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license: unknown
task_categories:
- object-detection
- image-segmentation
tags:
- pdf
- document-layout-analysis
- data-extraction
language:
- en
- fr
- es
size_categories:
- n<1K
configs:
- config_name: annotations
data_files:
- split: unhcr
path: "annotations/unhcr/*.json"
- split: prwp
path: "annotations/prwp/*.json"
- split: refugee
path: "annotations/refugee/*.json"
- config_name: metadata
data_files:
- split: unhcr
path: "metadata/unhcr/*.json"
- split: prwp
path: "metadata/prwp/*.json"
- split: refugee
path: "metadata/refugee/*.json"
---
# 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.
```json
{
// 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]
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