1.1. Contact organisation
Statistics Denmark
1.2. Contact organisation unit
Science, Technology and Culture
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Sejrøgade 11, DK-2100 Copenhagen, Denmark
1.6. Contact email address
Confidential because of GDPR
1.7. Contact phone number
Confidential because of GDPR
1.8. Contact fax number
Confidential because of GDPR
2.1. Metadata last certified
31 October 2021
2.2. Metadata last posted
31 October 2021
2.3. Metadata last update
31 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 | |
| Fields of Research and Development (FORD) | |
| Socioeconomic objective (SEO) |
3.3.2. Sector institutional coverage
| Higher education sector | The R&D activities of the university hospitals are included in the Higher education sector beginning 2002. Up through 2001 the Danish university hospitals are included in the Government sector. |
| Tertiary education institution | |
| University and colleges: core of the sector | Included |
| University hospitals and clinics | Included (beginning 2002)Medical university faculties have no clinics. The clinical R&D and education takes place within different hospitals as part of their general activities. Funds are allocated from the Higher Education Sector to the hospital as payment for this R&D and education (salaries, current expenditures and overhead). The R&D performed at these hospitals is classified in the Higher education sector beginning with the 2002 data; previously the R&D in the university hospitals was classified in the Government sector. |
| HE Borderline institutions | Included |
| Inclusion of units that primary don`t belong to HES |
3.3.3. R&D variable coverage
| R&D administration and other support activities | Corresponds to Frascati Manual. Administration carried out by researchers in direct connection with R&D is considered as R&D and included in expenditure and personnel data. R&D administration undertaken at central level within the Administration is excluded from the personnel series but taken into account in the calculation of R&D shares in overhead costs. |
| External R&D personnel | Post-graduate students who receive salaries or grants are recorded as a separate category and included in the data. In some cases, their activities take place outside the higher education sector, and the corresponding R&D is then classified in the sector/industry where the R&D is performed. |
| Clinical trials | Corresponds to the concepts of the Frascati Manual. |
3.3.4. International R&D transactions
| Receipts from Rest of the world by sector - availability | Yes, separated in enterprises, EU and Governments |
| Payments to Rest of the world by sector - availability | Yes, separated in enterprises, EU and Governments |
| R&D expenditure of foreign affiliates - coverage | Yes, though some validity problems in the information. |
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) | Statistics on extramural R&D is compiled. First, enterprises are asked whether they perform R&D, acquire R&D from other part of the group or acquire form others. Next, the expenditure is asked in separate tables for intramural and extramural R&D, the latter divided in the sources. |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | |
| 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 year |
| Source of funds | More sources used |
| Type of R&D | Based on FTE |
| Type of costs | Capital expenditures are divided in buildings and other capital costs |
| Defence R&D - method for obtaining data on R&D expenditure | Institutions performing defence R&D are included in the relevant sectors and receive the same questionnaire as other institutions in the same sector. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | End of year |
| Function | More detailed than needed for international reporting |
| Qualification | Not included |
| Age | Two categories: Danish/foreign |
| Citizenship | Not included |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | average number of persons employed during the calendar year |
| Function | |
| Qualification | |
| Age | |
| Citizenship |
3.4.2.3. FTE calculation
We ask for estimates from each unit. Some institutions still seem to be using ratios according to the employment category. Post-graduate students performing R&D are included
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
3.5. Statistical unit
Medical university faculties in Denmark have no clinics. The clinical R&D and education takes place within different hospitals as part of their general activities. Funds are allocated from the Higher Education Sector to the hospital as partial payment for this R&D and education (salaries, current expenditures and overhead), while another part is financed by the regional government. All R&D performed at these hospitals is classified in the Higher Education Sector beginning with the 2002 data; previously all R&D in the university hospitals was classified in the Government Sector.
All institutions are contacted and asked to identify all departments, centres etc. that perform any R&D. All identified units receive the set of questionnaires for the HES-sector.
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 | All higher educational institutions | All |
| Estimation of the target population size |
3.7. Reference area
Not requested.
3.8. Coverage - Time
Not requested. See point 5.
3.9. Base period
Not requested.
The statistical unit is defined in the Frascati manual as being the smallest homogeneous unit predominantly involved in only one of the six fields of science and for which a complete (or almost complete) set of factor input data can be obtained. In the Danish terminology this means the individual institute in universities, a centre and the individual department in university hospitals.
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
| Existence of R&D specific statistical legislation | No specific statistical legislation. |
| Legal acts | No legal act. |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | |
| Planned changes of legislation | None to our knowledge. |
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:
b) Confidentiality commitments of survey staff:
7.2. Confidentiality - data treatment
No need to report unit response level.
8.1. Release calendar
The publication date appears in the release calendar. The date is confirmed some weeks before.
8.2. Release calendar access
The Release Calender can be accessed on Statistics Denmarks English website: https://www.dst.dk/en/Statistik/planlagte.
8.3. Release policy - user access
Statistics are always published at 8:00 a.m. at the day announced in the release calendar. No one outside of Statistics Denmark can access the statistics before they are published.
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 | ||
| Ad-hoc releases |
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) |
Yes | The results are published with the results for the Gov and PNP sectors. |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
1) Y – Yes, N - No
10.3. Dissemination format - online database
StatBank Denmark, available on http://www.statistikbanken.dk
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 | All have access to the tables. Access to the micro data of the BES is only for researchers through our Safe Centre or through the access for researchers at Statistics Denmark. |
| Access cost policy | The paper publication is priced, but can be downloadet from our home page. |
| 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 | Yes | Statbank Denmark includes statistics on R&D. The questionnaires and the methodological notes on our website | |
| CD-ROMs | No | ||
| Data prepared for individual ad hoc requests | Yes | Customer specific tables are produced. | |
| Other | Yes | A sample of tables (EXCEL) are provided on our website. See Annex |
1) Y – Yes, N - No
10.6. Documentation on methodology
See: https://www.dst.dk/da/Statistik/Publikationer/VisPub?cid=17628 where documents on the used methodology can be found.
The OECD's Frascati Manual defines concepts in research and development.
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.) | A quality report (Statistics Documentation) is available in English on www.dst.dk and updated annually |
| Request on further clarification, most problematic issues | CF. below |
| Measure to increase clarity | CF. below |
| Impression of users on the clarity of the accompanying information to the data | Due to the detailed information published we have had little feedback on clarity, but much contact to get more information. We assist all users, but may ask for a fee, if we need to use more resources |
11.1. Quality assurance
Statistics Denmark follows the principles in the Code of Practice for European Statistics (CoP) and uses the Quality Assurance Framework of the European Statistical System (QAF) for the implementation of the principles. This involves continuous decentralized and central control of products and processes based on documentation following international standards. The central quality assurance function reports to the Working Group on Quality. Reports include suggestions for improvement that are assessed, decided and subsequently implemented.
11.2. Quality management - assessment
N/A
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1 | The European commission (DG’s and Eurostat) |
Data used for the compilation of European statistics and policy analysis |
| 1 | Nordic providers | Data to compare the Nordic countries |
| 1 | Denmark, national level (Ministries, Parliament) |
Statistics for policy use: development and follow up |
| 2 | OECD | Data for international comparisons |
1) Users' class codificationOECD
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. Instead, we have a combined user/provider-group for the public R&D statistics (HES+GOV+PNP), where the statistics is discussed and the questionnaires finally agreed. |
| User satisfaction survey specific for R&D statistics | Yes |
| Short description of the feedback received | n/a |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
The statistics is complete according to the Commission Regulation and the guidelines from the Frascati Manual. Not applicable for sensus with 100 pct. responses.
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 | Yearly | 1996, 1998 | |||
| Type of R&D | Y | Yearly | 1996, 1998 | Estimated | ||
| Type of costs | Y | Yearly | 1996, 1998 | |||
| Socioeconomic objective | Y | Yearly | 1996, 1998 | NORDFORSK with a key to NABS | ||
| Region | Y | Yearly | 1996, 1998 | |||
| FORD | Y | Yearly | 1996, 1998 | Estimated | ||
| Type of institution | Y | Yearly | 1996, 1998 |
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 | ||||||
| Function | ||||||
| Qualification | ||||||
| Age | ||||||
| Citizenship | ||||||
| Region | ||||||
| FORD | ||||||
| Type of institution |
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 | Yearly | 1996, 1998 | |||
| Function | Y | Yearly | 1996, 1998 | |||
| Qualification | N | |||||
| Age | N | |||||
| Citizenship | N | Danish or foregin | ||||
| Region | Y | Yearly | 1996, 1998 | |||
| FORD | Y | Yearly | 1996, 1998 | |||
| Type of institution | Y | Yearly | 1996, 1998 |
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
No variance in a census.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | none (full target population in sample) |
| Government | none (full target population in sample) |
| Higher education | none (full target population in sample) |
| Private non-profit | none (full target population in sample) |
| Rest of the world | none (full target population in sample) |
| Total | none (full target population in sample) |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Occupation | Researchers | none (full target population in sample) |
| Technicians | none (full target population in sample) | |
| Other support staff | none (full target population in sample) | |
| Qualification | ISCED 8 | Not in survey (Qualification by national standard) |
| ISCED 5-7 | Not in survey (Qualification by national standard) |
|
| ISCED 4 and below | Not in survey (Qualification by national standard) |
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:
b) Measures taken to reduce their effect:
13.3.1.1. Over-coverage - rate
Over coverage is not possible.
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:
b) Measures taken to reduce their effect:
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
We have contacts at every institution and public institutions have to report to Statistics Denmark according to Act on Statistics Denmark.
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 | |
| Estimates of data entry errors | |
| Variables for which coding was performed | |
| Estimates of coding errors | |
| Editing process and method | |
| Procedure used to correct errors |
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:
b) Date of first release of national data:
c) Lag (days):
14.1.2. Time lag - final result
a) End of reference period:
b) Date of first release of national data:
c) Lag (days):
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) | ||
| Delay (days) | ||
| 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
Until 2002, University hospitals were included in the government sector.
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). | ||
| Researcher | FM2015, § 5.35-5.39. | ||
| Approach to obtaining Headcount (HC) data | FM2015, § 5.58-5.61 (in combination with the Eurostat's harmonised Methodological Guidelines). | ||
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with the Eurostat's harmonised Methodological Guidelines). | ||
| Reporting data according to formula: Total R&D personnel = Internal R&D personnel + External R&D personnel | FM2015, §5.25 | ||
| Intramural R&D expenditure | FM2015, Chapter 4 (mainly paragraph 4.2). | ||
| Statistical unit | FM2015 §3.70 (in combination with the Eurostat's harmonised Methodological Guidelines). | ||
| Target population | FM2015 §9.6 (in combination with the Eurostat's harmonised Methodological Guidelines). | ||
| Sector coverage | FM2015 §3.67-3.69 (in combination with the Eurostat's harmonised Methodological Guidelines). | ||
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with the Eurostat's harmonised Methodological Guidelines). | ||
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with the Eurostat's harmonised Methodological Guidelines). | ||
| Borderline research institutions | FM2015 §9.18-9.27 (in combination with the Eurostat's harmonised Methodological Guidelines). | ||
| Major fields of science and technology coverage and breakdown | Reg. 995/2012: Annex 1, section 1, § 7.3. | ||
| Reference period | Reg. 995/2012: Annex 1, section 1, § 4-6. |
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 | ||
| Survey questionnaire / data collection form | ||
| Cooperation with respondents | ||
| Coverage of external funds | ||
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | ||
| Data processing methods | ||
| Treatment of non-response | ||
| Variance estimation | ||
| Method of deriving R&D coefficients | ||
| Quality of R&D coefficients | ||
| Data compilation of final and preliminary data |
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) | |||
| Function | |||
| Qualification | |||
| R&D expenditure | |||
| 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
Besides the statistics concerning the public sector there are also statistics regarding the business sector and in National Accounts. The Research and Development in the public sector statistics covers research and development performed in the public sector. I.e. it also includes some research and development financed by private sources. Research and development in the public sector in National Accounts covers research and development financed by the public sector. I.e. it also includes some research and development performed by the private sector.
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) | |||
| Final data (delivered T+18) | |||
| Difference (of final data) |
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 | |
| Type of survey | |
| 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 | |
| Description of collected data / statistics | |
| Reference period, in relation to the variables the survey contributes to |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | |
| Description of collected information | |
| Data collection method | |
| 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: | Questionnaire for higher education institutions. |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
Annexes:
Questionnire_2019_HES
18.4. Data validation
The methodology is decribed in danish in the attached publication:"Forskning og udvikling i den offentlige sektor, Kvalitetshåndbog" Summary in english.
Annexes:
kvalitetshåndbog_fouoff
18.5. Data compilation
See below.
18.5.1. Imputation - rate
not relevant
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | |
| Data compilation method - Preliminary data |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | |
| Revision policy for the coefficients | |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). |
18.5.4. Measurement issues
| Method of derivation of regional data | |
| Coefficients used for estimation of the R&D share of more general expenditure items | |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics |
18.5.5. Weighting and estimation methods
| Description of weighting method | |
| Description of the estimation method |
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.
31 October 2021
See below.
Medical university faculties in Denmark have no clinics. The clinical R&D and education takes place within different hospitals as part of their general activities. Funds are allocated from the Higher Education Sector to the hospital as partial payment for this R&D and education (salaries, current expenditures and overhead), while another part is financed by the regional government. All R&D performed at these hospitals is classified in the Higher Education Sector beginning with the 2002 data; previously all R&D in the university hospitals was classified in the Government Sector.
All institutions are contacted and asked to identify all departments, centres etc. that perform any R&D. All identified units receive the set of questionnaires for the HES-sector.
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.
The statistical unit is defined in the Frascati manual as being the smallest homogeneous unit predominantly involved in only one of the six fields of science and for which a complete (or almost complete) set of factor input data can be obtained. In the Danish terminology this means the individual institute in universities, a centre and the individual department in university hospitals.
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.
