Human Capital & Responsibility

Challenge

The European compute and data continuum depends on people as much as technology. Technical capabilities require skilled professionals to implement, operate, and evolve them. Environmental sustainability requires not just efficient hardware, human expertise to optimise software and operations but also the management of environmental impacts over the whole life cycle of infrastructures or scientific projects, as well as the means to ensure actors at all levels (end users, data centre operators, policy makers and funders) adopt environmentally responsible behaviours. Cross-infrastructure coordination requires communities that collaborate effectively across organisational boundaries.

Three interconnected challenges constrain human capital and organisational capacity:

• Workforce skills gap: The drivers described in Section 3 require expertise that is in short supply. Heterogeneous computing demands specialists in portability and performance engineering. AI/ML adoption depends on machine learning engineers who can bridge proof-of-concept and production. Sustainability goals require transverse hardware-software expertise to minimise impacts through continuous code optimisation, impact-aware scheduling and infrastructure operation. Sustained progress requires a triangular collaboration model: researchers driving concept and validation, code developers responsible for implementation and optimisation, and digital-infrastructure specialists owning operations and performance. This collaboration needs to be sustained by science funders as a multi-year commitment rather than assembled from ad-hoc project additions, with return on investment evaluated where appropriate. Community surveys indicate that most research groups identify technical capacity as a concern, yet lack resources and expertise to address it.
• Career structure misalignment: Scientific career structures do not recognise or reward contributions to infrastructure, software development, or operational efficiency. Research software engineers, who bridge domain science and computing expertise, typically lack defined career paths. This creates retention challenges as the same skills command premium salaries in the commercial sector. The Amsterdam Declaration on Funding Research Software Sustainability identifies recognition of software as a first-class research output, but operational uptake has been uneven: most large-scale digital infrastructures provide only limited code-optimisation support, and deep domain-level RSE work is rarely embedded in their core remit. Partial implementations exist via EuroHPC Centres of Excellence, RI Train, and HPC Train, but no European-level funding line yet sustains the triangular researcher–RSE–DI collaboration the priority calls for. The Declaration treats recognition as a research contribution as essential to workforce sustainability.
• Fragmented community coordination: Research infrastructures and the federated digital infrastructures serving them are coordinated unevenly: WLCG and the Grid stack have a mature multi-party governance, but the coupling between data-intensive science needs and EuroHPC remains under-articulated, and structured engagement between scientific RIs and the broader European digital-infrastructure providers is still partial. Scientific requirements are often invisible when infrastructure decisions are made. Effective co-design requires sustained collaboration that current funding models do not reliably support.

Priorities Overview

Pillar 4 builds collaborative communities through co-design processes, establishes environmental sustainability through impact-aware management of facilities, and develops career profiles combining domain science with computing expertise. Three priorities address the human and organisational dimensions of the continuum. The pillar combines the two strands that its title names: human capital, P11 and P13, which concern the people and communities that operate the continuum; and responsibility, P12, which is different in kind. P12 is a technical and operational priority addressing the environmental footprint of computing rather than the workforce. It sits here because environmental sustainability, like community and skills, is a continuous responsibility and an enabling condition rather than a one-off deliverable, and its technical detail is developed in the Technical Blueprint (D6.1) and the Environmental Sustainability driver (Section 3.2.3).

P11: Community Collaboration and Co-Design. Establish structured mechanisms for scientific communities to feed requirements, roadmaps and white papers into the cross-infrastructure coordination forum (P01), building on the SPECTRUM CoP and the existing networks (e.g., APPEC, ASTRONET, RadioNet, JENA). These networks contribute strategy and requirements but hold no operational mandate, so commitment and uptake depend on the research facilities and digital infrastructure providers convened in that forum. The European Commission should give the forum formal standing and recognise data-intensive research infrastructures as stakeholders in EuroHPC and EOSC governance. (Detail: Annex A, P11)

P12: Environmental Sustainability. Embed environmental accountability for multiple impacts into research computing operations, using lifecycle assessment methodologies (ISO 14040:2006 and ISO 14044:2006 on Environmental Management, Life Cycle Assessment; the EU Product Environmental Footprint (PEF) method) to track impacts across procurement, fabrication, operation, and end-of-life. For this accounting to drive behaviour rather than displacement, it must be paired with policy on research conduct (procurement criteria, allocation rules), otherwise raised EU-tracked costs risk pushing workloads to non-European providers and net-out the sustainability gain.

Allocate environmental impacts as credits flowing from policy makers to data centres and end users, replacing core hours and abstract metrics with visible impact accounting. The credit metaphor, rather than direct euros, allows top-down political targets (e.g. EU Green Deal limits) to flow through the allocation chain without entangling with market-cost dynamics; the two metrics are complementary, not substitutable, that supports responsible behaviour.

Multi-objective optimisation balancing performance with carbon emissions, energy consumption, and resource utilisation is a candidate output, with best-practices for European data centres (best practices and shared reference patterns rather than rigid blueprints, given the diversity of facility designs) an option for community discussion. The priority addresses growing requirements for environmental responsibility as a condition of public funding and social licence to operate. (Detail: Annex A, P12).

Relaxing the environmental tension requires addressing the whole value chain leading to the generation of science data products in order to reduce the environmental impact: to jointly optimise the research problem (recognising that responsibility for resource-appropriateness lies with the researcher's problem framing as well as with software and hardware) in combination with software and hardware, to make maximal use of minimal resources, to tailor execution to environmental impact in the sense of choosing what resources to use where and at what moment in time, to reduce digital waste through the reuse of data and software, framing failures during execution and resilient execution by adapting to changing operating conditions. Optimising scheduling and hence avoiding idle cycles is equally important. This relies on combining tools, policies and a structured collaborative effort between research and digital infrastructures.

Key to their successful application is a change of paradigm which consists in allocating environmental impacts at all levels, instead of core hours for end users and only funds to data centres. This change of metric is intended, on one hand, to ensure these impacts are visible in order to foster responsible behaviours by the actors and, on the other hand, to establish a framework allowing a high-level allocation made by policy makers in line with European or national objectives to then be used by data centres, typically through hardware procurement and operations, with the latter itself resulting from impact allocation to end users. These allocations serve as credit which each actor is then free to manage (within a framework that is collaborative set in e.g., a Commons approach by science funders, research infrastructures and digital infrastructures) and consumed by arbitrating between their priorities. At the level of the compute and data continuum, this is expected to require coordination between institutional actors for the continuum to not just provide extended resources to users but also a lever to reduce the global and local environmental impacts of their activities.

P13: Skills, Careers and Training. Develop the hybrid expertise combining domain science with computing skills that the continuum requires, with training programmes aligned with emerging drivers (AI/ML integration, heterogeneous computing, sustainability practices), delivered through a mix of integrated curricula and AI-assisted training tools (which can extend reach where curriculum capacity is the bottleneck), and career frameworks following the Research Software Engineer model. The current arrangement carries an avoidable systemic cost: trained experts leave at later career stages, taking with them critical, slow-to-rebuild institutional knowledge, and forcing complete system replacements that would not otherwise be needed. The intent is not to compete with industry on salary but to retain the specific, hard-to-substitute knowledge that the long-lived experiments (HL-LHC, SKA) depend on. A modest number of stable, well-placed career positions would deliver a better return on the substantial investments already committed to scientific computing. Short-term actions may leverage current EU instruments (e.g., RI Train, HPC Train, EuroHPC Centres of Excellence); medium-term actions integrate training into European university curricula (Detail: Annex A, P13).

Interdependencies: Community collaboration (P11) provides the foundation for this pillar and connects across all pillars through co-design processes that shape governance decisions (Pillar 1), technical architecture (Pillar 2), and software priorities (Pillar 3). Within Pillar 4, P11 coordinates sustainability practices (P12) and enables knowledge transfer for skills development (P13). These priorities create the organisational foundation on which technical capabilities depend.

Recommendations

For Scientific Communities and Research Infrastructures

• Increase and incentivise participation in co-design processes and CoPe Working Groups (P11)
• Integrate and reward environmental impact awareness into software development practices (P12)
• Recognise and value research software engineering contributions within projects and collaborations (P13)

For e-Infrastructures and Service Providers

• Maintain community engagement mechanisms through dedicated liaison roles and coordination structures (P11)
• Implement a sustainability governance through impact reporting and impact-driven resource allocation (P12), supported where possible by EU-level regulation that institutions can act on (the post-CSRD/Omnibus reporting landscape is still settling, so the regulatory hook for research computing remains to be defined)
• Provide automation (e.g, AI-driven), training and documentation enabling efficient use of heterogeneous resources (P13)

For Policy Makers and Funding Bodies

• Fund sustained community coordination mechanisms beyond project timelines (P11)
• Define admissible impact budgets for infrastructures and evaluate investments and operation in this frame (e.g., by implementing reporting requirements at the EU-level) (P12)
• Establish career frameworks for research software engineers (across emerging skill areas including AI/ML, performance engineering, and scientific code preservation) and for data stewards (responsible data management, hierarchical-storage management, and long-term curation) (P13)