# From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data

arXiv: https://arxiv.org/abs/2607.16845 · HTML: https://mlsanity.com/paper/2607.16845/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 64 references that are in the map.
- First posted on arXiv 18 Jul 2026.

## Builds on (most cited first, 27 of 27)

- [The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search](https://mlsanity.com/paper/2504.08066/) (2025)
- [AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges](https://mlsanity.com/paper/2505.10468/) (2025)
- [Empowering Biomedical Discovery with AI Agents](https://mlsanity.com/paper/2404.02831/) (2024)
- [A Survey on Code Generation with LLM-based Agents](https://mlsanity.com/paper/2508.00083/) (2025)
- [Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents](https://mlsanity.com/paper/2503.24047/) (2025)
- [AgentRxiv: Towards Collaborative Autonomous Research](https://mlsanity.com/paper/2503.18102/) (2025)
- [Risks of AI Scientists: Prioritizing Safeguarding Over Autonomy](https://mlsanity.com/paper/2402.04247/) (2024)
- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [LLMs for Science: Usage for Code Generation and Data Analysis](https://mlsanity.com/paper/2311.16733/) (2023)
- [Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition](https://mlsanity.com/paper/2407.02651/) (2024)
- [On the Design of AI-powered Code Assistants for Notebooks](https://mlsanity.com/paper/2301.11178/) (2023)
- [How Do Data Analysts Respond to AI Assistance? A Wizard-of-Oz Study](https://mlsanity.com/paper/2309.10108/) (2023)
- [AutoDev: Automated AI-Driven Development](https://mlsanity.com/paper/2403.08299/) (2024)
- [AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation](https://mlsanity.com/paper/2305.12050/) (2023)
- [Towards Evaluation Guidelines for Empirical Studies involving LLMs](https://mlsanity.com/paper/2411.07668/) (2024)
- [From Copilot to Pilot: Towards AI Supported Software Development](https://mlsanity.com/paper/2303.04142/) (2023)
- [How Do Programming Students Use Generative AI?](https://mlsanity.com/paper/2501.10091/) (2025)
- [Can Developers Prompt? A Controlled Experiment for Code Documentation Generation](https://mlsanity.com/paper/2408.00686/) (2024)
- [Generating Java Methods: An Empirical Assessment of Four AI-Based Code Assistants](https://mlsanity.com/paper/2402.08431/) (2024)
- [The AI Collaborator: Bridging Human-AI Interaction in Educational and Professional Settings](https://mlsanity.com/paper/2405.10460/) (2024)
- [Model Cards Revisited: Bridging the Gap Between Theory and Practice for Ethical AI Requirements](https://mlsanity.com/paper/2507.06014/) (2025)
- [A Qualitative Study of User Perception of M365 AI Copilot](https://mlsanity.com/paper/2503.17661/) (2025)
- [From Code Generation to Software Testing: AI Copilot with Context-Based RAG](https://mlsanity.com/paper/2504.01866/) (2025)
- [GenAI Is No Silver Bullet for Qualitative Research in Software Engineering](https://mlsanity.com/paper/2603.08951/) (2026)
- [The Role of GitHub Copilot on Software Development: A Perspective on Productivity, Security, Best Practices and Future Directions](https://mlsanity.com/paper/2502.13199/) (2025)
- [JELAI: Integrating AI and Learning Analytics in Jupyter Notebooks](https://mlsanity.com/paper/2505.17593/) (2025)
- [Virtuous Machines: Towards Artificial General Science](https://mlsanity.com/paper/2508.13421/) (2025)

## Abstract (arXiv)

Scientists at European XFEL conduct experiments that generate very large and complex datasets. The subsequent data analysis is challenging as scientists must combine their domain expertise with facility- and software-specific knowledge scattered across documentation, tools, and support channels. To address this problem, we designed and evaluated an agentic AI system tailored to the scientists' needs and integrated with the high-performance computing environment of European XFEL. Using a design science research approach, we conducted a rapid literature review, a systematic evaluation of 16 AI tools, multiple interviews, a focus group, and a user study with experts at European XFEL to develop and evaluate two prototypes. Our study identifies key knowledge challenges in scientific data analysis, derives requirements for an AI agent that supports knowledge retrieval and source code generation, and proposes design recommendations for a specialized system adaptable to the evolving AI tool landscape. These findings provide guidance for developing maintainable AI support in highly specialized scientific environments.