher Education Sector (HES) includes universities (WO), universities of applied sciences (HBO), research institutes associated with higher education institutes and university hospitals and clinics. - Private institutions of post-secondary education are not part of the target population. This is however not of so much relevance for the R&D of the HES. - Other to the HES related institutions performing R&D show up if they become part of the annual report of the higher education institutes. They are treated as research institutes associated with higher education institutes. - Other publicly financed institutes performing R&D but operating independently from higher education institutes are part of the government sector (GOV). |
The Higher Education Sector (HES) includes universities (WO), universities of applied sciences (HBO), research institutes associated with higher education institutes and university hospitals and clinics. - Private institutions of post-secondary education are not part of the target population. This is however not of so much relevance for the R&D of the HES. - Other to the HES related institutions performing R&D show up if they become part of the annual report of the higher education institutes. They are treated as research institutes associated with higher education institutes. - Other publicly financed institutes performing R&D but operating independently from higher education institutes are part of the government sector (GOV). |
| Estimation of the target population size | All R&D performers. | All R&D performers. |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested. See point 5.
3.9. Base period
Not requested.
Thousands of Euro, number of persons/FTE.
Calendar year.
6.1. Institutional Mandate - legal acts and other agreements
See below.
6.1.1. European legislation
| Legal acts / agreements |
Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021. |
6.1.2. National legislation
Yes. Restricted to a limited number of research institutes and under certain restrictions (e.g. anonymization of data).
| Existence of R&D specific statistical legislation | The production of national R&D statistics is governed by the general national statistical legislation. | |
| Legal acts | Wet op het Centraal Bureau voor de Statistiek. | |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Yes. | |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Yes, for enterprises that are sampled it is obligatory to provide data. | |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Yes. | |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Yes, but restricted to a limited number of research institutes and under certain restrictions (e.g. anonymisation of data). | |
| Planned changes of legislation | not available |
6.1.3. Standards and manuals
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:
Information about individual firms can never be disclosed.
b) Confidentiality commitments of survey staff:
7.2. Confidentiality - data treatment
8.1. Release calendar
8.2. Release calendar access
N/A.
8.3. Release policy - user access
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
| Mean of dissemination | Availability (Y/N)1 | Content, format, links, ... |
| General publication/article (paper, online) |
Y |
Yearly publication “ICT, kennis en economie 2021” (“ICT, knowledge and economy 2021”). Only available in Dutch. To be downloaded for free via ICT, kennis en economie 2021 (cbs.nl).
|
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Tables are available in the online-database of Statistics Netherlands: https://opendata.cbs.nl/#/CBS/en/.
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 | Tables on the website are freely available. Microdata is available for research purposes under restrictions. External researchers have access to our microdata on-site or through remote access facilities. They cannot export any microdata, only aggregated data and statistical/analytical output can be exported. Information that outside users export from our microdata is checked by Statistics Netherlands experts first in order to get permission to export. |
| Access cost policy | not applicable |
| Micro-data anonymisation rules | not applicable |
10.5. Dissemination format - other
See below.
10.5.1. Metadata - consultations
Not requested.
10.5.2. Availability of other dissemination means
| Dissemination means | Availability (Y/N)1 | Micro-data / Aggregate figures | Comments |
| Internet: main results available on the national statistical authority’s website | Y | Aggregate figures. | |
| CD-ROMs | N | ||
| Data prepared for individual ad hoc requests | Y | Aggregate figures. | |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
Description statistical process on the Statistics Netherlands website (only Dutch): Research & Development (cbs.nl).
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.) |
Statistics Netherlands makes microdata on R&D available to external researchers. These microdata are accompanied by documentation of metadata, allowing users to better understand the data.
- Also the Statistics Netherlands website has a short methodological description of the survey (http://www.cbs.nl/nl-NL/menu/themas/bedrijven/methoden/dataverzameling/korteonderzoeksbeschrijvingen/research-development.htm).
- More metadata are available in the online statistical database of Statistics Netherlands, as clarifications to the variables that are included in the tables on R&D.
- Finally, there’s methodological information on the R&D survey in our theme publication “Kennis en economie 2015” (“Knowledge and economy”, only available in Dutch.
This publication contains a methodological section, describing the applied survey method.
|
| Request on further clarification, most problematic issues | Occasionally requests for more clarifications are issued at Statistics Netherlands’ information service. It’s hard to distinguish any especially problematic issues raised by our users, as their questions do not systematically cover the same areas. |
| Measure to increase clarity | No. |
| Impression of users on the clarity of the accompanying information to the data | Our general impression is that there are no major clarity issues. |
11.1. Quality assurance
Statistics Nethertlands is an ISO-certified insitution and the R&D statistics are produced under the same standards.
11.2. Quality management - assessment
It is a rather robust approach not based on a survey with detailed questions, but on time-use coefficients for R&D per university and the annual reports with the financial statements of the same universities. The time-use coefficients are crucial for the outcome. The quality of the coefficients rely very much on the consistency between the nominator (resources of staff spent on R&D) and the denominator of the quotient (number of staff on the payroll of the universities). However, over the years the overall time-use coefficient of the universities is rather stable around 0.61 (or 61%). The time-use coefficients for R&D are used for all current and capital costs of the universities. So, if the time-use coefficient is 0.61, this means that 61% of the personnel costs of the universities are spent on R&D. Also, if these costs increase by 10% the R&D expenditures – assuming an unchanged time-use coefficient for R&D – also increase by 10%. For the academic hospitals the time-use coefficients are only used for the current and capital costs of the department with teaching and research tasks (so, not the whole academic hospital which mainly performs health care).
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| Institutions | Ministries and local governments. |
Indicators for setting and assessing policy goals.
|
| Businesses | Enterprises/business groups. | Indicators for market assessment. |
| Researchers and students | Universities/higher education insitutions. | Primarily microdata for analyses/research. |
| Social actors | Employers' associations. | Indicators for business sector assessment. |
| Media | Newspapers, websites and other media outlets. | Output based on relevant and upto-date data. |
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 | not available |
| 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
All required, obligatory data cells are provided.
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 of 30 July 2020. The Regulation (EU) stipulates periodicity of variables that should be provided, the breakdowns and whether they should be provided mandatory or on voluntary basis.
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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Source of funds | Y | Annual | ||||
| Type of R&D | Y | Annual | ||||
| Type of costs | Y | Annual | ||||
| Socioeconomic objective | N | |||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| 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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| 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 | Modifications - Description | Modifications - Year of introduction | Modifications - Reasons | |
| Sex | Y | Annual | ||||
| Function | Y | Annual | ||||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | |||||
| Region | Y | Annual | ||||
| FORD | Y | Annual | ||||
| 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 |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 995/2012 (neither as 'optional').
2) Y-start year
13.1. Accuracy - overall
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
13.1.1. Accuracy - Overall by 'Types of Error'
| Sampling errors | 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 | |||||||
| Total R&D personnel in FTE | |||||||
| Researchers in FTE | |||||||
1) Ranking of the type(s) of errors that result in over/under-estimation, from the most important source of error (1) to the least important source of error (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 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' = In the event that at least one out of the three criteria above described would not be fully met.
3) 'Satisfactory' = In the event that the average rate of response would be lower than 60% even by meeting the two remaining criteria.
4) 'Poor' = In the event that the average rate of response would be lower than 60% and at least one of the two remaining criteria would 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
The main indicator used to measure sampling errors is the coefficient of variation (CV).
Definition of coefficient of variation:
CV= (Square root of the estimate of the sampling variance) / (Estimated value)
13.2.1.1. Variance Estimation Method
N/A.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | |
| Government | |
| Higher education | |
| Private non-profit | |
| Rest of the world | |
| Total |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Occupation | Researchers | |
| Technicians | ||
| Other support staff | ||
| Qualification | ISCED 8 | |
| ISCED 5-7 | ||
| ISCED 4 and below |
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:
N/A.
b) Measures taken to reduce their effect:
N/A.
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:
N/A.
b) Measures taken to reduce their effect:
N/A.
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)
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) |
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 variable/breakdown | Item non-response rate (un-weighted) (%) | Comments |
13.3.3.3. Measures to increase response rate
N/A.
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 | not applicable |
| Estimates of data entry errors | not applicable |
| Variables for which coding was performed | not applicable |
| Estimates of coding errors | not applicable |
| Editing process and method | not applicable |
| Procedure used to correct errors | not applicable |
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 of year T
b) Date of first release of national data: T + 10 months
c) Lag (days): 0
14.1.2. Time lag - final result
a) End of reference period: 31 December of year T
b) Date of first release of national data: T + 18 months
c) Lag (days): 0
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 divergences from FM.
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 995/2012 or Frascati manual 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 paragraph 5.2). | No. | |
| Researcher | FM2015, § 5.35-5.39. | No. | |
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with the Eurostat's harmonised Methodological Guidelines). | No. | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with the Eurostat's harmonised Methodological Guidelines). | No. | |
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | No. | |
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | No. | |
| Statistical unit | FM2015 §3.70 (in combination with the Eurostat's harmonised Methodological Guidelines). | No. | |
| Target population | FM2015 §9.6 (in combination with the Eurostat's harmonised Methodological Guidelines). | No. | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with the Eurostat's harmonised Methodological Guidelines). | No. | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with the Eurostat's harmonised Methodological Guidelines). | No. | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with the Eurostat's harmonised Methodological Guidelines). | No. | Only the department of the academic hospitals with explicit tasks in the field of education and research. |
| Borderline research institutions | FM2015 §9.18-9.27 (in combination with the Eurostat's harmonised Methodological Guidelines). | No. | Borderline research institutes become part of the university if they are part of the annual report of the university (this is the ultimate expression of ‘associated with’ or ‘administered by’). |
| Major fields of science and technology coverage and breakdown | Reg. 995/2012: Annex 1, section 1, § 7.3. | No. | |
| Reference period | Reg. 995/2012: Annex 1, section 1, § 4-6. | No. |
15.1.4. Deviations from recommendations
The following table lists a number of key methodological issues, which may affect the international comparability of national R&D statistics. The table gives the references in the Frascati manual, where related recommendations are made. Countries are asked to report on the existence of any deviations from existing recommendations and comment upon.
| Methodological issues | Deviation from recommendations | Comments on national treatment / treatment deviations from recommendations |
| Data collection method | No. | Almost entirely based on publicly available information. |
| Survey questionnaire / data collection form | No. | Information drawn from websites, annual reports and provided by professional associatons representing (parts of) the sector. |
| Cooperation with respondents | No. | Only with professional associations from within the HES. Seldom with individual universities or academic hospitals. |
| Coverage of external funds | No. | |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | No. | GUF is calculated as the balance of the intramural R&D expenditures minus all other sources of funds distinguished. |
| Data processing methods | No. | |
| Treatment of non-response | No. | |
| Variance estimation | not applicable | |
| Method of deriving R&D coefficients | No. | Based on a calculation and not on a real time-use survey among the staff of the HES. |
| Quality of R&D coefficients | No. | |
| Data compilation of final and preliminary data | No. |
15.2. Comparability - over time
See below.
15.2.1. Length of comparable time series
See below.
15.2.2. Breaks in time series
| Length of comparable time series | Break years1 | Nature of the breaks | |
| R&D personnel (HC) | |||
| Function | |||
| Qualification | |||
| R&D personnel (FTE) | 1999, 1990, 1982 | - As from 1999 universities became the formal employer of PhD-candidates. Before 1999 a large number of PhD-candidates were formally employed by research institutes who did finance their research. So, PhD-candidates and their research activities moved from the Government sector to the Higher Education sector. - As from 1990, the data reflect the 1994 change in survey methodology for HERD expenditure, and one consequence was an upward evaluation of government-financed R&D in the higher education sector. For 1990 and 1991, the breakdown by Type of cost, Type of R&D, Socio-economic objectives and Field of Science are not available as they were not revised. - As from 1982, change in estimation method for the Higher Education sector. |
|
| Function | |||
| Qualification | |||
| R&D expenditure | 1999, 1990, 1982 | - As from 1999 universities became the formal employer of PhD-candidates. Before 1999 a large number of PhD-candidates were formally employed by research institutes who did finance their research. So, PhD-candidates and their research activities moved from the Government sector to the Higher Education sector. - As from 1990, the data reflect the 1994 change in survey methodology for HERD expenditure, and one consequence was an upward evaluation of government-financed R&D in the higher education sector. For 1990 and 1991, the breakdown by Type of cost, Type of R&D, Socio-economic objectives and Field of Science are not available as they were not revised. - As from 1982, change in estimation method for the Higher Education sector. |
|
| Source of funds | |||
| Type of costs | |||
| Type of R&D | |||
| Other |
1) Breaks years are years for which data are not fully comparable to the previous period.
15.2.3. Collection of data in the even years
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.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
N/A.
15.3.3. National Coherence Assessments
| Variable name | R&D Statistics - Variable Value | Other national statistics - Variable value | Other national statistics - Source | Difference in values (of R&D statistics) | Explanation of / comments on difference |
15.3.4. Coherence – Education statistics
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) | 4675000 | 35000 | 23300 |
| Final data (delivered T+18) | 4900000 | 35963 | 23806 |
| Difference (of final data) | 375000 | 963 | 506 |
15.4.2. Consistency between R&D personnel and expenditure
| Average remuneration (cost¨in national currency) | |
| Consistency between FTEs of internal R&D personnel and R&D labour costs (1) | |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) |
(1) Calculate the average remuneration (cost) of individuals belonging to the internal R&D personnel, excluding those who are only formally ‘employees’ (university students, grant holders, etc.).
(2) Calculate the average remuneration (cost) of individuals belonging to the external R&D personnel (FTEs/other current R&D costs for external R&D personnel).
The assessment of costs associated with a statistical product is a rather complicated task since there must exist a mechanism for appointing portions of shared costs (for instance shared IT resources and dissemination channels) and overheads (office space, utility bills etc). The assessment must become detailed and clear enough so that international comparisons among agencies of different structures are feasible.
16.1. Costs summary
| Costs for the statistical authority (in national currency) | % sub-contracted1) | |
| Staff costs | ||
| Data collection costs | ||
| Other costs | ||
| Total costs | ||
| 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) | ||
| Average Time required to complete the questionnaire in hours (T)1 | ||
| Average hourly cost (in national currency) of a respondent (C) | ||
| Total cost |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
Not requested.
17.2. Data revision - practice
Not requested.
17.2.1. Data revision - average size
Not requested.
18.1. Source data
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
18.1.1. Data source – general information
| Survey name | N/A. |
| Type of survey | Compiling the R&D statistics of the HES in the Netherlands is not based on a survey in the true meaning of the word. It is based on publicly available information distributed by the higher education institutes themselves or by the professional associations of the sector. |
| Combination of sample survey and census data | |
| Combination of dedicated R&D and other survey(s) | |
| Sub-population A (covered by sampling) | |
| Sub-population B (covered by census) | |
| Variables the survey contributes to | |
| Survey timetable-most recent implementation |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | |||
| Stratification variables (if any - for sample surveys only) | |||
| Stratification variable classes | |||
| Population size | |||
| Planned sample size | |||
| Sample selection mechanism (for sample surveys only) | |||
| Survey frame | |||
| Sample design | |||
| Sample size | |||
| Survey frame quality |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | Association of Universities in the Netherlands (VSNU): - Resources of staff spent on R&D (fte’s) by university and field of science (KUOZ). - Personnel on the payroll of universities per university (head counts and fte’s) broken down by gender and field of science (WOPI). Annual reports of universities and academic hospitals: Financial statement of the universities and academic hospitals on their revenues, expenditures and investments. The Netherlands Association of Universities of Applied Sciences (HBO-raad):Aggregated overview of R&D expenditures of the universities of applied sciences by field of science and source of funds. Association of University Medical Centers in The Netherlands (NFU): Financial statement of the academic hospitals on their revenues broken down by their main activities (healthcare, education, R&D, other). |
| Description of collected data / statistics | |
| Reference period, in relation to the variables the survey contributes to | Calendar year. |
18.2. Frequency of data collection
Yearly.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Association of Universities in The Netherlands (VSNU) The Netherlands Association of Universities of Applied Sciences (HBO-raad) Association of University Medical Centers in The Netherlands (NFU) Annual reports of universities and academic hospitals |
| Description of collected information | - Resources of staff spent on R&D (fte’s) by university and field of science (KUOZ). Aggregated overview of R&D expenditures of the universities of applied sciences by field of science and source of funds. Financial statement of the academic hospitals on their revenues broken down by their main activities (healthcare, education, R&D, other) Financial statement of the universities and academic hospitals on their revenues, expenditures and investments. |
| Data collection method | Publicly available information. Provided by The Netherlands Association of Universities of Applied Sciences (HBO-raad). Provided by the Association of University Medical Centers in The Netherlands (NFU) Publicly available information. |
| Time-use surveys for the calculation of R&D coefficients | |
| Realised sample size (per stratum) | |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | |
| Incentives used for increasing response | |
| Follow-up of non-respondents | |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | |
| R&D national questionnaire and explanatory notes in the national language: | |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
18.4. Data validation
Validation is done in EDAMIS
18.5. Data compilation
See below.
18.5.1. Imputation - rate
0
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | Data are compiled in the same way on a yearly basis. |
| Data compilation method - Preliminary data | Preliminary data are based on the actual financial statements of year t of the universities, the actual number of staff on their payroll of year t and the estimated R&D time-use coefficients of year t-1. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | Basically the resources of staff spent on R&D in fte’s (KUOZ) are divided by the number of staff on the payroll of the universities also in fte’s (WOPI). This quotient is the time-use coefficient for the time spent on R&D by the staff of the universities. This time-use coefficient is calculated for every individual university and every individual field of science. The time-use coefficients are calculated on a yearly basis. These time-use coefficients refer to the R&D of the universities including the academic hospitals and research institutes associated with the universities or academic hospitals. R&D expenditure and R&D personnel of the universities of applied sciences are estimated separately. |
| Revision policy for the coefficients | The time-use coefficients are calculated on a yearly basis |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | The consistency between the resources of staff spent on R&D (nominator) and the total number of staff on the payroll of the universities (denominator). Both figures are collected separately from different sources. |
18.5.4. Measurement issues
| Method of derivation of regional data | In the survey, respondents are asked to provide a breakdown of R&D personnel hc by gender and province. This breakdown is used to estimate the other variables by gender and province. |
| Coefficients used for estimation of the R&D share of more general expenditure items | R&D coefficients are calculated based on two sources: statistical data about total university personnel on December 31 and statistical data about the actual time spent on R&D by scientific personnel at the universities in a specific year. The new method results in a higher estimate of the R&D expenditures (on the average an increase in 20-25%) than the former one. THis is because post-graduate students are now on the payroll of the universities (in the past they were employed by the governement sector). |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | not applicable |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | For the HES there is information on the different sources of funds. The General University Fund (GUF) however is calculated as the balance of the intramural R&D expenditures minus all other sources of funds distinguished. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | not applicable |
18.5.5. Weighting and estimation methods
| Description of weighting method | not applicable |
| Description of the estimation method | not applicable |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
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 units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
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.
Statistics on science, technology and innovation were collected based on Commission Implementing Regulation (EU) Regulation (EU) No 995/2012 concerning the production and development of Community statistics on science and technology until the end of 2020. 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. Please note that according to Article 12(4) of Regulation (EU) 2020/1197, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021.
1 October 2021
See below.
Both the reporting unit and the main statistical unit are the enterprise.
See below.
Not requested.
Calendar year.
Accuracy in the statistical sense denotes the closeness of computations or estimates to the exact or true values. Statistics are not equal with the true values because of variability (the statistics change from implementation to implementation of the survey due to random effects) and bias (the average of the possible values of the statistics from implementation to implementation is not equal to the true value due to systematic effects).
Several types of statistical errors occur during the survey process. The following typology of errors has been adopted:
1. Sampling errors. These only affect sample surveys. They are due to the fact that only a subset of the population, usually randomly selected, is enumerated.
2. Non-sampling errors. Non-sampling errors affect sample surveys and complete enumerations alike and comprise:
a) Coverage errors,
b) Measurement errors,
c) Non response errors and
d) Processing errors.
Model assumption errors should be treated under the heading of the respective error they are trying to reduce.
Thousands of Euro, number of persons/FTE.
See below.
Several separate activities are used for the collection of raw data or pre-compiled administrative data and statistics related to R&D. For simplicity, we call them surveys irrespective of whether they are sample surveys, censuses, collections of administrative data/pre-compiled statistics. This section presents the names of the surveys by sector of performance as well as methodological information for each survey. Depending on the type of survey and sector of performance, only the sections corresponding to that survey and sector are filled in.
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.
