electricsheepafrica/africa-egypt-capmas-patents-and-trademarks-efed9124

Patents and Trademarks | Africa (CAPMAS Egypt Open Data)

5,615 rows - 1 Africa country/area - 2010-2023 - 26 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

TL;DR

This dataset contains 5,615 rows from CAPMAS Egypt Open Data, covering Patents and Trademarks. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Economic datasets help analysts examine production, prices, public finance, trade flows, market conditions, and macroeconomic change.

This dataset covers Patents and Trademarks from CAPMAS Egypt Open Data. Use the source and schema sections below to confirm definitions, units, and collection methodology before sensitive analytical use.

How To Read This Dataset

• —One row means: one indicator observation for one geography, time period, and optional source dimensions.
• —Primary geography column: source metadata (EGY).
• —Best time column: year.
• —Time coverage basis: year.
• —Recommended join keys: year, indicator_id, plus source-specific dimension columns.

Coverage

Geographic Coverage

Top areas shown below, sorted by row count when available:

Indicators, Variables, Or Resource Contents

• —949 - The numerical distribution of patent applications submitted by country(Number)
• —951 - The numerical distribution of patents granted by the Egyptian Patent Office according to countries.(Number)
• —950 - Number of applications submitted for obtaining patents by country(Number)
• —952 - Number of patents granted by the Egyptian Patent Office according to countries(Number)
• —956 - The development of the number of Egyptian patents transferred to the Innovation Development Authority and those that have been marketed.(Number)
• —957 - Development of the number of Egyptian patents by field and what has been marketed(Number)
• —958 - Development of the number of Egyptian patents by field without marketing(Number)
• —978 - Number of research studies conducted by various entities(Number)
• —1005 - The numerical distribution of patents granted to Egyptians and foreigners by the Egyptian Patent Office.(Number)
• —961 - The numerical distribution of trademark applications submitted according to countries.(Number)
• —1008 - The numerical distribution of patents granted to Egyptians and foreigners by the Egyptian Patent Office according to international agreements.(Number)
• —1012 - The numerical distribution of patents granted to Egyptians and foreigners by the Egyptian Patent Office according to the international patent classification.(Number)
• —1023 - The numerical distribution of applications submitted by Egyptians and foreigners for obtaining patents according to international agreements.(Number)
• —1025 - The numerical distribution of trademarks granted by the Egyptian Trademark Office(Number)
• —953 - The numerical distribution of patents granted to Egyptians according to the field of specialization.(Number)
• —8543 - Names of Egyptian Patents and Their Marketing Status(Number)
• —8545 - Number of trademarks granted according to international classification(Number)
• —964 - Number of patents granted to Egyptians by type(Number)
• —954 - Number of patents granted to Egyptians classified by the source agency(Number)
• —965 - Number of patents granted to foreigners classified by the source organization(Number)
• —... 6 more indicators

Schema

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-egypt-capmas-patents-and-trademarks-efed9124")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

print(df.info())
print(df.head())

Filter By Geography

# This dataset is scoped to Egypt in source metadata.
sample = df.copy()

Time-Series Pattern

if "value" in df.columns and "year" in df.columns:
    trend = df.sort_values("year")

Pivot For Analysis

if {"indicator_id", "year", "value"}.issubset(df.columns):
    matrix = df.pivot_table(index="year", columns="indicator_id", values="value")
    print(matrix.tail())

Data Quality Notes

• —Canonical time field: year.
• —Missing values are preserved rather than silently imputed.
• —Column names are standardized for machine use; source meanings are preserved where known.
• —Always confirm source methodology, units, and collection definitions before policy, production, or redistribution-sensitive use.

Source And Provenance

• —Source: CAPMAS Egypt Open Data
• —Publisher: Central Agency for Public Mobilisation and Statistics (CAPMAS), Egypt
• —Portal: https://www.capmas.gov.eg
• —Resource: Patents and Trademarks
• —License: Source-specific or other license
• —Retrieved/generated: 2026-08-08T13:12:42Z
• —Hugging Face repo: electricsheepafrica/africa-egypt-capmas-patents-and-trademarks-efed9124

Transformations Applied

• —Converted the source table to Parquet for efficient analytics and ML workflows.
• —Added or preserved source provenance columns where available.
• —Standardized README metadata, dataset loading configuration, schema documentation, and citation format.
• —Preserved source-reported values without analytical imputation.

Suggested Analyses

• —Build time-series dashboards
• —Compare economic indicators
• —Join with population or sector data
• —Build time-series views and period-over-period comparisons
• —Pivot to indicator x period matrices
• —Check missingness before modeling
• —Use source or repo metadata for country scope when a geography column is not present

Citation

@misc{electric_sheep_africa_africa_egypt_capmas_patents_and_trademarks_efed9124_2023,
  title        = {Patents and Trademarks | Africa (CAPMAS Egypt Open Data)},
  author       = {Central Agency for Public Mobilisation and Statistics (CAPMAS), Egypt},
  year         = {2023},
  url          = {https://www.capmas.gov.eg},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-egypt-capmas-patents-and-trademarks-efed9124}}
}

License

Released under Source-specific or other license.

Original data is published by Central Agency for Public Mobilisation and Statistics (CAPMAS), Egypt. Electric Sheep Africa engineering standardizes the data for discovery, loading, and analysis on Hugging Face. Cite both the original source and this ML-ready dataset when used.

About Electric Sheep Africa

Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face.

Provenance: README standardized 2026-08-11 by the Electric Sheep Africa README system. Source URL: https://www.capmas.gov.eg