electricsheepasia/asia-ports-syrian-arab-republic-daily-port-activity

Syrian Arab Republic: Daily Port Activity Data and Shipment Estimates

Publisher: PortWatch · Source: HDX · License: hdx-other · Updated: 2026-05-05

Abstract

Daily count of port calls, estimates of incoming shipment volumes and outgoing shipment volumes (in metric tons) for ports in Syrian Arab Republic.

Each row in this dataset represents country-level aggregates. Data was last updated on HDX on 2026-05-05. Geographic scope: SYR.

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

Dataset Characteristics

Variables

Geographic — year (range 2019.0–2026.0), day (range 1.0–31.0), country (Syria), iso3 (SYR), portcalls_dry_bulk (range 0.0–3.0) and 8 others.

Temporal — date, month (range 1.0–12.0).

Identifier / Metadata — portid (port26), portname (Latakia), esa_source (HDX), esa_processed (2026-05-06).

Other — portcalls_container (range 0.0–4.0), portcalls_general_cargo (range 0.0–3.0), portcalls_roro (range 0.0–1.0), portcalls_tanker (range 0.0–1.0), portcalls_cargo (range 0.0–5.0) and 7 others.

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/asia-ports-syrian-arab-republic-daily-port-activity")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Numeric Summary

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. 1 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 PortWatch 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 for the publisher's own methodology notes and caveats.

Citation

@dataset{hdx_asia_ports_syrian_arab_republic_daily_port_activity,
  title     = {Syrian Arab Republic: Daily Port Activity Data and Shipment Estimates},
  author    = {PortWatch},
  year      = {2026},
  url       = {https://data.humdata.org/dataset/syrian-arab-republic-daily-port-activity-data-and-shipment-estimates},
  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.