datasets

Contemporary dataset papers frequently suffer from limited originality, insufficient diversity, inadequate quality control, and poor transparency regarding construction methodologies; existing datasheets are largely descriptive and lack quantifiable evaluation criteria or enforceable accountability mechanisms. Method: We propose DataRubrics, the first rubric-based framework for structured data quality assessment, integrating LLM-as-a-judge (e.g., GPT-4) with synthetic data techniques to enable automated, reproducible, and standardized quality scoring for both human- and model-generated datasets. Contribution/Results: The framework delivers an open-source evaluation toolkit (github.com/datarubrics/datarubrics), facilitating collaborative, measurable data review by reviewers and authors alike. It significantly enhances rigor, transparency, and trustworthiness in data-centric research through objective, interpretable, and auditable quality metrics.

Public datasets in the LLM4RE (Large Language Models for Requirements Engineering) domain are fragmented, poorly documented, and lack systematic description, hindering comparability and reuse. Method: We conduct the first systematic dataset mapping study in LLM4RE, analyzing 62 publicly available datasets drawn from 43 scholarly publications along dimensions including document type, granularity, RE task phase, domain, and language. We propose the first domain-specific dataset classification and characterization framework for LLM4RE. Contribution/Results: Our framework identifies critical research gaps—particularly in requirements elicitation, requirements management, and multilingual support—and we release an open dataset catalog alongside a standardized featureization schema. This work significantly enhances dataset visibility, structural consistency, and cross-study comparability, laying the foundation for a unified benchmarking repository in LLM4RE.

Existing dataset documentation tools struggle to achieve real-world adoption due to ambiguous value propositions, misalignment with practical contexts, insufficient attention to human labor costs, and a lack of systemic integration. This study addresses these challenges through a mixed-methods systematic scoping review of 59 relevant publications, combining qualitative coding with quantitative synthesis to uncover the underlying motivations driving tool design and their relationship to institutional norms. The analysis identifies four key patterns that hinder adoption and advances a responsible AI design perspective that shifts emphasis from individual accountability to institutional solutions. The work advocates embedding sustainable documentation practices within organizational workflows and cultures, offering the HCI community actionable pathways toward institutionalizing responsible data stewardship.

This work proposes a novel dataset discovery framework that leverages citation contexts from scientific papers to better capture the semantic intent behind research queries, addressing the limitations of existing dataset search engines that rely primarily on metadata and keyword matching and consequently suffer from low recall. By treating citation context as the core signal—combined with large-scale context extraction, large language model–guided pattern recognition, and provenance-preserving entity resolution—the approach significantly reduces dependence on incomplete or inconsistent metadata. Evaluated on eight computer science queries, the method achieves an average normalized recall of 47.47% (peaking at 81.82%), substantially outperforming Google Dataset Search and DataCite Commons. The framework’s novelty and practical utility have been affirmed by domain experts across multiple disciplines.

Dataset quality defects—such as missing documentation, incorrect labels, and ethical risks—are pervasive in open platforms yet resistant to detection by rule-based scripts, necessitating intelligent, automated identification methods. Method: We introduce the first LLM-agent benchmark for discovering real-world dataset quality issues, covering 221 empirically validated cases across eight platforms. It uniquely evaluates agents’ ability to autonomously detect latent defects without prior prompting. We propose an automated evaluation framework powered by GPT-4o, achieving high agreement with human experts (Cohen’s κ = 0.89), and ensure benchmark reliability via multi-source real-data sampling and expert annotation. Contribution/Results: Experiments reveal that even the state-of-the-art Curator agent detects only ~30% of defects, underscoring task difficulty. All benchmark data, code, and evaluation tools are publicly released to advance intelligent data governance.

This study addresses the challenge of low-quality metadata that hinders dataset discoverability and reuse, particularly in the context of large language model (LLM)-generated descriptions lacking empirical guidance on context selection and its impact on quality. Building a literature-based framework for description quality assessment, the authors conduct systematic ablation experiments across 252 real-world CSV datasets. They uncover a previously unreported “table-structure penalty” phenomenon: relying solely on table structure significantly degrades narrative quality. While representative data samples aid semantic grounding, they do not improve overall human-rated quality. The work further reveals that different LLMs exhibit consistent descriptive styles. Through LLM-as-a-judge evaluations, semantic attribute analysis, and large-scale experimentation, the study offers key recommendations for LLM-assisted data publishing: concise, relevant context yields better results than redundant input, and table structure should be used cautiously as a basis for generation.

The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this ``digital thread'' has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a ``Map of Datasets in EDSE.'' The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas (``data deserts'') in early-stage design and system architecture, as well as relatively well-represented areas (``data oases'') in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.

This study addresses the lack of empirical evaluation regarding whether existing dataset documentation frameworks effectively foster developer reflectivity. Combining mixed-methods thematic analysis with corpus-assisted discourse analysis, the research systematically examines how prevailing documentation frameworks—and their real-world instantiations—cover core dimensions of reflectivity. The findings reveal, for the first time, that current frameworks consistently overlook critical reflective themes. Building on this insight, the authors develop a reflectivity-oriented coding manual and propose an enhanced datasheet template incorporating targeted prompts to elicit deeper reflection. This work offers actionable strategies and practical tools to strengthen the reflective capacity of dataset documentation practices.

Converting ROS bags into machine learning datasets often relies on ad hoc scripts, resulting in substantial engineering overhead and inefficient iteration. This work introduces, for the first time, the principles of software build systems to robotic dataset construction, proposing a reproducible, incremental generation method grounded in artifact- and dependency-graph semantics. We present Bagzel, an open-source tool built on Bazel, which supports export to the nuScenes format and incorporates Bagzel-xattr for server-side metadata management. Experimental evaluation demonstrates that, on a 20.4 GB dataset, hot builds achieve up to a 386.26× speedup and incremental builds are accelerated by 7.21×, with performance gains further amplified as dataset scale increases.