# SetGo: Metadata Readiness for Scientific AI Datasets

arXiv: https://arxiv.org/abs/2607.22677 · HTML: https://mlsanity.com/paper/2607.22677/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 10 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Data Readiness for AI: A 360-Degree Survey](https://mlsanity.com/paper/2404.05779/) (2024)
- [Data Readiness Levels](https://mlsanity.com/paper/1705.02245/) (2017)
- [Applying the FAIR Principles to computational workflows](https://mlsanity.com/paper/2410.03490/) (2024)
- [AI Data Readiness Inspector (AIDRIN) for Quantitative Assessment of Data Readiness for AI](https://mlsanity.com/paper/2406.19256/) (2024)
- [Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability](https://mlsanity.com/paper/2308.09004/) (2023)
- [Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN](https://mlsanity.com/paper/2406.12909/) (2024)
- [WorkflowHub: a registry for computational workflows](https://mlsanity.com/paper/2410.06941/) (2024)
- [Automated Data Readiness for Scientific AI](https://mlsanity.com/paper/2607.02771/) (2026)

## Abstract (arXiv)

Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse. The Readiness Engine for Data Integration (REDI) addresses computational readiness, but no corresponding tool evaluates whether a dataset's metadata are sufficiently complete, governed, and standards-compliant for publication and agent-based consumption. Existing FAIR assessors operate only on published repository records, and no single system covers FAIR compliance, licensing, provenance, governance, reproducibility, and catalog readiness together. We present SetGo, an open-source Python toolkit that assesses and repairs metadata readiness across these six dimensions before a dataset is published or archived. Applied to four scientific corpora, SetGo surfaces deficiencies that general-purpose tools do not detect: ERA5 climate metadata scores 4% on ACDD 1.3 compliance; materials datasets fail OPTIMADE species-definition requirements; and PDB-derived proteomics data carries licensing terms incompatible with standard SPDX identifiers. Guided enrichment raises overall FAIR scores from 52-57% to 81-91%, and a single setgo publish command pushes to Hugging Face Hub, CKAN, or OpenMetadata with ML Commons Croissant 1.0 metadata sidecars. To support interactive and automated workflows, SetGo integrates with coding agents powered by large language models (LLMs) through a /setgo skill that enables natural-language execution of the full assess-enrich-publish loop, with user involvement limited to supplying missing metadata values.