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PengfeiLi
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WaterEfficientDatasetForAfricanDataCenters

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Water

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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 11 new columns ({'W_on (L)', 'γ_off', 'Leak frac', 'E (Wh)', 'W_total (L)', 'W_leak (L)', 'γ_on', 'Scenario', 'E (kWh)', 'PUE', 'W_off (L)'}) and 17 missing columns ({'share\nsolar', 'Leakage\nfraction', 'Onsite WUE\n(L/kWh)', 'share\nwind', 'WB (°F)', 'WB (°C)', 'share\noil', 'PUE\n[3]/Table2', 'Climate\nRegion', 'share\ncoal', 'share\nhydro', 'share\nbioenergy', 'Total Fuel\nTWh', 'Offsite WUE\n(L/kWh)', 'share\nnuclear', 'share\nother_renewables', 'share\ngas'}).

This happened while the csv dataset builder was generating data using

hf://datasets/PengfeiLi/WaterEfficientDatasetForAfricanDataCenters/LLM_Water_Footprints.csv (at revision 365c1b55124cfb1823349235690f40b727940a32), ['hf://datasets/PengfeiLi/WaterEfficientDatasetForAfricanDataCenters@365c1b55124cfb1823349235690f40b727940a32/Country_Summary.csv', 'hf://datasets/PengfeiLi/WaterEfficientDatasetForAfricanDataCenters@365c1b55124cfb1823349235690f40b727940a32/LLM_Water_Footprints.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/python3.12/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Country: string
              Scenario: string
              E (Wh): double
              E (kWh): double
              γ_on: double
              W_on (L): double
              Leak frac: double
              W_leak (L): double
              γ_off: double
              PUE: double
              W_off (L): double
              W_total (L): double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1665
              to
              {'Country': Value('string'), 'WB (°C)': Value('float64'), 'WB (°F)': Value('float64'), 'Onsite WUE\n(L/kWh)': Value('float64'), 'share\nother_renewables': Value('float64'), 'share\nbioenergy': Value('float64'), 'share\nsolar': Value('float64'), 'share\nwind': Value('float64'), 'share\nhydro': Value('float64'), 'share\nnuclear': Value('float64'), 'share\noil': Value('float64'), 'share\ngas': Value('float64'), 'share\ncoal': Value('float64'), 'Offsite WUE\n(L/kWh)': Value('float64'), 'Leakage\nfraction': Value('float64'), 'PUE\n[3]/Table2': Value('float64'), 'Total Fuel\nTWh': Value('string'), 'Climate\nRegion': 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 1361, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 940, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              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 11 new columns ({'W_on (L)', 'γ_off', 'Leak frac', 'E (Wh)', 'W_total (L)', 'W_leak (L)', 'γ_on', 'Scenario', 'E (kWh)', 'PUE', 'W_off (L)'}) and 17 missing columns ({'share\nsolar', 'Leakage\nfraction', 'Onsite WUE\n(L/kWh)', 'share\nwind', 'WB (°F)', 'WB (°C)', 'share\noil', 'PUE\n[3]/Table2', 'Climate\nRegion', 'share\ncoal', 'share\nhydro', 'share\nbioenergy', 'Total Fuel\nTWh', 'Offsite WUE\n(L/kWh)', 'share\nnuclear', 'share\nother_renewables', 'share\ngas'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/PengfeiLi/WaterEfficientDatasetForAfricanDataCenters/LLM_Water_Footprints.csv (at revision 365c1b55124cfb1823349235690f40b727940a32), ['hf://datasets/PengfeiLi/WaterEfficientDatasetForAfricanDataCenters@365c1b55124cfb1823349235690f40b727940a32/Country_Summary.csv', 'hf://datasets/PengfeiLi/WaterEfficientDatasetForAfricanDataCenters@365c1b55124cfb1823349235690f40b727940a32/LLM_Water_Footprints.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)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

Country string	WB (°C) float64	WB (°F) float64	Onsite WUE (L/kWh) float64	share other_renewables float64	share bioenergy float64	share solar float64	share wind float64	share hydro float64	share nuclear float64	share oil float64	share gas float64	share coal float64	Offsite WUE (L/kWh) float64	Leakage fraction float64	PUE [3]/Table2 float64	Total Fuel TWh string	Climate Region string
Algeria	14.132752	57.438954	1.211959	0	0	0.0009	0.000014	0.000014	0	0.309446	0.68666	0.002967	1.407563	0.397	2.3	6,423,001.38	Mediterranean
Benin	22.02328	71.641904	1.441566	0	0	0.021978	0	0	0	0	0.978022	0	0.778033	0.397	1.7	18,127.08	Savanna
Botswana	14.971115	58.948007	1.226563	0	0	0.003953	0	0	0	0	0	0.996047	2.000154	0.55	1.8	50,397.26	Steppe
Burkina Faso	18.157104	64.682787	1.303294	0	0	0.5	0	0.5	0	0	0	0	2.6715	0.25	1.6	4,780.77	Savanna
Burundi	15.991449	60.784608	1.247477	0	0	0.043478	0	0.956522	0	0	0	0	5.089696	0.42	1.6	4,581.57	Rainforest
Cameroon	18.932089	66.07776	1.327043	0	0	0.002703	0	0.675676	0	0	0.321622	0	3.850346	0.525	1.5	126,348.86	Rainforest
Cape Verde	22.135359	71.843646	1.446313	0	0	0.142857	0.857143	0	0	0	0	0	0.004143	0.397	2	1,394.39	Desert
Central African Republic	20.819828	69.47569	1.393218	0	0	0	0	1	0	0	0	0	5.32	0.397	2	2,561.13	Rainforest
Chad	17.547251	63.585052	1.286004	0	0	0	1	0	0	0	0	0	0.001	0.397	1.4	108.65	Desert
Egypt	17.120199	62.816358	1.27463	0	0	0.005049	0.005161	0.012449	0	0.398314	0.567558	0.011469	1.641926	0.397	2.3	12,759,523.01	Desert
Equatorial Guinea	22.140176	71.852317	1.446518	0	0	0	0	0.328767	0	0	0.671233	0	2.282671	0.397	1.9	24,928.29	Rainforest
Eritrea	16.584787	61.852617	1.261224	0	0	1	0	0	0	0	0	0	0.023	0.397	1.7	99.60	Desert
Ethiopia	13.036109	55.464996	1.196372	0	0	0.002601	0.038362	0.959038	0	0	0	0	5.102179	0.192	1.5	183,820.52	Steppe
Gabon	21.909189	71.43654	1.436777	0	0	0	0	0.464789	0	0	0.535211	0	2.898169	0.397	1.9	25,457.59	Rainforest
Ghana	22.263381	72.074086	1.451786	0	0	0.005947	0	0.343092	0	0	0.650961	0	2.342902	0.397	1.6	373,241.36	Savanna
Guinea	19.957735	67.923923	1.361533	0	0	0.009901	0	0.990099	0	0	0	0	5.267554	0.397	1.8	40,238.13	Rainforest
Kenya	16.159275	61.086695	1.251247	0	0	0.068345	0.384892	0.546763	0	0	0	0	2.910734	0.258	1.6	110,754.45	Savanna
Lesotho	10.81851	51.473318	1.177025	0	0	0	0	1	0	0	0	0	5.32	0.252	1.4	4,979.97	Steppe
Liberia	21.97719	71.558942	1.439626	0	0	0	0	1	0	0	0	0	5.32	0.397	2	9,049.31	Rainforest
Libya	16.317391	61.371304	1.254884	0	0	0.000465	0	0	0	0	0.999535	0	0.794641	0.397	2.3	188,244.57	Desert
Madagascar	17.087983	62.758369	1.273797	0	0	0.042105	0	0.821053	0	0	0	0.136842	4.643747	0.276	1.8	18,923.87	Savanna
Malawi	16.241054	61.233897	1.253118	0	0	0.139344	0	0.860656	0	0	0	0	4.581893	0.24	1.2	24,302.24	Savanna
Mali	16.743641	62.138554	1.265103	0	0	0.020979	0	0.979021	0	0	0	0	5.208874	0.264	1.5	15,537.49	Desert
Mauritania	17.92804	64.270472	1.296656	0	0	0.27451	0.313725	0.411765	0	0	0	0	2.197216	0.397	1.9	3,585.58	Desert
Morocco	14.67462	58.414316	1.221132	0	0	0.005666	0.020946	0.002657	0	0.624539	0.008703	0.337489	2.425735	0.397	2.3	2,241,554.29	Mediterranean
Mozambique	20.917685	69.651833	1.39697	0	0	0.003737	0	0.827015	0	0	0.169247	0	4.53436	0.37	1.8	373,099.08	Savanna
Namibia	13.342383	56.016289	1.200324	0	0	0.37037	0.014815	0.577778	0	0	0	0.037037	3.156681	0.397	2.1	16,135.09	Desert
Niger	16.381633	61.486939	1.256386	0	0	0.15625	0	0	0	0	0.1875	0.65625	1.470406	0.17	1.6	3,476.92	Desert
Nigeria	20.611596	69.100873	1.385339	0	0	0.001353	0	0.214827	0	0	0.78382	0	1.766047	0.469	1.5	631,061.33	Savanna
Senegal	20.83037	69.494666	1.393621	0	0	0.351464	0.309623	0.129707	0	0	0.012552	0.196653	1.103293	0.252	1.9	47,608.48	Savanna
Seychelles	23.252078	73.85374	1.495883	0	0	0.888889	0.111111	0	0	0	0	0	0.020556	0.397	2	1,792.79	Rainforest
Sierra Leone	22.217828	71.99209	1.449833	0	0	0.052632	0	0.947368	0	0	0	0	5.041211	0.397	1.8	3,244.09	Rainforest
Somalia	21.29007	70.322126	1.411539	0	0	0.75	0.25	0	0	0	0	0	0.0175	0.397	1.7	398.40	Desert
South Africa	13.770934	56.787681	1.206376	0	0	0.004749	0.00743	0.002374	0.007736	0.226125	0.032707	0.718878	2.119215	0.222	1.4	11,436,237.08	Mediterranean
South Sudan	20.625514	69.125925	1.385861	0	0	1	0	0	0	0	0	0	0.023	0.397	1.5	398.40	Savanna
Sudan	19.652329	67.374192	1.350899	0	0	0.003623	0	0.996377	0	0	0	0	5.300808	0.397	1.5	263,898.37	Desert
Tanzania	17.414656	63.346381	1.282408	0	0	0.005599	0	0.315789	0	0	0.678611	0	2.219625	0.216	1.4	177,884.40	Savanna
Togo	21.030924	69.855663	1.401352	0	0	0.074074	0	0.197531	0	0	0.728395	0	1.631642	0.397	1.6	16,135.09	Savanna
Tunisia	16.035357	60.863643	1.248454	0	0	0.015116	0.015116	0.000472	0	0	0.969296	0	0.773466	0.397	2.3	185,441.49	Mediterranean
Uganda	17.992152	64.385874	1.298496	0	0	0.026316	0	0.973684	0	0	0	0	5.180605	0.18	1.9	98,404.13	Rainforest
Zambia	15.572025	60.029645	1.238462	0	0	0.00722	0	0.881382	0	0	0	0.111398	4.912806	0.47	2.1	386,246.19	Savanna
Zimbabwe	14.340357	57.812643	1.215359	0	0	0.003398	0	0.665912	0	0	0	0.330691	4.206755	0.252	2.1	175,892.41	Steppe
Republic of the Congo	20.676127	69.217029	1.387765	0	0.002717	0.000906	0	0.996377	0	0	0	0	5.306439	0.4	2	3770491.8	Rainforest
Rwanda	17.051	62.6918	1.272844	0	0.006	0.365	0	0.466	0	0.135	0.028	0	2.89562	0.46	1.7	82000	Rainforest
United States	null	null	0.55	0	0	0	0	0	0	0	0	0	3.141	0.1	1.17	null	—
Global	null	null	1.07	0	0	0	0	0	0	0	0	0	4.807	0.39	1.42	null	—
Algeria	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Algeria	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Algeria	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Algeria	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Benin	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Benin	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Benin	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Benin	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Botswana	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Botswana	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Botswana	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Botswana	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Burkina Faso	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Burkina Faso	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Burkina Faso	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Burkina Faso	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Burundi	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Burundi	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Burundi	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Burundi	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Cameroon	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Cameroon	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Cameroon	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Cameroon	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Cape Verde	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Cape Verde	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Cape Verde	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Cape Verde	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Central African Republic	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Central African Republic	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Central African Republic	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Central African Republic	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Chad	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Chad	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Chad	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Chad	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Egypt	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Egypt	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Egypt	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Egypt	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Equatorial Guinea	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Equatorial Guinea	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Equatorial Guinea	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Equatorial Guinea	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Eritrea	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Eritrea	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Eritrea	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Eritrea	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Ethiopia	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Ethiopia	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Ethiopia	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Ethiopia	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Gabon	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null
Gabon	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null	null

End of preview.

Dataset Card for Water Efficiency Dataset Creation: Africa-wide Study

Dataset Summary

This dataset focuses on improving water management practices for data centers across Africa. It includes nation-level time-series data on energy consumption and weather conditions from various African countries. The goal is to estimate water usage efficiency (WUE) in different climate regions and inform policymakers about sustainable water practices for data centers and AI computing.

The dataset allows detailed analysis of how local climatic conditions affect data center operations, particularly their cooling systems, and how different energy sources contribute to the water footprint of data centers. This helps optimize water usage in a region known for its water scarcity.

Dataset Creation

Authors: Noah Shumba, Opelo Tshekiso, Pengfei Li, Giulia Fanti, Shaolei Ren
Institution: Upanzi Network-Carnegie Mellon University Africa/University of California Riverside
Source: WeatherAPI, African Energy Data Sources (OurWorldInData,ENERGYDATA.INFO )

The dataset was developed as part of ongoing research into the environmental impact of AI computing in resource-constrained regions like Africa. It is curated from both public datasets and proprietary weather data from the WeatherAPI.

Dataset Details

Languages: English
Domains: Water management, energy consumption, climate data, AI sustainability
Data Types: Average water usage effectiveness across different countries
Tasks: Analysis of water usage efficiency in data centers across multiple African countries, with potential applications in AI model impact analysis

The data covers a wide range of African climates, including rainforests, deserts, and savannas, to help assess the impact of geographical factors on data center water consumption.

BibTeX entry and citation info

@inproceedings{shumba2025water,
  author       = {Shumba, Noah and Tshekiso, Opelo and Li, Pengfei and Fanti, Giulia and Ren, Shaolei},
  title        = {A Water Efficiency Dataset for African Data Centers},
  booktitle    = {Proceedings of the ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS25)},
  year         = {2025},
  month        = jul,
  day          = {22--25},
  location     = {Toronto, ON, Canada},
  publisher    = {ACM},
  address      = {New York, NY, USA},
  pages        = {1--8},
  doi          = {10.1145/3715335.3735483},
}

References

OurWorldInData: Energy Mix in Africa
ENERGYDATA.INFO
WeatherAPI
Sanchez, R.G., et al. "Freshwater use of the energy sector in Africa," Applied Energy, 2020.

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