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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"
- config_name: documents
data_files:
- split: unhcr
path: "documents/unhcr/*.pdf"
- split: prwp
path: "documents/prwp/*.pdf"
- split: refugee
path: "documents/refugee/*.pdf"
- config_name: snapshots
data_files:
- split: unhcr
path: "snapshots/unhcr/*.png"
- split: prwp
path: "snapshots/prwp/*.png"
- split: refugee
path: "snapshots/refugee/*.png"
---
# 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
├── documents/<source>/*.pdf # Actual PDFs
├── metadata/<source>/*.json # Document-level metadata
├── schemas/*.json # Provides the schema of the annotation and metadata files
├── snapshots/<source>/*.png # Image files corresponding to the annotations
└── 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 `schemas/data-snapshot-eval-v1.3.schema.json`
- Provided on a per-document basis; documents that do not have data snapshots will still have an annotation file present but list of bounding boxes will be empty.
- `documents`
- Actual PDF files that were annotated
- `metadata`
- Document-level metadata following the [World Bank Metadata Standards (Chapter 5 — Documents)](https://worldbank.github.io/schema-guide/chapter05.html), schema provided in `schemas/metadata_schema.json`.
- Provided on a per-document basis
- All files across all sources share a uniform schema (same keys at every nesting level)
- `snapshots`
- PNG files extracted from the documents and bounding box locations
### Sources
- UNHCR
- PRWP
- Refugee
## Loading the dataset using HF's `datasets` library
### Annotations
```python
>>> from datasets import load_dataset
>>> annotations = load_dataset("ai4data/data-snapshot", name="annotations", split="unhcr")
>>> annotations[0] # Inspect the first record
{'label_map': {'1': 'Figure', '2': 'Table'}, 'info': {'schema_version': '1.3', 'type': 'ground_truth', 'created_at': datetime.datetime(2026, 5, 20, 13, 44, 29), 'run_id': 'human-annotation-combined-e3432dce89', 'model': {'name': 'human annotation'}, 'coordinate_system': {'type': 'normalized_xyxy', 'range': [0.0, 1.0], 'origin': 'top_left'}}, 'documents': [{'doc_id': '06072015-baalbek-hermelgovernorateprofile.pdf', 'doc_name': '06072015-baalbek-hermelgovernorateprofile.pdf', 'doc_path': 'pdf_input/06072015-baalbek-hermelgovernorateprofile.pdf'}], 'predictions': [{'page_id': '06072015-baalbek-hermelgovernorateprofile.pdf::p000', 'doc_id': '06072015-baalbek-hermelgovernorateprofile.pdf', 'page_index': 0, 'objects': [{'id': '1d69f693', 'label': 'Figure', 'bbox': [0.029415499554572243, 0.1766403810171256, 0.5954839424856321, 0.7354445202645015], 'score': None}, ...}
```
### Metadata
```python
>>> metadata = load_dataset("ai4data/data-snapshot", name="metadata", split="unhcr")
>>> metadata[0] # Inspect the first record
{'type': 'document', 'metadata_information': {'title': 'Lebanon: Baalbek-Hermel Governorate Profile (June 2015)', 'idno': '06072015-baalbek-hermelgovernorateprofile', 'producers': [{'name': 'UNHCR', 'abbr': 'UNHCR', 'affiliation': 'UNHCR', 'role': 'Source'}], 'production_date': datetime.datetime(2026, 5, 21, 0, 0), ...}
```
### Documents
```python
>>> docs = load_dataset("ai4data/data-snapshot", data_dir="documents/unhcr") # Or simply data_dir="documents/" for all files
>>> docs.save_to_disk("path/to/docs_directory") # Files are saved as an Arrow file
```
### Snapshots
```python
>>> snapshots = load_dataset("ai4data/data-snapshot", data_dir="snapshots/unhcr") # Or simply data_dir="snapshots/" for all snapshots
>>> snapshots.save_to_disk("path/to/snapshots_directory") # Files are saved as an Arrow file
```
## Schema
### Annotations
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
"created_at": "2026-05-20T13:44:29",
"run_id": "human-annotation-combined-e3432dce89",
"model": {
"name": "human annotation"
},
"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
"objects": [
{
"id": "obj_001",
"label": "Figure", // Matches a label_map entry
"bbox": [0.029, 0.177, 0.595, 0.735], // Normalized [x_min, y_min, x_max, y_max]
"score": null // Always null for ground truth
}
]
}
]
}
```
### Metadata
The metadata files follow the [**World Bank Document Metadata Schema**](https://worldbank.github.io/schema-guide/chapter05.html). See `schemas/metadata_schema.json` for the formal JSON schema definition.
All metadata files across all sources share a uniform schema (same keys at every nesting level, same types) to ensure compatibility with Apache Arrow and HuggingFace streaming.
Top-level fields:
- `type`
- `metadata_information`
- `document_description`
- `provenance`
- `tags`
- `schematype`
- `additional` - contains source-specific fields (e.g. `additional.unhcr_*` for UNHCR, `additional.wds_*` for WDS API-sourced datasets).
## Dataset creation
The annotations were produced through human labeling using Label Studio.
## Licensing information
[TBD]
## Citation information
[TBD]
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