electricsheepafrica/africa-ner-ibtracs-tropical-storm-tracks

---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license: cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
task_ids: []
tags:
- africa
- humanitarian
- hdx
- electric-sheep-africa
- cyclones-hurricanes-typhoons
- hxl
- ner
pretty_name: "Niger: IBTrACS Storm Tracks"
dataset_info:
splits:
- name: train
num_examples: 4468
- name: test
num_examples: 1117
---

# Niger: IBTrACS Storm Tracks

**Publisher:** HDX · **Source:** [HDX](https://data.humdata.org/dataset/ner-ibtracs-tropical-storm-tracks) · **License:** `cc-by-igo` · **Updated:** 2025-08-26

---

## Abstract

The International Best Track Archive for Climate Stewardship (IBTrACS) project is the most complete global collection of tropical cyclones available. It merges recent and historical tropical cyclone data from multiple agencies to create a unified, publicly available, best-track dataset that improves inter-agency comparisons.

Fields available:
SID: A unique storm identifier (SID) assigned by IBTrACS algorithm.
ISO_TIME: Time of the observation in ISO format (YYYY-MM-DD hh:mm:ss)
BASIN: Basin of the current storm position
SUBBASIN: Sub-basin of the current storm position
NATURE: Type of storm (a combination of the various types from the available sources)
NUMBER: Number of the storm for the year (restarts at 1 for each year
LAT: Mean position - latitude (a combination of the available positions)
LON: Mean position - longitude (a combination of the available positions)
WMO_WIND: Maximum sustained wind speed assigned by the responsible WMO agency
WMO_PRES: Minimum central pressure assigned by the responsible WMO agency.

Each row in this dataset represents geolocated point observations. Temporal coverage is indicated by the `iso_time` column(s). Geographic scope: **NER**.

*Curated into ML-ready Parquet format by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica).*

---

## Dataset Characteristics

| | |
|---|---|
| **Domain** | Climate and environment |
| **Unit of observation** | Geolocated point observations |
| **Rows (total)** | 5,585 |
| **Columns** | 12 (3 numeric, 8 categorical, 1 datetime) |
| **Train split** | 4,468 rows |
| **Test split** | 1,117 rows |
| **Geographic scope** | NER |
| **Publisher** | HDX |
| **HDX last updated** | 2025-08-26 |

---

## Variables

**Geographic** — `iso_time`, `lat` (range 8.3–70.7), `lon` (range -95.9–33.0).

**Outcome / Measurement** — `number` (range 12.0–114.0).

**Identifier / Metadata** — `sid` (2000217N11342, 1877247N14341, 2018242N13343), `esa_source` (HDX), `esa_processed` (2026-04-06).

**Other** — `basin` (North Atlantic, , North India), `subbasin` (North Atlantic, Caribbean Sea, Gulf of Mexico), `nature` (Tropical, Extratropical, Disturbance), `wmo_wind` ( , 30, 25), `wmo_pres` ( , 1006, 1004).

---

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ner-ibtracs-tropical-storm-tracks")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()
```

---

## Schema

| Column | Type | Null % | Range / Sample Values |
|---|---|---|---|
| `sid` | object | 0.0% | 2000217N11342, 1877247N14341, 2018242N13343 |
| `number` | float64 | 0.0% | 12.0 – 114.0 (mean 61.8994) |
| `basin` | object | 0.0% | North Atlantic, , North India |
| `subbasin` | object | 0.0% | North Atlantic, Caribbean Sea, Gulf of Mexico |
| `iso_time` | datetime64[ns] | 0.0% | |
| `nature` | object | 0.0% | Tropical, Extratropical, Disturbance |
| `lat` | float64 | 0.0% | 8.3 – 70.7 (mean 22.518) |
| `lon` | float64 | 0.0% | -95.9 – 33.0 (mean -41.6266) |
| `wmo_wind` | object | 0.0% | , 30, 25 |
| `wmo_pres` | object | 0.0% | , 1006, 1004 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-06 |

---

## Numeric Summary

| Column | Min | Max | Mean | Median |
|---|---|---|---|---|
| `number` | 12.0 | 114.0 | 61.8994 | 67.0 |
| `lat` | 8.3 | 70.7 | 22.518 | 18.8 |
| `lon` | -95.9 | 33.0 | -41.6266 | -39.8 |

---

## Curation

Raw data was downloaded from HDX via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (`N/A`, `null`, `none`, `-`, `unknown`, `no data`, `#N/A`) were unified to `NaN`. 4 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

---

## Limitations

- Data originates from HDX and has not been independently validated by ESA.
- Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
- Refer to the [original HDX dataset page](https://data.humdata.org/dataset/ner-ibtracs-tropical-storm-tracks) for the publisher's own methodology notes and caveats.

---

## Citation

```bibtex
@dataset{hdx_africa_ner_ibtracs_tropical_storm_tracks,
title = {Niger: IBTrACS Storm Tracks},
author = {HDX},
year = {2025},
url = {https://data.humdata.org/dataset/ner-ibtracs-tropical-storm-tracks},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}
```

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — Africa's ML dataset infrastructure. Lagos, Nigeria.*

annotations_creators:
- 无注释
language_creators:
- 公开数据源采集
language:
- 英语
license: cc-by-4.0
multilinguality:
- 单语言
size_categories:
- 1000<n<10000
source_datasets:
- 原创数据集
task_categories:
- 其他
task_ids:
- 无
tags:
- 非洲
- 人道主义
- HDX
- electric-sheep-africa
- 气旋、飓风、台风
- HXL
- 命名实体识别（NER）
pretty_name: "尼日尔：IBTrACS风暴路径"
dataset_info:
splits:
- name: train
num_examples: 4468
- name: test
num_examples: 1117

---

# 尼日尔：IBTrACS风暴路径

**发布方：HDX · **来源：[HDX](https://data.humdata.org/dataset/ner-ibtracs-tropical-storm-tracks) · **许可协议：**`cc-by-igo` · **更新时间：**2025-08-26

---

## 摘要

国际最佳路径档案气候管理计划（International Best Track Archive for Climate Stewardship, IBTrACS）是当前全球最完整的热带气旋公开数据集集合。该项目整合多机构的最新及历史热带气旋数据，构建了统一的公开最佳路径数据集，可提升跨机构的比对效率。

可用字段：
- SID：IBTrACS算法分配的唯一风暴标识符（SID）
- ISO_TIME：采用ISO 8601格式的观测时间（YYYY-MM-DD hh:mm:ss）
- BASIN：当前风暴位置所属的洋盆
- SUBBASIN：当前风暴位置所属的次级洋盆
- NATURE：风暴类型（整合各来源的多种风暴类型分类）
- NUMBER：当年度的风暴编号（每年从1重新计数）
- LAT：平均位置纬度（整合所有可用位置数据）
- LON：平均位置经度（整合所有可用位置数据）
- WMO_WIND：由世界气象组织（World Meteorological Organization, WMO）指定机构分配的最大持续风速
- WMO_PRES：由世界气象组织（World Meteorological Organization, WMO）指定机构分配的最低中心气压

本数据集的每一行均代表一条地理定位的点观测记录。时间覆盖范围由`iso_time`列标注。地理覆盖范围：**尼日尔（NER）**。

*由[Electric Sheep Africa](https://huggingface.co/electricsheepafrica)整理为适用于机器学习的Parquet格式。*

---

## 数据集特征

| | |
|---|---|
| **领域** | 气候与环境 |
| **观测单元** | 地理定位点观测数据 |
| **总数据行数** | 5585 |
| **列数** | 12列（3列数值型、8列分类型、1列日期时间型） |
| **训练集划分** | 4468行 |
| **测试集划分** | 1117行 |
| **地理覆盖范围** | 尼日尔（NER） |
| **发布方** | HDX |
| **HDX最后更新时间** | 2025-08-26 |

---

## 变量说明

**地理与时间变量**：`iso_time`、`lat`（取值范围8.3–70.7）、`lon`（取值范围-95.9–33.0）。
**结果/测量变量**：`number`（取值范围12.0–114.0）。
**标识符与元数据**：`sid`（示例值：2000217N11342、1877247N14341、2018242N13343）、`esa_source`（HDX）、`esa_processed`（2026-04-06）。
**其他字段**：`basin`（北大西洋、北印度洋）、`subbasin`（北大西洋、加勒比海、墨西哥湾）、`nature`（热带、温带、扰动）、`wmo_wind`（示例值：空值、30、25）、`wmo_pres`（示例值：空值、1006、1004）。

---

## 快速上手

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-ner-ibtracs-tropical-storm-tracks")
train = ds["train"].to_pandas()
test = ds["test"].to_pandas()

print(train.shape)
train.head()

---

## 数据架构

| 列名 | 数据类型 | 空值占比 | 取值范围/示例值 |
|---|---|---|---|
| `sid` | object | 0.0% | 2000217N11342、1877247N14341、2018242N13343 |
| `number` | float64 | 0.0% | 12.0 – 114.0（均值61.8994） |
| `basin` | object | 0.0% | 北大西洋、空值、北印度洋 |
| `subbasin` | object | 0.0% | 北大西洋、加勒比海、墨西哥湾 |
| `iso_time` | datetime64[ns] | 0.0% | 无 |
| `nature` | object | 0.0% | 热带、温带、扰动 |
| `lat` | float64 | 0.0% | 8.3 – 70.7（均值22.518） |
| `lon` | float64 | 0.0% | -95.9 – 33.0（均值-41.6266） |
| `wmo_wind` | object | 0.0% | 空值、30、25 |
| `wmo_pres` | object | 0.0% | 空值、1006、1004 |
| `esa_source` | object | 0.0% | HDX |
| `esa_processed` | object | 0.0% | 2026-04-06 |

---

## 数值统计摘要

| 列名 | 最小值 | 最大值 | 均值 | 中位数 |
|---|---|---|---|---|
| `number` | 12.0 | 114.0 | 61.8994 | 67.0 |
| `lat` | 8.3 | 70.7 | 22.518 | 18.8 |
| `lon` | -95.9 | 33.0 | -41.6266 | -39.8 |

---

## 数据整理流程

原始数据通过CKAN API从HDX下载，并转换为Parquet格式。将列名统一转换为小写蛇形命名法（snake_case）。将常见的缺失值标记（`N/A`、`null`、`none`、`-`、`unknown`、`no data`、`#N/A`）统一替换为`NaN`。基于解析成功率（阈值>85%），将4列从字符串类型转换为数值型或日期时间型。采用固定随机种子（42）将数据集按80/20比例划分为训练集与测试集，并以Snappy压缩格式保存为Parquet文件。

---

## 局限性说明

- 数据源自HDX，未由Electric Sheep Africa进行独立验证。
- 自动化清洗无法修正原始数据集中的错误报告值、定义不一致或采样偏差问题。
- 请参阅[原始HDX数据集页面](https://data.humdata.org/dataset/ner-ibtracs-tropical-storm-tracks)查看发布方提供的方法论说明与免责条款。

---

## 引用格式

bibtex
@dataset{hdx_africa_ner_ibtracs_tropical_storm_tracks,
title = {Niger: IBTrACS Storm Tracks},
author = {HDX},
year = {2025},
url = {https://data.humdata.org/dataset/ner-ibtracs-tropical-storm-tracks},
note = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

---

*[Electric Sheep Africa](https://huggingface.co/electricsheepafrica) — 非洲机器学习数据集基础设施提供商。尼日利亚拉各斯。*