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For any question on data and metadata, please contact: Eurostat user support |
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1.1. Contact organisation | Swiss Federal Statistical Office (FSO) |
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1.2. Contact organisation unit | Division WI/ section WSA |
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1.5. Contact mail address | Office Fédéral de la Statistique (OFS) Espace de l'Europe 10 2010 Neuchâtel SWITZERLAND |
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2.1. Metadata last certified | 14/04/2023 | ||
2.2. Metadata last posted | 14/04/2023 | ||
2.3. Metadata last update | 14/04/2023 |
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3.1. Data description | ||||||||||||||||
Statistics on Government Budget Allocations for R&D (GBARD) measure government support to research and experimental development (R&D) activities, and thereby provide information about the priority Governments give to different public R&D funding activities. This type of funder-based approach for reporting R&D involves identifying all the budget items that may support R&D activities and measuring or estimating their R&D content (FM 2015, Chapter 12). 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 1197/2020 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 (Commission Implementing Regulation (EU) 2020/ 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 (europa.eu)). Please note that according to Article 12(4) of Regulation (EU) 1197/2020, the provisions of Regulation (EU) 995/2012 continue to apply for the reference years that fall before 1 January 2021. |
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3.2. Classification system | ||||||||||||||||
Distribution by socioeconomic objectives (SEO) is based on the Nomenclature for the Analysis and Comparisons of Scientific Programmes and Budgets (NABS) at one digit level. |
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3.2.1. National classification | ||||||||||||||||
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3.2.2. NABS classification | ||||||||||||||||
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3.3. Coverage - sector | ||||||||||||||||
See below. |
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3.3.1. General coverage | ||||||||||||||||
1) Science & Technology |
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3.3.2. Definition and coverage of government | ||||||||||||||||
GBARD statistics are assumed to report detailed data on all the government's budget items that may support R&D activities and to measure or estimate their R&D content. For the purposes of GBARD, the Government sector comprises (a) the central (federal) government, (b) regional (state) government and (c) local (municipal) government subsectors (FM2015, Chapter 12).
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3.4. Statistical concepts and definitions | ||||||||||||||||
Not requested. |
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3.5. Statistical unit | ||||||||||||||||
institutional unit |
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3.6. Statistical population | ||||||||||||||||
See below. |
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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.
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3.7. Reference area | ||||||||||||||||
Not requested. |
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3.8. Coverage - Time | ||||||||||||||||
Not requested. See point 5. |
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3.9. Base period | ||||||||||||||||
Not requested. |
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Not requested. |
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a) Calendar year: Y
b) Fiscal year: Start month: End month: |
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6.1. Institutional Mandate - legal acts and other agreements | |||
See below. |
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6.1.1. European legislation | |||
GBARD statistics are based on the 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. |
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6.1.2. National legislation | |||
There is no special national law. |
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6.1.3. Standards and manuals | |||
Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development. |
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6.2. Institutional Mandate - data sharing | |||
Not requested. |
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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: Ordonnance concernant l'exécution des relevés statistiques du 30 juin 1993 b) Confidentiality commitments of survey staff:
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7.2. Confidentiality - data treatment | |||
Not applicable |
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8.1. Release calendar | |||
Not applicable |
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8.2. Release calendar access | |||
Not applicable |
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8.3. Release policy - user access | |||
Not applicable |
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The collection of GBARD data is conducted and disseminated on an yearly basis |
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10.1. Dissemination format - News release | ||||||||||||||||||||
See below. |
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10.1.1. Availability of the releases | ||||||||||||||||||||
1) Y - Yes, N – No |
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10.2. Dissemination format - Publications | ||||||||||||||||||||
See below. |
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10.2.1. Availability of means of dissemination | ||||||||||||||||||||
1) Y – Yes, N - No |
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10.3. Dissemination format - online database | ||||||||||||||||||||
Free online database (S-T Indicators) accessible on our website S&T Indicators: In the indicator, GBARD. indicator in French and in German. https://www.bfs.admin.ch/bfs/fr/home/statistiques/education-science/technologie/systeme-indicateurs/acces-indicateurs/input-s-t/credits-budgetaires-r-d.html |
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10.3.1. Data tables - consultations | ||||||||||||||||||||
Not requested. |
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10.4. Dissemination format - microdata access | ||||||||||||||||||||
See below. |
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10.4.1. Provisions affecting the access | ||||||||||||||||||||
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10.5. Dissemination format - other | ||||||||||||||||||||
See below. |
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10.5.1. Metadata - consultations | ||||||||||||||||||||
Not requested. |
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10.5.2. Availability of other dissemination means | ||||||||||||||||||||
1) Y – Yes, N - No |
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10.6. Documentation on methodology | ||||||||||||||||||||
- |
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10.6.1. Metadata completeness - rate | ||||||||||||||||||||
In 2021, the FSO introduced a methodological revision of the calculation of GBARD, based on administrative data. The new methodology was applied retrospectively to the series of all national GBARD data from 2017 onwards. The GBARD methodology is described in a FSO publication (link bellow) https://www.bfs.admin.ch/bfs/fr/home/statistiques/education-science/technologie/systeme-indicateurs/acces-indicateurs/input-s-t/credits-budgetaires-r-d.assetdetail.21364556.html |
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10.7. Quality management - documentation | ||||||||||||||||||||
See below. |
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10.7.1. Information and clarity | ||||||||||||||||||||
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11.1. Quality assurance | |||
- |
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11.2. Quality management - assessment | |||
The overall assessment of the GBARD data is good. Some weaknesses might appear by using R&D coefficients |
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12.1. Relevance - User Needs | ||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.1.1. Needs at national level | ||||||||||||||||||||||||||||||||||||||||||
1) Users' class codification 1- Institutions: 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.) |
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12.2. Relevance - User Satisfaction | ||||||||||||||||||||||||||||||||||||||||||
To evaluate if users' needs have been satisfied, the best way is to use user satisfaction surveys. |
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12.2.1. National Surveys and feedback | ||||||||||||||||||||||||||||||||||||||||||
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12.3. Completeness | ||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.3.1. Data completeness - rate | ||||||||||||||||||||||||||||||||||||||||||
- |
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12.3.2. Completeness - overview | ||||||||||||||||||||||||||||||||||||||||||
Completeness is assessed via comparison of the data delivered against the requirements of Commission Implementing Regulation (EU) No 995/2012.
1) Criteria: Obligatory data (provisional budget and final budget). Only 'Very Good' = 100% and 'Very Poor' <100% apply. 2) Criteria: Optional data (final budget). 'Very Good' = 100%; 'Good' = >75%;'Satisfactory' 50 to 75%%; 'Poor' 25 to 50%; 'Very Poor' 0 to 25%. |
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12.3.3. Data availability | ||||||||||||||||||||||||||||||||||||||||||
See below. |
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12.3.3.1. Data availability – Provisional data | ||||||||||||||||||||||||||||||||||||||||||
1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year. 2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled |
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12.3.3.2. Data availability – Final data | ||||||||||||||||||||||||||||||||||||||||||
1) Availability of the data: N: No, data are not available, Y: Yes, data are available + start year. 2) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled |
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12.3.3.3. Data availability – Other special categories | ||||||||||||||||||||||||||||||||||||||||||
1) Stage: P - provisional, F - final. 2) Availability of the data: No, data are not available, Y: Yes, data are available + start year. 3) Time of compilation: T is assumed to represent the end of reference period, x expresses the number of months after (positive) or before (negative) T when data is compiled |
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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. |
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13.1.1. Accuracy - Overall by 'Types of Error' | ||||||||||||||||||
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 GBARD. |
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13.1.2. Assessment of the accuracy | ||||||||||||||||||
1) High level of coverage (At least all national or federal ministries and the ministries and agencies responsible for R&D funding at state or regional level). High rate of response (>90%) in data collection. All figures broken down by NABS. 2) If at least one out of the three criteria above described would not be fully met. 3) In the event that the rate of response would be lower than 80% even by meeting the two remaining criteria. 4) In the event that the average rate of response would be lower than 70% and at least one of the two remaining criteria would not be met. 5) If all the three criteria above described are not met. |
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13.2. Sampling error | ||||||||||||||||||
Not requested. |
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13.2.1. Sampling error - indicators | ||||||||||||||||||
Not requested. |
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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. |
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13.3.1. Coverage error | ||||||||||||||||||
Coverage errors are due to divergences between the target population and the frame population. The frame population is the set of target population members that has a chance to be selected into the survey sample. It is a listing of all items in the population from which the sample is drawn that contains contact details as well as sufficient information to perform stratification and sampling.
a) Description/assessment of coverage errors:
b) Measures taken to reduce their effect:
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13.3.1.1. Over-coverage - rate | ||||||||||||||||||
not applicable |
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13.3.1.2. Common units - proportion | ||||||||||||||||||
Not requested. |
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13.3.2. Measurement error | ||||||||||||||||||
Measurement errors occur during data collection and generate bias by recording values different than the true ones. The survey questionnaire used for data collection may have led to the recording of wrong values.
a) Description/assessment of measurement errors:
b) Measures taken to reduce their effect:
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13.3.3. Non response error | ||||||||||||||||||
Non response errors: occur 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.
a) Problems in obtaining data from targeted information providers: Not applicable (census)
b) Measures taken to reduce their effect:
c) Effect of non-response errors on the produced statistics:
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13.3.3.1. Unit non-response - rate | ||||||||||||||||||
Not requested. |
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13.3.3.2. Item non-response - rate | ||||||||||||||||||
Not requested. |
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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.
a) Data processing and editing processes: Not applicable
b) Description of errors:
c) Measures taken to reduce their effect: |
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13.3.5. Model assumption error | ||||||||||||||||||
Model assumption errors occur when the assumptions made for the estimation of parameters, models, the testing of statistical hypotheses, etc., are violated. As a result, the quality of the resulting statistics is affected (e.g. degrees of confidence might be inflated). Description/assessment: |
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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. |
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14.1.1. Time lag - first result | |||||||||||||||
Date of first release of national data: |
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14.1.2. Time lag - final result | |||||||||||||||
Date of first release of national data: |
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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. |
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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) |
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14.2.1.1. Deadline and date of data transmission | |||||||||||||||
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15.1. Comparability - geographical | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.1.1. Asymmetry for mirror flow statistics - coefficient | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.1.2. Survey Concepts Issues | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
The following table lists a number of key survey concepts and conceptual issues; it gives reference to the Commission Regulation No 995/2012 or Frascati manual paragraphs with recommendations about these concepts / issues.
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15.1.3. Deviations from recommendations | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
GBARD encompass all spending allocations met from sources of government revenue foreseen within the budget, such as taxation. Spending allocations by extra-budgetary government entities are within the scope only to the extent that their funds are allocated through the budgetary process (FM2015 §12.9). The following table lists a number of key methodological issues, which may affect the international comparability of national GBARD statistics.
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15.2. Comparability - over time | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.1. Length of comparable time series | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
See below. |
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15.2.2. Breaks in time series | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1) Breaks years are years for which data are not fully comparable to the previous period. |
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15.3. Coherence - cross domain | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
GBARD data are higher than GOVERD, for GBARD also includes: |
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15.3.1. Coherence - sub annual and annual statistics | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.3.2. Coherence - National Accounts | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Not requested. |
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15.4. Coherence - internal | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
This part compares GBARD statistics from the provisional and final budget for the reference year.
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15.4.1. Comparison between provisional and final data according to NABS 2007 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
In million national currency |
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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. |
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16.1. Costs summary | |||||||||||||||||||||
1) The shares of the figures given in the first column that are accounted for by payments to private firms or other Government agencies. |
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16.2. Components of burden and description of how these estimates were reached | |||||||||||||||||||||
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’) |
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17.1. Data revision - policy | |||
- |
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17.2. Data revision - practice | |||
Not requested. |
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17.2.1. Data revision - average size | |||
Not requested. |
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18.1. Source data | ||||||||||||||||||||||||||||
a) Provisional data:
b) Final data:
c) General University Funds (GUF): |
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18.2. Frequency of data collection | ||||||||||||||||||||||||||||
See 12.3.3. |
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18.3. Data collection | ||||||||||||||||||||||||||||
See below. |
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18.3.1. Data collection overview | ||||||||||||||||||||||||||||
1) Time of data collection (T+x): T is assumed to represent the end of reference period. x expresses the number of months after (positive) or before (negative) T when data is collected. |
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18.3.2. General University Funds (GUF) | ||||||||||||||||||||||||||||
The data comes from administrative data of the Higher Education Sector. |
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18.3.3. Distribution by socioeconomic objectives (SEO) | ||||||||||||||||||||||||||||
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18.3.4. Questionnaire and other documents | ||||||||||||||||||||||||||||
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18.4. Data validation | ||||||||||||||||||||||||||||
- Outlier detection (early in the process) - Checking the population coverage - Benchmark the responses with the responses of the previous year;
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18.5. Data compilation | ||||||||||||||||||||||||||||
See below. |
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18.5.1. Imputation - rate | ||||||||||||||||||||||||||||
- |
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18.5.2. Data compilation methods | ||||||||||||||||||||||||||||
See below. |
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18.5.2.1. Identifying R&D | ||||||||||||||||||||||||||||
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18.5.2.2. General University Funds (GUF) | ||||||||||||||||||||||||||||
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18.5.2.3. Other issues | ||||||||||||||||||||||||||||
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18.6. Adjustment | ||||||||||||||||||||||||||||
Not requested. |
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18.6.1. Seasonal adjustment | ||||||||||||||||||||||||||||
Not requested. |
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