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REC-00180760
2024-11-03
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REC-00553302
2024-12-06
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88.9
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REC-00884670
2022-08-02
Gombe
58.4
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REC-00959399
2022-06-21
Enugu
59
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REC-00451968
2022-09-08
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69.9
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REC-00336911
2023-11-06
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87.4
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2024-10-04
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48.8
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REC-00829856
2022-05-09
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84.4
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REC-00556583
2025-02-07
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89.1
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REC-00084844
2023-06-27
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100
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REC-00535995
2024-06-30
Anambra
59.1
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REC-00713509
2023-06-11
Edo
87.5
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REC-00368516
2023-11-04
Katsina
43.5
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REC-00019496
2025-03-20
Bauchi
74.1
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REC-00447259
2023-01-11
Anambra
60.1
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REC-00536947
2024-01-12
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69
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REC-00401503
2022-03-03
Bauchi
73.3
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REC-00780278
2023-04-13
Rivers
90.6
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REC-00800852
2023-04-21
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REC-00177210
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2024-02-06
Yobe
82.8
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REC-00516213
2023-11-04
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REC-00364660
2023-09-15
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REC-00310359
2025-01-18
Ogun
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REC-00021332
2025-01-08
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85.3
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REC-00049513
2022-06-12
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REC-00298209
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REC-00107944
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REC-00482816
2024-03-21
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REC-00451207
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Zamfara
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REC-00172462
2023-01-02
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47.9
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REC-00532476
2023-12-24
Niger
88.3
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2023-03-06
Kano
65.6
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REC-00185106
2022-02-12
Taraba
75.5
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REC-00787823
2022-06-02
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66.2
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REC-00431084
2022-05-16
Sokoto
75.7
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REC-00816563
2024-03-18
Imo
80.6
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REC-00535320
2022-02-13
Rivers
68.1
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REC-00730757
2022-08-11
Kwara
62
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REC-00040803
2024-11-29
Lagos
78.5
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REC-00828910
2023-07-07
Kogi
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REC-00280964
2024-12-05
Rivers
76.1
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REC-00545620
2022-02-20
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REC-00309408
2023-07-29
Ondo
53
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100
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REC-00449038
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REC-00127391
2023-09-30
Kano
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2023-05-06
Kaduna
79.3
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REC-00526191
2024-11-27
Yobe
65.5
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REC-00390517
2022-02-24
Ondo
48.4
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REC-00397219
2024-08-21
Oyo
73.3
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REC-00272120
2023-04-08
Niger
66.5
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REC-00153935
2023-11-19
Borno
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REC-00458490
2024-01-12
Lagos
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REC-00893284
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REC-00132040
2023-11-04
Yobe
54.4
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REC-00304658
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Kogi
58
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REC-00825201
2024-09-01
Nasarawa
68.2
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REC-00750080
2023-04-24
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REC-00390990
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Gombe
49.7
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REC-00754263
2024-08-24
Katsina
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REC-00886791
2025-02-05
Bayelsa
48.2
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REC-00571166
2022-08-15
Ogun
89
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REC-00200464
2024-03-28
Zamfara
86
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REC-00057777
2023-01-10
Yobe
76.6
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REC-00343656
2024-04-23
Ogun
80.7
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REC-00894950
2022-03-02
Gombe
92.5
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REC-00193339
2022-10-12
Jigawa
100
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REC-00736380
2023-12-09
Ondo
45
A
REC-00075047
2024-03-11
FCT
74.7
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REC-00113481
2022-09-30
Kano
81.9
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REC-00426701
2023-12-14
Anambra
64.4
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REC-00443278
2024-09-05
Abia
78.9
C
REC-00239411
2024-01-02
Katsina
36.9
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REC-00314027
2022-04-23
Oyo
79.3
A
REC-00800318
2023-05-07
Rivers
79.2
C
REC-00512779
2024-11-27
Oyo
67.2
A
REC-00692161
2022-09-22
Borno
63.2
A
REC-00147622
2023-02-13
Bauchi
92.7
B
REC-00654579
2022-01-20
Niger
78
B
REC-00315446
2024-04-22
Zamfara
59.1
A
REC-00384421
2022-10-12
Osun
65.5
C
REC-00314040
2023-05-09
Kogi
92
C
REC-00456391
2022-10-06
Enugu
75.7
B
REC-00669692
2024-07-19
Gombe
59
B
REC-00183975
2023-12-18
Anambra
54.4
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REC-00078156
2023-03-08
Nasarawa
67.6
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REC-00780779
2023-03-08
Akwa Ibom
77.5
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REC-00545073
2023-06-11
Lagos
62.8
A
REC-00621075
2023-07-03
Gombe
70
A
REC-00718494
2022-03-11
Enugu
83.5
A
REC-00978766
2024-09-16
Ekiti
52.3
B
REC-00689422
2023-06-09
Delta
62.8
C
REC-00167534
2024-05-17
Bauchi
61.9
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REC-00973848
2023-02-22
Anambra
62.4
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REC-00504742
2024-06-29
Bayelsa
49
C
REC-00578946
2022-10-31
Yobe
54.6
A
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Africa Synth Education Curriculum Implementation Nigeria | Africa (Electric Sheep Africa metadata inventory)

Size category: 100K<n<1M - Formats: parquet - Sector: education - Engineered by Electric Sheep Africa

size sector downloads license

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

Education datasets help researchers study access, participation, attainment, learning systems, staffing, and infrastructure.

Dataset context from the existing Hugging Face card: ⚠️ Synthetic dataset — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference. Nigeria Education – Curriculum Implementation Dataset Description Synthetic Curriculum & Subjects data for Nigeria education sector. Category: Curriculum & SubjectsRows: 100,000Format: CSV, ParquetLicense: MITSynthetic: Yes (generated using reference data from WAEC, JAMB, UBEC, NBS, UNESCO) Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-curriculum-implementation-nigeria.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-synth-education-curriculum-implementation-nigeria
Sector education
Topic tags nigeria, education, waec, jamb, synthetic, curriculum-and-subjects
Modalities text
Formats parquet
Size category 100K<n<1M
Countries Nigeria
ISO3 coverage NGA
Last modified on HF 2026-04-14 22:25:05+00:00
Inventory snapshot 2026-07-16T16:00:34Z

How To Read This Dataset

  • Start from the repository files and the dataset viewer when available.
  • Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling.
  • Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis.
  • Preserve missing values until you have a defensible imputation rule.

Usage

from datasets import load_dataset

ds = load_dataset("electricsheepafrica/africa-synth-education-curriculum-implementation-nigeria")
print(ds)

split_name = next(iter(ds))
table = ds[split_name]
print(table.features)
print(table[:3])

Convert To Pandas When Tabular

from datasets import Dataset

first_split = ds[next(iter(ds))]
if isinstance(first_split, Dataset):
    df = first_split.to_pandas()
    print(df.head())

Data Quality Notes

  • This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory.
  • Exact schema, row counts, and source files should be inspected in the repository data files.
  • Metadata gaps from the inventory: upstream_publisher, language.
  • Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Suggested Analyses

  • Inspect schema and missingness before modeling.
  • Profile variables by geography, time, and subgroup columns where present.
  • Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available.
  • Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo.

Citation

@misc{electric_sheep_africa_africa_synth_education_curriculum_implementation_nigeria_2026,
  title        = {Africa Synth Education Curriculum Implementation Nigeria | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-curriculum-implementation-nigeria},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-curriculum-implementation-nigeria}}
}

License

Released under mit.

Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face.

About Electric Sheep Africa

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


Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: catalog/esa_metadata_inventory/master_metadata.jsonl.

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