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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 25 new columns ({'battery_chemistry_use', 'artisanal_production_percent', 'production_volume_tonnes', 'data_source', 'domestic_processing_rate_percent', 'processing_capacity_tonnes', 'extraction_cost_usd_per_tonne', 'project_status', 'infrastructure_quality_index', 'corruption_perception_index', 'substitution_risk_index', 'mineral', 'investment_flows_usd_millions', 'ev_demand_share_percent', 'country_code', 'market_price_usd_per_tonne', 'mine_type', 'quarter', 'political_stability_index', 'region', 'grade_percent', 'supply_concentration_hhi', 'recycling_rate_percent', 'chinese_investment_share_percent', 'geopolitical_risk_index'}) and 10 missing columns ({'value_addition_score', 'production_tonnes', 'price_usd_per_tonne', 'mineral_type', 'supply_risk_score', 'royalty_rate_pct', 'demand_growth_pct', 'domestic_processing_pct', 'artisanal_mining_pct', 'strategic_importance_index'}).

This happened while the csv dataset builder was generating data using

hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger/data/african_critical_minerals_all.csv (at revision 7e36421e8210a648117df7cd87118d5cb682d857), ['hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african-critical-minerals-reserves.csv', 'hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african_critical_minerals_all.csv', 'hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african_critical_minerals_baseline_demand.csv', 'hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african_critical_minerals_ev_acceleration.csv', 'hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african_critical_minerals_supply_chain_disruption.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              record_id: int64
              country: string
              country_code: string
              region: string
              mineral: string
              scenario: string
              year: int64
              quarter: int64
              mine_type: string
              project_status: string
              reserve_estimate_tonnes: int64
              production_volume_tonnes: int64
              grade_percent: double
              extraction_cost_usd_per_tonne: double
              market_price_usd_per_tonne: double
              processing_capacity_tonnes: int64
              domestic_processing_rate_percent: double
              export_volume_tonnes: int64
              investment_flows_usd_millions: double
              chinese_investment_share_percent: double
              esg_compliance_score: double
              artisanal_production_percent: double
              geopolitical_risk_index: double
              infrastructure_quality_index: double
              corruption_perception_index: double
              political_stability_index: double
              battery_chemistry_use: string
              ev_demand_share_percent: double
              recycling_rate_percent: double
              substitution_risk_index: double
              supply_concentration_hhi: double
              data_source: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 4700
              to
              {'record_id': Value('string'), 'country': Value('string'), 'mineral_type': Value('string'), 'year': Value('int64'), 'reserve_estimate_tonnes': Value('float64'), 'production_tonnes': Value('float64'), 'export_volume_tonnes': Value('float64'), 'domestic_processing_pct': Value('float64'), 'royalty_rate_pct': Value('float64'), 'price_usd_per_tonne': Value('float64'), 'demand_growth_pct': Value('float64'), 'esg_compliance_score': Value('float64'), 'artisanal_mining_pct': Value('float64'), 'value_addition_score': Value('float64'), 'strategic_importance_index': Value('float64'), 'supply_risk_score': Value('int64'), 'scenario': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 25 new columns ({'battery_chemistry_use', 'artisanal_production_percent', 'production_volume_tonnes', 'data_source', 'domestic_processing_rate_percent', 'processing_capacity_tonnes', 'extraction_cost_usd_per_tonne', 'project_status', 'infrastructure_quality_index', 'corruption_perception_index', 'substitution_risk_index', 'mineral', 'investment_flows_usd_millions', 'ev_demand_share_percent', 'country_code', 'market_price_usd_per_tonne', 'mine_type', 'quarter', 'political_stability_index', 'region', 'grade_percent', 'supply_concentration_hhi', 'recycling_rate_percent', 'chinese_investment_share_percent', 'geopolitical_risk_index'}) and 10 missing columns ({'value_addition_score', 'production_tonnes', 'price_usd_per_tonne', 'mineral_type', 'supply_risk_score', 'royalty_rate_pct', 'demand_growth_pct', 'domestic_processing_pct', 'artisanal_mining_pct', 'strategic_importance_index'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger/data/african_critical_minerals_all.csv (at revision 7e36421e8210a648117df7cd87118d5cb682d857), ['hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african-critical-minerals-reserves.csv', 'hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african_critical_minerals_all.csv', 'hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african_critical_minerals_baseline_demand.csv', 'hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african_critical_minerals_ev_acceleration.csv', 'hf://datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger@7e36421e8210a648117df7cd87118d5cb682d857/data/african_critical_minerals_supply_chain_disruption.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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record_id
string
country
string
mineral_type
string
year
int64
reserve_estimate_tonnes
float64
production_tonnes
float64
export_volume_tonnes
float64
domestic_processing_pct
float64
royalty_rate_pct
float64
price_usd_per_tonne
float64
demand_growth_pct
float64
esg_compliance_score
float64
artisanal_mining_pct
float64
value_addition_score
float64
strategic_importance_index
float64
supply_risk_score
int64
scenario
string
f123b387f8f7d3c0
Namibia
rare_earths
2,024
455,509.6
462.1
252.4
36.68
5.779
11,598.04
3.5
61.1
5.54
37.8
92.8
100
baseline
f6b3490f2cfd7536
Mali
lithium
2,022
101,883.5
486.8
277.4
13.1
4.492
23,583.69
6.14
40.6
30.54
14.3
90.2
100
baseline
289344a315f05c04
Guinea
chromium
2,029
2,940,456.7
222,938.3
130,760
13.71
6.479
540.08
6.94
34.5
29.18
13
70.3
100
baseline
190286fe6907ef5f
Namibia
rare_earths
2,030
411,426.2
632.4
468.2
33.21
5.189
63,420.45
3.14
64.8
3.89
39.5
98.6
100
baseline
dec65193349732d7
Ghana
manganese
2,029
12,720,485.8
3,277,583.9
2,605,506.8
29.37
7.542
2.77
4.99
56.4
23.98
28.8
74.5
100
baseline
273937973d33b3c6
Zimbabwe
lithium
2,020
536,110.3
4,305.7
2,462.8
25.9
5.913
50,428.99
3.56
36.2
16.35
21.1
97.7
100
baseline
0a88853e26e7e66c
Nigeria
tungsten
2,025
1,752.4
107.1
75.4
24
7.205
32,295.31
8.31
35.7
26.74
23
73.7
100
baseline
0736f29be27308d7
Nigeria
tungsten
2,020
2,039.6
99.5
54.8
21.4
8.17
23,504.34
4.72
40.6
28.95
26.2
78.4
100
baseline
2ba1a8a346fc0c27
Zimbabwe
nickel
2,028
858,383.4
23,456.8
18,131.3
27.32
7.84
27,108.03
4.62
30.8
18
24.5
81.6
100
baseline
873fd12bb69bb049
South Africa
chromium
2,029
171,815,912.8
17,595,157.3
13,794,538.7
52.02
7.798
257.75
5.5
51.6
6.09
56.6
71.7
100
baseline
f33416a9ec813d30
Nigeria
nickel
2,023
89,129.9
2,254.8
1,486.9
21.61
6.993
22,992.58
2.53
41.4
20.12
19.3
85.5
100
baseline
850dec77d4d79e6e
Zimbabwe
chromium
2,027
229,700,639.8
1,066,407.3
745,854.7
26.81
5.493
516.34
8.81
35.3
12.5
24.9
68.7
100
baseline
e69f1beae532cac5
Ghana
manganese
2,026
11,003,267.7
3,212,726.8
2,945,855.7
31.71
4.513
4.6
5.95
49.9
17.18
24.7
80.9
100
baseline
81972d5bca079239
DRC
cobalt
2,020
3,558,924.3
140,159.6
123,862.4
13.97
7.476
35,889.81
5.57
31
35.85
13.5
96.4
100
baseline
e2f13a6d56bafa40
Zambia
nickel
2,024
180,643.6
5,867
3,563.7
19.58
6.231
18,952.45
2.4
48.3
18.72
16.6
81.1
100
baseline
c5434dc45581138f
Tanzania
cobalt
2,021
159,764.3
2,900.3
1,656
23.47
6.59
84,192.01
2.24
42.3
21.63
25.9
89.4
100
baseline
d02b0540c4cac1c7
South Africa
tantalum
2,022
19,274.9
107.3
84.2
52.4
5.663
135,547.1
4.24
49.4
5.68
58
77
100
baseline
ead828bae972857d
Ethiopia
nickel
2,026
161,746.6
4,730.6
4,377.1
18.69
3.572
20,032.02
3.95
38.4
24.33
21.4
78.7
100
baseline
9fb9e25a5301654c
Madagascar
nickel
2,030
1,325,604.6
53,378.2
47,926.2
17.66
5.892
33,598.51
8.59
46.8
19.06
19.7
82.7
100
baseline
0b2bfdab82d937e3
Botswana
nickel
2,024
438,337.3
23,339
15,687.2
40.85
5.193
26,006.45
7.69
59.6
3.24
43.2
82
100
baseline
ad2aa583841b3e40
South Africa
nickel
2,025
361,446.2
42,234.3
39,764.4
55.4
7.4
26,487.7
7.23
51.2
4.84
60
82.5
100
baseline
833d5b65eeeab8da
Zambia
lithium
2,026
37,982.2
28.7
26.3
20.15
6.757
63,410.48
2.54
39.9
13.6
22.8
95.2
100
baseline
a4cf323f46dc6be2
Tanzania
rare_earths
2,025
2,701,222.6
1,010.1
562.9
21.65
7.838
54,254.18
3.86
38.3
17.87
21.4
95.2
100
baseline
69e191e9c940853e
Namibia
graphite
2,027
2,888,000
57,254.8
49,077.7
38.22
6.786
1,334.1
5.02
68.1
4.34
32.3
78.7
100
baseline
fcb4b313de00f71b
Zimbabwe
platinum
2,027
1,140.8
18.9
13.3
26.93
6.007
1,130.69
3.05
35.8
17.7
28.9
91.9
100
baseline
3adf1719d0b26d30
Mozambique
tantalum
2,028
2,740.4
118.7
108.3
18.49
3.654
147,014.64
4.2
39.3
23.15
23.1
79.1
100
baseline
c6cc7a70b8923e08
Botswana
nickel
2,027
451,149.4
25,096.2
22,032.9
43
6.878
18,063.47
5.78
61.8
3.59
37.5
80.3
100
baseline
27aaa61687f4876b
Ghana
manganese
2,021
12,650,870
2,852,998.8
2,024,676.1
27.37
7.085
4.27
2.36
56.2
19.77
35.3
75.5
100
baseline
648149a05e461ae6
Ghana
platinum
2,028
46,939.6
431.7
258.5
29.62
3.094
1,303.42
2.96
48.9
21.22
24.3
86.5
100
baseline
f325bb8d3a1893c1
Zambia
cobalt
2,030
239,530.6
1,376.1
800.6
19.04
4.616
60,931.19
4.17
41
17.51
19.8
98.8
100
baseline
e41a05acb525c755
DRC
chromium
2,023
43,582.6
583.9
541.5
14.1
2.994
253.84
4.93
37.1
34
13
73.2
100
baseline
0a765001b8c3cbb6
Ethiopia
manganese
2,024
7,468.8
561
522.4
17.63
6.296
4.1
2.82
44.2
21
15.3
84
100
baseline
8ae27997220804dc
Nigeria
tungsten
2,026
1,940.7
92.7
73.4
19.86
5.095
32,898.68
7.26
34
20.95
22.2
79.9
100
baseline
a717354aee5b1f35
Zambia
tantalum
2,027
29,803.7
406.6
248.2
21.74
4.373
162,605.63
5.71
41.9
13.69
19.9
78.1
100
baseline
e97e1f856908e3fa
Tanzania
cobalt
2,028
124,480.2
4,103.7
2,210
22.97
5.583
80,378.55
5.38
39.5
17.22
23.7
92.1
100
baseline
edebd5f6556ce4e6
Zambia
nickel
2,026
206,673.3
5,712.9
3,760.9
19.81
4.411
24,293.96
7.33
44.4
12.69
21.9
82
100
baseline
c0478660247c0c70
Zambia
tantalum
2,029
16,698.7
458.4
266.2
18.06
5.543
190,223.41
5.49
46.6
14.93
23
80.9
100
baseline
f8f211830ca78959
Mali
cobalt
2,022
53,561.9
937
831.9
11.07
3.324
62,382.98
3.18
43.1
23.78
10.8
95.9
100
baseline
cfd7ac5223c6ab05
Ghana
manganese
2,028
11,673,864.2
4,048,638.1
2,566,062.9
30.53
5.179
2.28
2.8
57.1
17.65
25
74.8
100
baseline
c625c1a8e85bf690
Guinea
nickel
2,026
2,605,945.2
69,721.6
43,999
14.19
6.667
21,916.54
8.19
36.3
27.01
17.6
84
100
baseline
583a6b5c769c5d95
Guinea
nickel
2,024
2,863,596.9
53,690.9
30,854.3
14.42
6.791
16,316.73
6.5
39.6
25.01
15
79.3
100
baseline
4445a5e6f422595c
DRC
chromium
2,029
17,304.7
170
129
16.35
8.136
292.8
6.48
36.6
36.24
13.1
73
100
baseline
c806e1b0d15e00b9
Namibia
lithium
2,027
180,344.2
2,128
1,705.7
37.5
4.739
42,553.76
6.98
59.2
4.54
32.5
98.9
100
baseline
60e2508e4118a28b
Ghana
cobalt
2,030
30,847.2
41.9
35.3
30.75
6.598
77,907.95
4.9
48.8
24.73
31.3
91
100
baseline
f6ca5ff1c7438565
Zimbabwe
lithium
2,028
451,535.1
5,161.3
4,534.9
23.07
7.85
32,656.17
3.98
31.2
14.87
24.7
91.4
100
baseline
597c84dc89651b4b
Zimbabwe
platinum
2,021
1,134.8
17.1
11.7
27.17
5.716
902.64
6.35
31.9
12.36
25.4
85.8
100
baseline
88644ca58ed8df52
Guinea
platinum
2,027
19,468.8
398.8
353
14.73
4.578
1,376.61
8.63
36.6
20.65
17.8
82.7
100
baseline
e3e6731a5a183a9a
Zambia
cobalt
2,027
230,997.5
1,327.2
672.7
18.58
7.7
37,213.44
3.72
40.7
11.85
18.6
97.7
100
baseline
0d5cc1c0d18a177a
Guinea
manganese
2,030
9,393,691.4
1,324,512.9
1,182,223.6
13.61
3.386
6.09
6.35
30.8
26.67
17.2
77.3
100
baseline
6fe0115d9fff23bf
Ethiopia
tantalum
2,020
1,929.7
65.8
43.5
17.75
4.184
119,553.01
3.15
38.6
21.83
18
81.1
100
baseline
b4f08728d6eb37a6
Mozambique
rare_earths
2,027
1,768,981.5
4,581.4
3,949.6
21.6
6.17
32,445.64
5.76
39.1
17.5
22.8
89.5
100
baseline
4e92fffc9226b71d
Ghana
lithium
2,023
24,183.9
347.9
177.4
31.98
4.734
76,984.61
8.81
59
23.33
34.8
96.1
100
baseline
e8a5d2282d23ebe8
Mozambique
rare_earths
2,023
1,993,007.7
5,556.3
4,472.8
20.88
6.509
41,381.01
5.54
39.3
20.8
21.9
87.8
100
baseline
e926660067d0da96
Tanzania
rare_earths
2,022
2,671,931.8
1,159.2
1,036.8
21.41
5.384
19,032.2
8.74
42.2
23.7
24.7
93.5
100
baseline
3c9c13b7793d1b8c
South Africa
chromium
2,030
188,071,801.6
25,746,701
24,080,152.7
58.35
4.969
265.55
4.97
61
5.21
62.1
77.1
100
baseline
53442934a0c766b2
South Africa
nickel
2,020
378,846.5
47,090.7
30,496.1
56.21
6.323
18,760.78
5.22
50.1
3.84
65.9
79.8
100
baseline
750eb88c88c04532
Mozambique
manganese
2,026
30,289.9
654.6
450.8
20.97
3.741
2.12
5.05
34.4
18.71
19.5
83.6
100
baseline
e36056c88e2886d7
Rwanda
nickel
2,025
47,922.4
655.1
433.3
27.43
3.8
26,238.3
6.23
53.5
19.02
31.2
81.5
100
baseline
d863a84ef899e6b6
Zimbabwe
graphite
2,021
39,260.1
109.3
92.8
26.44
5.847
2,789.97
5.71
28.3
17.75
21.3
73.3
100
baseline
c21513039579ac32
Nigeria
nickel
2,020
93,484.9
1,641.9
1,369
22.11
6.712
22,172.77
2.66
43.1
19.29
24.5
90
100
baseline
40384a6a4ea0cd8f
Guinea
chromium
2,020
2,736,495.2
198,318.6
135,000.9
15.56
5.675
196.7
4.91
32.6
26.66
15.3
70
100
baseline
d4949caba36732b5
Zambia
nickel
2,028
187,879.7
4,870.1
3,973
21.41
6.423
24,989.57
4.33
40
14.65
19.3
79.5
100
baseline
4fc60a165f7555d6
Ghana
chromium
2,025
518,091.3
59,110.1
42,170.3
27.55
3.403
391.04
2.41
62.5
20.62
32
71
100
baseline
3b5026ebdb347d47
Rwanda
tungsten
2,020
2,734.2
520.1
342.3
28.44
4.127
22,566.35
6.79
56.1
25.93
31.4
82.3
100
baseline
f946277881879254
Nigeria
tungsten
2,030
1,851.4
97.3
89.3
23.45
4.199
42,578.91
3.44
42.2
25.83
21.1
73.5
100
baseline
56be71faa2a449ee
Namibia
lithium
2,028
173,516.9
2,245.6
1,467.3
34.09
6.791
55,779.63
2.08
62.1
5.43
31.8
94.4
100
baseline
65c4af90321aaaf5
Mali
cobalt
2,024
45,269.5
1,085.3
711.2
11.07
6.451
58,142.58
3.81
32.9
25.57
14.1
99.1
100
baseline
12a212d6e4f6bf49
Nigeria
nickel
2,021
89,253.1
2,241.6
1,298
21.47
6.519
24,958.89
2.7
32.8
18.98
25.7
89.9
100
baseline
b601a902811d8ab0
Zambia
nickel
2,030
177,037
5,076.4
2,585.9
19.3
7.089
32,151.42
2.79
38.8
15.67
20.2
83.6
100
baseline
a83023d3c97b1e76
Tanzania
graphite
2,025
18,054,583.1
45,680.3
35,856.8
22.13
4.079
1,509.23
5.25
39.6
17.56
19.1
70.8
100
baseline
6f0c3f3bee4e142c
Nigeria
tantalum
2,030
898.9
66.1
55.7
21.78
7.67
165,841.97
7.46
34
19.6
21.5
77.5
100
baseline
6b6502ec81622190
Tanzania
cobalt
2,028
126,247.7
3,426.7
1,771.4
21.42
4.5
89,654.49
5.37
46.1
24.54
24.1
88
100
baseline
543680194c95f6e7
Botswana
graphite
2,025
42,151.7
93.8
81.1
48.04
7.583
1,871.12
7.17
65.5
3.16
42.6
80
100
baseline
dd19b9c27b6fa4fe
Ghana
chromium
2,030
458,809.7
66,941.4
52,969.9
27.4
3.534
348.82
6.66
61.2
18.33
31
67.9
100
baseline
b9fe39c5c6ca2564
Madagascar
graphite
2,030
1,408,883.9
12,456.2
11,485.9
17.31
5.026
2,858.33
7.47
44.3
25.33
19.1
74.1
100
baseline
416f8fd7d53ff546
Madagascar
cobalt
2,022
106,842.9
1,948.3
1,645.9
16.89
7.724
46,265.42
5.11
45
25.33
19.2
100
100
baseline
4e14f49fb93d98b3
Zambia
nickel
2,022
182,760.1
5,889.2
4,988.1
21.24
3.804
19,030
3.37
39.3
16.86
19.6
85.5
100
baseline
708c4337adec09c5
Tanzania
rare_earths
2,023
2,738,647.3
1,249.6
1,178.4
21.83
4.357
37,058.29
7.53
40.1
17.5
23.2
99.6
100
baseline
04d6cb83441eadaa
Guinea
tantalum
2,021
18,598.8
109.1
61.8
14.81
7.517
111,469
1.87
35
21.7
18
78.4
100
baseline
b7277a04d4fe2ac9
DRC
cobalt
2,030
3,046,232.9
144,214.5
74,593
16.25
5.54
86,221.16
6.39
29.9
25.73
12.9
100
100
baseline
52101636705bcee0
Zambia
cobalt
2,024
245,599.3
916.6
556.7
20.56
5.847
79,537.25
5.42
42.9
16.64
18.3
87.4
100
baseline
e47d8bd6c020ebe7
Madagascar
cobalt
2,022
101,221.7
1,735.2
1,628.7
16.44
5.634
85,030.78
4.3
43.7
31.01
16.2
87.6
100
baseline
342f76b07bea5c5f
Nigeria
tantalum
2,030
923.1
66.5
55.3
21.26
3.237
166,460.58
6.6
40.2
26.65
24.7
74.4
100
baseline
81719e4ad7338d22
Nigeria
cobalt
2,024
14,695.2
112
71.7
21.55
7.058
31,144.76
4.12
32.8
26.95
20
99.6
100
baseline
c8233df1728bcf28
Ghana
rare_earths
2,026
22,664.3
280
240.5
28.6
3.387
35,305.63
2.89
48.4
18.28
27.5
93.2
100
baseline
9c945232e5875714
Ethiopia
nickel
2,027
153,405.9
4,656.6
4,151.4
17.47
7.811
32,617.51
2.55
38.4
23.01
19.3
91.2
100
baseline
5107060617b1461d
Zimbabwe
rare_earths
2,020
9,541.2
385.5
357.5
22.66
3.748
24,560.09
2.19
30.7
13.61
22.8
100
100
baseline
73e79c63af772615
DRC
rare_earths
2,028
23,096.6
73.4
51.5
16.49
4.612
33,988.89
7.01
39.2
29.18
12.5
96.7
100
baseline
4b263bb8cdf91aa0
Rwanda
chromium
2,021
35,206.3
410.2
336.1
29.12
3.817
276.66
6.22
51.9
23.26
28.8
71.6
100
baseline
3b48de6152d07285
Guinea
nickel
2,021
2,666,270.8
73,430.1
60,469
15.08
7.067
14,608.54
5.77
36.9
23.06
17.3
88.8
100
baseline
58509ba4c4d5266d
Namibia
lithium
2,026
188,107.7
2,569.9
2,246.4
35.13
4.368
79,273.96
7.73
56.1
3.95
30.7
98.7
100
baseline
4502bda358a6fc09
Tanzania
cobalt
2,029
137,414
3,543.6
2,040.7
23.94
3.831
83,812.29
7.19
49.4
15.28
18
97.2
100
baseline
1d126abd68f096ff
Zambia
cobalt
2,029
241,601.1
1,245.6
902
20.51
4.651
69,474.12
5.74
36.6
11.7
19.5
94.8
100
baseline
5b47a834caf761d9
Mozambique
graphite
2,025
25,467,054.1
102,049.9
96,397.4
19.38
5.124
1,620.87
3.06
39.4
16.41
16.4
80.3
100
baseline
c1894a4546bfa590
Rwanda
tantalum
2,022
5,207.2
366.1
301.9
26.26
3.985
128,492.87
4.37
50.3
24.47
25.1
81.1
100
baseline
714b133c454491b5
DRC
lithium
2,029
48,979.3
125.3
89.2
15.11
7.186
90,219.6
6.32
34
31.05
13.2
92.8
100
baseline
74abb75acb600ede
South Africa
nickel
2,028
325,403.4
55,477.5
44,724.8
50.81
7.792
26,802.48
8.22
52.8
5.02
55
78.8
100
baseline
cc21ff5b7a6e2ab6
Zimbabwe
rare_earths
2,028
28,561.4
504.4
391.4
23.31
8.143
26,350.52
7.04
34
13.5
28.2
92
100
baseline
108ac137e9c5dca2
Namibia
graphite
2,029
2,624,992.1
68,410.9
61,052.9
35.9
5.844
2,517.37
5.02
51.9
4.95
35.3
77.7
100
baseline
33539932131aa318
Nigeria
manganese
2,030
15,793.8
520.5
363.6
21.64
4.915
3.99
5.33
40.6
27.57
22.2
78.8
100
baseline
End of preview.

African Critical Minerals Reserves | Africa (Electric Sheep Africa metadata inventory)

Size category: 100K<n<1M - Formats: not declared - Sector: energy - 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

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 Critical Minerals Reserves Comprehensive synthetic dataset modeling critical mineral reserves, production, trade flows, governance indicators, and supply chain dynamics across 20 African countries, 10 critical minerals, and 3 demand/supply scenarios spanning 2020-2030. Designed for EV/battery companies, mining investors, policy… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger.

Dataset Profile

Field Value
Hugging Face repo electricsheepafrica/africa-synth-critical-minerals-reserves-niger
Sector energy
Topic tags extractives, critical-minerals, ev-battery, lithium, cobalt, graphite, nickel, manganese, platinum-group-metals, rare-earths, vanadium, chromium
Modalities not declared
Formats not declared
Size category 100K<n<1M
Countries Niger
ISO3 coverage NER
Last modified on HF 2026-04-14 22:59:13+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-critical-minerals-reserves-niger")
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, modality, format.
  • 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_critical_minerals_reserves_niger_2026,
  title        = {African Critical Minerals Reserves | Africa (Electric Sheep Africa metadata inventory)},
  author       = {Public dataset metadata},
  year         = {2026},
  url          = {https://huggingface.co/datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger},
  publisher    = {Hugging Face Datasets, engineered by Electric Sheep Africa},
  howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-synth-critical-minerals-reserves-niger}}
}

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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