AIDRIN/CITATION.cff at develop · idtlab/AIDRIN · GitHub

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cff-version: 1.2.0
message: If you use this software, please cite it using the metadata from this file.
title: "AIDRIN - AI Data Readiness Infrastructure"
type: software
authors:
  - family-names: Hiniduma
    given-names: Kaveen
    affiliation: The Ohio State University
    orcid: "https://orcid.org/0009-0006-8516-0215"
  - family-names: Byna
    given-names: Suren
    email: sbyna@lbl.gov
    affiliation: The Ohio State University
    orcid: "https://orcid.org/0000-0003-3048-3448"
  - family-names: Bez
    given-names: Jean Luca
    email: jlbez@lbl.gov
    affiliation: Lawrence Berkeley National Laboratory
    orcid: "https://orcid.org/0000-0002-3915-1135"
  - family-names: Madduri
    given-names: Ravi
    affiliation: Argonne National Laboratory
    orcid: "https://orcid.org/0000-0003-2130-2887"
url: https://aidrin.io
repository-code: https://github.com/idtlab/AIDRIN
identifiers:
  - type: doi
    value: 10.5281/zenodo.21798062
    description: Zenodo archive of AIDRIN
abstract: "AIDRIN, the AI Data Readiness Infrastructure, is an open-source framework for assessing whether a dataset is ready for AI, remediating the issues it finds, and transforming data into the form an AI application needs. It evaluates data along seven readiness dimensions. Six of them are quality, understandability and usability, fairness and representativeness, structure and organization, impact on AI through feature relevance and correlation, and governance covering ethics, security, and privacy. The seventh, readiness for a specific AI application, cuts across the other six rather than sitting beside them, and users can extend it with their own metrics so that assessments reflect the requirements of their field. AIDRIN generates a report across these dimensions with visualizations, machine-readable JSON, and suggested remedies, and users can define their own remedies as well. The same engine is available from a browser dashboard, the command line, a Python library, and an MCP server for agentic clients, and it can run on remote datasets that stay in place."
keywords:
  - data readiness
  - AI
  - data management
preferred-citation:
  type: software
  authors:
    - family-names: Hiniduma
      given-names: Kaveen
    - family-names: Byna
      given-names: Suren
    - family-names: Bez
      given-names: Jean Luca
    - family-names: Madduri
      given-names: Ravi
  title: "AIDRIN - AI Data Readiness Infrastructure"
  doi: "10.5281/zenodo.21798062"