The convergence of laboratory automation, artificial intelligence (AI), and data-driven science has catalyzed the emergence of self-driving laboratories (SDLs), autonomous platforms capable of designing, executing, and analyzing experiments with minimal human input. While early SDLs (SDL 1.0) demonstrated the feasibility of closed-loop discovery, their impact has been constrained by limited scope, poor interoperability, and reliance on human-curated heuristics. This review outlines the vision of SDL 2.0: a new generation of flexible, scalable, and collaborative discovery engines for chemistry and materials science. We discuss recent advances in modular hardware design, AI-driven decision-making including Bayesian optimization, computer vision, and large language models, and orchestration software that integrate scheduling, data management, and safety protocols. Building on these foundations, we propose six defining characteristics for SDL 2.0: interoperable, collaborative, generalizable, orchestrated, safe, and creative. Together, these features establish SDLs as globally networked platforms, enabling reproducible experimentation, accelerated innovation, and democratized access to advanced research infrastructure. By embedding modularity, AI reasoning, and community-driven standards into their core, SDLs 2.0 promise to transform not only how experiments are conducted, but also who can participate in and benefit from the accelerating pace of scientific discovery.

A Blueprint for Improving the Research Infrastructure of a Pediatric Hospital Medicine Division

OVERVIEW: The fourth-generation university (4GU) represents a fundamental shift in how universities engage with innovation ecosystems. While entrepreneurial universities emphasize commercialization and direct economic engagement, 4GUs explicitly organize their teaching, research, and valorization activities around societal transformation missions while orchestrating regional innovation ecosystems. For R&D managers, this transition creates new strategic opportunities: Small and medium-sized enterprises (SMEs) gain access to research infrastructure and collaborative networks otherwise beyond their reach, while large corporations can externalize exploratory research and participate in system-level solutions to grand challenges. We develop a working definition of the 4GU and demonstrate its practical implications through Eindhoven University of Technology’s evolution within the Brainport ecosystem, showing how this model creates value for R&D managers through ecosystem participation rather than bilateral knowledge transfer. We provide a staged implementation framework that guides firms from ecosystem assessment to co-orchestration, supported by multitier performance metrics that balance traditional innovation outputs with ecosystem development indicators. This article contributes to innovation management practice by reframing university–industry collaboration as ecosystem development—essential for addressing the complex, interdependent challenges that define contemporary innovation.

A roadmap for federated learning projects using health data to guide sustainable artificial intelligence development in the European Union.

This policy analysis examines the framework and strategic objectives of the World Health Organization (WHO) Global Traditional Medicine Strategy 2025-2034, with particular attention to its articulation of a people-centered health systems approach. The analysis explores how the strategy represents an evolution from earlier WHO strategies by embedding principles of holism, equity, cultural respect, and autonomy within its operational guidance for traditional, complementary, and integrative medicine (TCIM), while also interrogating the assumptions and implementation challenges associated with this shift. A qualitative policy document analysis was conducted using thematic synthesis. The WHO Global Traditional Medicine Strategy 2025-2034 was analyzed as the primary source, with supplementary WHO documents used for contextualization. Text segments related to people-centered care were systematically coded and mapped against established domains of patient-centered practice and people-centered health systems, including autonomy, informed choice, cultural appropriateness, coordinated care, and equity. Themes were iteratively refined to assess how people-centered principles are operationalized within the strategy. The analysis finds that people-centeredness is structurally embedded across the strategy's vision, guiding principles, and strategic action areas. Key operational mechanisms include an emphasis on risk-based regulation and safety to support informed choice, integration of TCIM into primary health care to improve equitable access and coordination, and a broadened approach to evidence generation that incorporates patient-reported outcome measures alongside other methodological approaches. However, the analysis also identifies potential vulnerabilities related to variable regulatory capacity, evidence heterogeneity, resource constraints, and contextual differences across health systems. The WHO Global Traditional Medicine Strategy 2025-2034 provides a coherent framework for advancing people-centered integration of TCIM, but its effectiveness depends on careful, context-sensitive implementation. Member States must balance autonomy with safety, cultural respect with scientific rigor, and inclusivity with accountability. Actionable priorities include strengthening risk-based regulatory systems, investing in TCIM research and development infrastructure using pluralistic methodologies, and cultivating interprofessional and multi-stakeholder governance to translate people-centered principles into sustainable health system practice.

Since the completion of the Human Genome Project, genome-based medicine has progressed from a predominantly research- driven endeavor to a field of increasing clinical relevance. In Korea, the Korea National Institute of Health (KNIH) has played a central role in the establishment of the necessary research infrastructure that supports the secure and responsible use of genomic and clinical data. These efforts have enabled the generation of comprehensive genomic datasets representative of the Korean population and, together with the Korea Biobank Array optimized for population-specific variants, have strengthened discovery-driven research and accelerated advances in disease gene identification and risk prediction. More recently, KNIH has expanded analyses based on whole-genome sequencing data to support clinical translation, enabling more comprehensive variant detection and facilitating the application of genomic information to disease diagnosis and precision medicine research. These national genomic resources provide an important foundation for improving the diagnosis and management of genetically mediated conditions, including pediatric kidney diseases, where early etiologic diagnosis can substantially influence clinical decision-making and long-term outcomes. Further strengthening of institutional and regulatory frameworks will be essential to support routine clinical implementation and maximize the public health impact of genomics in Korea.

Assessing the health impacts of climate stressors is challenging due to the inherently interdisciplinary nature of the effort and the complexity of data, methods, and software involved. We surveyed researchers who published in the climate and health space to identify major barriers to using and sharing climate and health data and code resources. Participants were identified using a PubMed query to return articles related to research in the field of climate and health. Using the PubMed API, we scraped email addresses for authors of matching published articles. 9195 authors were emailed a link to the online survey instrument, which took approximately 7 min to complete. We had an 11.8% response rate resulting in 1041 useable responses. Respondents were from over 75 different countries with only 16.4% working with US populations and were evenly represented between early, mid, and established career. The most desired resources were analysis-ready datasets and educational materials on data management and analysis. Personal constraints such as lack of time were a major barrier to sharing data or code. Our survey results suggest that investment in data creation as a professional service, knowledge sharing and collaboration, and research infrastructure will be enthusiastically adopted and help to accelerate the pace of research to practice.

BackgroundThis study comprehensively evaluated occupational health, safety, and welfare conditions in small and medium enterprises.ObjectiveThe research used an integrated framework combining physical, mechanical, and psychological risk evaluation. Given the limited research infrastructure in informal industrial sectors, the investigation aimed to establish baseline occupational health data for policy intervention development.MethodsThis cross-sectional exploratory study employed a mixed methods approach designed specifically for resource-constrained informal sector research. Data were collected from 10 randomly selected SMEs and 1 large-scale industry for comparative analysis. A culturally adapted questionnaire was developed and validated after reviewing existing international tools that proved inadequate for the Indian SME context, including 50 workers selected through stratified sampling.ResultsWorkers faced severe multi-dimensional occupational hazards with alarming injury rates: cut injuries (26%, n = 13), hearing impairment from noise exposure (26%, n = 13), respiratory disorders from metal dust (20%, n = 10), skin allergies from metal handling (14%, n = 7), and thermal burns (12%, n = 6). 86% of workers (n = 43) showed complete unawareness of psychological health impacts, while 57% (n = 29) worked excessive 12-h shifts, violating regulatory standards. Critical safety gaps included 52% of workers operating without any personal protective equipment and zero pre-employment health screenings across all surveyed industries.ConclusionsThis study reveals critical systemic failures in occupational safety within SME steel industries, with workers experiencing simultaneous physical, mechanical, and psychological hazards. The integrated assessment approach uncovered previously underreported psychological health neglect alongside documented physical risks. The findings establish baseline data for this understudied sector and demonstrate significant associations between safety practices and health outcomes.

The National Institute for Health and Care Research (NIHR) has 20 Biomedical Research Centres (BRCs) and 28 Clinical Research Facilities (CRFs) that work with NHS organisations and universities to translate cutting-edge research into new interventions. As mandated by NIHR, all BRCs/CRFs have an Equality, Diversity and Inclusion (EDI) strategy which details how they will implement EDI through their practices, research and organisational systems. This UK-based study aimed to conduct a pilot qualitative analysis of EDI strategies to compare all 20 NIHR BRCs/CRFs, identify common priorities and improve inclusion across research infrastructures. The analysis was presented at the first in-person seminar for NIHR EDI professionals (Birmingham, October 2024). Qualitative content analysis of publicly available EDI strategy documents. 48 research infrastructures (20 BRCs and 28 CRFs). EDI strategies were collated into NVivo and Microsoft Excel where inductive coding and content analysis was executed for objectives, action plans and success measures. Both quantitative and qualitative content analyses were conducted to analyse the prevalence of categories and similarities or differences between them. Logic models were developed to map the process of implementing EDI for each main category generated. The most common main category across objectives was 'Cultural change in workplaces' for BRCs and 'Leadership, governance and policy' for CRFs. For action plans, codes for 'Collaborations and Networks' and 'Research development and delivery' were most prevalent for BRCs-for CRFs, it was 'Workforce culture change' and 'Research development and delivery'. Success measures for both BRCs and CRFs most often related to 'Summary reports, feedback, audits and monitoring'. Differences between BRCs and CRFs reflected their organisational roles and strategic maturity, with BRCs tending to have more comprehensive, measurable strategies. This study provides the first systematic analysis of EDI strategies across all NIHR BRCs and CRFs, offering a comprehensive mapping of how EDI priorities are articulated and operationalised across objectives, action plans and success measures. While both infrastructures align with NIHR's inclusion goals, BRCs generally showed more strategic maturity than CRFs. As the analysis was based solely on publicly available strategy documents, it could not determine the extent to which any strategy had been implemented in practice. Future research is needed to examine implementation and impact. The contribution of this work lies in demonstrating systematically and for the first time the ways in which EDI commitments are framed across NIHR infrastructures and their varying levels of depth and maturity. Our findings support the development of more measurable EDI frameworks and highlight opportunities to strengthen inclusion across NIHR-funded research infrastructures.

Scientific protein data formats are widely assumed to be inert, yet their use in automated and AI-driven research environments creates overlooked pathways for cyberbiosecurity risk. This work examines sleeper code patterns, defined as structured non-executable strings embedded in FASTA files, CIF metadata, and protein sequences that persist through downstream processing. A controlled simulation framework models ten representative adversarial use cases and reveals that many workflow components carry these patterns forward without removal, including cloud alignment tools, high-performance computing pipelines, visualization utilities, and transformer-based protein models. Across the ten simulations, nine workflows preserved at least one embedded pattern, which confirms broad systemic tolerance for structured symbolic content. Results show that permissive parsing rules and AI prefix conditioning allow symbolic content to survive reformatting and, in some cases, to become further embedded within generated outputs. These findings indicate a structural blind spot in scientific workflows where biological trust assumptions obscure computational vulnerabilities. To address this gap, the paper introduces a multilayer mitigation framework that combines input sanitation, anomaly detection, AI model guardrails, workflow provenance, and federated containment. Taken together, the study reframes protein data formats as potential cyber vectors and highlights the need for interdisciplinary approaches that strengthen digital resilience across computational biology and national research infrastructure.

Abstract HTS has the potential of a game changer for many applications of superconductivity, not last in the field of particle accelerators and detectors. This paper explores the potential of HTS, with a focus on REBCO-coated conductors, in relation to the evolving demands of superconducting magnets for accelerators. HTS already have a spectacular current carrying ability at high field, demonstrated and available on relevant lengths. Recent advances in non-conventional winding techniques for solenoids, in particular non-insulated windings, have shown that it is possible to reach engineering current densities in the coil exceeding by far those of LTS . This approach seems to offer an extended field reach, as well as solutions to the challenges associated with magnet mechanics, quench management and cost. Most important, beyond the ability to reach a field range higher than what is possible with LTS, HTS offers an extended range of operating temperature, with large margin. This can be exploited to obtain higher availability and better cryogenic efficiency, a must for the future of sustainable large scale research infrastructures such as particle accelerators.

This paper examines how policy capacity shapes collaborative governance in mobility transitions through a qualitative case study of Delhi’s 2020 Electric Vehicle (EV) Policy. It addresses the policy problem of why collaborations around ambitious, high-visibility transition policies are difficult to sustain over time, despite strong early leadership and political support. This is important because governments increasingly rely on multi-actor arrangements to deliver low-carbon mobility, yet often overlook the underlying capacities required to maintain them. The study finds that collaboration in Delhi’s EV policy was initially enabled by strong analytical and political capacities concentrated in a small group of senior bureaucrats and an atypical state think tank, working closely with non-state knowledge organizations. These actors designed structured forums, attracted capable partners, and established early interest. Over time, however, limited operational capacity in line departments, fragmented and weakly organized industry representation, and missing systemic analytical capacities in road transport sector, eroded the collaborative momentum. The paper concludes that sustaining collaboration in emerging policy domains requires more than early champions and external expertise. It argues for deliberate investments in mid-level public managers within core agencies, stronger organization and analytical roles for industry and civil society, and the development of systemic data and research infrastructures. These recommendations speak directly to policymakers seeking to govern mobility transitions through durable, capacity-aware collaborative arrangements.

Background Large language models (LLMs) are increasingly explored for clinical applications in neurology, yet their real-world utility, safety, and optimal implementation remain uncertain. We systematically reviewed the literature to characterize current applications, evaluate evidence quality, and identify knowledge gaps regarding LLM use in clinical neurology. Methods Following PRISMA guidelines, we searched PubMed, Embase, Scopus, Web of Science, and CENTRAL from January 1, 2022 through February 1 2026. for peer-reviewed studies evaluating LLM applications in clinical neurology. We included studies using large language models for clinically relevant neurology tasks from text or multimodal inputs. Two independent reviewers screened records, extracted data, and assessed risk of bias using the QUADS-AI. We synthesized evidence narratively across application domains, validation approaches, and model performance. Results Thirty-six studies (published 2023-2026) spanning 8 neurology subspecialties met inclusion criteria; 13 were simulation or feasibility studies, 17 analyzed retrospective clinical data, and 6 reported prospective clinical validation. Proprietary models predominated; 7 studies used retrieval-augmented generation (RAG) and 3 used agentic frameworks. Performance was highest for constrained tasks, including binary diagnostic classification (area under the curve, AUC 0.75-0.94), information extraction (F1 score, 0.89-0.90), patient education question answering (accuracy, 68%-97%), and ischemic stroke thrombectomy decision support (AUC, 0.92). Open-ended case-based classification showed lower accuracy (42%-54%). Safety signals included hallucinations and fabricated citations, overconfident recommendations, and poor calibration; risk of bias was rated high in all included studies. Conclusion LLMs show promise for selected neurology workflows, but current evidence is early, heterogeneous, and limited by high risk of bias and scarce prospective validation. Clinical translation will likely require RAG and agentic architectures that can plan multi-step tasks, retrieve guidelines and local protocols, verify and calibrate outputs, and produce structured, auditable recommendations with source attribution, with clinician oversight and prospective evaluation. Primary Funding Source: This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. Registration: PROSPERO CRD420251082465.

Background LLMs are promising for clinical workflows but hallucinations limit deployment. Neuro-symbolic systems pair LLMs with explicit rules, ontologies, or knowledge graphs to constrain outputs, yet integration patterns are described inconsistently and their deployment trade-offs remain unclear. Methods We conducted a PRISMA 2020 systematic review (PROSPERO CRD420261296004) of peer-reviewed studies (PubMed/MEDLINE, CENTRAL, Web of Science, Scopus; English; January 1, 2022 to January 30, 2026) and assessed risk of bias using PROBAST adapted to neuro-symbolic evaluations. Results We identified 3,166 records; after screening, 21 studies were included. Studies spanned 18 clinical settings (N = 20 to 197,761; median 2,398) with external validation in 9/21 (42.9%). Four integration patterns were identified, ordered by increasing symbolic authority: structured output, rule-guided generation, knowledge retrieval, and iterative validation (symbolic veto or regeneration). Among studies reporting quantitative comparisons (n = 14), performance improvements ranged from + 3.1% to + 125.6% (median + 21.9%) and increased with symbolic authority (median gains: 9%, 16%, 26%, 40%). All studies reported explicit hallucination mitigation, and 17/21 (81.0%) reported guideline alignment. All studies were rated high risk of bias, driven mainly by analysis limitations (high in 19/21). Iterative validation approaches reported 2-88 s latency and up to 100x cost increases. Conclusion Across four neuro-symbolic LLM integration approaches, gains increase with symbolic authority: the strongest results come from iterative validation where the symbolic layer can veto, not just add context. That improves safety and auditability but raises latency, cost, and symbolic-stack failure risk. Primary Funding Source: This work was supported in part through the computational and data resources and staff expertise provided by Scientific Computing and Data at the Icahn School of Medicine at Mount Sinai and supported by the Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Research reported in this publication was also supported by the Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Registration: PROSPERO CRD420251120318.

Abstract. A decade-long (2015–2024) analysis of aerosol properties was conducted at RADO (Romanian Atmospheric 3D Observatory)-Bucharest station in Romania, a key atmospheric observational site at the Eastern border of the Aerosol, Clouds and Trace gases Research Infrastructure (ACTRIS). This study aims to characterize the optical and microphysical properties of aerosols, classify predominant aerosol types, and investigate their seasonal variability and transport pathways based on long-term multiwavelength Raman lidar and sun/sky/lunar photometer measurements. Results indicate a dominance of fine-mode aerosols, with an average Aerosol Optical Depth (AOD) of ∼ 0.2 and Ångström Exponent (AE) values between 1.5 and 2.0, highlighting pollution-driven aerosol regimes. Seasonal variations were observed, with continental aerosols prevailing in winter, dust transport peaking in spring (altitudes of 2–8 km), and biomass-burning aerosols increasing during summer. Analysis of 408 aerosol layers using the NATALI (Neural network Aerosol Typing Algorithm based on LIdar data) identified complex aerosol mixtures, with 63 high-resolution cases revealing a predominance of “dust polluted” and “continental smoke” types. In the lower troposphere, the extinction-related Ångström exponent shows a narrow mono-modal distribution with the peak at 0.9, indicating predominantly medium-sized particles, whereas in the high troposphere it becomes bi-modal, reflecting alternating occurrences of small and large particles. Lidar ratio values have a distribution peak at around 48–49 sr in both altitude regions, but their spread is much wider in the lower troposphere – revealing frequent layers of highly absorbing aerosols – while lofted layers in the high troposphere exhibit a narrower range typical of moderately absorbing particles. Distinct differences between fresh and aged biomass-burning aerosols (smoke) are identified through their altitude, depolarization, Ångström exponent, and lidar-ratio characteristics, demonstrating microphysical change during transport. FLEXPART (FLEXible PARTicle dispersion model) retro-plume simulations provided insights into aerosol source regions and transport patterns showing contributions from local emissions, long-range transported desert dust, and biomass burning events from Europe and North America. These findings emphasize the persistent influence of regional pollution and transported aerosols on air quality and climate. The integration of ground-based remote sensing and advanced retrieval algorithms like NATALI provides a robust framework for aerosol characterization, enhancing climate models and air quality assessments.

ABSTRACT OBJECTIVES The intra-arrest stage of out-of-hospital cardiac arrest is a poorly understood syndrome. We created an emergency medical services (EMS) clinical research infrastructure to systematically study its biological pathophysiology from the earliest medical contact in the prehospital setting. Our objectives were to evaluate the feasibility of intra-arrest blood collection at two timepoints by on-duty paramedics and qualitatively describe the preliminary results of laboratory analysis. METHODS Two urban fire-based EMS stations conducted this prospective observational pilot study. Intra-arrest patients necessitating intravenous medication administration were included. Traumatic arrests or children <8 years old were excluded. Paramedics obtained blood samples using preassembled blood collection kits immediately after intravenous access, and again after return of spontaneous circulation or resuscitative efforts were terminated on scene. Blood samples were rapidly transported, centrifuged, and frozen. Multiple pathways of biological pathophysiology were analyzed. Preexisting quality improvement activities monitored for interference with clinical care. RESULTS Throughout the 7-month study period, there were no episodes of interference with standard clinical care during 27 patient encounters. Initial and repeat blood samples from 10 subjects had a panel of blood-based biomarker levels measured. In initial samples, we observed 6 subjects with hyperkalemia, 6 with hypernatremia, 1 with hypocalcemia, 7 with complement activation, 2 with hyperreninemia, and 1 with low antithrombin. In repeat samples, we observed renormalization of potassium in 2 subjects despite no pharmacological treatment. Complement activation, fibrin degradation products, and lactate levels increased over time in all but 1 subject. CONCLUSIONS Serial intra-arrest blood sample collection by treating paramedics is feasible. Intra-arrest biological pathophysiology is heterogeneous.

Regional academic journals as a component of Russia’s research infrastructure

There is well-recognised inequity of care for people treated with inflammatory bowel disease (IBD) between metropolitan and regional areas. We aim to describe these barriers to care using Tasmania as a case example and to suggest solutions for addressing these barriers that can be used in Tasmania and beyond. Tasmania has some of the highest rates of IBD in the world, with high rates of advanced therapy use reflecting a more complex population. The landscape of IBD care in Tasmania is marked by diverse challenges, some of which are unique to the state and others shared across regional Australia, all of which we appreciate negatively affects IBD outcomes in Tasmania. We formed a multidisciplinary working group from all three health services in Tasmania and identified keys issues facing Tasmanian's with IBD. The issues identified and proposed solutions were guided by Australian IBD reports and care standards, major society guidelines and our experience within the health service. The key barriers we describe include the underserviced IBD workforce, lack of multidisciplinary care, the isolation of the North West, inadequate expertise in certain areas and the absence of research and administrative infrastructure. We have developed a set of solutions designed to provide an actionable framework for improving care for Tasmanians living with IBD. These solutions encompass the development of multidisciplinary care in a regional setting, enhancing the IBD workforce, developing support for remote areas, upgrading diagnostic output, creating statewide research databases and investing in local training pathways. This action plan may serve as a model for other regional settings to deliver more equitable care in Australia and beyond.

Adult social care in England has long lacked the research infrastructure and capacity common in health, limiting evidence-informed improvement. The Kent Research Partnership (KRP) implemented a dual, bi-directional Researcher-in-Residence (RiR) model (one university-employed researcher embedded in the local authority and one local-authority-employed researcher embedded in the university) to build research capacity. This study explored implementation challenges and facilitators over the first 32months of the partnership. Semi-structured interviews were conducted with eight participants (four current/former RiR; four core team/management). Interviews were recorded, transcribed and pseudonymized. Data were analysed using reflexive thematic analysis, then deductively mapped to the updated Consolidated Framework for Implementation Research (CFIR 2.0). Three themes described determinants of implementation. (a) Context and culture: system-level financial pressures, fragile regional research support and competing operational priorities limited engagement; post-coronavirus disease-19 (COVID) hybrid working and organisational restructuring impeded co-location and informal relationship-building. (b) Intervention design and implementation: dedicated, full-time RiR posts enabled proactive capacity-building; the dual, bi-directional structure conferred legitimacy and access across partnership settings. However, broad role definitions and unfamiliar terminology led to ambiguity and expectations of bespoke research delivery. Reframing the practice-based role as "Research Facilitator" improved clarity and was subsequently formalised within the local authority. (c) RiR personal and professional characteristics: effectiveness hinged on combined research expertise and practice/policy experience, plus relational skills (approachability, persistence, adaptability). A thoughtfully designed RiR model, with dual posts, protected time and individuals who bridge research and practice, can catalyse research capacity building in adult social care. However, persistent contextual barriers, such as resource constraints, cultural misalignment, remote/hybrid working patterns, can limit embedding and impact of research capacity building partnership in social care. Co-designed role clarity, alignment with service-improvement goals, innovative approaches to remote embedding and sustained infrastructure funding are recommended to lessen the impact of the contextual barriers.

Scientific data management is at a critical juncture, driven by exponential data growth, increasing cross-domain dependencies, and a severe reproducibility crisis in modern research. Traditional centralized data management approaches are not only struggling with data volume but also fail to address the fragmentation of research results across domains. This hinders scientific reproducibility and cross-domain collaboration and increases concerns about data sovereignty and governance. This article proposes FAIR and federated Data Ecosystems as an improved architectural pattern for future research data ecosystems. It tries to incorporate the latest advancements in decentralized, distributed systems into existing research infrastructure to promote cross-domain collaboration. Based on established patterns from Data Commons, Data Meshes, and Data Spaces, our approach focuses on a layered architecture that consists of governance, data, service, and application layers. With this, it could be possible to preserve domain-specific expertise and control while facilitating data integration through standardized interfaces and semantic enrichment. Key requirements include adaptive metadata management, simplified user interaction, robust security, and transparent data transactions. Our architecture supports compute-to-data as well as data-to-compute paradigms, implementing a decentralized peer-to-peer network that scales horizontally. This article aims to provide both an impulse for the technical architecture as well as concepts for a governance framework so that FAIR and federated Data Ecosystems could enable researchers to build on existing work while maintaining control over their data and computing resources. This could provide a practical path towards an integrated research infrastructure that respects domain autonomy as well as interoperability requirements.