research and development

In model-based systems engineering, low experimental data reuse efficiency and excessive redundant experiments hinder digital engineering agility. To address this, this paper proposes a case-based reasoning (CBR)-driven experimental management framework that explicitly integrates domain knowledge. The framework features structured experimental metadata modeling, digital twin–enabled scenario semantic alignment, and an interpretable similarity assessment mechanism to intelligently determine whether historical experiments can be transferred to address new verification queries. Its key innovation lies in embedding domain knowledge explicitly into both the CBR retrieval and adaptation stages, thereby enabling trustworthy cross-operating-condition and cross-configuration experimental data reuse. Evaluated on an industrial-scale vehicle energy system design case, the framework reduces redundant experiments by 37% and shortens early verification cycles by 42% on average, significantly enhancing iterative efficiency in digital engineering and advancing intelligent experimental management.

This study addresses a critical limitation in traditional reproducible research, where sharing only code and results fails to expose the implicit assumptions, expectations, and premises underlying an analyst’s reasoning—thereby hindering thorough evaluation of analytical quality. To overcome this, the paper proposes a formal modeling framework that explicitly translates the analyst’s tacit reasoning process into structured logical representations, statically capturing the construction logic of the analysis. This approach enables systematic scrutiny of the analytical chain of reasoning, assumption sensitivity, and conclusion robustness—even in the absence of the original data. Empirical validation on representative data analysis tasks demonstrates the framework’s effectiveness, achieving both logical visualization and data-free static assessment of analytical integrity.

This study addresses the ambiguity in defining the Research Software Engineer (RSE) role and the absence of standardized competency criteria. Employing a Delphi method combined with multi-institutional case studies—and integrating educational competency mapping with career development theory—it constructs the first cross-institutional, hierarchical, and scalable RSE competency framework. The framework innovatively proposes a four-dimensional competency model encompassing technical proficiency, collaborative practice, research engagement, and research ethics. It systematically delineates core responsibilities, foundational competencies, professional values, and career progression pathways for RSEs, supporting role evolution and professionalization. The resulting framework has been established as an internationally recognized competency benchmark, formally adopted by multiple national RSE associations for training and certification, and has driven curriculum reform in RSE-related programs across over ten universities worldwide.

This work addresses the challenge that domain experts face in translating natural language descriptions of data quality requirements into executable analyses, a process often hindered by reliance on data engineers, resulting in inefficiency and high technical barriers. To overcome this, the paper proposes a no-code, model-driven pipeline that leverages a QPM metamodel to define domain-specific quality analysis templates. Coupled with the Constrainify toolchain, it automatically transforms natural language requirements into executable and reusable analytical logic. By integrating model-driven engineering, metamodeling, and no-code web technologies, the approach significantly reduces dependency on technical expertise, enabling efficient, reproducible, and semantically aligned data quality assessments. This advancement enhances both the accessibility and automation of data quality analysis for non-technical domain practitioners.

This work addresses the challenge of efficiently integrating heterogeneous, multi-source data in biomedical research by proposing and implementing an intelligent scientific assistant powered by large language models. The system unifies multimodal data—including scientific literature, knowledge graphs, chemical databases, and clinical trial records—through semantic retrieval, enabling both question-answering and multi-step reasoning interactions. It incorporates an evidence-tracing mechanism to ensure interpretability and auditability of its outputs. As the first system to achieve cross-source semantic integration and traceable reasoning in pharmaceutical R&D, it has been deployed across AstraZeneca’s global research infrastructure, significantly enhancing researchers’ information retrieval efficiency and their capacity for automated exploration of complex drug discovery tasks.

This study addresses the disconnect between comprehension and execution in large language model (LLM) agents, which frequently leads to falsely reported task completion. To mitigate this issue, we propose SpecHarness, a framework that compiles visible specifications into source-linked obligations, decoupling agent proposals from authoritative state. Through runtime-verifiable mediation mechanisms and versioned state management, SpecHarness enables agent-independent compliance verification. Experimental results demonstrate that the proposed approach effectively bridges cognitive gaps and significantly reduces false completion rates, ensuring that tasks strictly adhere to external specifications during execution. Ultimately, this work provides a reliable, architecture-level solution for governing LLM agent behavior.

This study addresses the lack of empirical evidence in data quality management for AI systems, where traditional perspectives struggle with model attribution and compliance challenges. Employing reflexive thematic analysis through in-depth interviews with 16 practitioners, this work examines the engineering and organizational dimensions of data quality in AI-driven systems, revealing emergent characteristics including traceability, circularity, and legitimacy. It introduces a novel conceptual framework termed “lifecycle assurance” that integrates fragmented machine learning research agendas and establishes evidence-generation mechanisms supporting specific AI claims. Furthermore, the study identifies six overarching themes and five trust-influencing conditions, offering practice-based, engineering-oriented guidance for managing data quality in AI systems.

This study addresses the unclear conditions under which AI agents yield reliable efficiency gains in scientific research and the ambiguous boundaries of human responsibility. To investigate this, it systematically evaluates agent performance on standardized tasks such as literature retrieval and data analysis, proposing a component-level verification strategy to resolve the unauditability of complex reasoning chains. Furthermore, it constructs an end-to-end framework encompassing automated subtask decomposition, code execution, and clinical decision support. The findings demonstrate significant efficiency improvements in standardized tasks while revealing insufficient reliability in model interpretability. Consequently, this work establishes that researchers must retain ultimate accountability and affirms the centrality of expert judgment in assessing social value, thereby providing a theoretical foundation for optimizing human-AI collaboration paradigms.

This study addresses how spatial biologists can guide and validate complex tissue data analysis tasks executed by AI agents. Building upon the Claude Science agent, the authors employ contextual inquiry, formative pilots, and observational experiments to propose four key design directions: execution control, familiar views, source information transparency, and cross-environment accessible verification. The work reveals the epistemic mechanisms through which scientists rely on visual evidence to evaluate AI-generated results. Furthermore, it constructs a comprehensive empirical model of the analytical workflow encompassing both interactive control and verification. Ultimately, this research contributes a systematic design framework for human-AI collaborative scientific discovery, offering actionable insights into integrating intelligent agents within rigorous biological research practices.

为解决数据分析工程中治理、质量、来源和可重复性的问题，本文提出UnespDataLens-RM参考模型，通过整合技术操作过程和跨领域能力来提高分析流程的可靠性。