1.1. Contact organisation
Statistical Service of Cyprus (CYSTAT)
1.2. Contact organisation unit
Science and Technology Statistics Unit
1.3. Contact name
Confidential because of GDPR
1.4. Contact person function
Confidential because of GDPR
1.5. Contact mail address
Statistical Service of Cyprus
CY-1444
Nicosia
Cyprus
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 2023
2.2. Metadata last posted
31 October 2023
2.3. Metadata last update
31 October 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 institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
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. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
3.2. Classification system
- 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 |
| No additional classification used | Not applicable |
3.3. Coverage - sector
See below.
3.3.1. General coverage
| Definition of R&D | No deviations in definitions and recommendations of Frascati Manual. |
| Fields of Research and Development (FORD) | No deviations in definitions and recommendations of Frascati Manual. |
| Socioeconomic objective (SEO by NABS) | No statistics on R&D expenditure by socio-economic objective are produced. |
3.3.2. Sector institutional coverage
| Higher education sector | No deviations in definitions and recommendations of Frascati Manual (§3.67-3.74). |
| Tertiary education institution | No deviations in definitions and recommendations of Frascati Manual (§3.67-3.71). |
| University and colleges: core of the sector | No deviations in definitions and recommendations of Frascati Manual (§3.67-3.71). |
| University hospitals and clinics | No deviations in definitions and recommendations of Frascati Manual (FM §3.71, 3.72, 9.13-9.17). |
| HES Borderline institutions | No deviations in definitions and recommendations of Frascati Manual (FM §3.73, 9.18-9.27). |
| Inclusion of units that primarily do not belong to HES | No |
3.3.3. R&D variable coverage
| R&D administration and other support activities | No deviations from FM §2.122. |
| External R&D personnel | No deviations from FM §5.20-5.24, Table 5.2. External personnel is calculated only in R&D expenditure. R&D personnel is only the internal R&D personnel. |
| Clinical trials | Information for clinical trials is included (FM §2.61) and is calculated/distributed in the sector performing them. If R&D can not been separated, the R&D is distributed to the sector of the entity performing the clinical trial. |
3.3.4. International R&D transactions
| Receipts from rest of the world by sector - availability | Yes. Receipts from Rest of the world by sector (FM §4.108, Table 4.3) |
| Payments to rest of the world by sector - availability | Not applicable. No Payments to Rest of the world by sector (FM §4.133). No extramural R&D is 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) | No |
| Method for separating extramural R&D expenditure from intramural R&D expenditure | Not applicable. |
| Difficulties to distinguish intramural from extramural R&D expenditure | Not applicable. |
3.4. Statistical concepts and definitions
See below.
3.4.1. R&D expenditure
| Coverage of years | Calendar year |
| Source of funds | No divergence from FM (FM §4.104-4.108, Table 4.3.) |
| Type of R&D | No divergence from FM (FM section 2.5) |
| Type of costs | No deviations from FM (section 4.2). No more detailed breakdown of costs than in the FM exist. |
| Defence R&D - method for obtaining data on R&D expenditure | R&D survey. |
3.4.2. R&D personnel
See below.
3.4.2.1. R&D personnel – Head Counts (HC)
| Coverage of years | Total number of persons employed during the calendar year. |
| Function | No difficulties encountered with classifying personnel by Occupation (researcher, technicians, other support staff). |
| Qualification | No difficulties encountered with classifying personnel by qualification. |
| Age | No difficulties encountered with classifying personnel by Age. |
| Citizenship | No difficulties encountered with classifying personnel by Citizenship. |
3.4.2.2. R&D personnel – Full Time Equivalent (FTE)
| Coverage of years | Total number of persons employed during the calendar year. |
| Function | No difficulties encountered with classifying personnel by Occupation (researcher, technicians, other support staff). |
| Qualification | No difficulties encountered with classifying personnel by qualification. |
| Age | Not applicable. No data for age in FTE is collected in Higher Education sector. |
| Citizenship | Not applicable. No data for Citizenship in FTE is collected in Higher Education sector. |
3.4.2.3. FTE calculation
The Full-time Equivalent (F.T.E.) expresses the total time devoted to research by a person during one year. One F.T.E. may be thought of as one person-year which corresponds to one person working full-time on R&D during one year. Thus, a person who normally spends 30% of his time on R&D and the remaining 70% on other activities should be considered as 30/100 = 0,3 person-years. Three persons who spend 30%, 50% and 80% of their time on R&D activities correspond to 0,3 + 0,5 + 0,8 = 1,6 person-years.
3.4.2.4. R&D personnel - Cross-classification by function and qualification
| Cross-classification | Unit | Frequency |
| No Cross-classification exist | Not applicable | Not applicable |
3.5. Statistical unit
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
The statistical unit used is the faculty or department or research centre.
3.6. Statistical population
See below.
3.6.1. National target population
The target population is the population for which inferences are made. The frame (or frames, as sometimes several frames are used) is a device that permits access to population units. The frame population is the set of population units which can be accessed through the frame and the survey data really refer to this population of institutional units.
The objective of the European R&D statistics is to cover all intramural R&D activities. In line with this objective, the target population for the national R&D survey of the HES Sector should consist of all R&D performing institutional units (including known R&D performers or assumed to perform R&D). In practise however, countries in their R&D surveys might have difficulty in identifying R&D activities at the municipality level.
| Target population when sample/census survey is used for collection of raw data | Target population when administrative data or pre-compiled statistics are used | |
| Definition of the national target population | The target population of national R&D statistics on HES comprises all universities and other institutions of post-secondary education, regardless of their source of finance or legal status. It includes research institutes operating under the direct control of or administered by or associated with higher education institutions. All higher education institutions registered at the Ministry of Education and Culture are being covered. | The target population of national R&D statistics on HES comprises all universities and other institutions of post-secondary education, regardless of their source of finance or legal status. It includes research institutes operating under the direct control of or administered by or associated with higher education institutions. All higher education institutions registered at the Ministry of Education and Culture are being covered. |
| Estimation of the target population size | No estimation of the target population size can be made. | No estimation of the target population size can be made. |
3.7. Reference area
Government controlled areas of the Republic of Cyprus.
3.8. Coverage - Time
Not requested. See point 3.4.
3.9. Base period
Not requested. The base year for the unit Purchasing Power Standard (PPS) and PPS per inhabitant at constant prices is currently 2005. All calculations of non-basic unit (national currencies) are done by Eurostat.
The units of measures used for the data set disseminated are Euro, %, number of persons.
Calendar year 2021
6.1. Institutional Mandate - legal acts and other agreements
Article 3 of the national Official Statistics Law, No. 25(I) of 2021 defines the functions of the Statistical Service of Cyprus regarding the production and dissemination of official statistics. Moreover, Article 13, explicitly stipulates the mandate for data collection and introduces a mandatory response to statistical enquiries by stipulating the obligation of respondents to reply to surveys and provide the data required. This relates not only to national but also to European statistics which, by virtue of Article 8 of the said Law, are incorporated in the annual and multiannual programmes of work without any further procedure.
6.1.1. European legislation
| Legal acts / agreements | 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 laying down technical specifications and arrangements pursuant to Regulation (EU) 2019/2152 of the European Parliament and of the Council on European business statistics repealing 10 legal acts in the field of business statistics. The Regulation sets the framework for the collection of R&D statistics and specifies the main variables of interest and their breakdowns at predefined level of detail. Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology was in force until the end of 2020. |
| Nature of the “obligations” of responsible national organisations to produce statistics and report to international organisations | Provisions of the above Legal acts / agreements |
6.1.2. National legislation
| Existence of R&D specific statistical legislation | Official Statistics Law No. 25(I) of 2021: https://www.cystat.gov.cy/en/StaticPage?id=1074 |
| Legal acts | Provitions of the above Law. |
| Obligation of responsible organisations to produce statistics (as derived from the legal acts) | Provitions of the above Law. |
| Right of responsible organisations to collect data – obligation of (natural / legal) persons to provide raw and administrative data (as derived from the legal acts) | Provitions of the above Law. |
| Obligation of responsible organisations to protect confidential information from disclosure (as derived from the legal acts) | Provitions of the above Law. |
| Rights of access of third organisations / persons to data and statistics (as derived from the legal acts) | Provitions of the above Law. |
| Planned changes of legislation | No |
6.1.3. Standards and manuals
- Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development
- European Business Statistics Methodological Manual on R&D Statistics
6.2. Institutional Mandate - data sharing
Not requested.
7.1. Confidentiality - policy
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: Official Statistics Law No. 25(I) of 2021
b) Confidentiality commitments of survey staff: Official Statistics Law No. 25(I) of 2021
Official statistics are released in accordance to all confidentiality provisions of the following:
- National Official Statistics Law No. 25(I) of 2021 (especially Article 16 on statistical confidentiality).
- Regulation (EC) No 223/2009 of the European Parliament and of the Council of 11 March 2009 on European statistics and its later amendments (especially Chapter 5 on statistical confidentiality).
- European Statistics Code of Practice (especially Principle 5 on statistical confidentiality).
- Guidelines for the Protection of Confidential Data.
- Official Statistics Law No. 25(I) of 2021:https://www.cystat.gov.cy/en/StaticPage?id=1074
- Regulation (EC) No 223/2009 on European statistics (consolidated text): http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:02009R0223-20150608&qid=1504858409240&from=EN
- European Statistics Code of Practice:http://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-32-11-955
- Guidelines for the Protection of Confidential Data: https://www.cystat.gov.cy/en/StaticPage?id=1066
7.2. Confidentiality - data treatment
The survey is carried out in accordance to the Official Statistics Law No. 25(I) of 2021. The Statistical Service is bound, under the provisions of the Statistics Law, to treat all information collected as confidential. All collected information and data are used solely for statistical purposes. Data on individual enterprise cannot be published or disclosed to either public bodies or private individuals.
The treatment of confidential data is regulated by Guidelines for the Protection of Confidential Data.
- Guidelines for the Protection of Confidential Data: https://www.cystat.gov.cy/en/StaticPage?id=1066
8.1. Release calendar
Notifications about the dissemination of statistics are published in the release calendar, which is available on CYSTAT’s web portal. The annual release calendar, announced during the 4th quarter of the year, includes provisional dates of publication for the following year, which are finalized the week before publication.
8.2. Release calendar access
Link to CYSTAT’s release calendar: https://www.cystat.gov.cy/en/AnnouncementList
8.3. Release policy - user access
According to the Dissemination and Pricing Policy of the Statistical Service of Cyprus (section 2.3) CYSTAT΄s main channel for dissemination of statistics is the web portal, which offers the same conditions to everyone and is updated at the same time every working day (12:00 noon). No privileged pre-released access is granted.
In addition to the annual release calendar, users are informed of the various statistical releases through the “Alert” service provided by CYSTAT.
- Dissemination and Pricing Policy of the Statistical Service of Cyprus: https://www.cystat.gov.cy/en/StaticPage?id=1064
Frequency of data dissemination: 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 | Y | Yes a press release is issued |
| Ad-hoc releases | N |
1) Y - Yes, N – No
10.2. Dissemination format - Publications
See below.
10.2.1. Availability of means of dissemination
| Means of dissemination | Availability (Y/N)1 | Content, format, links, ... |
| General publication/article (paper, online) |
Y | The results of the national R&D surveys are published in the annual report “Research and Development Statistics”. The key results are also published in our statistical yearbook entitled “Statistical Abstract”. Both publications can be purchased in paper form or can be downloaded for free from our website. |
| Specific paper publication (e.g. sectoral provided to enterprises) (paper, online) |
N |
1) Y – Yes, N - No
10.3. Dissemination format - online database
Not available.
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 | There is no Micro-data access to outside users. Statistical micro-data from CYSTAT’s surveys are accessible for research purposes only and under strict provisions as described below: Under the provisions of the Official Statistics Law, CYSTAT may release microdata for the sole use of scientific research. Applicants have to submit the request form "APPLICATION FOR DATA FOR RESEARCH PURPOSES" giving thorough information on the project for which micro-data are needed. The application is evaluated by CYSTAT’s Confidentiality Committee and if the application is approved, a charge is fixed according to the volume and time consumed for preparation of the data. Micro-data may then be released after an anonymization process which ensures no direct identification of the statistical units but, at the same time, ensures usability of the data. The link for the application is attached below.
|
| Access cost policy | See above |
| Micro-data anonymisation rules | See above |
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 | The results of the national R&D surveys are published in the annual report “Research and Development Statistics”. The publication can be downloaded free of charge from the web site of the Statistical Service of Cyprus (www.cystat.gov.cy) in PDF format. The main R&D indicators are also included, as MS Excel files, in the “Key Figures” section of the web site, under the statistical theme “Science and Technology”. Finally, the key results are also published in our statistical yearbook entitled “Statistical Abstract” which can be downloaded from the web site. | |
| Data prepared for individual ad hoc requests | Y | Further information to interested users is given upon request. | |
| Other | N |
1) Y – Yes, N - No
10.6. Documentation on methodology
https://www.cystat.gov.cy/en/MethodologicalDisplay?s=49
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.) | Users can download free of charge from the web site of the Statistical Service of Cyprus the annual publication “Research and Development Statistics”. This contains a textual description of latest developments in R&D activities, a number of graphical displays and numerous tables, including a comparison with corresponding international statistics. It also contains a comprehensive methodological note, giving information on the national R&D survey and its scope, concepts and definitions, as well as a copy of the questionnaire used. Every time that new data is disseminated at the national level, a press release is issued. |
| Request on further clarification, most problematic issues | For any further information, users can make a request to the Statistical Service (by phone, mail, e-mail or via the enquiries facility on the web site). |
| Measure to increase clarity | No intention to take any further measures. |
| Impression of users on the clarity of the accompanying information to the data | Users seem to be fully satisfied. |
11.1. Quality assurance
The quality of statistics in CYSTAT is managed in the framework of the European Statistics Code of Practice which sets the standards for developing, producing and disseminating European Statistics as well as the ESS Quality Assurance Framework (QAF). CYSTAT endorses the Quality Declaration of the European Statistical System. In addition, CYSTAT is guided by the requirements provided for in Article 11 of the Official Statistics Law No. 25(I) of 2021 as well as Article 12 of Regulation (EC) No 223/2009 on European statistics, which sets out the quality criteria to be applied in the development, production and dissemination of European statistics.
- European Statistics Code of Practice: http://ec.europa.eu/eurostat/web/products-manuals-and-guidelines/-/KS-32-11-955
- ESS Quality Assurance Framework (QAF): http://ec.europa.eu/eurostat/documents/64157/4392716/ESS-QAF-V1-2final.pdf/bbf5970c-1adf-46c8-afc3-58ce177a0646
- Quality Declaration of the European Statistical System: http://ec.europa.eu/eurostat/documents/4031688/8188985/KS0217428ENN_corr.pdf/116f7c85-cd3e-4bff-b695-4a8e71385fd4
- Official Statistics Law No. 25(I) of 2021: https://www.cystat.gov.cy/en/StaticPage?id=1074
- Regulation (EC) No 223/2009 on European statistics (consolidated text): http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:02009R0223-20150608&qid=1504858409240&from=EN
11.2. Quality management - assessment
The R&D data on the HES sector in Cyprus are assessed as being of high quality. The definitions, concepts and methodology used are in compliance with the requirements of Eurostat and follow the guidelines of the Frascati Manual 2015. The national R&D survey is a well established survey which yields the maximum of the information required on an annual basis and with a relevantly short time lag from the end of the reference period. Information collected from administrative sources is of very good quality and is complemented with data provided by the research staff itself, thus rendering the statistics produced of an adequate standard.
12.1. Relevance - User Needs
See below.
12.1.1. Needs at national level
| Users’ class1 | Description of users | Users’ needs |
| 1 | European institutions: Council, Commission (Eurostat, DG Research), European Parliament. International organisations: OECD, UNESCO etc. National: Ministry of Finance, Ministry of Energy, Commerce and Industry, Deputy Ministry of Research, Innovation and Digital Strategy, Directorate General for European Programmes, Coordination and Development, Research & Innovation Foundation |
European institutions: Formulating the needs and assessing the implementation of Community research policies, especially with regard to the EU goals in R&D, as set by the Lisbon summitstrategy. International organisations: Economic analysis and monitoring. National: Assessing the implementation of the national reform programme for the Lisbon strategy, strategic programming, economic analysis and monitoring. |
| 2 | Social actors: various employers’ associations, trade unions and lobby groups. | Economic analysis and monitoring, interested both in figures and comments. |
| 3 and 4 | Media: Economic newspapers, TV channels. Researchers and students: Higher education institutions, researchers, students and private individuals. | Interested in figures, comments and analyses. |
| 5 | Enterprises or businesses: Business enterprises, consultancy offices. | Market analysis, marketing strategy, offering consultancy services. |
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 | Since 2008 (with the exception of 2010, 2013 and 2020) CYSTAT carries out an annual online “Users Satisfaction Survey”. The results of the surveys are available on CYSTAT’s web portal at the link attached below. Overall, there is a high level of satisfaction of the users of statistical data published by CYSTAT. Results of CYSTAT’s User Satisfaction Surveys: https://www.cystat.gov.cy/en/StaticPage?id=1144 |
| User satisfaction survey specific for R&D statistics | In the latest national user satisfaction survey, R&D statistics were listed down explicitly as one of the main statistical fields to be commented on. |
| Short description of the feedback received | However, the number of questionnaires with relevant comments was too low to allow for any concrete conclusions to be drawn. No specific problems were reported. There were no findings specific to the HES sector. |
12.3. Completeness
See below.
12.3.1. Data completeness - rate
See below.
12.3.2. Completeness - overview
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 2020/1197. The Regulation (EU) stipulates periodicity of variables that should be provided, breakdowns and if they should be provided mandatory or on voluntary basis.
| 5 (Very Good) |
4 (Good) |
3 (Satisfactory) |
2 (Poor) |
1 (Very poor) |
Reasons for missing cells |
|
| Preliminary variables | X | No missing cells | ||||
| Obligatory data on R&D expenditure | X | No missing cells | ||||
| Optional data on R&D expenditure | X | No missing cells | ||||
| Obligatory data on R&D personnel | X | No missing cells | ||||
| Optional data on R&D personnel | X | No missing cells | ||||
| Regional data on R&D expenditure and R&D personnel | X | No missing cells |
Criteria:
A) Obligatory data. Only 'Very Good' = 100%, Poor' >95%; 'Very Poor' <100% apply.
B) Optional data. 'Very Good' = 100%; 'Good' = >75%; 'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%.
12.3.3. Data availability
See below.
12.3.3.1. Data availability - 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-1998 | Annual | No gap years | No modifications | ||
| Type of R&D | Y-1998 | Annual | No gap years | No modifications | ||
| Type of costs | Y-1998 | Annual | No gap years | No modifications | ||
| Socioeconomic objective | N – data not available | |||||
| Region | Not apllicable. Cyprus is one region | |||||
| FORD | Y-1998 | Annual | No gap years | No modifications | ||
| Type of institution | Y-1998 | Annual | No gap years | No modifications |
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-2001 | Annual | No gap years | No modifications | ||
| Function | Y-1998 | Annual | No gap years | No modifications | ||
| Qualification | Y-1998 | Annual | No gap years | No modifications | ||
| Age | Y-2002 | Annual | No gap years | No modifications | ||
| Citizenship | Y-2002 | Annual | No gap years | No modifications | ||
| Region | Not apllicable. Cyprus is one region | |||||
| FORD | Y-1998 | Annual | No gap years | No modifications | ||
| Type of institution | Y-1998 | Annual | No gap years | No modifications |
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-2001 | Annual | No gap years | No modifications | ||
| Function | Y-1998 | Annual | No gap years | No modifications | ||
| Qualification | Y-1998 | Annual | No gap years | No modifications | ||
| Age | N – data not available | |||||
| Citizenship | N – data not available | |||||
| Region | Not apllicable. Cyprus is one region | |||||
| FORD | Y-1998 | Annual | No gap years | No modifications | ||
| Type of institution | Y-1998 | Annual | No gap years | No modifications |
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 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
1) This question is optional. It refers to variables and breakdowns NOT asked by the Commission Implementing Regulation (EU) No 2020/1197 (neither as 'optional').
2) Y-start year
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 | - |
1 | 2 | - | - | - | +/- |
| Total R&D personnel in FTE | - | 1 | 2 | - | - | - | +/- |
| Researchers in FTE | - | 1 | 2 | - | - | - | +/- |
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
Not applicable, since no sample survey is conducted.
13.2.1.2. Confidence interval for R&D expenditure by source of funds
| Source of funds | R&D expenditure |
| Business enterprise | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted |
| Government | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted |
| Higher education | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted |
| Private non-profit | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted |
| Rest of the world | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted |
| Total | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted |
13.2.1.3. Confidence interval for R&D personnel by occupation and qualification
| R&D personnel (FTE) | ||
| Function | Researchers | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted |
| Technicians | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted | |
| Other support staff | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted | |
| Qualification | ISCED 8 | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted |
| ISCED 5-7 | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted | |
| ISCED 4 and below | Not applicable. No coefficients of variation can be calculated since no sample survey is conducted |
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 applicable. None of the HES R&D performing units is being omitted.
b) Measures taken to reduce their effect: Not applicable.
13.3.1.1. Over-coverage - rate
Not applicable.
13.3.1.2. Common units - proportion
Not requested.
13.3.2. Measurement error
Measurement errors occur during data collection and generate bias by recording values different than the true ones (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: No measurement errors exist.
b) Measures taken to reduce their effect: The data collection and processing phase is managed by a highly skilled person who is working in the field for more than 22 years. Information providers, in most cases, stay the same for years and, consequently, are very well aware of the questionnaire and the relevant concepts and definitions. Regular contacts by telephone or e-mail and, in some cases, personal interviews, are also made to provide clarifications and assistance and to check and correct possible inconsistencies and oversights in the questionnaires received. The data reported are also checked against administrative records kept by the Research & Innovation Foundation, which is the national institute for the promotion of scientific and technological research in Cyprus. The CORDIS database, as well as the web sites of various funding agencies and programmes, research compendia and journals and academic reports on research, is also frequently consulted in order to identify any possible research activities that the information providers may have failed to report.
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) |
| 11 | 11 | 0% |
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 |
| Not applicable, there is no non-response. | Not applicable, there is no non-response. | Not applicable, there is no non-response. |
13.3.3.3. Measures to increase response rate
Not applicable, there is no non-response.
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 entry is done in MS Excel spreadsheets. |
| Estimates of data entry errors | No processing errors exist. |
| Variables for which coding was performed | All variables included on the questionnaires are being coded. |
| Estimates of coding errors | No coding errors exist. |
| Editing process and method | The MS Excel files used incorporate various cross-checking and validation capabilities. Controls and checks for logical inconsistencies are used to eliminate any remaining errors. Comparisons are also made with the responses provided by the same unit in the previous years’ surveys. |
| Procedure used to correct errors | Errors detected in the questionnaires are corrected by further contacting the information providers. |
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: No release of provisional national data.
b) Date of first release of national data: No release of provisional national data.
c) Lag (days): No release of provisional national data.
14.1.2. Time lag - final result
a) End of reference period: 2021 (T)
b) Date of first release of national data: T+19 months
c) Lag (days): 0 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) | 10 | 18 |
| Delay (days) | 0 | 18 |
| 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 comments.
15.1.3. Survey Concepts Issues
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Implementing Regulation (EU) No 2020/1197 or Frascati manual and EBS Methodological Manual on R&D Statistics paragraphs with recommendations about these concepts/issues.
| Concept / Issues | Reference to recommendations | Deviation from recommendations | Comments on national definition / Treatment – deviations from recommendations |
| R&D personnel | FM2015 Chapter 5 (mainly 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 Eurostat'EBS Methodological Manual on R&D Statistics). | No | |
| Approach to obtaining Full-time equivalence (FTE) data | FM2015, § 5.49-5.57 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| 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 Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Target population | FM2015 §9.6 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Sector coverage | FM2015 §3.67-3.69 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Post-secondary (non university / college) education institutions | FM2015 §9.12 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Hospitals and clinics | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Borderline research institutions | FM2015 §9.13-9.17, §9.109-9.112 (in combination with Eurostat's EBS Methodological Manual on R&D Statistics). | No | |
| Major fields of science and technology coverage and breakdown | Reg. 2020/1197 : Annex 1, Table 18 | No | Production of annual data for all variables. |
| Reference period | Reg. 2020/1197 : Annex 1, Table 18 | No | Production of annual data for all variables. |
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 | See present report |
| Survey questionnaire / data collection form | No | See present report |
| Cooperation with respondents | No | See present report |
| Coverage of external funds | No | See present report |
| Distinction between GUF and other sources – Sector considered as source of funds for GUF | No | See present report |
| Data processing methods | No | See present report |
| Treatment of non-response | No | See present report |
| Variance estimation | No | See present report |
| Method of deriving R&D coefficients | No | See present report |
| Quality of R&D coefficients | No | See present report |
| Data compilation of final and preliminary data | No | See present report |
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 | 1998-2021 | No break years | |
| Qualification | 1998-2021 | No break years | |
| R&D personnel (FTE) | |||
| Function | 1998-2021 | No break years | |
| Qualification | 1998-2021 | No break years | |
| R&D expenditure | 1998-2021 | No break years | |
| Source of funds | 1998-2021 | No break years | |
| Type of costs | 1998-2021 | No break years | |
| Type of R&D | 1998-2021 | No break years | |
| Other | 1998-2021 | No break years |
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 are produced in the same way in the odd and 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. The education statistics (UNESCO/OECD/Eurostat (UOE)) include R&D expenditure in tertiary educational institutions and follow the recommendations of the Frascati manual regarding the definition of R&D expenditure. Due to the differences in the coverage some differences in the two datasets (UOE questionnaire and the R&D HES surveys) are expected. However, there is a need to ensure that a harmonised approach is used for compiling data in the two domains. The two statistical domains should aim for a consistent use of R&D coefficients for splitting teaching and research time.
15.3.1. Coherence - sub annual and annual statistics
Not requested.
15.3.2. Coherence - National Accounts
R&D statistics are fully reconcilable with National Accounts.
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 |
| Not applicable | Not applicable | Not applicable | Not applicable | Not applicable | Not applicable |
15.3.4. Coherence – Education statistics
R&D statistics are fully reconcilable with Education Statistics (R&D only). All other education data (UOE) (institutions/departments involved) are collected by the Education Statistics Section of CYSTAT.
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) | 70000 | 875.0 | 765.0 |
| Final data (delivered T+18) | 76616 | 880.0 | 768.2 |
| Difference (of final data) | 6616 | 5.0 | 3.2 |
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) | Not available |
| Consistency between FTEs of external R&D personnel and other current costs for external R&D personnel (2) | 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 | Not available | 0 |
| Data collection costs | Not available | 0 |
| Other costs | Not available | 0 |
| Total costs | Not available | 0 |
| 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) | 11 | Count the number of bodies (universities and other institutions of post-secondary education, regardless of their source of finance or legal status), from which request R&D data. |
| Average Time required to complete the questionnaire in hours (T)1 | Not available | Not available |
| Average hourly cost (in national currency) of a respondent (C) | Not available | Not available |
| Total cost | Not available | Not available |
1) T = the time required to provide the information, including time spent assembling information prior to completing a form or taking part in interview and the time taken up by any subsequent contacts after receipt of the questionnaire (‘Re-contact time’)
17.1. Data revision - policy
A data revision policy is in place at CYSTAT. It is published on CYSTAT’s web portal, at the following link: https://www.cystat.gov.cy/en/StaticPage?id=1072
CYSTAT also publishes a list of scheduled revisions (regular or major revisions), also published on its web portal, at the following link: https://www.cystat.gov.cy/en/AnnouncementList
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 | SURVEY ON SCIENTIFIC RESEARCH AND EXPERIMENTAL DEVELOPMENT |
| Type of survey | The national R&D survey is incorporated in the regular programme of work of the Statistical Service of Cyprus and it can be considered as a census. |
| Combination of sample survey and census data | Not applicable. No sampling is used. |
| Combination of dedicated R&D and other survey(s) | Not applicable. |
| Sub-population A (covered by sampling) | Not applicable. |
| Sub-population B (covered by census) | Not applicable. |
| Variables the survey contributes to | Not applicable. |
| Survey timetable-most recent implementation | Not applicable. |
18.1.2. Sample/census survey information
| Stage 1 | Stage 2 | Stage 3 | |
| Sampling unit | Not applicable. No sampling is used. |
Not applicable. No sampling is used. | Not applicable. No sampling is used. |
| Stratification variables (if any - for sample surveys only) | Not applicable. No sampling is used. | Not applicable. No sampling is used. | Not applicable. No sampling is used. |
| Stratification variable classes | Not applicable. No sampling is used. | Not applicable. No sampling is used. | Not applicable. No sampling is used. |
| Population size | Not applicable. No sampling is used. | Not applicable. No sampling is used. | Not applicable. No sampling is used. |
| Planned sample size | Not applicable. No sampling is used. | Not applicable. No sampling is used. | Not applicable. No sampling is used. |
| Sample selection mechanism (for sample surveys only) | Not applicable. No sampling is used. | Not applicable. No sampling is used. | Not applicable. No sampling is used. |
| Survey frame | Not applicable. No sampling is used. | Not applicable. No sampling is used. | Not applicable. No sampling is used. |
| Sample design | Not applicable. No sampling is used. | Not applicable. No sampling is used. | Not applicable. No sampling is used. |
| Sample size | Not applicable. No sampling is used. | Not applicable. No sampling is used. | Not applicable. No sampling is used. |
| Survey frame quality | Not applicable. No sampling is used. | Not applicable. No sampling is used. | Not applicable. No sampling is used. |
18.1.3. Information on collection of administrative data or of pre-compiled statistics
| Source | The survey is based on a register of all possible R&D performers, following the FM recommendations. This register includes all universities and other institutions of post-secondary education, regardless of their source of finance or legal status. It includes research institutes operating under the direct control of or administered by or associated with higher education institutions. All higher education institutions registered at the Ministry of Education and Culture are being covere |
| Description of collected data / statistics | Information is collected from administrative sources and registers of higher education institutions, which includes (i) a list of the academic staff by department, position, year of birth and citizenship, (ii) the annual gross emoluments for each member of the academic staff, (iii) the R&D expenses and the names of the research co-ordinator and of the other members of the research team for each internal research programme, (iv) a list of all research programmes funded from external sources and the level of funding by each body, and (v) a list of all postgraduate research students by department, date of enrolment, year of birth and citizenship. The time-use survey requires from staff members information on faculty/department, sex, age, citizenship, level of formal qualification and an estimate of their working time devoted to research, the area of their research interests and their participation in research programmes. In addition, coordinators of each research programme provide information on its timeframe, classification by type of research and field of science and technology, personnel involved by occupation, sex, age, citizenship, level of formal qualification, remuneration and full-time equivalence and expenditure by type of costs and source of funding. |
| Reference period, in relation to the variables the survey contributes to | All parameters collected are reported on an annual basis. |
18.2. Frequency of data collection
See 12.3.3.
18.3. Data collection
See below.
18.3.1. Data collection overview
| Information provider | Data is collected from university central administration offices (finance, personnel, service for research) and, in the case of time-use surveys, from individual staff members. |
| Description of collected information | Information is collected from administrative sources and registers of higher education institutions, which includes (i) a list of the academic staff by department, position, year of birth and citizenship, (ii) the annual gross emoluments for each member of the academic staff, (iii) the R&D expenses and the names of the research co-ordinator and of the other members of the research team for each internal research programme, (iv) a list of all research programmes funded from external sources and the level of funding by each body, and (v) a list of all postgraduate research students by department, date of enrolment, year of birth and citizenship. The time-use survey requires from staff members information on faculty/department, sex, age, citizenship, level of formal qualification and an estimate of their working time devoted to research, the area of their research interests and their participation in research programmes. In addition, co-ordinators of each research programme provide information on its timeframe, classification by type of research and field of science and technology, personnel involved by occupation, sex, age, citizenship, level of formal qualification, remuneration and full-time equivalence and expenditure by type of costs and source of funding. |
| Data collection method | Information is collected from administrative sources and registers of higher education institutions, as described previously. Two questionnaires are then addressed to each member of the academic staff; the first questionnaire aims at being completed by everybody and is basically a time-use survey, while the second questionnaire pertains to the research programmes themselves and is meant to be completed by the co-ordinators of each programme only. Regular contacts by telephone or e-mail and, in some cases, personal interviews, are also used to provide clarifications and assistance and to check and correct possible inconsistencies and oversights in the questionnaires received. The data reported are also checked against administrative records kept by the Research & Innovation Foundation, which is the national institute for the promotion of scientific and technological research in Cyprus. The CORDIS database, as well as the web sites of various funding agencies and programmes, research compendia and journals and academic reports on research, is also frequently consulted in order to identify any possible research activities that the information providers may have failed to report. |
| Time-use surveys for the calculation of R&D coefficients | All parameters collected are reported on an annual basis. |
| Realised sample size (per stratum) | Not applicable. No sampling is used. |
| Mode of data collection (face-to-face interviews; telephone interviews; postal surveys, etc.) | Information is collected from administrative sources and registers of higher education institutions, as described previously. Two questionnaires are then addressed to each member of the academic staff; the first questionnaire aims at being completed by everybody and is basically a time-use survey, while the second questionnaire pertains to the research programmes themselves and is meant to be completed by the co-ordinators of each programme only. Regular contacts by telephone or e-mail and, in some cases, personal interviews, are also used to provide clarifications and assistance and to check and correct possible inconsistencies and oversights in the questionnaires received. The data reported are also checked against administrative records kept by the Research & Innovation Foundation, which is the national institute for the promotion of scientific and technological research in Cyprus. The CORDIS database, as well as the web sites of various funding agencies and programmes, research compendia and journals and academic reports on research, is also frequently consulted in order to identify any possible research activities that the information providers may have failed to report. |
| Incentives used for increasing response | No incentives used for increasing response. |
| Follow-up of non-respondents | Regular contacts by telephone or email and, in some cases, personal interviews, are also used. |
| Replacement of non-respondents (e.g. if proxy interviewing is employed) | No replacement of non-respondents. |
| Response rate (ratio of completed "interviews" over total number of eligible enterprises or enterprises of unknown eligibility) | 100% |
| Non-response analysis (if applicable -- also see section 18.5. Data compilation - Weighting and Estimation methods) | There was no need to carry out a non-response survey. |
18.3.2. Questionnaire and other documents
| Annex | Name of the file |
| R&D national questionnaire and explanatory notes in English: | Quest_2021_HES_EN.pdf -(R&D Questionnaire 2021 - Higher Education Sector (English)) |
| R&D national questionnaire and explanatory notes in the national language: | Quest_2021_HES_GR.pdf -(R&D Questionnaire 2021 - Higher Education Sector (Greek)) |
| Other relevant documentation of national methodology in English: | |
| Other relevant documentation of national methodology in the national language: |
Annexes:
R&D Questionnaire 2021 - Higher Education Sector (English)
R&D Questionnaire 2021 - Higher Education Sector (Greek)
18.4. Data validation
Validation activities include: checking that the population coverage and response rates are as required; comparing the statistics with previous cycles; confronting the statistics against other relevant data (both internal and external); investigating inconsistencies in the statistics; verifying the statistics against expectations and domain intelligence, outlier detection.
18.5. Data compilation
See below.
18.5.1. Imputation - rate
Not applicable.
18.5.2. Data compilation methods
| Data compilation method - Final data (between the survey years) | The national R&D survey is carried out on an annual basis. |
| Data compilation method - Preliminary data | An estimate is made on the basis of the previous year’s figures and data derived from administrative records. No use of coefficients is made. |
18.5.3. Methodology for derivation of R&D coefficients
| National methodology for their derivation. | No coefficients are being used. |
| Revision policy for the coefficients | Not applicable. |
| Issues that affect their quality (e.g. date of last update, aggregation level at which they are computed, etc). | Not applicable. |
18.5.4. Measurement issues
| Method of derivation of regional data | Not applicable. Cyprus is one region. |
| Coefficients used for estimation of the R&D share of more general expenditure items | Not applicable. |
| Inclusion or exclusion of VAT and provisions for depreciation in the measurement of expenditures | No deviations from FM §4.40-4.43 (VAT), and FM §4.38-4.39 (depreciation) recomendations. |
| Treatment and calculation of GUF source of funds / separation from “Direct government funds” | No deviations from FM recomendations. |
| Differences between national and Frascati Manual classifications not mentioned above and impact on national statistics | No differences. |
18.5.5. Weighting and estimation methods
| Description of weighting method | Not applicable. No sampling is used. |
| Description of the estimation method | Not applicable. No sampling is used. |
18.6. Adjustment
Not requested.
18.6.1. Seasonal adjustment
Not requested.
Statistics on higher education R&D (HERD) measure research and experimental development (R&D) performed in the higher education sector, i.e. R&D expenditure and R&D personnel. In line with this objective the target population for the national R&D survey of the higher education sector should consist of all R&D performing institutional units (including all R&D performers – occasional and continuous, known and unknown - in all branches and size classes) belonging to this sector.
The main concepts and definitions used for the production of R&D statistics are given by the OECD (2015), Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development, The Measurement of Scientific, Technological and Innovation Activities, which is the internationally recognised standard methodology for collecting R&D statistics and Eurostat’s European Business Statistics Methodological Manual on R&D Statistics(EBS Methodological Manual on R&D Statistics) complements this with guidelines for further harmonisation among EU, EFTA and candidate countries.
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. Statistics on science, technology and innovation were collected until the end of 2020 based on Commission Implementing Regulation (EU) No 2012/995 concerning the production and development of Community statistics on science and technology.
31 October 2023
See below.
The statistical unit is the institutional unit as defined by Council Regulation (EEC) No 1993/696 of 15 March 1993, if there are deviations please explain.
The statistical unit used is the faculty or department or research centre.
See below.
Government controlled areas of the Republic of Cyprus.
Calendar year 2021
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 units of measures used for the data set disseminated are Euro, %, number of persons.
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.
Frequency of data dissemination: 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.
