Macroeconomic dataset for comparative studies on coastal and inland regions in innovation space of Russia

This article presents regional-level data that can be used for comparative territorial studies on innovation dynamics. The dataset covers a series of 50 indicators grouped into a matrix of 5 elements of regional innovation system (human resources – HR, infrastructure, research & development sector – R&D, innovative milieu, framework conditions) and 5 components of innovation security (economic, scientific and technological – S&T, social, political, geo-ecological). This complex set of interrelated data enables to grasp the catalyst and inhibitor factors that have a significant impact on the sustainable development of a particular regional innovation system. The innovation security approach used enables to consider the locus of innovation processes, account for the relationship between individual components of regional innovation systems and acknowledge for the unique properties of the regions. The database includes statistics for a total set of 85 regions of the Russian Federation over a period of 2015 and 2016. Spatial differentiation is made on to coastal and inland regions. This enables to identify the development patterns as influenced by the global trend of coastalization.


Specifications
Geography, Planning and Development Specific subject area Knowledge geography Type of data Table  Figure How Data is structured by merging information from the aforementioned sources. Sample construction involved conversion of raw data collected from the various sources into indicators and coefficients of a comparable form. Extrapolation is applied for periods where data were not available. Data is aggregated by elements of regional innovation system and components of innovation security. Innovation security matrices are calculated for all regions of the Russian Federation. The ranking approach is applied to all regions according to the level of innovation security, identifying the strengths and weaknesses of their regional innovation systems. A comparative analysis of the innovation security of coastal and inland regions of the Russian Federation is undertaken. Description of data collection Presented data covers a series of regional-level data on the most important indicators used in socio-economic and geo-economic research in conducting a comparative assessment of the level of regional innovation development and innovation security.

Data
The data cover a sample of 85 regions of the Russian Federation, of which 23 are coastal regions and 62 inland regions. The coverage data period is 2015e2016. The data are grouped: 1) by components of innovation security: economic, scientific and technological e S&T, social, political, geoecological; 2) by elements of regional innovation system: human resources e HR, infrastructure, research & development sector e R&D, innovative milieu, framework conditions); and 3) by types of regions, selected on the basis of their economic and geographical position: coastal, inland.
Innovation security assessed using 50 indicators. A number of factors determine the choice of indicators. Firstly, the comprehensiveness of the research e the need to evaluate all components of innovation security: economic, S&T, social, political, geoecological in their relationship with the components of RIS: HR, infrastructure, R&D sector, innovative milieu, framework conditions. Secondly, the implementation of the principle of sufficiency e 2 major indicators are used to assess each component of innovation security in each of 5 aspects (HR, infrastructure, R&D sector, innovative milieu, framework conditions) characterizing the development of regional innovation system (RIS) components. Thirdly, the availability of dynamic data series for all subjects of the Russian Federation in order to conduct annual monitoring of innovation security. Supplement 1 presents the typology of Russian regions to coastal and inland types. Supplements 2e9 present the matrix of innovation security for all administrative units of the Russian Federation for 2015, 2016.
The original series of aggregated macroeconomic data for innovation security matrices are available in separate Excel spreadsheets (Supplements 10, 11).
Figs. 1e10 are presenting the development of regional innovation system elements across regions of the Russian Federation in 2015e2016, indicating RIS elements that excel national average values. Coastal regions are marked with blue filling.

Value of the Data
Traditional approach to the evaluation of regional innovation development implies consideration of a narrow range of indicators generally focused on determining the economic competitiveness, innovation infrastructure, and scientific productivity or R&D expenditure [1e11]. To a large extent, this factor predetermines the research results with a ranking table invariably led by core regions e the major financial and industrial centres. These results rarely differ from general assessments of socio-economic development [12e18]. The database presented addresses such research limitations by taking into account the full range of factors affecting the innovation development of regions and their innovation security. As a result, a different picture of the national innovation system is obtained featuring heterogeneity of the innovation space given the broad scope of indicators evaluated. The dataset covers a series of 50 indicators for a total set of 85 regions of the Russian Federation that is structured in a regional innovation security evaluation matrix [19e21]. The wide spectrum of parameters used ensures a comprehensive assessment of each of the 5 innovation security components (economic, scientific and technological, social, political, geoecological) being interrelated to the values of regional innovation system elements (human resources, infrastructure, R&D, innovative milieu, framework conditions). The data provided enables regional scientists to conduct comparative studies using individual criteria selected, including the assessment of the region's position relative to average values for federal districts or nationwide. Of particular value would be research on the typologies of regions regarding their geoeconomic position e coastal and inland, borderland and midland, central and peripheral, etc. This dataset may have important policy implications. The detailed perspective over the regional innovation divergence enabled to isolate gaps that threaten the innovation security of a region and inhibit the development of its innovation system. Correlations may be found between certain policy instruments implemented and the change in macroeconomic indicators. The data may be applied to the elaboration of a territorial-adaptive approach to regional development taking into account the differences in the territorial capital of regions.  Fig. 11 features an evaluation matrix applied for measuring the level of regional innovation security. Tables 1e5 contain detailed data on indicators and evaluation procedure for assessing the components of the innovation security of a region.
The economic component of innovation security was most efficiently implemented in 2015 in 43 administrative units of the Russian Federation, of which 9 are coastal (in 2016, these are 44 regions, including 10 coastal). Thus, the value of the index of the economic component of innovation security in these regions is higher than the median value for all regions of the Russian Federation (for 2015 it is  In 2016, there were some changes in the innovation security systems of coastal regions compared to 2015 (Fig. 2). St. Petersburg and Kamchatka Territory strengthened their economic component of innovation security by increasing the convergence of economic development of RIS subsystems. St. Petersburg strengthened the R&D subsystem by significantly increasing the expenditure of organizations for patents, licenses for the use of inventions, industrial designs, utility models. And the Kamchatka Territory has improved the infrastructure subsystem of the RIS by increasing the digitalization of business. The Chukotka Autonomous Area and the Khabarovsk Territory, on the contrary, demonstrated a decrease in the level of innovation security in the context of the economic component due to the deterioration of the RIS innovative milieu (a significant decrease in entrepreneurial innovation activity).
The majority of coastal regions of the Russian Federation have favourable economic framework conditions for conducting innovation activities and for realizing human potential for the development of innovations. However, in general, the remaining major RIS subsystems (infrastructure, R&D, innovative milieu) have weak economic development.

Innovation security components
Economic S&T Social PoliƟcal Geoecological HR  I1, I2  I11, I12  I21, I22  I31, I32  I41, I42   Infrastructure  I3, I4  I13, I14  I23, I24  I33, I34  I43, I44   R&D  I5, I6  I15, I16  I25, I26  I35, I36  I45, I46   InnovaƟve milieu  I7, I8  I17, I18  I27, I28  I37, I38  I47, I48   Framework  condiƟons  I9, I10  I19, I20  I29, I30  I39, I40 I49, I50 In 2016, 6 coastal regions were able to strengthen the political component of their innovative security while 7 regions became more vulnerable (Fig. 8)    In 2016, 5 coastal regions were able to strengthen the geoecological component of RIS; 7 regions, on the contrary, became more vulnerable (Fig. 10). The Krasnoyarsk Territory, Republic of Daghestan, and Sakhalin region improved their indicators of the geoecological development of RIS in the context of the innovation milieu, the Astrakhan region had improved HR subsystem, Kaliningrad region developed infrastructure subsystem, and Sakhalin region developed its framework conditions. The decrease in the index of the geoecological component of RIS occurred in the Magadan region, the Republic of Krym (due to indicators of the HR subsystem RIS), the Chukotka Autonomous Area and Leningrad region (due to indicators of the infrastructure subsystem), the Yamal-Nenets Autonomous Area (due to the indicators of the innovation milieu), the Rostov region and the Republic of Kalmykia (due to the indicators of the RIS framework conditions). The Arkhangelsk region underwent a restructuring in the internal composition of the geoecological subindex.

Experimental design, materials and methods
The data covers a sample of 85 regions of the Russian Federation, coverage period is 2015e2016. These macroeconomic data are collected from several reliable sources, such as the Federal Service of State Statistics of the Russian Federation (Rosstat), Scopus database, SciVal, Scientific Research Institute e Federal Research Centre for Projects Evaluation and Consulting Services (SRI FRCEC), Single portal of the budget system of the Russian Federation (Electronic budget), Scientific and technological infrastructure of the Russian Federation e Centers for collective use of scientific equipment and unique scientific installations (Ministry of Education and Science of the Russian Federation), Association of Accelerators and Business Incubators of Russia, Information and communication support system for young innovators (ICS). When building a database, comparability of indicators by size units is ensured. Standard data extrapolation method is performed whenever possible to construct a complete data series in case of missing values.
The evaluation algorithm includes 6 stages:   Electronic budget, Rosstat/Annual  The following formula is applied for normalization of the raw data for indicators characterizing a negative attribute: The following indicators are considered to be of a negative attribute: degree of depreciation of fixed assets in a full range of organizations; proportion of the population not using the Internet for security reasons; share of the total number employed in manufacturing industries working in unhealthy and hazardous conditions; payment for excess emissions of pollutants per organization; concentration of pollutant emissions from stationary and mobile sources. 4) calculation of integral indices for each cell of the matrix by the arithmetic mean method; Z ij ¼ P n j¼1 Z ij n ; with Z ij e the value of the integral index for the matrix cell; Z ij e normalized j-value for i-region; n e number of indicators in the subgroup (in this case n ¼ 2). 5) calculation of structural indices in rows and columns of the matrix as arithmetic means of integral indices (3); 6) calculation of the final (closing) total index of innovation security level as the arithmetic mean of the structural indexes of rows and columns of the innovation security matrix of a region (Fig. 11). Furthermore, the level of innovation security of coastal regions is analyzed separately in comparison with the inland regions.
Firstly, there are indicators of the economic component of the regional innovation security, which made it possible to assess the security status of a region in 5 aspects (Table 1): Human resources: I1, I2 e the level of investment of economic entities in people e holders of implicit knowledge, the effectiveness of the use of human potential; Infrastructure: I3, I4 e the conditions for creating an information environment and establishing inter-organizational interactions with the subsequent exchange of explicit and implicit knowledge through personal and/or remote contacts; Research and development: I45, I46 e the level of creation of new knowledge in the field of environmental protection and environmental management; Innovative milieu: I47, I48 e the presence of a culture for ecological innovation; Framework conditions: I49, I50 e environmental quality and potential for improvement.