Datasets:

electricsheepafrica
/

africa-synth-governance-election-infrastructure-all

country stringclasses 12 values	scenario stringclasses 3 values	population_millions float64 13.1 225	registered_voters_millions float64 6.41 99.4	polling_stations_total int64 25.6M 398M	polling_stations_per_100k float64 400 400	voter_turnout_rate float64 15 99	biometric_verification_pct float64 0 100	electronic_voting_pct float64 0 95	observer_rating_score float64 1 10	result_transmission_speed_hours float64 0.5 168	election_disputes_count int64 0 80	dispute_resolution_days float64 1 365	election_cost_per_voter_usd float64 0.05 46.3	election_compliance_score float64 2.33 100	election_quality_class stringclasses 3 values	n_political_parties int64 1 35	international_observer_missions int64 2 68	internet_penetration_pct float64 5 98	security_incidents int64 0 145	voter_education_index float64 5 98	female_candidate_pct float64 0 69.8
Uganda	baseline	48.11	21.8	87,217,832	400	53.36	85.68	19.18	2.99	17.16	6	63.9	4.33	41.95	poorly_managable	10	14	38.64	20	45.09	35.93
Cameroon	baseline	27.4	6.87	27,462,956	400	54.12	82.22	2.92	2.96	2.08	15	98.2	8.83	47.9	partially_managable	4	7	51.74	21	43.14	12.77
Nigeria	baseline	219.41	93.02	372,075,192	400	34.64	93.15	2.05	5.01	44.59	17	105.8	8.41	45.68	partially_managable	15	41	52.16	32	37.92	5.26
Rwanda	baseline	13.76	8.16	32,635,789	400	88.48	97.52	41.06	6.01	21.69	2	19.4	1.34	66.62	partially_managable	6	15	53.71	0	69.41	59.92
Kenya	baseline	52.93	21.52	86,091,833	400	65.97	96.03	41.74	6.55	11.81	10	86	32.19	67.1	partially_managable	3	38	68.12	13	70.75	17.66
Rwanda	baseline	13.67	8.04	32,148,929	400	84.71	97.26	58.61	4.73	4.53	1	14.3	1.8	76.03	well-managed	7	14	54.32	1	65.66	56.18
DR Congo	baseline	101.93	45.98	183,939,554	400	46.33	63.43	4.58	3.88	41.81	25	104.7	10.26	46.41	partially_managable	21	40	23.58	43	22.32	6.19
Uganda	baseline	48.17	21.34	85,373,302	400	55.78	90.07	16.69	3.86	0.56	7	128.9	3.61	56.09	partially_managable	7	23	29.82	15	48.96	29.66
Ghana	baseline	31.81	18.37	73,485,082	400	85.02	90.7	24.81	7.09	12.83	2	45.3	4.32	70.44	well-managed	3	16	75.85	5	64.39	13.43
DR Congo	baseline	101.9	47.54	190,164,204	400	44.61	59.47	0.86	1.9	15.39	22	79.8	6.42	34.66	poorly_managable	21	37	24.38	40	22.45	8.14
Nigeria	baseline	214.33	95.06	380,223,135	400	35.99	80.01	12.01	3.96	41.14	13	142.3	6.11	43.37	poorly_managable	18	44	46.36	32	56.12	1.07
Ghana	baseline	32.02	19.13	76,514,524	400	80.83	84.69	21.22	6.7	9.82	9	57.3	3.31	68.52	partially_managable	7	21	62.57	7	63.47	12.9
Nigeria	baseline	216.37	91.66	366,649,062	400	34.34	90.88	15.74	3.16	42.34	17	126.1	6.75	45.69	partially_managable	24	50	59.2	36	38.89	7.06
Zambia	baseline	20.11	7.21	28,854,349	400	72.47	80.26	26.97	6.04	46.31	2	66.6	6.13	62.07	partially_managable	3	9	43.14	2	50.47	12.72
Rwanda	baseline	13.51	8.38	33,510,764	400	88.47	100	41.85	4.53	137.99	1	22.6	0.97	70	partially_managable	8	21	59.66	3	66.25	58.36
Rwanda	baseline	13.46	8.11	32,428,347	400	89.69	99.5	57.47	2.46	11.38	1	24.4	0.13	68.39	partially_managable	7	11	65.91	4	66.72	61.13
Nigeria	baseline	217.46	94.69	378,756,460	400	36.75	84.8	11.86	4.16	4.85	19	140.9	12.07	48.11	partially_managable	16	50	56.55	34	30.21	6.49
DR Congo	baseline	102.44	46.66	186,637,275	400	44.78	60.01	22.01	1.75	24.57	23	144.7	12.48	33.48	poorly_managable	22	43	25.94	41	23.76	8.6
Cameroon	baseline	27.56	6.97	27,891,258	400	50.64	74.38	0	4.7	7.1	4	142	14.88	49.88	partially_managable	1	11	49.21	13	40.89	20.08
Uganda	baseline	47.87	21.2	84,784,189	400	57.86	83.62	21.6	4.57	12.43	13	131	2.51	57.67	partially_managable	7	23	38.31	17	50.83	37.17
Zambia	baseline	20.29	6.79	27,174,847	400	63.94	80.16	18.53	5.07	2.51	7	63.6	3	57.81	partially_managable	11	20	37.39	3	48.87	11.49
DR Congo	baseline	100.21	47.22	188,894,943	400	52.72	58.3	5.55	2.99	45.06	18	121.7	7.73	39.79	poorly_managable	15	31	26.44	52	45.53	13.75
Ethiopia	baseline	126.45	52	207,980,284	400	63.6	65.43	13.13	3.99	13.12	17	65.3	9.38	41.73	poorly_managable	11	17	31.26	37	33.23	37.7
Cameroon	baseline	28.3	6.73	26,926,047	400	56.41	88.1	3.44	3.94	14.94	7	125	10.32	51.45	partially_managable	2	8	45.16	12	49.45	13.54
Rwanda	baseline	13.79	8.2	32,812,962	400	90.25	99.34	57.99	3.86	5.58	0	16.7	1.52	75.44	well-managed	12	14	58.91	2	84.12	60.04
Senegal	baseline	17	7.39	29,547,027	400	53.03	95.31	41.19	6.39	20.58	1	26	7.13	75.17	well-managed	1	26	56.2	1	57.19	41.39
Tanzania	baseline	65.78	29.87	119,483,109	400	70.26	88.23	42.78	6.29	22.43	6	120.1	9.7	69.81	partially_managable	2	15	51.99	5	52.99	32.67
Uganda	baseline	47.72	21.33	85,311,954	400	54.64	87.05	24.99	4.78	29.97	8	73.7	4.36	60.96	partially_managable	10	23	23.94	14	51.59	34
Senegal	baseline	16.84	7.21	28,856,466	400	51.49	78.12	49.33	7.59	17.81	2	46.8	7.55	72.78	well-managed	5	20	57.82	4	71.6	45.41
South Africa	baseline	60.02	28.17	112,695,252	400	65.7	85.95	54.17	7.07	6.99	1	54.5	7.16	70.91	well-managed	14	37	74.68	2	69.44	43.09
Ethiopia	baseline	126.42	53.29	213,154,202	400	60.57	53.3	0	4.73	2.59	18	102.3	8.66	41.49	poorly_managable	10	29	26.65	20	47.06	37.06
Kenya	baseline	53.88	21.59	86,375,509	400	61.5	92.86	52.85	6.59	14.07	8	40.2	29.68	74.61	well-managed	6	35	81.66	10	68.17	15.3
Ethiopia	baseline	126.6	50.25	201,019,657	400	78.58	49.68	7.64	1.19	6.06	13	91.6	6.88	38.79	poorly_managable	8	23	25.57	31	36.14	43.37
Cameroon	baseline	27.7	7.04	28,159,621	400	43.87	72.38	1.04	3.25	22.17	6	166.5	14.33	47.34	partially_managable	1	16	42.8	20	46	15.33
Ghana	baseline	31.94	18.35	73,411,254	400	77.42	93.04	33.04	7.46	47.56	3	50.8	1.7	75.68	well-managed	1	24	72.39	2	67.22	13.01
DR Congo	baseline	102.04	46.41	185,651,054	400	38.14	73.3	2.92	3.29	11.31	18	177.1	5.83	41.02	poorly_managable	19	46	28.45	41	32.85	15.65
DR Congo	baseline	100.89	47.85	191,391,580	400	50.36	77.57	14.68	2.62	5.25	28	86.1	4.59	39.01	poorly_managable	14	55	23.01	37	31.39	15.8
Ethiopia	baseline	126.17	50	199,988,661	400	62.32	24.15	0.94	5.62	6.38	9	65.5	9.17	33.21	poorly_managable	3	15	26.1	24	30.53	37.37
Rwanda	baseline	13.31	8.01	32,022,819	400	88.27	96.43	63.9	5.79	30.29	1	18.1	0.93	77.04	well-managed	8	19	61	0	69.8	61.06
Ghana	baseline	32.39	18.48	73,931,864	400	77.69	90.86	44.32	6.41	12.38	5	65.1	2.23	71.97	well-managed	4	28	68.79	4	64.14	3.27
Ethiopia	baseline	126.13	52.75	211,018,379	400	62.1	36.52	10.12	3.79	3.8	16	71.2	8.68	33.44	poorly_managable	11	18	23.52	21	35.68	36.18
Ghana	baseline	32.51	19.03	76,133,170	400	78.86	92.61	36.15	6.65	22.84	4	21.2	2.54	68.48	partially_managable	1	18	65.78	5	65.93	14.45
Senegal	baseline	17.12	7.22	28,882,863	400	56.08	92.46	48.82	6.35	8.7	5	73	4.76	64.93	partially_managable	3	21	54.57	4	61.8	44.02
Nigeria	baseline	216.96	94.17	376,670,861	400	32.83	83.09	14.19	4.99	4.97	11	148.8	10.83	54.1	partially_managable	20	39	58.1	30	46.31	7.09
Rwanda	baseline	13.71	8.29	33,173,219	400	94.91	98.06	67.27	4.99	18.21	0	25.4	1.33	79.48	well-managed	5	17	58.72	0	72.08	53.76
Tanzania	baseline	65.5	29.61	118,443,185	400	74.54	81.42	4.15	3.39	12.71	5	90.5	4.81	57.11	partially_managable	2	17	47.71	12	59.53	35.26
Kenya	baseline	52.68	21.9	87,600,789	400	63.33	96.29	54.46	7.14	0.5	4	59.7	26.39	72.19	well-managed	10	30	82.72	12	65.69	10.24
DR Congo	baseline	101.27	47.81	191,243,000	400	50.65	68.33	15.92	2.87	35.64	24	127	2.6	41.2	poorly_managable	18	56	17.36	46	25.93	11.3
Ghana	baseline	31.68	19.35	77,398,443	400	78.78	91.16	28.71	6.53	55.07	5	14.9	3.4	79.25	well-managed	2	19	65.54	5	72.15	12.31
Ethiopia	baseline	127.38	50.73	202,910,373	400	60.28	37.79	6.35	2.5	5.01	14	106.5	5.75	38.81	poorly_managable	8	17	18.5	21	42.39	37.32
Nigeria	baseline	217.11	93.31	373,251,899	400	25.5	78.27	2.71	3.94	0.5	21	62.6	9.01	41.65	poorly_managable	19	54	54.55	26	45.56	3.6
Ethiopia	baseline	126.36	51.95	207,787,464	400	59.12	52.25	2.17	3.42	2.43	20	119.2	5.61	38.37	poorly_managable	8	24	26.32	29	35.61	39.07
Ghana	baseline	32.2	18.81	75,249,556	400	81.3	86.82	41.82	6.34	45.64	3	52.2	5.21	70.61	well-managed	1	26	68.75	4	61.51	12.3
Cameroon	baseline	27.46	6.9	27,611,767	400	51.82	67.84	8.14	3.86	11.85	16	141.6	10.55	42.4	poorly_managable	2	13	41.86	15	39.98	15.98
Nigeria	baseline	217.49	93.52	374,070,753	400	35.84	90.45	10.13	3.86	34.48	22	102.4	7.95	44.97	poorly_managable	20	51	58.82	43	41.51	8.88
Ethiopia	baseline	125.76	51.34	205,346,257	400	58.02	17.88	3.01	4.01	9.29	8	122.3	6.27	33.33	poorly_managable	8	24	20.32	34	41.25	42.51
Ghana	baseline	31.88	18.53	74,119,476	400	75.92	93.79	15.45	8.02	5.71	0	77.6	4.17	73.7	well-managed	3	28	64.45	5	61.76	7.88
Tanzania	baseline	64.62	29.89	119,569,825	400	65.14	65	29.87	6.06	9.85	4	65.9	7.24	55.55	partially_managable	2	12	45.23	9	56.1	38.8
Rwanda	baseline	13.47	8.48	33,929,245	400	86.82	97.02	51.98	4.38	2.67	2	9.2	0.43	79.94	well-managed	9	14	74.75	1	80.15	67.07
Nigeria	baseline	218.53	93.74	374,954,779	400	39.14	93.62	21.32	4.38	5.75	25	133.8	11.69	49.82	partially_managable	12	45	54.19	34	45.25	7.03
Rwanda	baseline	13.39	8.17	32,680,594	400	88.48	100	65.43	5.42	0.54	2	22.2	0.88	84.7	well-managed	10	12	67.09	0	74.22	61.79
Uganda	baseline	48.01	21.99	87,952,806	400	59.54	94.79	23.95	5.68	5.2	10	70.3	4.78	62.08	partially_managable	9	23	34.92	17	39.06	38.54
Tanzania	baseline	65.02	29.82	119,279,468	400	72.9	85.43	34.29	3.48	1.19	8	60.4	7.83	66.66	partially_managable	4	23	53.1	11	54.59	35.42
South Africa	baseline	59.75	27.4	109,594,867	400	73.93	64.42	48.29	7.1	39.38	2	35.9	9.3	61.99	partially_managable	21	27	73.2	4	68.47	42.06
Rwanda	baseline	13.55	8.17	32,660,322	400	90.94	96.04	38.94	2.03	33.75	2	26.3	1.59	64.03	partially_managable	4	9	58.44	1	73.55	62.37
Rwanda	baseline	13.38	8.02	32,068,580	400	95.22	98.71	51.63	4.54	1.17	0	10.7	1.6	78.55	well-managed	6	16	57.39	0	66.67	60.69
Kenya	baseline	52.24	21.23	84,907,785	400	65.2	99.89	36.88	4.63	4.82	8	42.3	20.9	63.99	partially_managable	4	44	89.38	15	70.57	9.83
Rwanda	baseline	13.77	8.41	33,622,317	400	93.17	96.13	26.76	3.02	7.12	0	11.2	2.07	63.21	partially_managable	9	16	60.31	0	66.65	60.21
Ghana	baseline	31.59	19.11	76,453,009	400	76.48	97.32	32	6.84	6.73	3	31.9	2.58	73.53	well-managed	4	25	66.81	4	54.66	9.71
Ghana	baseline	31.87	18.83	75,322,031	400	79.71	93.17	23.64	6.72	14.91	1	57	0.29	72.02	well-managed	2	18	74.31	1	63.52	9.57
Nigeria	baseline	219.42	94.1	376,409,803	400	37.73	85.32	12.6	3.75	0.5	16	139.9	9.61	43.26	poorly_managable	16	39	62.13	36	42.7	5.26
DR Congo	baseline	102.69	48.95	195,815,139	400	46.43	60.93	15.26	4.55	106.28	28	79.2	11.54	38.11	poorly_managable	24	37	19.51	41	23.15	11.93
Rwanda	baseline	13.27	7.88	31,538,628	400	93.88	96.3	44.6	4.98	8.76	0	6.7	0.93	78.18	well-managed	10	18	63.07	0	68.32	64.7
South Africa	baseline	60.28	27.45	109,785,707	400	65.07	86.28	32.55	7.76	23.63	2	40.1	9.39	79.22	well-managed	17	32	70.56	4	71.52	48.84
Ethiopia	baseline	126.83	50.18	200,718,341	400	62.52	45.52	5.81	3.6	13.09	17	89.9	3.68	44.28	poorly_managable	13	21	21.64	27	31.75	45.39
Cameroon	baseline	28.48	7.03	28,123,106	400	49.96	77.01	5.02	3.73	1.31	12	137.4	13.66	40.96	poorly_managable	3	6	42.06	20	37.78	14.78
Cameroon	baseline	27.89	7.17	28,681,094	400	48.42	74.81	5.57	4.15	41.51	4	118.7	14.45	45.36	partially_managable	4	3	49.19	12	43.73	16.32
Rwanda	baseline	13.47	8.4	33,600,576	400	89.07	99.47	64.06	4.39	21.87	1	16.9	2.12	72.2	well-managed	5	11	58.73	0	65.95	58.19
Ethiopia	baseline	127.54	51.86	207,455,019	400	66.81	21.26	9.74	3.12	54.48	16	100.1	4.49	38	poorly_managable	7	26	30.03	36	38.56	37.69
Uganda	baseline	48.19	22.09	88,368,641	400	62.62	70.08	29.44	4.14	22.75	11	57.4	2.89	52.18	partially_managable	7	16	24.52	17	51.24	35.33
Ghana	baseline	32.37	18.49	73,953,840	400	77.77	89.02	25.46	6.97	1.85	1	26.2	0.37	69.21	partially_managable	3	25	63.4	4	67.33	13.17
Cameroon	baseline	27.96	7	28,009,918	400	58.43	62.26	0	2.65	13.78	5	150.1	6.76	42.44	poorly_managable	2	16	40.17	19	41.99	15.1
Ethiopia	baseline	127.41	50.06	200,227,927	400	61.8	24.46	5.36	4.02	27.18	13	111.5	7.58	40.83	poorly_managable	6	24	24.12	33	42.86	32.02
Ethiopia	baseline	128.01	50.32	201,289,927	400	55.18	53.44	5.62	4.7	2.87	11	84	3.28	48.05	partially_managable	7	17	19.32	24	44.31	38.92
Cameroon	baseline	27.76	6.94	27,766,371	400	52.28	68.41	4.86	3.77	28.9	7	104.2	17.65	43.61	poorly_managable	6	11	47.51	13	37.66	13.36
Senegal	baseline	17.07	7.48	29,904,961	400	53.59	92.27	60.18	5.75	19.28	0	28.2	6.08	72.25	well-managed	6	12	59.82	1	63.53	42.87
South Africa	baseline	59.39	27.71	110,845,615	400	63.08	93.82	18.89	7.37	1.6	1	60.9	2.77	67.65	partially_managable	15	29	77.78	5	75.69	39.09
Tanzania	baseline	64.53	30.06	120,223,553	400	64.98	78.6	24.47	6.38	8.18	4	73	3.54	64.08	partially_managable	2	16	48.04	14	60.74	35.43
Tanzania	baseline	64.87	29.71	118,843,141	400	74.2	82.37	41.21	2.22	20.75	8	77.1	1.71	56.72	partially_managable	6	32	49.8	6	44.31	35.37
Kenya	baseline	53.43	22.07	88,281,655	400	68.34	97.08	64.92	4.84	7.66	9	24.3	39.14	71.22	well-managed	10	34	76.88	10	62.22	14.99
Senegal	baseline	16.98	7.24	28,941,172	400	52.83	92.8	47.78	6.53	43.6	6	47.7	3.99	71.81	well-managed	2	19	54.54	4	57.66	50.72
Kenya	baseline	52.15	22.29	89,176,676	400	68.43	93.31	50.58	5.17	6.93	4	52.8	26.6	73.75	well-managed	6	39	95.97	17	63.94	6.8
Uganda	baseline	48.44	20.52	82,092,899	400	59.36	48.98	43.45	1.49	14.17	15	84.7	5.08	38.23	poorly_managable	9	21	31.47	12	42.43	30.64
Ghana	baseline	31.36	18.59	74,356,048	400	80.53	93.35	37.65	6.04	17.06	6	40	0.05	68.39	partially_managable	4	31	62.34	3	58.65	3.32
DR Congo	baseline	102.75	47.77	191,069,962	400	51.97	44.93	13.29	1	5.87	17	100.1	11.74	33.82	poorly_managable	25	49	26.57	46	25.32	10.84
Nigeria	baseline	220.85	93.74	374,947,679	400	30.73	78.3	34.94	2.71	77.31	24	103.2	16.26	52.69	partially_managable	10	42	57.14	38	54.3	2.75
Ethiopia	baseline	126.85	50.01	200,021,616	400	61.44	35.55	3.56	5.7	13.11	14	73.2	6.19	48.87	partially_managable	7	20	19.57	34	40.69	42.8
Cameroon	baseline	28.24	6.74	26,942,916	400	56.45	64.92	7.08	3.6	12.9	12	159.1	7.7	53.7	partially_managable	1	14	54.77	9	46.34	12.53
Cameroon	baseline	27.47	6.7	26,815,592	400	59.21	59.47	9.13	1.11	5.24	14	89.4	6.9	37.08	poorly_managable	3	13	46.58	25	49.16	16.55
Ethiopia	baseline	125.2	51.12	204,476,707	400	64.14	72.26	12.4	1	26.22	7	114.5	6.29	44.53	poorly_managable	7	23	23.91	31	32.87	39.48

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African Election Infrastructure | Africa (Electric Sheep Africa metadata inventory)

Size category: 10K<n<100K - Formats: csv - Sector: governance_security - 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

Public datasets help analysts inspect structured evidence, build reproducible workflows, and compare patterns across domains.

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. African Election Infrastructure Dataset Overview Synthetic dataset modeling electoral infrastructure across 12 Sub-Saharan African (SSA) countries under 3 scenarios. Each scenario contains 10,008 records (834 per country). Parameters are grounded in empirical literature on election technology, observer ratings, cost… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-election-infrastructure-all.

Dataset Profile

Field	Value
Hugging Face repo	`electricsheepafrica/africa-synth-governance-election-infrastructure-all`
Sector	governance_security
Topic tags	governance, elections, electoral-integrity, sub-saharan-africa, synthetic, lmic, democracy
Modalities	`tabular`, `text`
Formats	`csv`
Size category	`10K<n<100K`
Countries	Africa-wide or source-defined African coverage
ISO3 coverage	`not declared`
Last modified on HF	`2026-04-14 22:58:11+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-governance-election-infrastructure-all")
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: country, upstream_publisher, language.
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: CC BY 4.0
Hugging Face URL: https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-election-infrastructure-all
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_governance_election_infrastructure_all_2026,
  title        = {African Election Infrastructure | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-election-infrastructure-all},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-governance-election-infrastructure-all}}
}

License

Released under CC BY 4.0.

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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