Data source: Eurostat Comparison with EIS 2009: The comparable EIS 2009 indicator focuses on doctorate graduates in science and engineering (S&e) and social sciences and humanities (SSH) following the recommendations received from Member States and experts during the revision of the EIS in 2008
Data source: Eurostat Comparison with EIS 2009: The comparable EIS 2009 indicator is defined more broadly as it takes the share of population aged 25-64 with tertiary education.
) 2010 MAIN TYPE/Innovation dimension/indicator COMMENT Data source Reference year (s) latest year used for IUS 2010 ENABLERS ENABLERS Human resources Human resources
(IUS) 2010 MAIN TYPE/Innovation dimension/indicator COMMENT Data source Reference year s) latest year used for IUS 2010 FIRM ACTIVITIES FIRM
TYPE/Innovation dimension/indicator COMMENT Data source Reference year (s) latest year used for IUS 2010 OUTPUTS OUTPUTS Innovators Innovators 3. 1
Data source: Eurostat 1. 2. 1 International scientific co-publications as%of total scientific publications of the country Numerator:
Data availability for this indicator is limited to the EU27 Member States. Note: This indicator was introduced to better capture research performance.
Data source: Science Metrix/Scopus 1. 2. 2 Scientific publications among the top-10%most cited publications worldwide as%of total scientific publications of the country Numerator:
or English speaking countries given the coverage of Scopus'publication data. Countries like France and Germany, where researchers publish relatively more in their own language,
Data source: Science Metrix/Scopus 1. 2. 3 Non-EU doctorate holders as%of total doctorate holders of the country Numerator:
Data source: Eurostat 1. 3. 1 Public R&d expenditures(%of GDP) Numerator: All R&d expenditures in the government sector (GOVERD) and the higher education sector (HERD.
Data source: Eurostat 1. 3. 2 Venture capital(%of GDP) Numerator: Venture capital investment is defined as private equity being raised for investment in companies.
Data are broken down into two investment stages: Early stage (seed+start-up) and Expansion and replacement (expansion and replacement capital.
Data source: Eurostat (EVCA (European Venture capital Association) is the primary data source for VC expenditure data) 9 2. 1. 1 Business R&d expenditures(%of GDP) Numerator:
All R&d expenditures in the business sector (BERD), according to the Frascati-manual definitions, in national currency and current prices.
Data source: Eurostat 2. 1. 2 Non-R&d innovation expenditures(%of total turnover) Numerator: Sum of total innovation expenditure for enterprises, in national currency and current prices excluding intramural and extramural R&d expenditures.
Data source: Eurostat (Community Innovation Survey) 2. 2. 1 SMES innovating in-house(%of all SMES) Numerator:
Data are taken from CIS 2008 questions 2. 2 and 3. 2, i e. those SMES which are either:
Data source: Eurostat (Community Innovation Survey)( cf. Box 1) 10 Box 1: Calculation of the indicator on SMES innovating in-house Data on product
and/or process innovators innovating in-house are not directly available from Eurostat. The indicator has been estimated as follows.
From Eurostat data are extracted online from inn cis6 prod-Product and process innovation for size categories between 10 and 49 and between 50 and 249 (i e.
From Eurostat data are extracted online from inn cis6 type-Enterprises by type of innovation activity for SMES on:(
product and process innovators Data on (9) Total enterprises are used for the denominator. Step 5:
because almost all large firms are involved in innovation co-operation. 11 Data source: Eurostat (Community Innovation Survey) 2. 2. 3 Public-private co-publications per million population Numerator:
‘research articles',‘research reviews',notes'and‘letters')published in the Web of Science database. These co-publications have been allocated to one
Data are two-year averages. Data source: CWTS/Thomson Reuters database. All data manipulations have been done by CWTS (Leiden University, http://www. cwts. nl.
2. 3. 1 PCT patent applications per billion GDP (in PPP€) Numerator: Number of patents applications filed under the PCT,
at internationational pase, designating the European Patent office (EPO). Patent counts are based on the priority date, the inventor's country of residence and fractional counts.
Denominator: Gross domestic product in Purchasing Power Parity Euros. Rationale: The capacity of firms to develop new products will determine their competitive advantage.
Data source: OECD/Eurostat Comparison with EIS 2009: This indicator replaces the EIS 2009 indicator on number of EPO patent applications per million population.
Data source: OECD/Eurostat 2. 3. 3 Community trademarks per billion GDP (in PPP€) Numerator:
Data source: OHIM (Office of Harmonization for the Internal Market)/ Eurostat Comparison with EIS 2009:
Data source: OHIM (Office of Harmonization for the Internal Market)/ Eurostat Comparison with EIS 2009:
Data are taken from CIS 2008 questions 2. 1 and 3. 1, i e. those SMES which have introduced either:
Data source: Eurostat (Community Innovation Survey) 3. 1. 2 SMES introducing marketing or organisational innovations as%of SMES Numerator:
and/or organisational innovation to one of their markets Data are taken from CIS 2008 questions 8. 1 and 9. 1,
Data source: Eurostat (Community Innovation Survey) 14 3. 1. 1 High-growth innovative firms Numerator:
Data source: Not yet available 3. 2. 1 Employment in knowledge-intensive activities as%of total employment Numerator:
Knowledge-intensive activities are defined, based on EU Labour force Survey data, as all NACE Rev. 2 industries at 2-digit level where at least 25%of employment has a higher education degree (ISCED5A or ISCED6).
Data source: Eurostat Comparison with EIS 2009: The indicator on knowledge-intensive activities replaces EIS 2009 indicators 3. 2. 1 on employment in medium-high
Data source: UN Comtrade/Eurostat 3. 2. 3 Knowledge-intensive services exports as%of total services exports Numerator:
Data source: Eurostat (Balance of payments statistics)/ UN Service Trade 16 3. 2. 4 Sales of new to-market and new to-firm innovations as%of turnover Numerator:
Data source: Eurostat (Community Innovation Survey) Comparison with EIS 2009: This indicator combines EIS 2009 indicators 3. 2. 5 on sales of new to-market products and 3. 2. 6 on sales of new to-firm products. 3
Data source: Eurostat Note:.This is a highly skewed indicator and a square root transformation has been used to reduce the volatility and skewed distribution of this indicator.
Data availability The Innovation Union Scoreboard uses the most recent statistics from Eurostat and other internationally recognised sources as available at the time of analysis. International sources have been used wherever possible
Note that the most recent data for the indicators are available at different years (cf.
though the data relate to actual performance in 2007 (4 indicators), 2008 (10 indicators) and 2009 (10 indicators).
The availability of data country by country at each year is given in Table 2 showing that non-EU27 countries have lower availability.
The indicator Venture capital has the lowest data availability in the database (69%across all Countries.
Country by country data availability (in percentage) 2010 2009 2008 2007 2006 EU27 100 100 100 100 100 BE 100 100 100 100
Transforming data that have skewed highly distributions across Countries Most of the indicators are fractional indicators with values between 0%and 100%.
In the IUS 2010 report data are transformed using a square root transformation after outliers have been removed (cf.
Imputation of missing values If data for the latest year are missing, they are imputed with the data of the latest available year.
If data for a year-in-between are missing, they are imputed with the value of the previous year.
If data are not available at the beginning of the time series, they are imputed with the oldest available year (see Table 4). Table 4:
Examples of imputation Example 1 (latest year missing) 2010 2009 2008 2007 Available relative to EU27 score Missing 150 120 110 Use most recent year 150
130 120 Missing Substitute with oldest available year 150 130 120 120 In case the data for an indicator are not available for a given country at any time point
Transforming data highly skewed data Most of the indicators are fractional indicators with values between 0%and 100%.
and can have skewed data distributions (where most countries show low performance levels and a few countries show exceptionally high performance levels).
and data have been transformed using a square root transformation: Non-EU doctorate students, Venture capital, PCT patents in societal challenges and License and patent revenues from abroad.
(i e. 1/24 if data for all 24 indicators are available), contrary to option 1 above.
strategies to measure country progress over time, Joint Research Centre, mimeo. 9 A geometric mean is an average of a set of data that is different from the arithmetic average.
Data availability for this indicator is limited to the EU27 Member States. Belgium, Denmark, Finland, Netherlands and Sweden have more than 1000 copublications per million population.
or English speaking countries given the coverage of Scopus'publication data. Countries like France and Germany, where researchers publish relatively more in their own language,
For several countries data are not available as the domestic Venture capital markets are too small to collect such data.
25: 147-148.17 Department of Broadband, Communications and the Digital economy. Australia's digital economy: future directions.
Final report. Canberra: Commonwealth of australia, 2009. http://www. dbcde. gov. au/data/assets/pdf file/0006/117681/DIGITAL ECONOMY FUTURE DIRECTIONS FINAL REPORT. pdf (accessed Aug 2010). 18 Coye M,
Kell J. How hospitals confront new technology. Health Aff (Millwood) 2006; 25: 163-173.19 Christensen C, Bohmer R, Kenagy J. Will disruptive innovations cure health care?
since then a large database of original ICT innovation indicators, enriched with geographical information in order to allow localisation and aggregation at NUTS 3 and NUTS 2 level.
11 3. Data...12 4. Characteristics of the international R&d centres network...17 Connectivity...18 Centrality and clustering...
21 Countries'positions in the network...25 Core and periphery of the R&d network...28 5. The determinants of international R&d linkages...
This type of analysis has been applied, for example, to patent data (Chao-Chih 2009, Han and Park 2006, Lai et al. 2011, Stefano and Francesco 2004, De Prato and Nepelski 2012) and bibliometric
data (Glänzel and Schubert 2005, Glänzel et al. 1999, Kretschmer 2004. In this context, our work delivers a valuable contribution by extending the analysis of knowledge networks by using a different type of information
Section 3 introduces the data used in the study and Section 4 analyses the characteristics of the R&d network and countries'positions in the network.
Further measure of a node's position in the network used in this study relates to the extent of clustering between nodes.
This property of a network structure can by captured by the clustering coefficient (Watts and Strogatz 1998
In directed networks, the clustering coefficient cc i C of node i is defined as:((1)) i icc i i k ke C (13) where ki is the degree of Vi
The clustering coefficient of a node is always a number between 0 and 1, where for a fully connected network CC=1. International R&d centres as a network A straightforward way of representing international R&d centres as a network is through drawing a line connecting two countries that share an R&d centre
i. 3. Data The data used in this paper originates from the 2011 edition of an originally assembled company-level dataset dedicated to observe the internationalization of R&d.
The data on R&d locations was collected by isuppli, an industry consultancy. 1 In order to check how representative the sample is compared,
Data excluding loops. Source: Own calculations 4. Characteristics of the international R&d centres network Our analysis of the global network of R&d centres starts with its graphical illustration in Figure 1. The arcs represent the existence of a relationship
7 Average in-strength 141 Average out-strength 58 Closeness centralization 0, 107 Betweenness centralization 0, 038 Clustering centralization 0, 457 Source:
Centrality and clustering Turning to other measures of the network, we first consider the measure of closeness centrality.
Turning to the clustering centrality, it is worth noting that an analysis of this measure can be found, for example,
because networks with strong clustering properties are likely to reflect some strong geographical structure in which short-distance links count more than long-distance ones.
In the context of the R&d network, the value of clustering coefficient is 0. 46,
This type of clustering behaviour lets us conclude that'local'links tend to play an important role.
Furthermore, the negative skew of the clustering coefficient's distribution indicates that most of the countries tend to 0 100 200 300 Frequency 0. 02.04.06.08.1 Betweenness centrality Frequency kdensity betweennesscentrality 24 be members
Clustering coefficient distribution Note: The probability density function was estimated using the kdensity estimation procedure, i e. univariate kernel density estimation.
when we look at the relationship between the clustering coefficient and nodes'degree and strength (see Table 11).
where the Pearson 0 5 10 15 Frequency 0. 2. 4. 6. 8 1 Clustering coefficient Frequency kdensity clusteringcoefficient 25 correlation value is either close to or above 0
i e. closeness centrality and clustering coefficient, the problem of multicolinearity seems to be of lesser importance.
i j a, indicates the presence or absence of a connection in the observed data,
and the data is large. In order to detect the core/periphery structure in our data, we use a genetic algorithm to find a partition such that correlation between the data
and the pattern matrix induced by the partition is maximized. 3 The results of the analysis are reported in Table 7. After 50 iterations,
we find that the final fitness measure is 0. 906 at the significance level p<0. 001.
this indicates that the underlying data exhibits very strong core/periphery structure. This is further confirmed by the reported density measures for individual partitions.
and common language variables is CEPII bilateral trade data Head, K.,Mayer, T. & Ries, J. 2010.'
http://www. cepii. fr/anglaisgraph/bdd/distances. htm 7 Data stems from the IMF. For more information please refer to:
http://www. imf. org/external/data. htm 34 R&d and hence are likely to have more links.
This type of clustering reveals that there are strong'local'links, which however do not imply geographical or cultural proximity,
First of all, the data used in the study cover only a selection of companies. Second, the richness of information on R&d activities of these companies is limited also.
2 Closeness centrality 0, 496*1 3 Clustering coefficient 0, 322*-0, 097 1 4 In-degree 0, 796*0, 465
Regression analysis of Count Data. Cambridge university Press. Chao-Chih, H. 2009.''The Use of Social network Analysis in Knowledge Diffusion Research from Patent Data.'
'Paper presented at International Conference on Advances in Social network Analysis and Mining. Dachs, B. & Pyka, A. 2010.'
Evidence from European patent data.''Economics of Innovation and New Technology, 19:1, 71-86. De Benedictis, L. & Tajoli, L. 2011.'
'The internationalisation of technology analysed with patent data.''Research Policy, 30:8, 1253-66. Han, Y.-J. & Park, Y. 2006.'
'Disambiguation and Co-authorship Networks of the U s. Patent Inventor Database (1975-2010.''In H. B. School (Ed.).Laumann, E. & Pappi, F. 1977.'
A gravity model using patent data.''Research Policy, 39:8, 1070-81. Reagans, R. & Mcevily, B. 2003.'
'Knowledge networks from patent data: Methodological issues and research targets.''KITES, Centre for Knowledge, Internationalization and Technology Studies, Universita'Bocconi, Milano, Italy.
Most capital demands are restricted to web server farms (see the web services value chain in Appendix 2) and even these can be hosted by third party data centres who offer website services for start-ups.
which brought high-level personnel for technical positions, for building and then running web services in vast data centres with fast transaction processing, large customer databases and data mining for refined data analysis,
identifying specialist staff to manage large data centres and high performance databases. 20 Well-developed infrastructure was also important,
e g. the supply chain for physical delivery with its infrastructure of warehousing, roads and airports for national delivery.
Amazon's customer databases hold names, addresses, telephone numbers, email addresses, profiled preferences and, most important, credit card details of its 137 million customers.
based on recent data from the UK compared with the USA. The historical US EU gap cannot be explained by the characteristics of the funds alone.
This is the case in the Oracle plea against Google in April/May 2012 over the use of the Java language to write APIS for the Android operating system. 21 Patent pools may be used where a standard complex technology is being assembled
The use of a programming language to create the APIS has been considered‘fair use'in the past. Moreover the knowledge of how APIS work is essential for the ICT industry to build compatible software modules for instance the PC industry could not have been created without it,
yet is could be argued that this is proprietary knowledge (Waters, 2012). 41 proprietary technology. Note that there is also a risk of the patent pool being declared illegal,
From our case studies, especially in robotics it is evident that open source IPR is important in innovation, specifically for software and for standard design libraries of structured data.
and its future here is still unclear. 22 The European commission's Interchange of Data between Public Administrations Programme (IDA) in 2002 financed an independent study on the opportunity of making software specific to the public administrations
such as operating systems, web servers, databases and office systems. Note that the role of the European commission in stimulating research development and innovation, RDI,
and EU education systems and the possible impacts on innovation. 28 A study by Florian (2007) found that the results emerging from the ARWU data were not replicable,
calling into question the comparability and methodology of the data used in the ranking. 46 example of innovation as an admired pursuit,
This may take the form of direct funding of research, public procurement or support for clustering,
Stancik J. and Biagi F,(2012), Characterizing the Evolution of the EU R&d Intensity Gap Using Data From Top R&d Performers, mimeo.
which rely on some of the world's largest data centres with intensive use of computer hardware and storage,
Using its data mining and profiling tools, it tries to detect market trends early and then 62 translates those trends and needs into new products and services.
and relied on data to hire who they thought were the best people by objective criteria.
Ahti Heinla, Priit Kasesalu and Jaan Tallinn, working for a small codeshop, Bluemoon Interactive, the same team behind the controversial file sharing service, Kazaa.
The sites are prohibited also from sharing certain user data with advertisers. But US companies are exempt from these rules under safe harbour agreements between the US and the European union.
In addition to electronic books, E Ink's Vizplex imaging film is used in mobile phones, signage, smartcards, memory devices,
This portfolio provides wide coverage as E-ink holds patents not only for displays, but also for methods of manufacturing the required materials, processes for assembling finished displays,
and technologies from Ricoh, Pixel Qi and Qualcomm might easily have taken the lead. E-Ink leads with its improved Pearl greyscale technology and then with its Triton full colour form launched in 2010 to maintain its grip on the e-reader market.
who previously had worked together at MIT's Artificial intelligence Laboratory. It was incorporated in 2000 when it merged with Real world Interface
Microrig, the result of partnership with the oil industry to develop a fully autonomous robot to collect data from functioning land-based oil wells;
market and profitability standards discouraged risk-taking and data driven research replaced intuitive visions as the main decision making tool.
Why do you think we published the API for the serial port? It's an expense
Further advances have been in combining machine intelligence with innovative gripper and sensor technologies to expand applications and industrial sectors.
Real-time Simulation for Design of Multi-physics Systems (Real-Sim) DLR and KUKA worked together, with others to perfect methods and tools for design of new robots or variants of existing
and distribution systems, including apps/content stores Data centres development Design Custom systems development (eg CPUS) Webfarms 24x7 operation Design Build Test Operate 3rd
Over this time, it developed a tool based on a database of original ICT activity indicators,
129 7. 1 Normalization and Rescaling of Data...129 7. 2 European ICT Poles of Excellence Composite Indicators...
Data Sources...131 8. 1 QS WORLD UNIVERSITY RANKINGS by QS...131 8. 2 ICT FP7 by EC DG Connect...
133 8. 6 Patent Data: REGPAT by OECD...134 8. 7 Company-level Information: ORBIS by Bureau Van dijk...
By using the data collected in the project and organized along three types of ICT activities (see Figure 2),
and chapter 8 Annex 3) describes the data sources used in the EIPE study. Methodological note:
Strong clustering of ICT activity Larger areas of intensive ICT activities, sometimes including a 1st tier region,
A deeper case-study level of analysis of the data shows that EIPES are characterised by several commonalities
definition of ICT patents following IPC taxonomy (OECD 2008b) Unit of observation NUTS 3 Source Patent data:
Unit of observation NUTS 3 Source Patent data: REGPAT by OECD (see Section 8. 6) Reference year (s) considered 2000-2009 79 Figure 43:
Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data:
Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data:
Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data:
Unit of observation NUTS 3 for EU and TL3 for the remaining OECD countries Source Patent data:
They are presented together with a first indication of the data sources used and their time coverage.
as well as the data sources used, is given in detail in the Annexes. For methodological details, please refer to the second EIPE Report (De Prato and Nepelski 2013a.
The data source on ICT FP7 programmes is described in Section 8. 2. Network design: A straightforward way of representing the locations of ICT FP7 programme participants as a network is through drawing a line connecting two different regions
Data source: The analysis is conducted using the data on ICT FP7 programmes by DG Connect
and is described in Section 8. 2. Network measures: According to the above defined methodology, based on the number of connections between regions and a subsequent analysis of these connections, indicators are constructed.
To the extent allowed by the availability of indicators and data, a mix of measures capturing the input
Venturesource by Dow jones (Section 8. 8) Patent data: REGPAT by OECD (Section 8. 6) Reference year 2005-2012 2000-2012 2000-2009 6. 2. 2 Internationalisation of ICT Innovation (Intin
The data on regional patents represents the input to innovation activities and the relevant data originates from the Regpat database (see Section 8. 6). Table 101:
ICT Innovation Internationalisation indicators (Intin) Indicator ID Intin 1 Name of indicator International co-inventions What does it measure?
Unit of observation NUTS 3 Source Patent data: REGPAT by OECD (Section 8. 6) Reference year (s) considered 2000-2009 6. 2. 3 Networking in ICT Innovation (Netin) A set of networking measures addressing innovation
a network of technological collaborations between inventors based on patent data has been built. The methodology was proposed by Breschi, Cassi and Malerba (2007) and used by De Prato and Nepelski (2012.
or international coinventions by using patent data as a network is to draw a line connecting two regions that share a patent developed by their residents.
1) Data source: The analysis is conducted using the data on REGPAT by OECD (see section 8. 6). Network measures:
In the above context, based on the number of connection of a region, we can define the measures of regions'centrality.
In addition to the extent allowed by the availability of indicators and data, a mix of measures capturing the input
Data source: The analysis is conducted using the EIM data on foreign investments (see Section 8. 5). 3 In the following,
we focus our attention on bilateral relationships between regions and do not take into account loops, i e. when a company's new investment and headquarter is located in the same region. 128 Network measures:
R&d, innovation and business. 7. 1 Normalization and Rescaling of Data Before aggregating the information,
In order to normalise the data used in this study, a standardization method, i e. z-scores, is used. This method is the most commonly used
Data Sources 8. 1 QS WORLD UNIVERSITY RANKINGS by QS The Computer science and Electronic Faculties rankings originate from the QS WORLD UNIVERSITY RANKINGS,
which was formed in 2008 to meet the increasing public interest for comparative data on universities and organisations,
Like any ranking at the global level, it is constrained by the availability of data from every part of its scope.
the QS uses its proprietary datasets that enable to drill down by subject area, namely academic and employer reputation surveys and the Scopus data for the Citations per Faculty indicator in the global rankings.
The data for citations originate from Scopus by Elsevier E. V. 5 Papers in Scopus are tagged with an ASJC (All Science Journal Classification) code
and the depth of data available to evaluate it. Aggregation, similarly to the approach used in the overall QS WORLD UNIVERSITY RANKINGS a z-score is calculated for each indicator with the results scaled between 0 and 100
The analysis of the Framework Programme 7 programmes and participants is based on the database provided by the DG Connect in November 2011,
Web of Science by Thomson Reuters Web of Science is an online academic citation index provided by Thomson Reuters. It is designed for providing access to multiple databases, cross-disciplinary research,
Web of Science has indexing coverage from the year 1900 to the present. Regarding the coverage,
it encompasses over 11,000 journals selected on the basis of impact evaluations. This selection includes open-access journals and over 12
Coverage includes the sciences, social sciences, arts, and humanities, and across disciplines. For the purpose of the EIPE exercise, journals classified in the Computer science research area are considered. 8. 4 R&d Centre Location by IHS isuppli The data used for the purpose of identification of R&d centre locations
originates from the 2011 IHS isuppli database, a company-level dataset dedicated to observe the internationalization of R&d.
It includes a list of R&d centres belonging to a number of high-tech companies together with their exact location
and additional information on the type of R&d activity performed in these centres. 133 The data on R&d locations was collected by IHS isuppli,
the dataset itself represents a unique collection of data for its coverage with a great level of details provided. 8. 5 European Investment Monitor by Ernst & young The European Investment Monitor (EIM) is a unique monitor
Since 1997, data is collected for all European countries and is published on a quarterly basis. Up to 2011,
Projects included in the database have to comply with several criteria to be considered as international investments.
The basic description of each investment project described by the EIM data includes the name of the firm
The data collected by the EIM enables to: Review developments and movements in the inward investment marketplace, identify emerging sectors, industries and clusters,
but not creating any new employment), not-for-profit organisations. 134 8. 6 Patent Data:
REGPAT by OECD The OECD REGPAT database presents patent data that have been linked to NUTS3 regions according to the addresses of the applicants and inventors.
The data have been regionalised at a very detailed level so that more than 2 000 regions are covered across OECD countries.
The data from the REGPAT database, are constructed along the following principles: Inventor v. owner region:
Patent data can be regionalised on the basis of the address of either the inventor or the holder.
when interpreting the data. The methodology developed to identify regions on the basis of addresses of the patents inventor (s)
data (assets, capital stock, number of employees, etc.)and R&d expenditures. This information was included combines information in the following sources:
The Scoreboard includes data on R&d investment along with other economic and financial data from the last four financial years. 9 9 More information under:
Regarding the selection of companies out of the ORBIS database and the construction of indicators on the number of employees, turnover, intangible and R&d expenditures at the NUTS 3 level,
Geographic coverage: EU 27; The ICT industry was defined according to the NACE Rev 2 definition of the ICT sector (OECD 2007;
Time coverage between and 2011, the last available date. 8. 8 Venture capital: Venturesource by Dow jones Dow jones Venturesource provides comprehensive data on venture-backed and private equity-backed companies including their investors and executives in every region, industry sector and stage of development
throughout the world. This database contains information on venture capital transactions, the financed companies and the financing firms.
The data are reported largely self y VC firms, but several plausibility checks are conducted by the database providers.
According to Kaplan et al. 20022002), who provide a detailed overview of this database and compare it with an alternative source of information which is Venture Economics,
the Venturesource data are generally more reliable, more complete, and less biased than the Venture Economics data. 10 Primary codes only include:
261-Manufacture of electronic components and boards, 262-Manufacture of computers and peripheral equipment, 263-Manufacture of communication equipment, 264-Manufacture of consumer electronics, 268-Manufacture of magnetic
and optical media, 4651-Wholesale of computers, computer peripheral equipment and software, 4652-Wholesale of electronic and telecommunications equipment and parts, 582-Software publishing, 611-Wired telecommunications
activities, 612-Wireless telecommunications activities, 613-Satellite telecommunications activities, 619-Other telecommunications activities, 6201-Computer programming activities, 6202-Computer consultancy activities
'The internationalisation of technology analysed with patent data.''Research Policy, 30:8, 1253-66. Hidalgo, C a.,Klinger, B.,Barabási, A l. & Hausmann, R. 2007.'
'How Well do Venture capital Databases Reflect Actual Investments?''In SSRN (Ed.).Nepelski, D. & De Prato, G. 2013a.'
Guidelines for Collecting and Interpreting Innovation Data. OECD Publishing. OECD 2007.''INFORMATION ECONOMY-SECTOR DEFINITIONS BASED ON THE INTERNATIONAL STANDARD INDUSTRY CLASSIFICATION (ISIC 4).'Paris:
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