Research and development (R&D) (rd)

1.1. Contact organisation

Statistics Iceland

1.2. Contact organisation unit

Business trends and structure

1.3. Contact name

Restricted from publication

1.4. Contact person function

Restricted from publication

1.5. Contact mail address

Borgartun 21a

150 Reykjavik

Iceland

1.6. Contact email address

Restricted from publication

1.7. Contact phone number

Restricted from publication

1.8. Contact fax number

Restricted from publication

2.1. Metadata last certified

24 March 2023

2.2. Metadata last posted

24 March 2023

2.3. Metadata last update

24 March 2023

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	Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge – including knowledge of humankind, culture and society – and to devise new applications of available knowledge. e. For an activity to be an R&D activity, it must satisfy five core criteria. The activity must be: novel, creative, uncertain, systematic, transferable and/or reproducible.
Fields of Research and Development (FORD)	Breakdown by field of R&D by six main fields: natural sciences, engineering and technology, medical sciences, agricultural sciences, social sciences, humanities.
Socioeconomic objective (SEO)	-

3.3.2. Sector institutional coverage

Higher education sector
Tertiary education institution
University and colleges: core of the sector	Included
University hospitals and clinics	Included
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	No intentional deviation from manual.
External R&D personnel	Included in total personnel
Clinical trials	Clinical trials not excluded.

3.3.4. International R&D transactions

Receipts from Rest of the world by sector - availability	Not collected.
Payments to Rest of the world by sector - availability	Not collected.
R&D expenditure of foreign affiliates - coverage	Not collected.

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)	N
Method for separating extramural R&D expenditure from intramural R&D expenditure	Clear instructions on which cost to specify.
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	In accordance with FM.
Type of R&D	In accordance with FM.
Type of costs	In accordance with FM.
Defence R&D - method for obtaining data on R&D expenditure	n/a

3.4.2. R&D personnel

See below.

3.4.2.1. R&D personnel – Head Counts (HC)

Coverage of years	Calendar year.
Function	Data available by breakdown: researchers; other.
Qualification	N/A
Age	N/A
Citizenship	N/A

3.4.2.2. R&D personnel – Full Time Equivalent (FTE)

Coverage of years	Calendar year.
Function	Data available by breakdown: researchers; other.
Qualification	N/A
Age	N/A
Citizenship	N/A

3.4.2.3. FTE calculation

"Average % of time spent on R&D" asked in survey, then turned into FTE.

3.4.2.4. R&D personnel - Cross-classification by function and qualification

Cross-classification	Unit	Frequency
N/A

3.5. Statistical unit

Higher education institutions (department level), incl. university hospitals.

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 universities and university hospitals
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.

Expenditures: ISK (thousands)

Personnel: HC and FTE

Calendar year of the reference year: 2021

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	N/A
Legal acts	N/A
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	N/A

6.1.3. Standards and manuals

- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development

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:

Section III of regulation 'Lög um Hagstofu Íslands og opinbera hagskýrslugerð, nr. 163, 21. desember 2007' establishes that any information concerning identifiable individuals or legal units has to be treated as confidential.

b) Confidentiality commitments of survey staff:

All survey staff is bound by the data confidentiality.

7.2. Confidentiality - data treatment

Data cells that are considered confidential are flagged as such.

8.1. Release calendar

Release calendar is updated on a year-to-year basis. The R&D data is typically released nationally before the end of October.

8.2. Release calendar access

https://www.statice.is/publications/

8.3. Release policy - user access

A link to 'rules on statistical releases':

statice.is/rules-on-statistical-releases/

Yearly dissemination.

10.1. Dissemination format - News release

See below.

10.1.1. Availability of the releases

	Availability (Y/N)¹	Content, format, links, ...
Regular releases	Yes	In accordance with the code of conduct of Statistics Iceland, release of official statistics included a press release
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)	Yes	Statice website: http://www.statice.is/statistics/business-sectors/science-and-technology/rd/
Specific paper publication (e.g. sectoral provided to enterprises) (paper, online)

1) Y – Yes, N - No

10.3. Dissemination format - online database

Statice website: http://www.statice.is/statistics/business-sectors/science-and-technology/rd/

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	N/A
Access cost policy	https://statice.is/services/data-for-scientific-research/
Micro-data anonymisation rules	https://statice.is/services/data-for-scientific-research/

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
CD-ROMs	No
Data prepared for individual ad hoc requests	Yes
Other	No

1) Y – Yes, N - No

10.6. Documentation on methodology

http://hagstofan.s3.amazonaws.com/media/public/2021/aef7bf6a-75ea-4302-9b60-10628ee03ea7.pdf

10.6.1. Metadata completeness - rate

Not requested.

10.7. Quality management - documentation

See below.

10.7.1. Information and clarity

Type(s) of data accompanying information available (metadata, graphs, quality reports, etc.)	Description of methodology not available in English for the time being
Request on further clarification, most problematic issues	Not directly.
Measure to increase clarity	Not at this point.
Impression of users on the clarity of the accompanying information to the data	Clarity does not seem to be a problem.

11.1. Quality assurance

Centralization of tasks and responsibilities: a single expert in the unit of Business Enterprise Statistics has duties and responsibilities over every aspect of the statistical production. By minimizing the number of personnel involved in the process, organizational complications are kept to a minimum. This is both manageable and feasibly considering the small scale and scope of the R&D industry in Iceland.

11.2. Quality management - assessment

The methodology is tailored to the scene which is characterised by a low number of R&D performing units on the University level

12.1. Relevance - User Needs

See below.

12.1.1. Needs at national level

Users’ class¹	Description of users	Users’ needs
1	Various ministries	R&D expenditures: types of exp., fields of science, and source of funds.
1	Universities	R&D expenditures: types of exp., fields of science, and source of funds.

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	We haven't done a user satisfaction survey; results have been presented to some of the ministries.
User satisfaction survey specific for R&D statistics	Not applicable
Short description of the feedback received	The data collected, as published by Statistics Iceland seems to have met user needs.

12.3. Completeness

See below.

12.3.1. Data completeness - rate

Not available

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
Type of R&D	Y	Yearly
Type of costs	Y	Yearly
Socioeconomic objective	Y	Yearly
Region	N
FORD	Y-1995	Yearly
Type of institution	N

1) Y-start year, N – data not available

12.3.3.2. Data availability - R&D Personnel (HC)

	Availability¹	Frequency of data collection	Gap years – years with missing data	Modifications - Description	Modifications - Year of introduction	Modifications - Reasons
Sex	Y-1999	Yearly
Function	Y	Yearly
Qualification	Y	Yearly
Age	N
Citizenship	N
Region	N
FORD	Y-1995	Yearly
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-1993	Yearly
Function	Y	Yearly
Qualification	Y	Yearly
Age	N
Citizenship	N
Region	N
FORD	Y-1995	Yearly
Type of institution	N

1) Y-start year, N – data not available

12.3.3.4. Data availability - other

Additional dimension/variable available at national level¹⁾	Availability²	Frequency of data collection	Breakdown variables	Combinations of breakdown variables	Level of detail

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⁾
	Sampling errors	Coverage errors	Measurement errors	Processing errors	Non response errors	Model-assumption Errors¹⁾	Perceived direction of the error²⁾
Total intramural R&D expenditure	-	1	3	4	2		+/-
Total R&D personnel in FTE	-	3	2	4	1		+/-
Researchers in FTE	-	3	2	4	1		+/-

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 method, as there was no sampling

13.2.1.2. Coefficient of variation 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. Coefficient of variation for R&D expenditure by function 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 :

Not known, assumed to be small.

b) Measures taken to reduce their effect:

Detailed instructions accompany the survey questionnaire, respondent support by phone and email.

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:

Not known, assumed to be small.

b) Measures taken to reduce their effect:

Detailed instructions accompany the survey questionnaire, respondent support by phone and email.

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)
9	10	10%

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
R&D expenditure		In practice non-existent. All the units reporting yes to the R&D question do provide basic information on the R&D expenditure, FTE and personnel.
R&D personnel in FTE		In practice non-existent. All the units reporting yes to the R&D question do provide basic information on the R&D expenditure, FTE and personnel.
Researchers in FTE		In practice non-existent. All the units reporting yes to the R&D question do provide basic information on the R&D expenditure, FTE and personnel.

13.3.3.3. Measures to increase response rate

Follow-up emails & phone calls.

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	Data collected through questionnaire sent to respondents via email.
Estimates of data entry errors	The only data entry errors that we have become aware of had to do with reported expenditures not being entered as the right amount. All responses were reviewed in light of that. Follow-up phone calls were made for the confirmation of amounts for every case where it wasn't clear whether the amounts had been entered correctly. So the issue was addressed and beyond that we are not aware of any data entry errors.
Variables for which coding was performed	No coding was required for variables that were sent to Eurostat and OECD.
Estimates of coding errors	No coding errors
Editing process and method	Any editing done to the data would involve checking with the respondents.
Procedure used to correct errors	Phone calls to respondents.

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: 31.12.2021

b) Date of first release of national data: 17.11.2022

c) Lag (days): 332 days.

14.1.2. Time lag - final result

a) End of reference period: 31.12.2021

b) Date of first release of national data: 03.03.2023

c) Lag (days): 427 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)	11	14
Delay (days)	17 days
Reasoning for delay	Delay in the release of administrative data that was needed for the data processing.

15.1. Comparability - geographical

See below.

15.1.1. Asymmetry for mirror flow statistics - coefficient

Not requested.

15.1.2. General issues of comparability

All data produced are harmonized with OECD norms and therefore international comparability is ensured.

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).	N/A
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	No deviation
Survey questionnaire / data collection form	No deviation
Cooperation with respondents	No deviation
Coverage of external funds	No deviation
Distinction between GUF and other sources – Sector considered as source of funds for GUF	No deviation
Data processing methods	No deviation
Treatment of non-response	No deviation
Variance estimation	No deviation
Method of deriving R&D coefficients	No deviation
Quality of R&D coefficients
Data compilation of final and preliminary data	No deviation

15.2. Comparability - over time

See below.

15.2.1. Length of comparable time series

See below.

15.2.2. Breaks in time series

	Length of comparable time series	Break years¹	Nature of the breaks
R&D personnel (HC)
Function
Qualification
R&D personnel (FTE)
Function
Qualification
R&D expenditure
Source of funds
Type of costs
Type of R&D
Other		2013	R&D statistics were moved to Statistics Iceland and comparability with previous years could not be ensured.

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

All data is collected on a yearly basis.

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

Not available.

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)	23229151	1240	1078
Final data (delivered T+18)	23229151	1234	1072
Difference (of final data)	0	6	6

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)	Internal personnel are not excluded from external personnel and therefore this data is not available.
Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2)	Internal personnel are not excluded from external personnel and therefore this data is not available.

(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	Rannsókna- og þróunarstarf háskólastofnana 2021
Type of survey	Census survey, panel of known or supposed R&D performers
Combination of sample survey and census data	-
Combination of dedicated R&D and other survey(s)	Not applicable.
Sub-population A (covered by sampling)	-
Sub-population B (covered by census)	-
Variables the survey contributes to	The survey contributes to produce information about the main variables and their breakdowns at predefined level of detail as specified in Commission Regulation 2020/1197.
Survey timetable-most recent implementation	N/A

18.1.2. Sample/census survey information

	Stage 1	Stage 2	Stage 3
Sampling unit	Higher education institutions (department level), incl. university hospitals.
Stratification variables (if any - for sample surveys only)
Stratification variable classes
Population size	10 (census)
Planned sample size
Sample selection mechanism (for sample surveys only)
Survey frame	HES sector includes all known R&D performers or potential R&D performers in universities and teaching hospitals. The method used to define the HES population is based on information from the previous R&D survey (all R&D performing units are surveyed) and information available from the central departments and administration services.
Sample design
Sample size
Survey frame quality

18.1.3. Information on collection of administrative data or of pre-compiled statistics

Source	N/A
Description of collected data / statistics	N/A
Reference period, in relation to the variables the survey contributes to	N/A

18.2. Frequency of data collection

See 12.3.3.

18.3. Data collection

See below.

18.3.1. Data collection overview

Information provider	All units.
Description of collected information	The information collected is R&D expenditure and R&D personnel. All the variables requested by EU Regulation No 2020/1197.
Data collection method	Questionnaire sent to respondents via email.
Time-use surveys for the calculation of R&D coefficients	N/A
Realised sample size (per stratum)	N/A
Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.)	Data collected in excel files through email
Incentives used for increasing response	No
Follow-up of non-respondents	Yes
Replacement of non-respondents (e.g. if proxy interviewing is employed)	No
Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility)	90%
Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods)	N/A

18.3.2. Questionnaire and other documents

Annex	Name of the file
R&D national questionnaire and explanatory notes in English:	R&DQuestionnaireHES
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:

Annexes:
R&DQuestionnaireHES

18.4. Data validation

1) comparison of the responses against the previous year, checking any inconsistencies
2) checking the outliers in respect to overall distributions

3) If any inconsistencies they are brought up in conversation with respondents

18.5. Data compilation

See below.

18.5.1. Imputation - rate

10%

18.5.2. Data compilation methods

Data compilation method - Final data (between the survey years)	HES data collected annually. Data available T+10.
Data compilation method - Preliminary data	HES data collected annually. Data available T+10.

18.5.3. Methodology for derivation of R&D coefficients

National methodology for their derivation.	N/A
Revision policy for the coefficients	N/A
Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc).	N/A

18.5.4. Measurement issues

Method of derivation of regional data	N/A
Coefficients used for estimation of the R&D share of more general expenditure items	N/A
Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures	N/A
Treatment and calculation of GUF source of funds / separation from “Direct government funds”	N/A
Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics	N/A

18.5.5. Weighting and estimation methods

Description of weighting method	No weighting
Description of the estimation method	Previous year

18.6. Adjustment

Not requested.

18.6.1. Seasonal adjustment

Not requested.

24 March 2023

See below.

Higher education institutions (department level), incl. university hospitals.

See below.

Not requested.

Calendar year of the reference year: 2021

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.

Expenditures: ISK (thousands)

Personnel: HC and FTE

See below.

Yearly dissemination.

See below.