Aim and Scope

This call will provide funding for research investigating the human and social challenges of data-driven strategies developed and applied within the life sciences. The research should aim to deepen understanding of how artificial intelligence (AI), autonomous systems, and data-driven methodologies have effects and consequences in the life sciences, medicine, and/or society.

The research should be of a visionary nature and have a clear scientific contribution for either the life sciences or the humanities and social sciences, or both. The call aims to fund research at the intersection between the DDLS program (Data Driven Life Science) and the WASP-HS program (AI/AS/Sofware & Society). See below for information about the programs.

The call is open to researchers working in the life sciences, social sciences, and humanities, broadly construed. In the life sciences, the research must be related to data-driven approaches in molecular biology, evolution and biodiversity, prediction medicine, or epidemiology or biology of infection (see DDLS research areas below).

In the social sciences and humanities, research areas include, but are not limited to, philosophy, sociology, anthropology, political science, law, ethics, history, linguistics, languages, the creative arts, and theology. Examples of research topics include ELSI (ethical, legal, and social issues), organizational, epistemic, economic, and cultural challenges, as well as other topics not listed. Research could address more practical challenges to the DDLS community (like data access and use, literacy challenges, consequences of laws like the European Health Data Space, benefit sharing, responsible policies around environmental sustainability, etc.) or be more theoretical in nature.

The call is part of a long-term commitment by the Wallenberg Foundations, which aims to stimulate collaborative research between the data-driven life sciences program and the WASP-HS program.

Description of WASP-HS and DDLS

Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden. The program is funded with a total of 3.3 billion SEK over 12 years from the Knut and Alice Wallenberg (KAW) Foundation.

The DDLS program will recruit 50 high-profile young group leaders and launch over 210 postdoctoral positions and establish a research school for 260 PhDs, including industry PhDs and postdocs. Fellows will be recruited to the 11 participating host universities/organizations, but brought together under a national DDLS program coordinated by SciLifeLab. The DDLS program has four strategic areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection.

The Wallenberg AI, Autonomous Systems and Software Program – Humanity and Society (WASP-HS) is a research program in which researchers tackle the challenges and impacts of upcoming technology shifts and contribute to the development of theories and practices of human and societal aspects of AI and autonomous systems. The particular focus is on the ethical, economic, labor market, social, cultural, and legal aspects of the impacts that AI and autonomous systems bring.

The vision of WASP-HS is to realize excellent research and develop competence in the opportunities and challenges of artificial intelligence and autonomous systems with a strong investment in research in social science and humanities. This multidisciplinary approach is expected to advance our understanding of the challenges and impact of intelligent and autonomous technology, as well as contribute to the development of the theory and practice of human and societal aspects of AI and autonomous systems.

DDLS and WASP-HS are both committed to forming meaningful collaborations to bridge the gaps between the scientific disciplines of DDLS and WASP-HS. The Knut and Alice Wallenberg Foundation has earmarked funding for such joint efforts in its donation. This call is a part of this collaboration and is intended to give researchers from DDLS and WASP-HS resources to pursue joint research projects.

Budget and Project Duration

We expect to fund between 2-4 projects. Funded budgets are expected to lie typically between 2 and 4 MSEK per project. Projects are expected to last either 12 or 24 months.

Financial Information

Since KAW DDLS funding does not follow a full-cost model, DDLS funding should be regarded as partial funding. Certain caps therefore apply to salary overheads (LKP), indirect costs, and premises costs in relation to the total KAW funding granted.

• The grants will be funded by KAW through KTH. Any necessary co-funding will be covered by each university/department
• Overhead and premises costs will be covered according to the program’s terms and conditions. Maximum 20% of the awarded grant can be used for indirect and premises costs.
• There is also a maximum coverage of 52,5% for LKP (payroll overhead) on personnel costs.
• All estimated project costs for the project period are to be specified in the budget template that will be provided.
• The grant will be transferred retroactively for reported costs according to the approved funding. Requisitions and cost reporting will be sent biannually to the SciLifeLab Operations Office/KTH. KTH, as SciLifeLab/DDLS program host university, coordinates the cost reporting and reimbursement processes. All necessary templates will be provided for these purposes at a later stage.

Proposal Structure/Format

Submission – how to apply
One person applies as the main PI of the project with a commitment letter from the co-PI (co- applicant.

Application form and word template

1. Name of project
2. Name, affiliations of main and co-PI
3. Project plan (max 5 pages, including references)
4. The scientific and societal impact and relevance for DDLS and WASP-HS fields (max 1 page)
5. Short CV and selected most relevant publications (max 10 publications) for all applicants main and co-applicants.
6. Commitment letter from receiving departments
7. Budget (template for overall budget)

Evaluation Criteria

Projects will be evaluated according to the assessment of the combination of the
following criteria:

• Scientific quality and novelty from data-driven life sciences,
• humanities, and social sciences perspectives
• Feasibility, including the interdisciplinary competence of the team
• Societal and scientific impact
• Merits of the applicants in relation to their career stage
• Merits and experience of the applicants in relation to the research questions posed
• Alignment with the visions and goals of DDLS and WASP- HS

We will also consider:

• Open science and data sharing aspects
• Future impact on and relevance for WASP-HS/DDLS community
• Complementarity of the applications (for example, gender balance, career
  stage, geographical distribution and project topic)

AI Based Application Success Predictor

1. Strong alignment with AI, autonomous systems, and society (highest predictor)

This is the most important predictor.

Competitive proposals should investigate the interaction between AI/autonomous systems/software and society, including topics such as:

• AI ethics
• AI governance
• Law and regulation
• Human–AI interaction
• Labor markets
• Democracy and public policy
• Trustworthy AI
• Privacy
• Education
• Cultural and societal impacts
• Responsible innovation

Projects focused solely on AI technology without a human or societal dimension are generally less competitive.

2. Genuine interdisciplinary research (very high predictor)

Interdisciplinarity is a defining feature of WASP-HS.

Strong proposals integrate expertise across fields such as:

• Computer science
• Artificial intelligence
• Social sciences
• Humanities
• Law
• Philosophy
• Economics
• Political science
• Sociology
• Psychology
• Ethics

Reviewers look for true intellectual integration rather than parallel disciplinary contributions.

3. Scientific quality and novelty (very high predictor)

Evaluation criteria explicitly include scientific quality and novelty.

Competitive projects should:

• Address an important research gap
• Pose ambitious research questions
• Develop new concepts or theories
• Introduce innovative methodologies
• Advance international scholarship

Incremental studies are generally less competitive.

4. Strong societal impact and relevance (very high predictor)

A hallmark of WASP-HS is the expectation that research contributes to understanding or addressing societal challenges arising from AI and autonomous systems.

Strong proposals explain how the work could:

• Inform public policy
• Improve responsible AI development
• Guide regulation
• Benefit organizations or communities
• Strengthen democratic governance
• Improve public understanding of AI

Societal impact is evaluated alongside scientific excellence.

5. High-quality interdisciplinary team (very high predictor)

Reviewers explicitly assess whether the project team has the interdisciplinary competence needed to complete the work.

Competitive teams typically demonstrate:

• Complementary expertise
• Clearly defined roles
• Experience in interdisciplinary collaboration
• Balanced disciplinary representation
• Effective project management

For several calls, collaboration between researchers from different Swedish universities is mandatory.

6. Feasibility and realistic project design (very high predictor)

Applications should include:

• Clear objectives
• Well-defined methodology
• Realistic milestones
• Appropriate timeline
• Achievable deliverables

Reviewers explicitly evaluate project feasibility alongside scientific merit.

7. Alignment with WASP-HS strategic vision (high predictor)

Projects should contribute to the program's broader mission of understanding and shaping the societal implications of AI and autonomous systems.

Reviewers assess:

• Relevance to WASP-HS goals
• Contribution to the research community
• Long-term value for the WASP-HS ecosystem

For the DDLS–WASP-HS joint call, alignment with both programs is essential.

8. Potential to establish new long-term collaborations (high predictor)

Many WASP-HS calls are specifically designed to initiate new interdisciplinary partnerships.

Strong proposals demonstrate potential for:

• Long-term research collaborations
• Future external funding
• International partnerships
• New interdisciplinary networks
• Sustainable research programs

Project Initiation Grants, in particular, are intended to lay the groundwork for larger future projects.

9. Open science and community engagement (high predictor)

For several funding schemes, applicants are expected to:

• Share research outputs openly where appropriate
• Follow FAIR data principles
• Present findings to the WASP-HS community
• Participate in seminars and workshops

A commitment to responsible data sharing and community participation strengthens an application.

10. Strong investigator profile relative to career stage (high predictor)

Reviewers consider:

• Applicant qualifications
• Relevant expertise
• Publications
• Experience related to the proposed research
• Career stage

Rather than emphasizing seniority alone, WASP-HS evaluates whether the applicant's experience is appropriate for the proposed work.