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electricsheepafrica/africa-mauritius-gross-tertiary-enrolment-rate-gter-in-tertiary-education-b-fa6715f1

Gross Tertiary Enrolment Rate Gter in Tertiary Education B | Africa (MDPA) 59 rows - 1 Africa country/area - 2000-2014 - 4 indicators - Engineered by Electric Sheep Africa TL;DR This dataset contains 59 rows from MDPA, covering Gross Tertiary Enrolment Rate Gter in Tertiary Education B. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples. What This Dataset Measures… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-gross-tertiary-enrolment-rate-gter-in-tertiary-education-b-fa6715f1.

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Dataset Card

Gross Tertiary Enrolment Rate Gter in Tertiary Education B | Africa (MDPA)

59 rows - 1 Africa country/area - 2000-2014 - 4 indicators - Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)

rows countries period indicators license

TL;DR

This dataset contains 59 rows from MDPA, covering Gross Tertiary Enrolment Rate Gter in Tertiary Education B. It is published as ML-ready Parquet with consistent Hugging Face metadata, source provenance, and analysis-friendly loading examples.

What This Dataset Measures

Education datasets help analysts study access, participation, learning systems, infrastructure, and outcomes across places and periods.

Source-provided context: Gross Tertiary Enrolment Rate (GTER) in Tertiary Education by sex, 2000 - 2015

How To Read This Dataset

  • —One row means: one indicator observation for one geography, time period, and optional source dimensions.
  • —Primary geography column: country_iso3.
  • —Best time column: year.
  • —Time coverage basis: year.
  • —Recommended join keys: country_iso3, year, indicator_id.

Coverage

DimensionValue
Rows59
Countries/areas1
First period2000
Last period2014
Indicators4
Columns19
Source formatXLSX

Geographic Coverage

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

AreaRowsFirst yearLast yearName
MU5920002014Mauritius

Indicators, Variables, Or Resource Contents

  • —gross-tertiary-enrolment-rate-gter-in-tertiary-education-by-sex-2000-201-564d3069 - Gross Tertiary Enrolment Rate (GTER) in Tertiary Education by sex, 2000 - 2015 - public funded institutions(sourceunitsunspecified)
  • —gross-tertiary-enrolment-rate-gter-in-tertiary-education-by-sex-2000-201-1f9bd21e - Gross Tertiary Enrolment Rate (GTER) in Tertiary Education by sex, 2000 - 2015 - private(sourceunitsunspecified)
  • —gross-tertiary-enrolment-rate-gter-in-tertiary-education-by-sex-2000-201-ea1bc9c7 - Gross Tertiary Enrolment Rate (GTER) in Tertiary Education by sex, 2000 - 2015 - overseas(sourceunitsunspecified)
  • —gross-tertiary-enrolment-rate-gter-in-tertiary-education-by-sex-2000-201-dd2440a3 - Gross Tertiary Enrolment Rate (GTER) in Tertiary Education by sex, 2000 - 2015 - total(sourceunitsunspecified)

Schema

ColumnTypeDescriptionExample
indicator_idstringStable source or Electric Sheep Africa indicator identifier.gross-tertiary-enrolment-rate-gter-in-tertiary-education-by-sex-2000-...
indicator_namestringHuman-readable indicator name.Gross Tertiary Enrolment Rate (GTER) in Tertiary Education by sex, 20...
country_iso3stringISO3 country or area code.MU
source_sheetstringSource column from the original resource.data-new-admissions-on-tertiary
country_namestringCountry or area name.Mauritius
yearint64Observation year.2000
valuedoubleNumeric observation value.4976.0
unitstringMeasurement unit, when supplied by the source.source_units_unspecified
source_period_start_yearint64Start year inferred from source metadata.2000
source_period_end_yearint64End year inferred from source metadata.2015
source_period_labeldictionary<values=string, indices=int8, ordered=0>Source column from the original resource.2000-2015
source_providerdictionary<values=string, indices=int8, ordered=0>Publishing organization.MDPA
source_datasetdictionary<values=string, indices=int8, ordered=0>Source dataset or package title.Gross Tertiary Enrolment Rate (GTER) in Tertiary Education by sex, 20...
source_resourcedictionary<values=string, indices=int8, ordered=0>Source resource title, table name, or file name.table1.xlsx
source_package_iddictionary<values=string, indices=int8, ordered=0>Source package identifier.d05f04ec-f191-486e-ba47-1dd4cf204f8a
source_resource_iddictionary<values=string, indices=int8, ordered=0>Source resource identifier.3e13a474-07f6-4524-8f53-00865d3cad7d
source_urldictionary<values=string, indices=int8, ordered=0>Original source URL or download URL.https://data.govmu.org/dataset/d05f04ec-f191-486e-ba47-1dd4cf204f8a/r...
license_iddictionary<values=string, indices=int8, ordered=0>Source license identifier.CC-BY-SA-4.0
retrieved_atdictionary<values=string, indices=int8, ordered=0>UTC source retrieval timestamp from the Electric Sheep Africa pipeline.2026-08-08T16:26:20Z

Usage

python
from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-mauritius-gross-tertiary-enrolment-rate-gter-in-tertiary-education-b-fa6715f1")
df = ds["train"].to_pandas()
print(df.head())

Inspect Columns

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

Filter By Geography

python
if "country_iso3" in df.columns:
    sample = df[df["country_iso3"] == "MU"]

Time-Series Pattern

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

Pivot For Analysis

python
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

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

  • —Compare education indicators by geography
  • —Track participation or completion trends
  • —Join with population and poverty indicators
  • —Build time-series views and period-over-period comparisons
  • —Pivot to geography x period or indicator x period matrices
  • —Check missingness before modeling
  • —Use country_iso3 as the safest geography join key when present

Citation

bibtex
@misc{electric_sheep_africa_africa_mauritius_gross_tertiary_enrolment_rate_gter_in_tertiary_education_b_fa67_2014,
  title        = {Gross Tertiary Enrolment Rate Gter in Tertiary Education B | Africa (MDPA)},
  author       = {MDPA},
  year         = {2014},
  url          = {https://data.govmu.org/dataset/gross-tertiary-enrolment-rate-gter-tertiary-education-sex-2000-2015},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mauritius-gross-tertiary-enrolment-rate-gter-in-tertiary-education-b-fa6715f1}}
}

License

Released under CC BY-SA 4.0.

Original data is published by MDPA. 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://data.govmu.org/dataset/gross-tertiary-enrolment-rate-gter-tertiary-education-sex-2000-2015