Datasets:

electricsheepafrica
/

africa-open-data-barometer-2016

prevalence_and_impact_of_open_data_initiatives_2013_2017 float64 2.01k 2.02k ⌀	unnamed_1 stringlengths 5 12 ⌀	unnamed_2 float64 22 104 ⌀	unnamed_3 float64 4.23 43.1 ⌀	unnamed_4 float64 12 57 ⌀	unnamed_5 float64 3 46 ⌀	unnamed_6 float64 0 58 ⌀	unnamed_7 float64 19 53 ⌀	unnamed_8 float64 12 52 ⌀	unnamed_9 float64 2 57 ⌀	unnamed_10 float64 11 74 ⌀	unnamed_11 float64 2 59 ⌀	unnamed_12 float64 34 108 ⌀	unnamed_13 float64 5 50 ⌀	unnamed_14 float64 7 48 ⌀	unnamed_15 float64 7 40 ⌀	unnamed_16 float64 8.21 45.7 ⌀	unnamed_17 float64 17 110 ⌀	unnamed_18 float64 0 63 ⌀	unnamed_19 float64 0 45 ⌀	unnamed_20 float64 0 31 ⌀	unnamed_21 float64 18 109 ⌀	esa_source stringclasses 1 value	esa_processed stringdate 2026-04-27 00:00:00 2026-04-27 00:00:00
2,013	Ethiopia	66	8.7	15	11	0	31	null	null	11	12	67	11.25	10	19	13.57	63	0	0	0	53	HDX	2026-04-27
2,015	Nigeria	67	14.13	29	13	3	null	26	26	36	38	65	10	35	11	18.67	65	10	0	0	61	HDX	2026-04-27
2,013	Zambia	75	4.23	12	5	0	24	null	null	16	6	71	5	7	13	8.57	73	0	0	0	53	HDX	2026-04-27
2,014	Nigeria	68	11.53	39	6	6	34	null	null	40	44	56	8	8	10	8.67	82	0	0	13	45	HDX	2026-04-27
2,013	Senegal	71	6.46	29	5	0	22	null	null	51	22	60	5	8	11	8.21	74	0	0	0	53	HDX	2026-04-27
2,014	Ethiopia	78	7.75	16	9	0	26	null	null	16	14	80	8	20	7	11.67	75	0	0	0	65	HDX	2026-04-27
2,016	Botswana	104	5.89	21	4	0	null	38	11	25	19	98	9	11	9	9.67	110	0	0	0	95	HDX	2026-04-27
2,014	Kenya	49	25.8	42	23	20	43	null	null	47	40	52	9	43	22	24.67	52	24	14	13	32	HDX	2026-04-27
2,016	Nigeria	70	20.97	31	7	41	null	25	27	40	37	81	13	11	11	11.67	102	19	45	31	25	HDX	2026-04-27
2,016	Kenya	35	40.42	57	22	58	null	52	57	54	59	37	15	37	21	24.33	67	63	45	31	18	HDX	2026-04-27
null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	HDX	2026-04-27
2,015	Senegal	70	10.33	22	12	0	null	23	11	43	26	73	18	22	11	17	71	0	0	0	84	HDX	2026-04-27
2,013	Botswana	55	16.08	12	22	0	24	null	null	13	9	70	8.75	25	34	23.57	49	0	0	0	53	HDX	2026-04-27
2,013	Tanzania	58	14.51	20	18	0	29	null	null	41	2	65	26.25	22	13	20	56	0	0	0	53	HDX	2026-04-27
null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	HDX	2026-04-27
2,015	Zambia	86	4.91	16	3	0	null	30	5	21	22	83	8	10	11	9.67	88	0	0	0	91	HDX	2026-04-27
2,014	Zambia	78	7.73	19	8	0	19	null	null	24	21	76	15	7	10	10.67	78	0	0	0	65	HDX	2026-04-27
null	Country	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	HDX	2026-04-27
2,016	Benin	103	7.32	13	13	0	null	12	2	37	15	108	19	20	11	16.67	84	0	0	0	93	HDX	2026-04-27
2,016	South Africa	46	34.43	51	28	29	null	24	50	74	57	46	30	24	36	30	59	19	30	20	32	HDX	2026-04-27
null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	HDX	2026-04-27
2,016	Senegal	98	8.74	24	9	0	null	31	15	35	21	92	18	11	11	13.33	96	0	0	0	109	HDX	2026-04-27
2,015	Botswana	78	6.51	18	5	0	null	27	19	20	22	79	10	13	11	11.33	82	0	0	0	69	HDX	2026-04-27
2,016	Ethiopia	81	16.14	47	9	0	null	47	47	49	46	51	9	22	9	13.33	95	0	0	0	98	HDX	2026-04-27
2,013	South Africa	52	19.2	35	18	10	27	null	null	46	40	54	30	9	25	20.71	53	16	12	0	40	HDX	2026-04-27
2,015	Benin	76	8.47	14	13	0	null	16	3	40	13	84	18	17	20	18.33	67	0	0	0	68	HDX	2026-04-27
2,014	Senegal	74	10.56	34	8	0	19	null	null	48	40	62	7	16	8	10.33	79	0	0	0	65	HDX	2026-04-27
2,014	Tanzania	68	11.69	17	15	3	21	null	null	21	15	79	9	27	14	16.67	66	0	0	6	51	HDX	2026-04-27
2,013	Kenya	22	43.06	50	46	22	53	null	null	47	51	34	50	48	40	45.71	17	25	12	23	27	HDX	2026-04-27
2,014	Botswana	78	8.39	26	7	0	29	null	null	27	27	69	8	11	10	9.67	80	0	0	0	65	HDX	2026-04-27
2,015	South Africa	47	26.77	41	20	24	null	17	35	66	52	50	38	13	23	24.67	54	0	43	26	31	HDX	2026-04-27
2,016	Tanzania	67	21.73	40	17	14	null	44	48	37	34	62	13	40	9	20.67	75	28	0	7	54	HDX	2026-04-27

Open Data Barometer 2016

Publisher: World Wide Web Foundation · Source: OpenAfrica · License: cc-by · Updated: 2023-04-13

Abstract

A global measure of how governments are publishing and using open data for accountability, innovation and social impact.

Each row in this dataset represents tabular records. Data was last updated on OpenAfrica on 2023-04-13. Geographic scope: BENIN, BOTSWANA, CAPE-VERDE, ETHIOPIA, KENYA, SENEGAL, SOUTH-AFRICA, TANZANIA, and 2 others.

Curated into ML-ready Parquet format by Electric Sheep Africa.

Dataset Characteristics


Domain	Humanitarian and development data
Unit of observation	Tabular records
Rows (total)	41
Columns	24 (21 numeric, 3 categorical, 0 datetime)
Train split	32 rows
Test split	8 rows
Geographic scope	BENIN, BOTSWANA, CAPE-VERDE, ETHIOPIA, KENYA, SENEGAL, SOUTH-AFRICA, TANZANIA, and 2 others
Publisher	World Wide Web Foundation
OpenAfrica last updated	2023-04-13

Variables

Identifier / Metadata — unnamed_1 (South Africa, Benin, Botswana), unnamed_2 (range 22.0–108.0), unnamed_3 (range 3.82–43.06), unnamed_4 (range 12.0–57.0), unnamed_5 (range 0.0–46.0) and 18 others.

Other — prevalence_and_impact_of_open_data_initiatives_2013_2017 (range 2013.0–2017.0).

Quick Start

from datasets import load_dataset

ds    = load_dataset("electricsheepafrica/africa-open-data-barometer-2016")
train = ds["train"].to_pandas()
test  = ds["test"].to_pandas()

print(train.shape)
train.head()

Schema

Column	Type	Null %	Range / Sample Values
`prevalence_and_impact_of_open_data_initiatives_2013_2017`	float64	9.8%	2013.0 – 2017.0 (mean 2014.5676)
`unnamed_1`	object	7.3%	South Africa, Benin, Botswana
`unnamed_2`	float64	9.8%	22.0 – 108.0 (mean 67.2973)
`unnamed_3`	float64	9.8%	3.82 – 43.06 (mean 15.2392)
`unnamed_4`	float64	9.8%	12.0 – 57.0 (mean 28.4324)
`unnamed_5`	float64	9.8%	0.0 – 46.0 (mean 14.0)
`unnamed_6`	float64	9.8%	0.0 – 58.0 (mean 7.7838)
`unnamed_7`	float64	56.1%	6.0 – 53.0 (mean 27.1667)
`unnamed_8`	float64	53.7%	12.0 – 52.0 (mean 28.8421)
`unnamed_9`	float64	53.7%	2.0 – 57.0 (mean 27.5263)
`unnamed_10`	float64	9.8%	11.0 – 74.0 (mean 37.7027)
`unnamed_11`	float64	9.8%	0.0 – 59.0 (mean 28.2703)
`unnamed_12`	float64	12.2%	34.0 – 108.0 (mean 68.0556)
`unnamed_13`	float64	9.8%	2.5 – 50.0 (mean 15.3986)
`unnamed_14`	float64	9.8%	1.0 – 48.0 (mean 19.7568)
`unnamed_15`	float64	9.8%	7.0 – 44.0 (mean 17.0811)
`unnamed_16`	float64	12.2%	3.93 – 45.71 (mean 16.8922)
`unnamed_17`	float64	12.2%	17.0 – 110.0 (mean 71.3056)
`unnamed_18`	float64	9.8%	0.0 – 63.0 (mean 7.4324)
`unnamed_19`	float64	9.8%	0.0 – 45.0 (mean 6.4865)
`unnamed_20`	float64	9.8%	0.0 – 31.0 (mean 6.1081)
`unnamed_21`	float64	12.2%
`esa_source`	object	0.0%	HDX
`esa_processed`	object	0.0%	2026-04-27

Numeric Summary

Column	Min	Max	Mean	Median
`prevalence_and_impact_of_open_data_initiatives_2013_2017`	2013.0	2017.0	2014.5676	2015.0
`unnamed_2`	22.0	108.0	67.2973	69.0
`unnamed_3`	3.82	43.06	15.2392	10.77
`unnamed_4`	12.0	57.0	28.4324	24.0
`unnamed_5`	0.0	46.0	14.0	12.0
`unnamed_6`	0.0	58.0	7.7838	0.0
`unnamed_7`	6.0	53.0	27.1667	26.5
`unnamed_8`	12.0	52.0	28.8421	25.0
`unnamed_9`	2.0	57.0	27.5263	27.0
`unnamed_10`	11.0	74.0	37.7027	37.0
`unnamed_11`	0.0	59.0	28.2703	22.0
`unnamed_12`	34.0	108.0	68.0556	69.5
`unnamed_13`	2.5	50.0	15.3986	10.0
`unnamed_14`	1.0	48.0	19.7568	17.0
`unnamed_15`	7.0	44.0	17.0811	11.0

Curation

Raw data was downloaded from OpenAfrica via the CKAN API and converted to Parquet. Column names were lowercased and standardised to snake_case. Common missing-value markers (N/A, null, none, -, unknown, no data, #N/A) were unified to NaN. 21 column(s) were cast from string to numeric or datetime based on parse-success rate (>85% threshold). The dataset was split 80/20 into train and test partitions using a fixed random seed (42) and saved as Snappy-compressed Parquet.

Limitations

Data originates from World Wide Web Foundation and has not been independently validated by ESA.
Automated cleaning cannot correct for misreported values, definitional inconsistencies, or sampling bias in the original collection.
The following columns have >20% missing values and should be treated with caution in modelling: unnamed_7, unnamed_8, unnamed_9.
This dataset spans 10 countries; geographic and methodological inconsistencies across national boundaries may affect cross-country comparability.
Refer to the original HDX dataset page for the publisher's own methodology notes and caveats.

Citation

@dataset{openafrica_africa_open_data_barometer_2016,
  title     = {Open Data Barometer 2016},
  author    = {World Wide Web Foundation},
  year      = {2023},
  url       = {https://open.africa/dataset/open-data-barometer-2016},
  note      = {Repackaged for machine learning by Electric Sheep Africa (https://huggingface.co/electricsheepafrica)}
}

Electric Sheep Africa — Africa's ML dataset infrastructure. Lagos, Nigeria.

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