Full papers
Up to 8 pages; present complete work: a scientific data system, benchmark, or evaluation, with methods, results, and analysis.
Machine Learning/CORE Unranked
AIDaR @ NeurIPS 2026
AIDaR: AI Data Readiness for Scientific Discovery
AIDaR @ NeurIPS 2026 is a one-day in-person workshop in Paris focused on AI data readiness for scientific discovery, addressing how scientific data should be organized for use by AI models and agents, and how evaluations can assess their reliability in real scientific workflows. It brings together researchers and engineers working on scientific data infrastructure, foundation models, biomedical evaluations, and industrial assay platforms.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Up to 4 pages; present focused contributions, work in progress, position pieces, or tool and dataset demos.
Research areas in scope
01
Data infrastructure for scientific AI
scientific foundation modelsrelational and graph-structured datamultimodal biomedical measurementsworkflow systemslarge industrial datasets used for model training and wet-lab feedback
02
Evaluations for scientific AI
biological reasoningpractical data analysisretrieval over structured scientific knowledgemultimodal biomedical datafailure analysisstudies of where performance changes across assays, platforms, or workflows
Policies worth checking twice
- Papers must use the NeurIPS 2026 style files; references and appendices are excluded from the page limit.
- Reviewing is double-blind; submissions and linked material must be anonymized.
- Disclose any use of large language models and their role.
- The workshop is non-archival.
- Submissions must be made via OpenReview.
- Authors may optionally add a machine-readable copy of their project through AIDaRS, a GitHub review pilot.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026