RAISE Doctoral Network for AI-Driven Scientific Discovery

Part B2 - Administrative Sections (No Page Limit)

Call: HORIZON-RAISE-2026-01-03

Section 4: Ethics and Security

4.1 Ethics Self-Assessment

[Describe any ethical issues identified and how they will be addressed. Consider the following dimensions:]

Research involving human participants:

Yes - [ ] No

If yes: Describe informed consent procedures, data anonymization, ethical approval status

Personal data processing:

Yes - [ ] No

If yes: GDPR compliance measures, data retention policies, third-country transfers (if applicable)

Research involving animals:

Yes - [ ] No

If yes: Describe justification, ethical approval, application of 3Rs principle

Dual-use research of concern (DURC):

Yes - [ ] No

If yes: Describe beneficial applications and safeguarding measures

AI systems with ethical implications:

Yes - [ ] No (LIKELY YES for RAISE proposals)

If yes: See Section 4.3 below for AI Ethics Considerations

4.2 Data Protection and Management

[Describe GDPR compliance and data governance measures]

Personal Data Processing:

No personal data handled (tick if applicable)
Lawful basis for processing: [Legitimate interest / Consent / Contract / Other]
Data categories processed: [e.g., contact details, biometric data, location data]
Data subject rights: [Describe how GDPR rights are ensured]
Data processor agreements: [Describe contracts with third parties]

Data Management Plan (Brief Overview):

[Outline approach to data storage, access control, retention, deletion, and backup for research data generated by doctoral candidates and network activities]

4.3 AI Ethics Considerations (RAISE-Specific)

[Describe responsible AI practices for AI systems developed within Doctoral Projects]

Transparency & Explainability:

AI models used in research: Describe methods for model interpretability and explainability
Documentation: How will doctoral candidates document AI methodology and limitations?

Fairness and Bias Mitigation:

Describe approach to identifying and mitigating algorithmic bias
Training requirements for DCs on fairness in AI
Bias testing and evaluation protocols

Accountability & Governance:

Responsibility assignment for AI system outcomes
Conflict resolution procedures if AI misuse is suspected
Alignment with NIST AI Risk Management Framework (or similar standards)

Safety and Robustness:

Adversarial testing approaches for AI systems
Validation and verification protocols
Documentation of limitations and failure modes

Human Oversight:

Describe supervision and oversight of AI system development by qualified researchers
Decision-making: Where are human-in-the-loop safeguards required?

4.4 Security Issues

[Describe any security-sensitive aspects of the research or network infrastructure]

No security issues identified (if applicable)
Research data classification: [Public / Restricted / Confidential]
Cybersecurity measures: [Firewalls, encryption, access controls, etc.]
Dual-use technology considerations: [If applicable]
Third-party data handling: [Security protocols for external collaborators]

Section 5: Network Organisation

5.1 Management Structure

[Describe governance bodies, roles, and decision-making hierarchy]

Supervisory Board:

Position	Represented Organisation	Role
Chair	[Coordinator]	Strategic leadership, final approval authority
Member	[Beneficiary 1]	Strategic representative
Member	[Beneficiary 2]	Strategic representative
External Observer	[Advisory Board representative]	Independent oversight

Management Committee:

Position	Organisation	Responsibility
Coordinator	[Lead Institution]	Day-to-day project management
WP1 Leader	[Institution]	Work Package 1 coordination
WP2 Leader	[Institution]	Work Package 2 coordination
Finance Manager	[Coordinator]	Budget and finance management
Training & Mobility Manager	[Institution]	Doctoral candidate progression and support

Advisory Board:

[3-5 external experts in AI, scientific discovery, and/or doctoral training]

Name	Affiliation	Expertise	Role
Prof./Dr. [Name]	[Institution/Company]	AI and scientific discovery	Biannual strategic advice
Prof./Dr. [Name]	[Institution/Company]	Responsible AI	Ethical oversight
Dr. [Name]	[Institution/Company]	Industry perspective	Research impact guidance

5.2 Decision-Making Procedures

[Describe voting mechanisms, quorum requirements, and conflict resolution]

Supervisory Board:

Voting: Simple majority (50% + 1) on strategic decisions
Quorum: 2/3 of members for valid decisions
Meeting frequency: Annually, plus ad hoc as needed
Decisions binding on all beneficiaries

Management Committee:

Voting: Consensus preferred; simple majority if necessary
Quorum: 2/3 of members
Meeting frequency: Monthly (virtual or in-person)
Coordinator has tie-breaking authority on operational matters

Conflict Resolution:

First: Resolution at Management Committee level
Second: Escalation to Supervisory Board
Third: Independent mediation (if required by contract)
Final: Arbitration per consortium agreement

IP and Publication Disputes:

[Describe procedures for resolving disputes over publication timing, IP ownership, and data use rights]

5.3 Communication Plan

Internal Communication:

Monthly Management Committee meetings (virtual, 60 minutes)
Quarterly all-hands network meetings for all DCs and supervisors
Online collaboration platform: [Specify tool - e.g., SharePoint, Teams, Slack]
Annual in-person coordinator meeting
Annual network conference/training school (see Section 2.4)

External Communication:

Project website: [Domain to be created] with updated information on DCs, publications, events
Social media: [Channels - e.g., Twitter, LinkedIn, YouTube]
Public deliverables: Open-access publications, toolkits, datasets (following FAIR principles)
Stakeholder engagement: Workshops with AI and scientific communities
Policy briefs and white papers on responsible AI in scientific discovery
Annual public report on network activities and outcomes

Document Management:

Central repository for all project documentation: [Tool to be specified]
Version control for governance documents
Regular distribution of meeting minutes and decisions to all partners

5.4 Quality Assurance and Monitoring

Monitoring Mechanisms:

Annual DC progress reports (to be reviewed by management committee)
Publication and dissemination tracking
Survey of DC satisfaction and training quality (biannually)
Mentor-mentee feedback sessions (quarterly)
External evaluation of research output and network impact (mid-term review)

Key Performance Indicators (KPIs):

KPI	Target	Review Period
DC completion on schedule	≥90%	Annual
Publications per DC	≥3 peer-reviewed papers	End of project
Intersectoral training activities	≥2 per DC over 3 years	Annual
Career outcomes	≥70% in R&I roles within 3 years post-PhD	Post-project evaluation
Gender balance in recruitment	≥40% underrepresented gender	Annual
Network cohesion score	≥4/5 (survey)	Biannual

Mid-Term Review:

External reviewer assessment of progress toward objectives (Month 18-24)
Adjustment of management procedures or DC projects if needed
Reallocation of secondment budgets based on actual mobility patterns

Section 6: Environmental Aspects

6.1 Alignment with MSCA Green Charter

[Describe how the Doctoral Network will minimize its environmental impact and promote sustainability]

Network Operations:

Mobility: Default to virtual meetings for Management Committee; encourage train travel over flights for distances <500 km; one annual in-person coordinator meeting
Events: Host annual network conference at a venue with verified carbon footprint reduction practices; digital-first registration and materials
Office & Lab: Ensure partner institutions follow ISO 14001 or equivalent environmental management standards
Procurement: Preference for recycled materials, open-source software (reducing licensing carbon footprint), energy-efficient hardware
Travel Grants: Mobility scheme encourages short-term secondments (reducing repeated long-distance travel); green travel incentives (e.g., train travel reimbursement parity with flights)

Documentation & Communication:

Digital publication of all project outputs (no printed reports unless specifically requested)
Open-access publishing to maximize dissemination efficiency
Virtual training schools and workshops where feasible
Carbon footprint tracking for all network-organized mobility and events

6.2 Environmental Sustainability in Research

[Describe how the research agenda contributes to environmental sustainability goals]

AI for Environmental Science:

DCs will develop AI applications for climate modeling, biodiversity monitoring, or sustainable materials discovery
Research outputs will contribute to understanding and mitigating environmental impacts of AI itself (e.g., carbon footprint of large language models, energy efficiency of deep learning)

Responsible AI in Scientific Discovery:

Emphasis on resource-efficient AI models (reduced computational requirements)
Data efficiency (maximizing information extraction from limited datasets)
Reusable AI tools and datasets (reducing redundant computation across research communities)

Open Science for Sustainability:

All datasets and models developed will be released open-source under appropriate CC licenses
Enables other research communities to reuse results, avoiding duplication of computational effort
Contributes to global knowledge commons for addressing planetary-scale challenges

Section 7: Participating Organisations

[For EACH beneficiary and associated partner, provide the following information. Copy this template for each organisation.]

Template: [Organisation Short Name] - [Full Name]

Field	Details
Country	[ISO Country Code]
Legal Entity Type	University / Research Institute / Company / SME / Other
Department/Division	[e.g., Department of Computer Science]
Scientist-in-Charge (NetAFF)	Prof./Dr. [Full Name], title, email, phone
Role in Network	Coordinator / Beneficiary / Associated Partner
PhD Awarding Capacity	Yes / No (must be "Yes" for at least beneficiaries)
Previous MSCA Hosting	Yes / No; if yes, briefly describe experience

Research Context and Infrastructure:

[Describe relevant research groups, laboratories, computing infrastructure (HPC facilities, GPU clusters, data repositories), and datasets available to doctoral candidates]

Research group(s) involved: [Names of relevant labs/groups]
Computational resources: [e.g., 500-GPU cluster, access to national HPC facility]
Datasets available: [e.g., databases of scientific simulations, observational data]
Research facilities: [e.g., high-performance computing center, experimental labs]
Equipment: [e.g., specialized instruments, software licenses]
Library and archive access: [e.g., institutional repositories, open-science platforms]

Expertise and Track Record:

[Describe research expertise directly relevant to the AI-in-scientific-discovery theme and the specific Doctoral Projects hosted]

Core research group expertise: [AI/ML, scientific domain (e.g., materials science, climate science), interdisciplinary training]
Key publications (last 5 years): [List 5-7 representative publications demonstrating expertise]
Relevant ongoing research projects: [List current grants and collaborations]
Expertise in responsible AI / AI ethics: [Describe prior work on fairness, explainability, safety, etc.]
Doctoral candidate supervision: [Describe track record in PhD training and mentorship]

Previous Involvement in EU and National R&I Projects:

Project	Acronym	Duration	Role	Budget (€)
[Project Title]	[ACRONYM]	[Years]	Coordinator / Partner	[Amount]
[MSCA Project, if any]	[Acronym]	[Years]	[Role]	[Amount]

Doctoral Candidates Hosted (Network Experience):

[Only fill this if the organisation has previously hosted Marie Curie DCs]

DC Number	Project Title	Host Organisation	Duration	Current Status
[e.g., DC-2022-1]	[Project Title]	[If different from current host]	[Months]	Completed / In Progress

Supervision and Training Capacity:

Established doctoral programme(s): [List PhD programmes offered]
Current number of PhD students supervised: [Number]
PhD completion rate (last 5 cohorts): [X%]
Average time to completion: [X years]
Supervisor experience: [Number of PhD students graduated by primary supervisors]
Mentoring and professional development: [Describe orientation programmes, career guidance, transferable skills training offered to DCs]
Intersectoral networks: [Industry collaborators, public institution partners, NGOs for secondment opportunities]
International experience: [Prior Erasmus Mundus, joint-degree, or other international programmes]

Support Infrastructure for Doctoral Candidates:

Graduate school or doctoral college: [Name, structure]
Counselling and well-being services: [Describe support for mental health, work-life balance]
Equality, diversity, and inclusion (EDI) initiatives: [Women in STEM support, disability services, mentoring for underrepresented groups]
Research integrity training: [Responsible conduct of research, open science, data management]

Link to Project Objectives:

[Briefly describe how this organisation's expertise and infrastructure support the overall network objectives and the specific Doctoral Projects to be hosted]

[REPEAT SECTION 7 TEMPLATE FOR EACH BENEFICIARY AND ASSOCIATED PARTNER]

Section 8: Pre-Agreement Letters (DN-JD Only)

Applicability:

This section applies ONLY to Joint Doctorate proposals (DN-JD) where doctoral candidates will receive joint, double, or multiple degrees from two or more institutions.

Instructions:

If NOT a DN-JD proposal: Write the following statement and proceed:

"Not applicable - this proposal is submitted as a standard Doctoral Network (DN) with single-degree awarding. Doctoral candidates will be enrolled exclusively at the host institution."

If DN-JD proposal: Include signed institutional pre-agreement letters (on official letterhead) from ALL institutions that will co-award or multiply-award degrees. Letters must confirm the following:

Commitment to joint supervision of enrolled doctoral candidates
Mutual recognition of doctoral studies credits (ECTS, coursework, research)
Joint or multiple degree awarding procedures and regulations
Alignment of doctoral regulations and quality standards across institutions
Arrangements for thesis examination by examiners from both/all institutions
Procedures for handling academic disputes or degree-awarding conflicts
Duration of joint-degree arrangement (co-terminus with project, or extended)

Pre-Agreement Letters:

[If DN-JD: Attach signed letters as appendix or upload separately in submission system. If DN: write N/A statement above.]

Section 9: Additional Information

9.1 Additional Context and Supplementary Information

[Provide any supplementary information that strengthens the proposal but does not fit within the page limits of Parts B1.1-B1.3. Examples include:]

Preliminary results or proof-of-concept: Early findings demonstrating feasibility of research approaches
Letters of support from external organisations: Industry partners, NGOs, policy makers endorsing the network's scientific and training agenda
Special mobility arrangements: Explanation of secondment logistics, visa sponsorship, or integration challenges for third-country doctoral candidates
Additional training activities: Detailed curriculum for specialised schools, mentoring programmes, or industry internships beyond those described in Part B1.3
Outreach and dissemination strategy: Public engagement initiatives, science communication projects, policy advocacy plans
Risk mitigation strategies: Contingency plans for major project risks identified in Part B1.3

9.2 References and Bibliography

[If supplementary references beyond the scope of Part B1 are needed, list them here with full bibliographic details. Use consistent citation style (e.g., APA, IEEE).]

Recommended References for RAISE Proposals:

European Commission. (2024). Responsible Research and Innovation in Horizon Europe. [URL]
NIST. (2023). AI Risk Management Framework. [URL]
ACM FAccT. (Annual). Conference on Fairness, Accountability, and Transparency. [Leading publications on responsible AI]
UNESCO. (2021). Recommendation on Open Science. [UNESCO Open Science Recommendation]
Horizon Europe. (2024). Guidelines on Open Science and Research Data Management. [EC publication]

SUBMISSION CHECKLIST FOR PART B2

Before submitting, verify the following:

Section 4 – Ethics & Security:

All ethics questions answered (human subjects, personal data, animals, DURC, AI ethics)
GDPR compliance described (if applicable)
AI ethics considerations explicitly addressed (mandatory for RAISE)
Security issues documented or marked "no security concerns"

Section 5 – Network Organisation:

Management structure clearly described (Supervisory Board, Management Committee, Advisory Board)
Decision-making procedures detailed (voting, quorum, conflict resolution)
Communication plan covers internal and external stakeholders
Quality assurance mechanisms and KPIs defined

Section 6 – Environmental Aspects:

Alignment with MSCA Green Charter explained
Environmental sustainability contributions described (research agenda + network operations)

Section 7 – Participating Organisations:

ALL beneficiaries described in detail (research expertise, infrastructure, supervision capacity)
ALL associated partners listed and described
PhD awarding capacity confirmed for at least all beneficiary institutions
Prior MSCA experience noted (if applicable)
Support infrastructure for DCs described

Section 8 – Pre-Agreement Letters:

If DN-JD: Signed pre-agreement letters from all co-degree-awarding institutions included
If DN (standard): "Not applicable" statement included

Section 9 – Additional Information:

Any supplementary materials included
Bibliography complete (if needed)

Cross-Cutting Checks:

All partner organisations listed in Part A of the online submission form
All beneficiaries and associated partners described in Section 7
No separate letters of commitment needed (post-2023 policy)
Page limits observed (Part B2 has no strict limit, but remain concise)
Language: English, spell-checked, consistent terminology

Final Quality Check:

Consistency between Part B1 and Part B2 (references to WPs, DCs, partners)
Font size ≥11pt, standard margins (MSCA standard)
All tables and figures clearly labelled and legible
PDF export verified (if applicable) for readability

Template Version: 1.0 (for HORIZON-RAISE-2026-01-03)

Last Updated: March 2026

Author: [Your Name / Research Administration Office]