Datasets:

electricsheepafrica
/

africa-synth-hospital-services-weekly-nigeria

week int64 1 52	month int64 1 12	service stringclasses 4 values	available_beds int64 8 74	patients_request int64 5 388	patients_admitted int64 5 74	patients_refused int64 0 363	patient_satisfaction int64 60 99	staff_morale int64 31 99	event stringclasses 4 values
1	1	emergency	32	76	32	44	67	70	none
1	1	surgery	45	130	45	85	83	78	malaria_peak
1	1	general_medicine	37	201	37	164	97	43	malaria_peak
1	1	ICU	22	31	22	9	84	91	malaria_peak
2	1	emergency	28	169	28	141	75	64	none
2	1	surgery	40	26	26	0	96	56	none
2	1	general_medicine	43	183	43	140	73	93	malaria_peak
2	1	ICU	16	7	7	0	79	85	ngo_donation
3	1	emergency	32	177	32	145	73	58	malaria_peak
3	1	surgery	27	66	27	39	63	72	malaria_peak
3	1	general_medicine	37	58	37	21	95	63	none
3	1	ICU	20	21	20	1	82	89	none
4	1	emergency	32	157	32	125	83	75	none
4	1	surgery	56	57	56	1	74	94	none
4	1	general_medicine	43	152	43	109	67	66	malaria_peak
4	1	ICU	20	21	20	1	64	85	malaria_peak
5	2	emergency	25	388	25	363	93	72	malaria_peak
5	2	surgery	26	70	26	44	81	83	ngo_donation
5	2	general_medicine	40	103	40	63	73	52	none
5	2	ICU	22	13	13	0	73	88	none
6	2	emergency	34	198	34	164	72	76	malaria_peak
6	2	surgery	27	58	27	31	96	81	ngo_donation
6	2	general_medicine	69	90	69	21	72	81	none
6	2	ICU	14	12	12	0	87	51	none
7	2	emergency	33	127	33	94	65	77	none
7	2	surgery	37	46	37	9	89	60	none
7	2	general_medicine	62	80	62	18	84	98	ngo_donation
7	2	ICU	21	47	21	26	72	83	malaria_peak
8	2	emergency	26	240	26	214	96	75	malaria_peak
8	2	surgery	51	20	20	0	91	61	strike
8	2	general_medicine	58	105	58	47	71	88	none
8	2	ICU	12	10	10	0	76	98	none
9	3	emergency	28	113	28	85	82	86	none
9	3	surgery	22	41	22	19	61	93	none
9	3	general_medicine	38	78	38	40	63	60	none
9	3	ICU	11	20	11	9	97	73	none
10	3	emergency	17	130	17	113	81	60	none
10	3	surgery	38	57	38	19	68	55	none
10	3	general_medicine	35	64	35	29	95	68	none
10	3	ICU	13	8	8	0	91	56	none
11	3	emergency	16	97	16	81	98	72	ngo_donation
11	3	surgery	48	48	48	0	82	80	none
11	3	general_medicine	35	50	35	15	75	75	none
11	3	ICU	14	17	14	3	61	50	none
12	3	emergency	28	347	28	319	68	83	malaria_peak
12	3	surgery	38	24	24	0	79	73	none
12	3	general_medicine	33	69	33	36	70	98	none
12	3	ICU	16	13	13	0	79	94	ngo_donation
13	4	emergency	23	142	23	119	77	95	none
13	4	surgery	41	54	41	13	75	90	none
13	4	general_medicine	33	72	33	39	73	70	none
13	4	ICU	17	12	12	0	66	52	none
14	4	emergency	15	97	15	82	71	71	none
14	4	surgery	33	49	33	16	97	87	none
14	4	general_medicine	51	47	47	0	86	76	none
14	4	ICU	10	16	10	6	92	54	none
15	4	emergency	17	53	17	36	94	98	none
15	4	surgery	31	124	31	93	65	76	malaria_peak
15	4	general_medicine	53	179	53	126	90	76	malaria_peak
15	4	ICU	14	11	11	0	92	52	none
16	4	emergency	23	144	23	121	62	89	none
16	4	surgery	37	28	28	0	82	31	strike
16	4	general_medicine	39	41	39	2	85	66	none
16	4	ICU	18	15	15	0	98	78	none
17	5	emergency	21	133	21	112	84	73	none
17	5	surgery	40	23	23	0	95	94	none
17	5	general_medicine	33	76	33	43	75	63	none
17	5	ICU	14	17	14	3	87	83	none
18	5	emergency	18	100	18	82	81	80	ngo_donation
18	5	surgery	43	36	36	0	81	98	none
18	5	general_medicine	31	25	25	0	96	62	strike
18	5	ICU	12	9	9	0	63	55	none
19	5	emergency	16	70	16	54	99	45	strike
19	5	surgery	42	44	42	2	78	66	none
19	5	general_medicine	43	60	43	17	85	86	none
19	5	ICU	14	17	14	3	68	61	none
20	5	emergency	21	45	21	24	93	81	none
20	5	surgery	31	39	31	8	99	94	none
20	5	general_medicine	40	70	40	30	64	71	none
20	5	ICU	10	14	10	4	85	65	none
21	6	emergency	18	95	18	77	88	63	none
21	6	surgery	30	44	30	14	64	67	ngo_donation
21	6	general_medicine	45	70	45	25	95	82	none
21	6	ICU	12	9	9	0	69	54	none
22	6	emergency	16	129	16	113	69	68	none
22	6	surgery	18	21	18	3	63	65	none
22	6	general_medicine	46	36	36	0	87	88	ngo_donation
22	6	ICU	9	6	6	0	92	82	none
23	6	emergency	22	54	22	32	62	50	none
23	6	surgery	26	43	26	17	72	61	none
23	6	general_medicine	27	81	27	54	94	77	ngo_donation
23	6	ICU	17	12	12	0	85	83	none
24	6	emergency	18	105	18	87	70	78	none
24	6	surgery	39	21	21	0	95	39	strike
24	6	general_medicine	47	66	47	19	94	97	none
24	6	ICU	9	9	9	0	91	73	none
25	7	emergency	15	48	15	33	72	72	none
25	7	surgery	22	39	22	17	76	98	none
25	7	general_medicine	36	51	36	15	81	62	none
25	7	ICU	8	6	6	0	71	90	none

End of preview. Expand in Data Studio

Nigeria Hospital - Weekly Service Metrics | Africa (Electric Sheep Africa metadata inventory)

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

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

Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance.

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. Services Weekly Dataset Dataset Description This collection tracks weekly performance metrics across four hospital services (emergency, surgery, ICU, general_medicine) for a full year. Metrics include bed availability, patient requests/admissions, satisfaction scores, staff morale, and special events. The data enables… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-hospital-services-weekly-nigeria.

Dataset Profile

Field	Value
Hugging Face repo	`electricsheepafrica/africa-synth-hospital-services-weekly-nigeria`
Sector	health
Topic tags	nigeria, healthcare, synthetic-data, hospital-operations, capacity-planning, resource-allocation, synthetic
Modalities	`tabular`, `text`
Formats	`parquet`
Size category	`n<1K`
Countries	Nigeria
ISO3 coverage	`NGA`
Last modified on HF	`2026-04-14 22:37:16+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-hospital-services-weekly-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.
Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material.

Source And Provenance

Source context: Electric Sheep Africa metadata inventory
Publisher/source attribution: Public dataset metadata
License: mit
Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/africa-synth-hospital-services-weekly-nigeria
Inventory retrieved at: 2026-07-16T16:00:34Z

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_hospital_services_weekly_nigeria_2026,
  title        = {Nigeria Hospital - Weekly Service Metrics | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-hospital-services-weekly-nigeria},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-hospital-services-weekly-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.

Downloads last month: 42

Collections including electricsheepafrica/africa-synth-hospital-services-weekly-nigeria