mlsanityData to 7 Oct 2026

Packaging research artefacts with RO-Crate

arXiv 2108.06503 ↗First posted 14 Aug 2021cs.DLcited by 22

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Abstract (arXiv)

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema.org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying "just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article is available at https://w3id.org/ro/doi/10.5281/zenodo.5146227

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Aug 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 14.

22 papers.

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  1. 2024Croissant: A Metadata Format for ML-Ready Datasetscited by 79
  2. 2024Reproducibility in Machine Learning-based Research: Overview, Barriers and Driverscited by 34
  3. 2026The Last Human-Written Paper: Agent-Native Research Artifactscited by 12
  4. 2025Provenance Tracking in Large-Scale Machine Learning Systemscited by 5
  5. 2024DISO: A Domain Ontology for Modeling Dislocations in Crystalline Materialscited by 2
  6. 2025yProv4ML: Effortless Provenance Tracking for Machine Learning Systemscited by 2
  7. 2026Artifact-centered Claim-aware Observability for Autonomous Scientific Agentscited by 2
  8. 2026Agora: Git as Shared Memory for Collective AutoResearchcited by 2
  9. 2025Mutual Understanding between People and Systems via Neurosymbolic AI and Knowledge Graphscited by 1
  10. 2023Halcyon -- A Pathology Imaging and Feature analysis and Management Systemcited by 0
  11. 2025TIB AIssistant: a Platform for AI-Supported Research Across Research Life Cyclescited by 0
  12. 2026paper.json: A Coordination Convention for LLM-Agent-Actionable Paperscited by 0
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  1. 2026AIMBio-Mat: An AI-Native FAIR Platform for Closed-Loop Materials Discovery and Biomedical Translationcited by 0
  2. 2026Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaborationcited by 0
  3. 2026Networked Intelligence: Active Shared Context Graphs for Human-AI Team Sciencecited by 0
  4. 2026F(AI)2R: Who Did What, and Who Checked? Verifiable AI Provenance as an Executable Skillcited by 0
  5. 2026CyberNeuro: A Privacy-Preserving Agentic Workbench for Cohort-Scale Neuroimage and Clinical Data Analysiscited by 0
  6. 2026Traceable Trust for action-ready artificial intelligence in biosciencecited by 0
  7. 2026Scientific Data Skills: Enabling Agent-Ready Scientific Data Services at Scalecited by 0
  8. 2026LLMs Interpret, Embeddings Organize, Graphs Emerge: Agent-Driven Compilation of Scientific Knowledgecited by 0
  9. 2026Can AI Agents Detect and Repair Artifact Drift in Network Experiments?cited by 0
  10. 2026A Policy Profile for Croissant: Refusal as a Property of the Datasetcited by 0