VI-SEEM Bundle Catalogue

Your one-stop-shop to choose your perfect service bundle

VI-SEEM project created a unique Virtual Research Environment and provided the related infrastructure, services and data to end-users from the academic and research sector in the fields of climate science, life sciences, and digital cultural heritage, through its open calls.

The consortium of organizations behind the VI-SEEM project now opens the possibility for customers coming from the private market / industrial sector to have access to these resources and services, for a fee.

This bundle catalogue contains specific bundles for HPC services, IaaS services and application-oriented services. The format of these services makes them attractive and affordable within the existing market. This marketplace gives the opportunity to interested customers to navigate through the bundles, see what these services can offer and at what price, and select a service most suitable to them.

As soon as the customer chooses a service, they click on “purchase order”, after which a document informing who the service provider is will be displayed, containing the service description, the monthly recurring charges and the initial terms.

The general terms and conditions under which these services are provided can be found here as well as the relevant Service Level Agreement here.

All the above documents (Terms and Conditions, Service Level Agreement, Purchase Order) must be physically signed by both parties (customer and provider) prior to service delivery.

High Performance Computing Data Management Infrastructure-as-a-Service Application services Consultancy
HPC Medium CPU
Type: HPC
Area: DCH, CL, LS
Provider: CyI (CY)

HPC Medium CPU: 1,900.00 € for 1 Month

Medium HPC bundle including CPU compute

Repository Service Small (10TB)
Type: Data
Area: DCH, CL, LS
Provider: GRNET (GR)

Repository Service: 240.00 € for 1 Month

Repository Service 10 TB

Type: Application
Area: DCH
Software: CH-CBIR
Provider: ETFBL (BA)

CHERE: 2,700.00 € for 1 Month

Classification of aerial images. Keywords: neural networks, image classification, remote sensing