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Datasets:
3iS
/
violence-and-conflict-events-in-colombia

Tasks:
Text Classification
Feature Extraction
Modalities:
Text
Tabular
Languages:
Spanish
Size:
1K<n<10K
Tags:
text
nlp
violence
spanish
entity
recognition
License:
Dataset card Files
xet
Community
Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 5 fields in line 6, saw 6

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2998, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1918, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2093, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1576, in __iter__
                  for key_example in islice(self.ex_iterable, self.n - ex_iterable_num_taken):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 279, in __iter__
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 190, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 5 fields in line 6, saw 6

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Humanitarian Dataset: Violence and IHL Violations in Colombia (2024)

Overview

This dataset contains records of violence and infractions to international humanitarian law (IHL) in Colombia during 2024. The dataset was compiled by OCHA Colombia from reports by key informants and news sources. The data has been structured and categorized according to IHL standards, including the extraction of the number of victims and events.

Content

The dataset includes the following information:

  • Event ID: A unique identifier for each event.
  • Event Type: Classification of the event (e.g., "Violencia").
  • Event Date: Date when the event occurred.
  • Event Title: A concise title summarizing the event.
  • Event Description: Detailed description of the event.
  • Categories: Classification of the event based on IHL categories, such as "Acciones armadas" or "Ataques contra la población civil."
  • Subcategories: More detailed classification, such as "Emboscada" or "Homicidio intencional en persona protegida."
  • Victim Information: Details about victims, including the number of individuals affected, their status (e.g., "Muerto," "Herido"), and their condition (e.g., "Civil," "Militar").
  • Source Information: Details about the sources reporting the event, including the type of source (e.g., "Prensa" or "Sociedad Civil"), the name of the source, and a link to the original article.
  • Location Information: Department and municipality where the event took place (if available).

Variables and Categories

The dataset includes the following key categories and variables:

  1. Categories:

    • "Acciones armadas" (Armed Actions)
    • "Ataques contra la población civil" (Attacks against the civilian population)

  2. Subcategories:

    • "Emboscada" (Ambush)
    • "Homicidio intencional en persona protegida" (Intentional homicide against a protected person)

  3. Victim Status:

    • "Muerto" (Dead)
    • "Herido" (Injured)

  4. Victim Condition:

    • "Civil"
    • "Militar"
    • "Desconocido" (Unknown)

Data Sources

This dataset was compiled from reports by key informants and news sources, including:

  • El Tiempo
  • Alerta Paisa
  • Analisisurbano.org

Each event record includes a link to the original source of the information.

How to Use

This dataset can be used to analyze trends in violence and IHL violations in Colombia, identify patterns in types of attacks, and assess the impact on different victim groups. It is suitable for researchers, humanitarian organizations, and policymakers interested in understanding the humanitarian situation in Colombia.

License

Please ensure compliance with applicable data usage policies and acknowledge OCHA Colombia when using this dataset.

license: MIT

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