The Regional Innovation Ecosystem Platform helps universities to understand and develop further their innovation ecosystem and manage the relationships with enterprises working with the innovation laboratories. The platform consists of two subsystems:

  1. A content management system which presents the actors that operating within the regional innovation ecosystem.
  2. A number of Intelligent Analytics tools which visualize the relationships, trends and activities of the nodes (actors of the ecosystem).

Five types of Organisations can be included in the platform: 1) University Labs, 2) Product or Service Providers, 3) Commercial Organisations, 4) Technology Transfer Organisations and 5) Funding Agents. The requested information for each organisation are the following:

  • Name
  • Type of organisation
  • Description
  • Website
  • Legal form
  • Products and services
  • Category (Science or Sector)
  • Annual Performance
  • Collaborations
  • Contact details
  • Region
  • Country

The Intelligent Analytics tools will use the information that is available in the CMS in order to produce online visualization network maps and diagrams. The mapping of the innovation ecosystem is considered as an open-ended dynamic process and continuously evolving, aiming to visualize the connectivity of each node and the characteristics of each connection from different perspectives. Four types of diagrams will be available:

  • Node Map – Entities in the innovation network are represented as nodes that are connected to one another based on partnerships. The type of an entity (lab, enterprise,etc.) is represented by the colour of its node, while its importance is denoted by the node’s relative size. Importance can be measured using one of the available variables, e.g. number of projects for labs, annual turnover for enterprises, or available funds for funding agents. The length of the lines connecting two nodes implies the strength of their connection, which is based on the number of activities that they share. Interactivity of the diagram is achieved by providing the user with the ability to filter the diagram based on time, region, or research area. For example, a user could ask to see all partnerships that took place in the last year in a specific region, regarding a particular research area. Based on the user’s choices, the diagram updates accordingly.
  • Word Cloud – The most important research interests in a region are represented in a word cloud. In addition to the “research area” field of each entity, the word cloud also gathers data from tags and free-text entries, in order to show a more complete picture of the interests in a region. As with the node map, interactivity is achieved by the ability to filter the word cloud by region or by time.
  • Bar charts and Pie Charts – This charts show the trends of the innovation network. Bar charts showing the amount of funding that has been granted and the number of activities in the region over time gives a general idea about the course of research. A pie chart of the main research areas as specified by the entities shows the main focuses of innovation in the region. Another pie chart helps identify the main funding agents.
  • Scatter-plots – The scatter-plots effectively display which entities are similar to one another in terms of two variables at a time. University labs are compared based on number of staff and number of projects completed. Enterprises are compared based on annual turnover and number of projects they have cooperated on. Funding agents are compared based on available funds and number of projects funded. The network shows what entities are related and the events that connect them.

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