Allocation of resources

1. What are the costs for making data FAIR in your project?

The costs for implementing FAIR data principles in Feel++ include:

Infrastructure Costs

  • Data repository hosting and maintenance through Cemosis e-Infrastructure

  • Cloud storage and bandwidth for large dataset distribution

  • Persistent identifier services (DOI registration fees)

  • Backup and long-term preservation systems

Personnel Costs

  • Data management specialist roles (0.2-0.5 FTE)

  • Software development for data tools and APIs

  • Documentation and metadata creation

  • Quality assurance and validation processes

Technical Costs

  • Software licenses for proprietary tools (when required)

  • Computing resources for data processing and validation

  • Security audits and compliance assessments

  • Training and capacity building

Estimated Annual Budget

  • Small projects: €5,000-€15,000

  • Medium projects: €15,000-€50,000

  • Large projects: €50,000-€100,000+

2. How will these be covered?

Data management costs are covered through multiple funding mechanisms:

Primary Funding Sources

  • European research grants (Horizon Europe, ERC) with dedicated data management budgets

  • French national funding (ANR, CNRS) including data infrastructure support

  • Regional funding from Grand Est region for Cemosis operations

  • University of Strasbourg institutional support

Collaborative Funding

  • Industrial partnerships covering domain-specific data management

  • International collaborations sharing infrastructure costs

  • Open source community contributions reducing development costs

Sustainable Funding Model

  • Core infrastructure funded through institutional commitments

  • Project-specific costs covered by research grants

  • Commercial licensing revenue supporting open data initiatives

  • Training and consulting services generating operational funding

Cost Optimization

  • Leveraging existing institutional infrastructure (University of Strasbourg)

  • Open source software reducing licensing costs

  • Shared resources through academic consortiums

  • Efficient data formats and compression reducing storage costs

that costs related to open access to research data are eligible as part of the Horizon 2020 grant (if compliant with the Grant Agreement conditions).

3. Who is responsible for data management in Feel++?

Data management responsibilities are distributed across the Feel++ organization:

Overall Coordination

  • Prof. Christophe Prud’homme (Project Leader, University of Strasbourg)

    • Strategic oversight and policy decisions

    • Coordination with funding agencies and partners

    • Final responsibility for data management compliance

Technical Implementation

  • Cemosis Team (www.cemosis.fr)

    • Data infrastructure development and maintenance

    • Repository management and technical support

    • Integration with University of Strasbourg systems

Domain-Specific Responsibilities

  • Research Group Leaders: Domain-specific data validation and quality

  • PhD Students and Postdocs: Day-to-day data generation and documentation

  • Software Engineers: Tool development and automation

  • System Administrators: Infrastructure security and backup

Institutional Support

  • University of Strasbourg IT Services: Core infrastructure and security

  • IRMA (Institute of Advanced Mathematical Research): Academic oversight

  • Legal and Ethics Office: Compliance and regulatory guidance

External Collaborations

  • Partner institutions: Joint data governance for collaborative projects

  • Industrial partners: Proprietary data handling protocols

  • International networks: Standards harmonization and best practices

4. Are the resources for long term preservation discussed (costs and potential value, who decides and how what data will be kept and for how long)?

Long-term preservation is a key component of the Feel++ data strategy:

Preservation Policies

  • Core datasets: Permanent preservation (software, benchmarks, key publications)

  • Research datasets: Minimum 10 years after publication

  • Educational materials: Permanent preservation with regular updates

  • Experimental data: 5-10 years depending on scientific value

Decision Framework

  • Data Review Committee: Annual assessment of preservation priorities

  • Scientific Advisory Board: Strategic guidance on valuable datasets

  • Community Input: User feedback on dataset importance

  • Automated policies: Rule-based retention for common data types

Cost Considerations

  • Storage costs: €0.10-€1.00 per GB per year depending on access tier

  • Migration costs: Periodic format updates and technology refresh

  • Curation costs: Quality assurance and metadata maintenance

  • Access infrastructure: Search, discovery, and distribution systems

Value Assessment

  • Citation metrics and usage statistics

  • Scientific impact and reproducibility value

  • Educational and training utility

  • Historical significance for computational science

Implementation

  • Tiered storage: Frequent access (fast), archive (slow), deep archive (tape)

  • Format migration: Proactive conversion to current standards

  • Redundancy: Multiple geographic locations and backup systems

  • Monitoring: Automated integrity checking and early warning systems