Automated Metadata Quality Report from CodeMetaSoft

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This issue was created automatically after running metadata quality checks. Several warnings or pitfalls were detected and may be worth reviewing.

This automated issue includes:

• Detected metadata pitfalls and warnings
• A suggested codemeta.json when no codemeta.json was detected
• Suggestions for fixing each issue

Context

This analysis is performed by the CodeMetaSoft project to help improve research software metadata quality.

This is a first initiative aimed at identifying and reporting metadata quality issues across research software repositories.
At this stage, we only provide diagnostics and recommendations.
In future iterations, we plan to propose automated fixes for the detected issues to further simplify the improvement process and reduce manual effort.

Each pitfall and warning is identified by a unique code (e.g. P001 for pitfalls, W004 for warnings) that corresponds to specific metadata quality issues.
You can find more details about these checks and how to address them in the RSMetacheck catalog.

Metadata Quality Report

Repository: https://github.com/oeg-upm/TINTOlib
Analysis Date: 2026-06-05
sw-metadata-bot version: 0.5.0
RSMetacheck version: 0.3.1

⚠️ Warnings (1)

W001

Evidence: W001 detected: requirements.txt contains software requirements without versions: hatchling

Suggestion: Add version numbers to your dependencies. This provides stability for users and allows reproducibility across different environments.

📄 Missing codemeta.json

No root codemeta.json file was detected in the repository. A generated suggestion is provided below.

{
  "@context": "https://w3id.org/codemeta/3.0",
  "@type": [
    "SoftwareSourceCode",
    "SoftwareApplication"
  ],
  "license": {
    "name": "Apache License 2.0",
    "url": "https://spdx.org/licenses/Apache-2.0",
    "identifier": "https://spdx.org/licenses/Apache-2.0"
  },
  "codeRepository": "https://github.com/oeg-upm/TINTOlib",
  "issueTracker": "https://api.github.com/repos/oeg-upm/TINTOlib/issues",
  "dateCreated": "2023-02-09",
  "dateModified": "2026-05-30",
  "downloadUrl": "https://github.com/oeg-upm/TINTOlib/releases",
  "name": "TINTOlib",
  "logo": "https://raw.githubusercontent.com/DCY1117/TEMP-Images/refs/heads/main/TINTOlib-images/logo.svg",
  "keywords": [
    "hybrid-neural-network",
    "synthetic-data",
    "synthetic-image",
    "tabular-to-image",
    "tabular2image"
  ],
  "programmingLanguage": [
    "Python"
  ],
  "softwareRequirements": [
    {
      "name": "hatchling",
      "@type": "SoftwareApplication"
    }
  ],
  "releaseNotes": "TINTOlib v1.1.0 Release Notes\r\n\r\nWHAT'S NEW\r\n==========\r\n\r\n\u2022 Two new synthetic image methods: Fotomics and DeepInsight\r\n\u2022 Customizable transformer system for flexible data preprocessing\r\n\u2022 New three-level class hierarchy (AbstractImageMethod \u2192 MappingMethod \u2192 ParamImageMethod)\r\n\u2022 Feature-to-pixel mapping with explicit CSV export\r\n\u2022 Support for multiple pixel assignment strategies and relevance scoring\r\n\r\nBREAKING CHANGES\r\n================\r\n\r\nProblem parameter updated:\r\n  OLD: problem=\"supervised\"\r\n  NEW: problem=\"classification\"\r\n  \r\n(Deprecated value still works with FutureWarning for backward compatibility)\r\n\r\n\r\nBUG FIXES\r\n=========\r\n\r\n\u2022 Fix LogScaler Class (#18)\r\n\u2022 Fix abstractImageMethod transformer reference in fit_transform()\r\n\u2022 Fix SuperTML uint8 rendering (#16)\r\n\u2022 Fix Random State for reproducibility\r\n\u2022 Fix CSV file generation\r\n\r\n\r\nDEPENDENCY CHANGES\r\n==================\r\n\r\n\u2022 numpy: 2.0.2 \u2192 1.26.4\r\n\u2022 mpi4py: NOW OPTIONAL (only needed for REFINED method)\r\n\u2022 Added: numba==0.62.0\r\n\r\n\r\nVERSION INFO\r\n============\r\n\r\n\u2022 Version: 1.0.6 \u2192 1.1.0\r\n\u2022 Status: Stable Release\r\n\r\n\r\nRESOURCES\r\n=========\r\n\r\nGitHub: https://github.com/oeg-upm/TINTOlib\r\nDocumentation: https://tintolib.readthedocs.io/\r\nPyPI: https://pypi.org/project/TINTOlib/\r\nIssues: https://github.com/oeg-upm/TINTOlib/issues\r\n",
  "softwareVersion": "v1.1.0",
  "datePublished": "2025-06-27",
  "buildInstructions": [
    "https://github.com/oeg-upm/TINTOlib/tree/main/docs",
    "https://raw.githubusercontent.com/oeg-upm/TINTOlib/main/README.md"
  ],
  "author": [
    {
      "@type": "Organization",
      "identifier": "oeg-upm",
      "@id": "https://github.com/oeg-upm"
    },
    {
      "@type": "Person",
      "email": "borjareinoso@gmail.com",
      "name": "BorjaRei"
    },
    {
      "@type": "Person",
      "email": "jcastillo@fi.upm.es",
      "name": "manwestc"
    },
    {
      "@type": "Person",
      "email": "david.g.f.2@gmail.com",
      "name": "DavidGonzalezFernandez"
    },
    {
      "@type": "Person",
      "email": "jiayun.liu@upm.es",
      "name": "JiayunLiu"
    },
    {
      "@type": "Person",
      "email": "javilecrin19@gmail.com",
      "name": "JavierLopez"
    }
  ],
  "referencePublication": [
    {
      "@type": "ScholarlyArticle",
      "identifier": "10.1016/j.inffus.2025.104088",
      "name": "A comprehensive benchmark of spatial encoding methods for tabular data with deep neural networks",
      "datePublished": "2026",
      "pagination": "104088"
    }
  ],
  "creditText": [
    "Liu, J., Gonz\u00e1lez-Fern\u00e1ndez, D., Castillo-Cara, M., Garc\u00eda-Castro, R. (tintolib: a python library for transforming tabular data into synthetic images for deep neural networks). https://doi.org/10.1016/j.softx.2025.102444. Available at: https://github.com/oeg-upm/TINTOlib"
  ],
  "url": [
    "https://github.com/oeg-upm/TINTOlib-Documentation"
  ],
  "readme": "https://raw.githubusercontent.com/oeg-upm/TINTOlib/main/README.md",
  "runtimePlatform": "Python>=3.8",
  "description": [
    "Pip package for converting tabular data into synthetic images"
  ]
}

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