Datasets:

ai4data
/

fcv-data-use-paper

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

@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}
}