Datasets:
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
Tags:
data-use
dataset-mention
named-entity-recognition
relation-extraction
text-classification
forced-displacement
License:
File size: 3,742 Bytes
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license: cc-by-4.0
language:
- en
task_categories:
- token-classification
- text-classification
tags:
- data-use
- dataset-mention
- named-entity-recognition
- relation-extraction
- text-classification
- forced-displacement
- fcv
- synthetic-data
- active-learning
- world-bank
- unhcr
size_categories:
- 1K-10K
configs:
- config_name: extraction
data_files:
- split: train
path: "extraction/train.jsonl"
- split: validation
path: "extraction/validation.jsonl"
- split: test
path: "extraction/test.jsonl"
- config_name: relation
data_files:
- split: train
path: "relation/train.jsonl"
- split: validation
path: "relation/validation.jsonl"
- split: holdout
path: "relation/holdout.jsonl"
- config_name: impact
data_files:
- split: train
path: "impact/train.jsonl"
- split: validation
path: "impact/validation.jsonl"
- split: holdout
path: "impact/holdout.jsonl"
- config_name: synthetic-relation
data_files:
- split: train
path: "synthetic-relation/train.jsonl"
- config_name: sources
data_files:
- split: train
path: "sources/train.jsonl"
---
# Dataset Card for FCV Data-Use Paper
Companion dataset for the paper **"Automated Tracking of Data Use in Fragile,
Conflict, and Violence Settings: A Joint World Bank and UNHCR Multitask
Extraction Framework"** (Macalaba, Solatorio, Brock).
Three-model swarm over the forced-displacement / FCV corpus: dataset-mention
extraction (Call 1), relation extraction (Call 1b, 5 types), and impact/usage
classification (Call 2, 3 tasks) -- plus the seed-based synthetic training data,
the PRWP training subset, and the per-source original extractions.
All splits are published verbatim exactly as used in the paper.
## Configs
| Config | Splits (records) |
|---|---|
| `extraction` | train: 22794, validation: 415, test: 1706 |
| `relation` | train: 640, validation: 91, holdout: 21 |
| `impact` | train: 604, validation: 151, holdout: 84 |
| `synthetic-relation` | train: 976 |
| `sources` | train: 5445 |
## Schemas
- `extraction` / `sources`: `{"input", "output": {"entities":
{"named_data", "descriptive_data", "vague_data"}, "entity_descriptions"}}`.
- `extraction` rows carry a `meta` field tagging provenance: `FCV`
(forced-displacement / FCV corpus), `PRWP` (Policy Research Working Papers --
manually annotated + reviewed/validated), or `synthetic` (seed-based synthetic
training data, flat-NER). `train` = FCV + PRWP + synthetic; `validation` and
`test` are FCV-only.
- `synthetic-relation`: relation-format synthetic training data (`{"input",
"output": {"entities": {"named_data", "organization"}, "relations":
[{"<type>": {"head", "tail"}}]}, "_meta"}`) from
`relation_training_synthetic` and `relation_llm_generated`, with a `source`
column.
- `relation`: `{"input", "output": {"entities": {"named_data",
"organization"}, "relations": [{"<type>": {"head", "tail"}}]}, "_meta"}`
with types `has_organization`, `used_by`, `has_acronym`, `has_timeframe`,
`has_geography`.
- `impact`: `{"input", "output": {"classifications": [{"task", "labels",
"true_label"}]}}` with tasks `purpose_action`, `usage`, `typology`.
- `sources`: adds a `source` column (`prwp`, `reliefweb`, `seis`, `esmap`,
`refugee_pads`) plus `document_id` and `active_learning`; the 57 active-learning
docs are emitted as flat-NER rows (invalid mentions filtered by verdict) flagged
`active_learning: true`.
## Citation
```bibtex
@misc{macalaba2026fcv,
title={Automated Tracking of Data Use in Fragile, Conflict, and Violence Settings},
author={Macalaba, Rafael and Solatorio, Aivin V. and Brock, Patrick Michael},
year={2026},
publisher={Hugging Face},
journal={ai4data/fcv-data-use-paper}
}
```
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