Automated Metadata Quality Report from CodeMetaSoft

[SergioZSZ]

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-Documentation
Analysis Date: 2026-06-28
sw-metadata-bot version: 0.5.0
RSMetacheck version: 0.3.3

⚠️ Warnings (1)

W001

Evidence: W001 detected: requirements.txt contains software requirements without versions: sphinx-rtd-theme

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-Documentation",
  "issueTracker": "https://api.github.com/repos/oeg-upm/TINTOlib-Documentation/issues",
  "dateCreated": "2023-03-08",
  "dateModified": "2026-05-20",
  "downloadUrl": "https://github.com/oeg-upm/TINTOlib-Documentation/releases",
  "name": "TINTOlib-Documentation",
  "logo": "https://raw.githubusercontent.com/DCY1117/TEMP-Images/refs/heads/main/TINTOlib-images/logo.svg",
  "programmingLanguage": [
    "Jupyter Notebook"
  ],
  "softwareRequirements": [
    {
      "name": "sphinx-rtd-theme",
      "@type": "SoftwareApplication"
    }
  ],
  "buildInstructions": [
    "https://raw.githubusercontent.com/oeg-upm/TINTOlib-Documentation/main/Readme.md",
    "https://tintolib.readthedocs.io/en/latest/"
  ],
  "author": [
    {
      "@type": "Organization",
      "identifier": "oeg-upm",
      "@id": "https://github.com/oeg-upm"
    }
  ],
  "referencePublication": [
    {
      "@type": "ScholarlyArticle",
      "identifier": "10.1109/JSTSP.2025.3555067",
      "name": "MIMO-Based Indoor Localisation with Hybrid Neural Networks: Leveraging Synthetic Images from Tidy Data for Enhanced Deep Learning",
      "author": [
        {
          "@type": "Person",
          "familyName": "Castillo-Cara",
          "givenName": "Manuel"
        },
        {
          "@type": "Person",
          "familyName": "Mart\u00ednez-G\u00f3mez",
          "givenName": "Jesus"
        },
        {
          "@type": "Person",
          "familyName": "Ballesteros-Jerez",
          "givenName": "Javier"
        },
        {
          "@type": "Person",
          "familyName": "Garc\u00eda-Varea",
          "givenName": "Ismael"
        },
        {
          "@type": "Person",
          "familyName": "Garc\u00eda-Castro",
          "givenName": "Ra\u00fal"
        },
        {
          "@type": "Person",
          "familyName": "Orozco-Barbosa",
          "givenName": "Luis"
        }
      ],
      "datePublished": "2025",
      "pagination": "1-13"
    },
    {
      "@type": "ScholarlyArticle",
      "identifier": "https://doi.org/10.1016/j.softx.2023.101391",
      "name": "TINTO: Converting Tidy Data into Image for Classification with 2-Dimensional Convolutional Neural Networks",
      "author": [
        {
          "@type": "Person",
          "familyName": "Castillo-Cara",
          "givenName": "Manuel"
        },
        {
          "@type": "Person",
          "familyName": "Talla-Chumpitaz",
          "givenName": "Reewos"
        },
        {
          "@type": "Person",
          "familyName": "Garc\u00eda-Castro",
          "givenName": "Ra\u00fal"
        },
        {
          "@type": "Person",
          "familyName": "Orozco-Barbosa",
          "givenName": "Luis"
        }
      ],
      "issn": "2352-7110",
      "datePublished": "2023",
      "pagination": "101391"
    }
  ],
  "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-Documentation"
  ],
  "readme": "https://raw.githubusercontent.com/oeg-upm/TINTOlib-Documentation/main/Readme.md",
  "description": [
    "**TINTOlib** is a state-of-the-art Python library that transforms **tidy data** (also known as tabular data) into **synthetic images**, enabling the application of advanced deep learning techniques, including **Vision Transformers (ViTs)** and **Convolutional Neural Networks (CNNs)**, to traditionally structured data. This transformation bridges the gap between tabular data and powerful vision-based machine learning models, unlocking new possibilities for tackling regression, classification, and other complex tasks. \n"
  ]
}

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