Datasets:

ai4data
/

manual_annotation_prwp

Couldn't cast array of type struct<json_structures: list<element: struct<dataset_mention: struct<acronym: string, author: string, data_type: struct<choices: list<element: string>, value: string>, dataset_name: string, dataset_tag: struct<choices: list<element: string>, value: string>, description: string, geography: string, is_used: struct<choices: list<element: string>, value: string>, producer: string, publication_year: string, reference_population: string, reference_year: string, usage_context: struct<choices: list<element: string>, value: string>>>>> to List({'dataset_mention': {'dataset_name': Value('string'), 'dataset_tag': {'value': Value('string'), 'confidence': Value('string')}, 'data_type': {'value': Value('string'), 'confidence': Value('string')}, 'acronym': Value('string'), 'author': Value('string'), 'producer': Value('string'), 'publication_year': Value('string'), 'reference_year': Value('string'), 'reference_population': Value('string'), 'geography': Value('string'), 'description': Value('string'), 'is_used': {'value': Value('bool'), 'confidence': Value('string')}, 'usage_context': {'value': Value('string'), 'confidence': Value('string')}}})

Error code:   UnexpectedError

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

World Bank PRWP - Refugee Data Manual Annotation

Dataset Description

This dataset consists of manually annotated excerpts from text describing data sources. It is intended for training and evaluating Named Entity Recognition (NER) models (specifically designed for the 13-field GLiNER2 data-mention schema) to extract mentions of datasets, databases, surveys, censuses, and other data sources.

The dataset focuses on the PRWP (Poverty and Equity Global Practice) refugee data contexts from the World Bank.

Organization

The dataset is divided into three splits:

train: 403 multi-mention records used for training models.
eval: 51 multi-mention records used for validation.
test: 46 multi-mention records used to benchmark model performance.

Data Instances

Each record corresponds to a text snippet (input) and contains a list of data mentions (output) complying with a strict 13-field JSON schema. The schema enforces verbatim grounding, where each dataset_name, along with non-classification metadata fields (acronym, author, producer, description, etc.), must be an exact substring of the input text.

Features:

input: The original text snippet.
output: A list of objects containing:
- dataset_name: The verbatim mention of the data source.
- dataset_tag: Classification (named, descriptive, vague).
- data_type: Inferred type of data (survey, census, administrative, database, indicator, geospatial, microdata, report, other).
- acronym, author, producer, description, geography, publication_year, reference_year, reference_population: Contextual entity string metadata fields representing properties of the dataset.
- is_used: Indication if this data source was utilized in the research/analysis.
- usage_context: Role of the data source (primary, supporting, etc.).

Quality Assurance

This ground-truth dataset underwent deep manual auditing and programmatic refinement:

Corrected categorization and unified data tags.
Verified 100% "verbatim" text grounding for spans.
Dropped false-positive non-data mentions, such as bare years, general organization references, or methodology fragments.
Detected and merged any duplicate entries or highly overlapping record snippets using Jaccard text similarity.

Usage

This dataset is structurally aligned to be used directly to fine-tune GLiNER2 adapter modules or to evaluate general data-mention entity extraction pipelines.

Downloads last month: 11

Size of downloaded dataset files:

891 kB

Size of the auto-converted Parquet files:

891 kB

Number of rows:

500