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Open research questions in Scientific Computing and Data Management

213 gap statements mined from Scientific Computing and Data Management papers in our 7.9M-paper local library, which holds 1,252 papers on the topic — drawn mostly from each paper's own stated research gap, future-work, challenge and limitation notes, and its abstract. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one.

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What the literature leaves open

  • The need for a comprehensive testing and validation framework for EOSC Beyond Pilot Nodes. The gap in establishing a federated European Open Science Cloud infrastructure.

    EOSC Beyond D16.1 Plans for the testing and validation of the EOSC Pilot Nodes · 2026 · DOI

  • The meta-scheduling function at Galaxy Australia already queries backend load in real time, but similar approaches could extend to memory or I/O characteristics. Combining static defaults with live data would yield more adaptive scheduling while maintaining interpretability.

    Right-Sizing Compute Resource Allocations for Bioinformatics Tools with Total Perspective Vortex · 2026 · DOI

  • The paper does not provide a comprehensive comparison with all existing early exiting methods. The evaluation is limited to twelve datasets and eight foundation models.

    EPEE: towards efficient and effective foundation models in biomedicine · 2026 · DOI

  • The need for a tamper-evident provenance system came from a lack of trust, traceability, and auditability in the current ML pipelining, prompting the introduction of this method to ensure the integrity issue posed by data tampering, preprocessing, and the modification of ML models. The system uses the blockchain-based off-chain approach for the efficient management of all machine learning processes.

    Tamper-Evident Data and Model Provenance for IoT-Based Machine Learning Using Blockchain and Off-Chain Storage · 2026 · DOI

  • It addresses a fundamental gap in the current landscape: while content provenance standards describe what was generated, and legal remedies operate after harm is done, no standardized mechanism exists for determining whether AI-generated content is authorized before it is produced.

    Prompt Protocol · 2026 · DOI

  • Conventional DAWs lack built-in proof layers. Music production and AI-assisted creative workflows require protected creative containers.

    Sovereign DAW — Defensive Concept Note · 2026 · DOI

  • Cross-version evaluation is limited to PutnamBench. Strategy-level metadata uses conservative cluster-level aggregation rather than marginal attribution.

    Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search · 2026

  • Future work should focus on training agents to be faithful end-to-end rather than only on individual reasoning traces. Better experiment-planning agents and scaffolds. The major challenge for auto-research is experimental rigor (§5.2). Closing this gap will require improvements: stronger agent capabilities and scaffolds that harness agents for designing and executing rigorous experiments end-to-end.

    How Far Are We From True Auto-Research? · 2026

  • Current implementations are limited to linear ChatBI systems. Prior work has limitations in handling complex, multi-step workflows and opacity in execution logic.

    DeepEye: A Steerable Self-driving Data Agent System · 2026 · DOI

  • The need for reproducible methods to study human-AI conversations. The challenge of implementing regulatory frameworks and provenance standards.

    The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance · 2026 · DOI

  • Future research should focus on developing reproducible and reusable computational approaches. The paper suggests establishing reproducible provenance as the foundation for countering GenAI disinformation.

    The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance · 2026 · DOI

  • One of the key challenges for such machine-assisted statistics is how to combine the general expertise of the machine with the subject matter knowledge of the user. No matter how many simulations the machine has been trained on, it would still likely miss key subject matter knowledge about the specific real data being analyzed, just as a statistics expert wouldn’t know all the intricate details of the data.

    Simulations in statistical workflows · 2026 · DOI

  • In particular, sufficient motivation has not yet been established for AIGC issuers to actively publish structured and contextualized AIGCs using our framework.

    A Prompt-Aware Structuring Framework for Reliable Reuse of AI-Generated Content in the Agentic Web · 2026 · DOI

  • The assumption that semantic continuity is guaranteed by similarity, geometry, usefulness, or convergence. The lack of a governance-relative formal framework for evaluating semantic continuity.

    Semantic Manifold Topology · 2026 · DOI

  • The lack of standardized mechanisms for tracking and recording attribution in derivative models. The need for jurisdiction-agnostic provenance data.

    IP-Superstate: A Reference Architecture for Continuous AI-Native Provenance, Attribution and Licensing · 2026 · DOI

  • Across these settings, NEI competence does not transfer reliably: models trained on shortcut-prone constructions fail to recognize semantically related insufficient evidence, and mixed-construction training narrows but does not close the gap.

    Evidence Absence Is Not Evidence Insufficiency: Diagnosing NEI Construction Artifacts in Fact Verification · 2026

  • The lack of a lightweight solution for protecting intellectual priority in AI-assisted software development. The need for a toolkit that provides timestamp evidence and prevents content leakage.

    Protecting Intellectual Priority in the Age of AI: Runnable Encrypted Code and Public Provenance for Private Repositories · 2026 · DOI

  • The package provides a fully documented public API, allowing users to locally test applications before deploying them on expensive clusters, ensuring cost efficiency. With binaries available for multiple Linux distributions, the tool is currently best suited for long-running processes.

    DistributedWorkflows.jl - A Julia interface to a task-based workflow management system. · 2026 · DOI

  • Despite this expansion, there exists no standardized, cryptographically verifiable infrastructure capable of proving the provenance, reproducibility, model origin, training lineage, or verification status of AI-generated artifacts.

    INTERGET v2.0: The Provenance and Autonomous Coordination Layer of the Sovereign Web — Intelligent Network for Trusted Execution of Recorded Generative Experiences and Trajectories · 2026 · DOI

  • Exploring the evolutionary trajectory from game-centric AI systems to broad applications anticipated with Q* approaches, - Integrating structured, goal-oriented learning with generative, creative…

    From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape · 2025 · DOI

  • The gap between the remarkable generative capabilities of Large Language Models and the complex demands of real-world decision making necessary for Artificial General Intelligence. The limitations of Large Language Models in forming persistent, dynamic models of the world.

    From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape · 2025 · DOI

  • HiRSE Seminar | Metadata for Research Software | M.Gruenpeter | 25/03/2026 | CC-BY 4.0 |…

    Making Research Software Visible, Citable, and Preserved: A Metadata Deep Dive for RSEs · 2026 · DOI

  • Evaluating EPEE on more datasets and foundation models. Investigating the application of EPEE in other domains beyond biomedicine. Developing more efficient and effective early exiting methods.

    EPEE: towards efficient and effective foundation models in biomedicine · 2026 · DOI

  • Further evaluation of Lean Refactor on larger benchmarks. Exploration of applications in collaborative, machine-verified mathematics. Development of new refactoring strategies and techniques.

    Lean Refactor: Multi-Objective Controllable Proof Optimization via Agentic Strategy Search · 2026

  • Machine-to-machine IP negotiation. Jurisdiction-agnostic provenance data. Improving the improvement mechanism itself through a dual-agent architecture.

    IP-Superstate: A Reference Architecture for Continuous AI-Native Provenance, Attribution and Licensing · 2026 · DOI

Most-cited papers in Scientific Computing and Data Management

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Open questions raised by papers listed on this page include: (1) Future research should focus on developing reproducible and reusable computational approaches (“The Verification Crisis: Expert Perceptions of GenAI Disinformation and the…”, 2026); (2) The lack of a governance-relative formal framework for evaluating semantic continuity (“Semantic Manifold Topology”, 2026); (3) The lack of standardized mechanisms for tracking and recording attribution in derivative models (“IP-Superstate: A Reference Architecture for Continuous AI-Native Provenance,…”, 2026); (4) The lack of a lightweight solution for protecting intellectual priority in AI-assisted software development (“Protecting Intellectual Priority in the Age of AI: Runnable Encrypted Code and…”, 2026); (5) Future work should focus on training agents to be faithful end-to-end rather than only on individual reasoning traces (“How Far Are We From True Auto-Research?”, 2026). 213 gap statements have been mined from Scientific Computing and Data Management papers in our 7.9M-paper local library, which holds 1,252 papers on the topic; the gaps come from whichever of those papers state one. They are mostly the research gaps the authors state and the papers' abstracts, plus future-work, limitations and challenges passages. The ones listed below are a selection still marked open; each names the study that raised it, with a DOI link where the paper has one, so you can read the original claim in context.

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