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AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science
Announce Type: replace Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial intelligence (AI) agents have significantly automated data science workflow. However, it remains unclear to what extent AI agents can match the performance of human experts on domain-specific data science tasks, and in which aspects human expertise continues to provide advantages.
AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science
arXiv:2603.19005v2 Announce Type: replace Abstract: Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial intelligence (AI) agents have significantly automated data science workflow. However, it remains unclear to what extent AI agents can match the performance of human experts on domain-specific data science tasks, and in which aspects human expertise continues to...
EvoDS: Self-Evolving Autonomous Data Science Agent with Skill Learning and Context Management
arXiv:2606.03841v1 Announce Type: new Abstract: Recent progress in Large Language Model (LLM) agents has enabled promising advances in automated data science. However, existing approaches remain fundamentally limited by their static action sets and lack of principled long-horizon context management, hindering their ability to accumulate reusable experience across tasks and operate reliably in multi-stage, iterative data science pipelines.
Preparing future math teachers to teach data science
When Eric Weber, professor and chair of mathematics at Iowa State University, talks about data science with future math teachers, he doesn't begin with code, algorithms, or buzzwords. Instead, he asks them to imagine the scientific method—form a hypothesis, collect data, conduct experiments—running in reverse.
Towards Persistent Case-Based Memory for Autonomous Data Science: A CBR-Augmented R&D-Agent with a Locally Deployable Small Language Model
Announce Type: new Abstract: Most top-performing autonomous data-science agents rely on frontier cloud models and lack persistent, cross-session memory. This paper addresses two open gaps: (1) the underexplored use of formally structured, quality-controlled Case-Based Reasoning (CBR) case bases coupling symbolic case records with executable code artefacts; and (2) the untested viability of Small Language Models (SLMs) as locally deployable agent backbones. We present CBR-augmented...
Pivoting the paradigm: the role of spreadsheets in K-12 data science
Announce Type: replace-cross Abstract: Spreadsheet tools are widely accessible to and commonly used by K-12 students and teachers. While spreadsheets are not ideal for many types of statistical analysis, they have an important role in data collection and organization. From a pedagogical standpoint, spreadsheets make data visible and easy to interact with, facilitating student engagement in data exploration, analysis, and computation.
Integrating citizen science with experimental data uncovers how switchgrass adapts flowering by region
Integrating citizen science with experimental data uncovers how switchgrass adapts flowering by region Gaby Clark Scientific Editor Robert Egan Associate Editor In its native habitat, switchgrass flowered earlier when growing farther north. In experiments with diverse genetic samples, it flowered earlier in the south. The discrepancy wasn't a welcome sight for a research team studying how prairie grasses respond in different environments, but resolving the apparent conflict led the...
Changing topic bias in biomedical science maps by linking documents through alternative data sources: policy documents, patents, authors, Facebook, and Twitter
arXiv:2412.07550v4 Announce Type: replace Abstract: Traditional science maps visualize topics by clustering documents within a network, but they are inherently biased toward clustering certain topics over others. If these topics could be chosen, then the science maps could be tailored for different needs. In this paper, we explore the extent to which the topic bias of a science map can be changed by choosing different data sources to build the document network.
Efficient Synthetic Network Generation via Latent Embedding Reconstruction
Announce Type: cross Abstract: Network data are ubiquitous across the social sciences, biology, and information systems. Generating realistic synthetic network data has broad applications from network simulation to scientific discovery. However, many existing black-box approaches for network generation tend to overfit observed data while overlooking characteristic network structure, and incur substantial computational overhead at scale.
Orange Lab: Lowering Barriers to Data Mining through Embedded Interactive Workflows
arXiv:2606.09239v1 Announce Type: new Abstract: While visual programming of data analysis workflows has become an important vehicle for the democratization of data science, such systems remain largely confined to standalone applications and offer limited support for transitioning their visual analytics solutions into interactive web environments. As a result, data analysis pipelines are difficult to share, embed, and adapt into user-facing analytical tools. We present Orange Lab, a web-based...