SetGo: Metadata Readiness for Scientific AI Datasets

• URL: http://arxiv.org/abs/2607.22677v1
• Date: Fri, 10 Jul 2026 15:44:03 GMT
• Title: SetGo: Metadata Readiness for Scientific AI Datasets
• Abstract summary: We present SetGo, an open-source Python toolkit that assesses and repairs metadata readiness before a dataset is published or archived.<n>To support interactive and automated, SetGo integrates with coding agents powered by large language models (LLMs) through a /setgo skill.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Automated Data Readiness for Scientific AI [0.0]
  We present REDI, an open-source framework for transforming scientific datasets into AI-ready data.<n>We show near-ideal parallel scaling to 100 nodes on Frontier for the climate case.<n>These results establish REDI as a cross-domain platform providing automated data readiness for scientific AI.
  arXiv  Detail & Related papers  (2026-07-02T21:09:13Z)
• Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models [63.96252564303143]
  Embodied-R1.5 is a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities.<n>We build a large-scale data system of over 15B tokens, and design a multi-task balanced RL recipe.<n>With only 8B parameters, Embodied-R1.5 SOTA on 16 out of 24 embodied VLM benchmarks, surpassing leading models like Gemini-Robotics-ER-1.5 and GPT-5.4.
  arXiv  Detail & Related papers  (2026-06-09T18:07:50Z)
• DeepXiv-SDK: An Agentic Data Interface for Scientific Literature [60.19264121557117]
  DeepXiv-SDK is a three-layer agentic data interface for scientific literature.<n>DeepXiv-SDK supports the complete ArXiv corpus, and is synchronized daily to incorporate new releases.
  arXiv  Detail & Related papers  (2026-02-14T23:07:28Z)
• OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value [74.80873109856563]
  OpenDataArena (ODA) is a holistic and open platform designed to benchmark the intrinsic value of post-training data.<n>ODA establishes a comprehensive ecosystem comprising four key pillars: (i) a unified training-evaluation pipeline that ensures fair, open comparisons across diverse models; (ii) a multi-dimensional scoring framework that profiles data quality along tens of distinct axes; and (iii) an interactive data lineage explorer to visualize dataset genealogy and dissect component sources.
  arXiv  Detail & Related papers  (2025-12-16T03:33:24Z)
• MeXtract: Light-Weight Metadata Extraction from Scientific Papers [48.73595915402094]
  We present MeXtract, a family of lightweight language models designed for metadata extraction from scientific papers.<n>MeXtract achieves state-of-the-art performance on metadata extraction on the MOLE benchmark.<n>We release all the code, datasets, and models openly for the research community.
  arXiv  Detail & Related papers  (2025-10-08T11:12:28Z)
• Scaling Generalist Data-Analytic Agents [95.05161133349242]
  DataMind is a scalable data synthesis and agent training recipe designed to build generalist data-analytic agents.<n>DataMind tackles three key challenges in building open-source data-analytic agents.
  arXiv  Detail & Related papers  (2025-09-29T17:23:08Z)
• Flexible metadata harvesting for ecology using large language models [3.4117490081172774]
  We develop a large language model (LLM)-based metadata harvester.<n>It flexibly extracts metadata from any dataset's landing page.<n>It converts these to a user-defined, unified format using existing metadata standards.
  arXiv  Detail & Related papers  (2025-08-21T10:10:29Z)
• Toward Total Recall: Enhancing FAIRness through AI-Driven Metadata Standardization [2.4347641401231126]
  We present a method that combines GPT-4 with structured metadata templates from the CEDAR knowledge base to automatically standardize metadata.<n>Our standardization process involves using CEDAR templates to guide GPT-4 in accurately correcting and refining metadata entries in bulk.
  arXiv  Detail & Related papers  (2025-02-13T21:58:27Z)
• Metadata Conditioning Accelerates Language Model Pre-training [76.54265482251454]
  We propose a new method, termed Metadata Conditioning then Cooldown (MeCo) to incorporate additional learning cues during pre-training.<n>MeCo significantly accelerates pre-training across different model scales (600M to 8B parameters) and training sources (C4, RefinedWeb, and DCLM)<n>MeCo is remarkably simple, adds no computational overhead, and demonstrates promise in producing more capable and steerable language models.
  arXiv  Detail & Related papers  (2025-01-03T18:59:23Z)
• Discourse Features Enhance Detection of Document-Level Machine-Generated Content [53.41994768824785]
  Machine-generated content poses challenges such as academic plagiarism and the spread of misinformation.<n>Existing MGC detectors often focus solely on surface-level information, overlooking implicit and structural features.<n>We introduce novel methodologies and datasets to overcome these challenges.
  arXiv  Detail & Related papers  (2024-12-17T08:47:41Z)
• Use of a Structured Knowledge Base Enhances Metadata Curation by Large Language Models [2.186740861187042]
  Metadata play a crucial role in ensuring the findability, accessibility, interoperability, and reusability of datasets.<n>This paper investigates the potential of large language models (LLMs) to improve adherence to metadata standards.<n>We conducted experiments on 200 random data records describing human samples relating to lung cancer from the NCBI BioSample repository.
  arXiv  Detail & Related papers  (2024-04-08T22:29:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.