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37 values
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REC-00294525
2023-01-24
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REC-00637883
2022-10-19
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59.7
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REC-00010592
2023-03-21
Enugu
60.6
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REC-00613677
2023-01-13
Bauchi
68.1
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REC-00513355
2024-01-11
Lagos
69.1
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REC-00910831
2024-04-27
Lagos
86.9
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REC-00087253
2023-03-17
Lagos
61.2
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REC-00826879
2023-07-07
Niger
50.4
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REC-00201016
2025-03-08
Anambra
100
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REC-00190335
2022-07-19
Kogi
70.5
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REC-00544373
2024-05-14
Bauchi
86.9
A
REC-00179737
2023-09-05
Edo
71.2
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REC-00451163
2023-10-20
Ogun
72.7
A
REC-00737974
2023-11-20
Ebonyi
94.6
A
REC-00081759
2022-03-22
Zamfara
85.1
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REC-00763279
2024-07-10
FCT
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REC-00743297
2023-10-23
Osun
62.4
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REC-00887065
2023-03-23
FCT
93.7
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REC-00306647
2022-07-02
Akwa Ibom
92.3
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REC-00971051
2022-02-10
Niger
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REC-00642190
2024-02-25
FCT
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REC-00430681
2023-11-08
Kogi
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REC-00912348
2023-06-18
Ondo
67.1
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REC-00030290
2022-12-08
Borno
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REC-00433025
2024-05-15
Adamawa
75.8
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REC-00100921
2024-12-23
Sokoto
79.9
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REC-00547360
2023-04-13
Lagos
57
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REC-00425818
2024-11-17
Kano
52
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REC-00431876
2023-06-19
Kaduna
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REC-00584125
2023-04-05
Nasarawa
48
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REC-00594105
2023-02-10
Jigawa
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REC-00615669
2024-04-25
Taraba
57.9
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REC-00744706
2023-08-30
Enugu
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REC-00548548
2022-10-06
Nasarawa
100
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REC-00507964
2024-04-01
Sokoto
76.3
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REC-00122110
2022-06-02
Bauchi
87.9
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REC-00855780
2024-01-13
Jigawa
45.5
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REC-00241719
2023-10-16
Jigawa
74
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REC-00214798
2024-08-28
Enugu
75.8
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REC-00190821
2024-09-19
Oyo
68.6
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REC-00368443
2022-03-17
Ebonyi
48.6
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REC-00825589
2023-05-02
Borno
63.8
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REC-00882641
2024-09-30
Taraba
65.3
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REC-00494055
2025-03-18
Katsina
77.7
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REC-00284850
2024-03-15
Plateau
95.3
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REC-00379813
2023-01-07
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47.6
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REC-00505190
2022-03-14
Ondo
87.2
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REC-00077237
2022-07-07
Katsina
77.1
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REC-00293877
2023-10-19
Kogi
76.4
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REC-00228667
2022-06-14
FCT
52.1
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REC-00477194
2022-07-03
Edo
65
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REC-00186212
2023-05-25
Kebbi
68.2
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REC-00664681
2023-09-07
Kaduna
89.8
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REC-00642589
2024-01-19
Gombe
69.2
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REC-00269433
2024-11-28
Zamfara
90.1
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REC-00113862
2023-04-10
Kebbi
66.6
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REC-00231050
2024-10-15
Benue
83.1
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REC-00645820
2022-10-29
Zamfara
80.1
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REC-00007875
2024-12-30
Enugu
53.1
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REC-00509066
2023-06-04
FCT
62.5
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REC-00580835
2022-08-06
Katsina
69.2
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REC-00239614
2024-09-28
Abia
81.2
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REC-00156864
2023-06-02
Bayelsa
44.2
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REC-00674926
2022-07-10
Kogi
65.5
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REC-00672755
2022-02-11
Ebonyi
77.4
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REC-00822087
2022-05-28
Borno
67.5
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REC-00626387
2022-03-25
Abia
100
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REC-00657381
2022-05-18
Akwa Ibom
86.6
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REC-00173471
2025-03-13
Akwa Ibom
56.9
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REC-00605091
2024-01-09
Nasarawa
60.4
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REC-00362663
2025-03-14
Gombe
82.6
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REC-00489468
2022-10-07
Sokoto
77.6
A
REC-00759027
2022-06-02
Lagos
63.4
B
REC-00280971
2022-01-21
Osun
93.5
A
REC-00688028
2023-01-17
Ekiti
63
A
REC-00145574
2023-08-27
Bayelsa
45
B
REC-00122606
2024-01-27
Ebonyi
51.8
A
REC-00650850
2024-05-22
Cross River
86.5
A
REC-00579452
2024-07-31
Gombe
79.5
C
REC-00687358
2022-06-16
Ondo
75.2
C
REC-00780138
2023-03-08
Lagos
76.4
C
REC-00645519
2022-04-06
Plateau
51.5
C
REC-00095114
2023-01-29
Ekiti
76.6
A
REC-00862614
2024-08-12
Kaduna
58.3
C
REC-00209118
2024-09-02
Yobe
80.1
A
REC-00138961
2023-10-17
Katsina
59
B
REC-00947978
2023-05-29
Borno
31.2
C
REC-00593672
2023-01-07
Bayelsa
74.9
A
REC-00378318
2024-10-17
Sokoto
73.5
B
REC-00759716
2022-01-09
Taraba
65.8
A
REC-00844176
2024-09-21
Kwara
61.2
A
REC-00894442
2023-11-20
Akwa Ibom
39.3
B
REC-00769677
2022-08-08
Kwara
83.7
A
REC-00403687
2023-03-07
Yobe
76.2
B
REC-00800294
2022-03-25
Taraba
54.5
A
REC-00021901
2024-06-30
Akwa Ibom
79.3
A
REC-00508881
2022-06-11
Bayelsa
58.5
B
REC-00580945
2023-11-16
Lagos
73.6
B
REC-00417420
2023-01-24
Katsina
94.5
A
REC-00066779
2022-08-29
Kogi
62.2
B
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Africa Synth Education Maintenance Budgets Nigeria | Africa (Electric Sheep Africa metadata inventory)

Size category: 10K<n<100K - 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 – Maintenance Budgets Dataset Description Synthetic School Infrastructure & Resources data for Nigeria education sector. Category: School Infrastructure & ResourcesRows: 80,000Format: CSV, ParquetLicense: MITSynthetic: Yes (generated using reference data from WAEC, JAMB, UBEC, NBS, UNESCO) Dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-maintenance-budgets-nigeria.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-synth-education-maintenance-budgets-nigeria
Sector education
Topic tags nigeria, education, waec, jamb, synthetic, school-infrastructure-and-resources
Modalities text
Formats parquet
Size category 10K<n<100K
Countries Nigeria
ISO3 coverage NGA
Last modified on HF 2026-04-14 22:25:47+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-maintenance-budgets-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_maintenance_budgets_nigeria_2026,
  title        = {Africa Synth Education Maintenance Budgets Nigeria | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-maintenance-budgets-nigeria},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-education-maintenance-budgets-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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