datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
disaster_tweetsDisaster_TweetsUrban-Disaster-Risk-Resilience-and-Land-Indicators-For-African-Countries
Urban Disaster Risk Resilience and Land Indicators For African Countries | Africa (World Health Organization)
Size category: 1K<n<10K - Formats: csv - Sector: climate_environment - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/Urban-Disaster-Risk-Resilience-and-Land-Indicators-For-African-Countries.africa-disaster-risk-all
African Flood Risk Urban Mapping | Africa (Electric Sheep Africa metadata inventory)
Size category: 10K<n<100K - Formats: csv - Sector: climate_environment - Engineered by Electric Sheep Africa
TL;DR
This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context.
What This Dataset Covers
Public… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-disaster-risk-all.twitter_disasterturkish-disaster-news-geonlp
Turkish Disaster News GeoNLP Dataset
Dataset Summary
This dataset was created and submitted as part of the Uncharted Data Challenge by Adaption. The LLM-enhanced instruction pairs (turkish_earthquake_news.csv) were generated using Adaptive Data by Adaption — an AI-powered data adaptation platform.
The first open-source Turkish-language disaster news dataset with district-level geocoding, humanitarian category labels, and multi-dimensional damage classification.… See the full description on the dataset page: https://huggingface.co/datasets/FatmaElik/turkish-disaster-news-geonlp.DisasterQADisasterQA is the first multiple choice question dataset on disaster response. This dataset covers a wide range of topics relating to the field including emergency management and GIS applications. All questions were obtained from open source online and we hope that this dataset is used to advance the field of disaster response.
Sources Used to Create the Dataset:
https://quizlet.com/710141954/iaem-cemaem-practice-exam-flash-cards/… See the full description on the dataset page: https://huggingface.co/datasets/Rajat1212/DisasterQA.natural-disasters-from-social-media
Description
Dataset created for Master's thesis "Detection of Catastrophic Events from Social Media" at the Slovak Technical University Faculty of Informatics.
Contains posts from social media that are split into two categories:
Informative - related and informative in regards to natural disasters
Non-Informative - unrelated to natural disasters
Other metadata include event type, source dataset etc. To balance classes, 50k tweets from twitter archive for years 2017-2022 were… See the full description on the dataset page: https://huggingface.co/datasets/melisekm/natural-disasters-from-social-media.disaster_tweets
README.md
data:
train.csv
validation.csv
test.csv
natural-disasters-from-social-media
Description
Dataset created for Master's thesis "Detection of Catastrophic Events from Social Media" at the Slovak Technical University Faculty of Informatics.
Contains posts from social media that are split into two categories:
Informative - related and informative in regards to natural disasters
Non-Informative - unrelated to natural disasters
Other metadata include event type, source dataset etc. To balance classes, 50k tweets from twitter archive for years 2017-2022 were… See the full description on the dataset page: https://huggingface.co/datasets/joker122322222/natural-disasters-from-social-media.disaster_editedannotations_creators:
expert-generated
language_creators:
found
languages:
en
licenses:
mit
multilinguality:
monolingual
paperswithcode_id: acronym-identification
pretty_name: disaster
size_categories:
10K<n<100K
source_datasets:
original
task_categories:
token-classification
task_ids: []
Natural_disaster_tweetsNatural_disaster_tweetsdisaster_response_messagesGlobal_disaster2018-2024
Global Natural Disaster Analysis (2018–2024)
Author: Omer LuzzattoCourse: Introduction to Data ScienceAssignment #1: EDA & DatasetDataset Source: Kaggle — Global Disaster Response Dataset (2018–2024)Dataset Size: ~50,000 rows × 12 columns
Project Goal
The goal of this project is to explore global natural disasters from 2018 to 2024 using Exploratory Data Analysis (EDA).This analysis explores global natural disaster data to uncover meaningful trends, patterns, and relationships… See the full description on the dataset page: https://huggingface.co/datasets/omerlu/Global_disaster2018-2024.Disaster-Type_Classification_Dataset_for_Automated_Fact-Checking
DTCD-AFC: Disaster-Type Classification Dataset for Automated Fact-Checking
Overview
The DTCD-AFC is a dataset designed for disaster-type classification evaluation for automated fact-checking.
It consists of multimodal social media posts collected based on past natural disasters, each labeled with the disaster type to which its content relates.
The social media posts are sourced from CrisisMMD.
Files
disaster_type_classification_dataset_for_afc.csv: The CSV… See the full description on the dataset page: https://huggingface.co/datasets/o-yas/Disaster-Type_Classification_Dataset_for_Automated_Fact-Checking.cleaned_disasterdisaster_classificationdisaster_classification_1disaster_combtwitter_disasterall_disasterGlobal_disaster_impact_analysDisasterStory