Dataset card for data-snapshot

Dataset summary

The data-snapshot dataset is an annotated corpus designed for the evaluation and development of models for extracting data snapshots from PDF documents. A data snapshot is defined as a figure or table that contains quantitative data derived from statistics, indicators, or structured data sources.

Dataset structure

The repository is organized as follows:

ai4data/data-snapshot/
├── annotations/<source>/*.json                 # Contains annotation files per document
├── metadata/<source>/*.json                    # Document-level metadata
├── schemas/data-snapshot-eval-v1.3.schema.json # Provides the schema of the annotation file					
└── README.md

Subsets

• annotations
  • JSON files that indicate the data snapshots: their object class (Figure / Table) and bounding box locations (in normalized [x1, y1, x2, y2] format, top-left origin)
  • Follows the schema provided in data-snapshot-eval-v1.3.schema.json
  • Provided on a per-document basis or a combined JSON file per source
• metadata
  • Provided on a per-document basis

Sources

• UNHCR
• PRWP (WIP)
• Refugee (WIP)

Schema

The annotation files follow the Data Snapshot Evaluation Format (v1.3). Below is a simplified, human-readable example of the JSON schema with explanatory comments for each field.

Note: You will notice a top-level field called predictions. In the context of this dataset, this is a misnomer because these are actually human-labeled annotations (ground truth). We use the key predictions because we borrow this schema from the project's evaluation codebase, which uses a unified structure for both ground truth and model predictions.

{
  // Canonical mapping of integer IDs to class names
  "label_map": {
    "1": "Figure",
    "2": "Table"
  },
  
  // High-level metadata about the file
  "info": {
    "schema_version": "1.3",
    "type": "ground_truth",  // Indicates these are human annotations
    "dataset_id": "data-snapshot_unhcr",
    "created_at": "2026-04-17T12:00:00Z",
    "coordinate_system": {
      "type": "normalized_xyxy",
      "range": [0.0, 1.0],  // Bounding boxes are normalized between 0 and 1
      "origin": "top_left"
    }
  },
  
  // List of documents referenced in this file
  "documents": [
    {
      "doc_id": "1_advocacy_note_mineaction_-_niger_eng.pdf",
      "doc_name": "1_advocacy_note_mineaction_-_niger_eng.pdf",
      "doc_path": "pdf_input/1_advocacy_note_mineaction_-_niger_eng.pdf"
    }
  ],
  
  // Per-page container of objects; these contain the ground truth annotations
  "predictions": [
    {
      "page_id": "1_advocacy_note_mineaction_-_niger_eng.pdf::p001",
      "doc_id": "1_advocacy_note_mineaction_-_niger_eng.pdf",
      "page_index": 0,  // 0-indexed page number
      // Image data for Label Studio (ignore this)
      "image": {
        "width_px": 2481,
        "height_px": 3508,
        "path": "images/1_advocacy_note_mineaction_-_niger_eng.pdf_p001.png"
      },
      "objects": [
        {
          "id": "obj_001",
          "label": "Figure",  // Matches a label_map entry
          "bbox": [0.1, 0.2, 0.8, 0.6],  // Normalized [x_min, y_min, x_max, y_max]
        }
      ]
    }
  ]
}

Dataset creation

The annotations were produced through human labeling using Label Studio.

Licensing information

[TBD]

Citation information

[TBD]