Add paper link, code link, and citation information

README.md

@@ -1,204 +1,217 @@
----
-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]
+---
+language:
+- en
+- fr
+- es
+license: unknown
+size_categories:
+- n<1K
+task_categories:
+- object-detection
+- image-segmentation
+tags:
+- pdf
+- document-layout-analysis
+- data-extraction
+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
+
+This dataset was introduced in the paper [Benchmarking Open-Source Layout Detection Models for Data Snapshot Extraction from Institutional Documents](https://huggingface.co/papers/2606.06242).
+
+The source code for the benchmark and dataset extraction is available on GitHub: [worldbank/ai4data](https://github.com/worldbank/ai4data/tree/main/experimental/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.
+
+The benchmark spans humanitarian reports, World Bank policy research working papers, and project appraisal documents, and includes annotations for figures and tables that contain reusable analytical information.
+
+## 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
+unknown
+
+## Citation information
+```bibtex
+@article{dy2026benchmarking,
+  title={Benchmarking Open-Source Layout Detection Models for Data Snapshot Extraction from Institutional Documents},
+  author={Dy, AJ Carl P. and Solatorio, Aivin V.},
+  journal={arXiv preprint arXiv:2606.06242},
+  year={2026}
+}
+```