The 2nd Workshop on AI-Ready Data for Science Discovery @ ICDM 2026

About the Workshop

Scientific discovery is entering a new era—powered by artificial intelligence (AI) and machine learning (ML). These technologies are enabling breakthroughs across disciplines such as biology, physics, chemistry, and materials science. However, one major bottleneck remains: the lack of high-quality, domain-specific datasets that are truly AI-ready.

The 2nd Workshop on AI-ready Data for Science Discovery (ADSD 2026) aims to address this critical challenge by building a vibrant, interdisciplinary community focused on the creation, curation, and benchmarking of scientific datasets. Hosted at ICDM 2026, ADSD will serve as a platform for researchers, practitioners, and data professionals to collaborate on shaping the future of scientific data mining.

Call for Papers

We welcome a wide array of submissions focused on AI-Ready Dataset for Science Discovery, encompassing topics such as theories, algorithms, applications, systems, and tools. These topics include but are not limited to:

Data Acquisition and Integration

Automated methods for constructing AI-ready data from experiments, simulations, and publications.
Methods for multimodal datasets integration, including text, images, tables, and numerical data.
Retrieval-augmented generation (RAG) and literature mining for scientific knowledge extraction and organization.
Scientific knowledge graphs, ontologies, and semantic data integration.

Data Curation, Quality Control, and Enrichment

Methods for dataset collection, annotation, documentation, and metadata generation.
Methods for synthetic data generation and data enrichment.
Methods for data consistency, completeness, provenance, and versioning.
Automated data refinement, outlier detection, and correction to improve dataset reliability.
Systems and tools for continuous dataset integrity monitoring, including expert-in-the-loop quality control.

Benchmarking and Evaluation Frameworks

Standardized benchmarks across scientific domains, such as biomedicine, materials science, and environmental modeling.
Methods for developing standardized metrics e.g. accuracy, robustness, scalability, and interpretability, tailored to domain-specific data characteristics.
Methods to evaluate data quality, AI-readiness, and suitability for downstream scientific tasks.
Methods to evaluate the data interpretability, robustness, and trustworthiness.
Open platforms for standardized benchmarking.
Benchmarks and protocols for evaluating end-to-end data pipelines and agentic workflows.

Applications in Scientific Research

Tools and agentic workflows for publication summary, knowledge discovery, and trend analysis.
AI-ready data and data-centric methods for scientific foundation models and domain-specific AI.
AI for scientific challenges like drug discovery, climate modeling, and material prediction, etc.
Closed-loop scientific workflows connecting data, AI models, simulations, experiments, and laboratory automation.

Submission Details

We invite the submission of regular research papers (6-10 pages), including the bibliography and any possible appendices. Submissions must be in PDF format, and formatted according to IEEE Conference Template. Submitted papers will be assessed based on their novelty, technical quality, potential impact, insightfulness, depth, clarity, and reproducibility. All the papers are required to be submitted via the ADSD Submission. By the unique ICDM tradition, all accepted workshop papers will be published in the dedicated ICDMW proceedings published by the IEEE Computer Society Press. For more questions about the workshop and submissions, please send email to pfwang@cnic.cn.