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CataTQA_Metadata

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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 9 new columns ({'light source', 'Bi type in photocatalyst', 'Reaction medium', 'SBET (m2 /g)', 'loading (g/l)', 'CB edge(V)', 'irradiation time(h)', 'Main product', 'Yield (μmol/g/h)'}) and 10 missing columns ({'Preparation method', 'Reaction solution', 'Light  intensity(W)', 'Ref', 'Calcination temperature(K)', 'Molecular formula', 'Photocatalyst dose(g L-1)', 'RH2(µmol h-1 g-1)', 'Calcination time(h)', 'Co-catalyst'}).

This happened while the csv dataset builder was generating data using

hf://datasets/kg4sci/CataTQA_Metadata/metadata/table_data/table10.csv (at revision 6f49b2b7b80c551a1c56830b9e567a7b32f38b46)

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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Bi type in photocatalyst: string
              loading (g/l): double
              SBET (m2 /g): double
              Eg(eV): double
              CB edge(V): string
              light source: string
              irradiation time(h): double
              Reaction medium: string
              Main product: string
              Yield (μmol/g/h): double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1554
              to
              {'Molecular formula': Value(dtype='string', id=None), 'RH2(µmol h-1 g-1)': Value(dtype='float64', id=None), 'Eg(eV)': Value(dtype='float64', id=None), 'Preparation method': Value(dtype='string', id=None), 'Calcination temperature(K)': Value(dtype='float64', id=None), 'Calcination time(h)': Value(dtype='float64', id=None), 'Light  intensity(W)': Value(dtype='string', id=None), 'Reaction solution': Value(dtype='string', id=None), 'Co-catalyst': Value(dtype='string', id=None), 'Photocatalyst dose(g L-1)': Value(dtype='float64', id=None), 'Ref': Value(dtype='string', id=None)}
              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 1428, 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 989, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, 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 9 new columns ({'light source', 'Bi type in photocatalyst', 'Reaction medium', 'SBET (m2 /g)', 'loading (g/l)', 'CB edge(V)', 'irradiation time(h)', 'Main product', 'Yield (μmol/g/h)'}) and 10 missing columns ({'Preparation method', 'Reaction solution', 'Light  intensity(W)', 'Ref', 'Calcination temperature(K)', 'Molecular formula', 'Photocatalyst dose(g L-1)', 'RH2(µmol h-1 g-1)', 'Calcination time(h)', 'Co-catalyst'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/kg4sci/CataTQA_Metadata/metadata/table_data/table10.csv (at revision 6f49b2b7b80c551a1c56830b9e567a7b32f38b46)
              
              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.

Molecular formula string	RH2(µmol h-1 g-1) float64	Eg(eV) float64	Preparation method string	Calcination temperature(K) float64	Calcination time(h) float64	Light intensity(W) string	Reaction solution string	Co-catalyst string	Photocatalyst dose(g L-1) float64	Ref string
null	null	null	null	null	null	null	null	null	null	null
BaTiO3	35	3	Solid state reaction	1,423	5	Xe lamp(300W),simulated sunlight	20% v/v CH3OH	Pt(0.4 wt％)	1.1	[1]
BaTi0.99Mo0.01O3	45	2.4	Solid state reaction	1,423	5	Xe lamp(300W),simulated sunlight	20% v/v CH3OH	Pt(0.4 wt％)	1.1	[1]
BaTi0.98Mo0.02O3	63	2.2	Solid state reaction	1,423	5	Xe lamp(300W),simulated sunlight	20% v/v CH3OH	Pt(0.4 wt％)	1.1	[1]
BaTi0.97Mo0.03O3	52	2.6	Solid state reaction	1,423	5	Xe lamp(300W),simulated sunlight	20% v/v CH3OH	Pt(0.4 wt％)	1.1	[1]
LaFeO3	144.7	null	Flux-growth	1,273	10	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.5 wt％)	1	[2]
LaFe0.85Ti0.15O3	309.3	2.1	Flux-growth	1,273	10	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.5 wt％)	1	[2]
La0.85Sr0.15FeO3	30.7	null	Flux-growth	1,273	10	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.5 wt％)	1	[2]
La0.9625Sr0.0375Fe0.8875Ti0.1125O3	62	null	Flux-growth	1,273	10	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.5 wt％)	1	[2]
La0.8875Sr0.1125Fe0.9625Ti0.0375O3	422.7	null	Flux-growth	1,273	10	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.5 wt％)	1	[2]
La0.925Sr0.075Fe0.925Ti0.075O3	554.7	2.1	Flux-growth	1,273	10	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.5 wt％)	1	[2]
Bi0.5Na0.5TiO3	325.4	2.92	Hydrothermal	433	24	Xe lamp(500W),346 nm±8.6 nm	20vol％ CH3OH	Pt(3 wt％)	0.75	[3]
Sr0.9Bi0.1Ti0.9Fe0.1O3	47	null	Hydrothermal	473	48	Hg lamp (500 W),λ ≥ 400 nm	0.05M Na2SO3	Pt(1 wt％)	1	[4]
Sr0.8Bi0.2Ti0.8Fe0.2O3	45	null	Hydrothermal	473	48	Hg lamp (500 W),λ ≥ 400 nm	0.05M Na2SO3	Pt(1 wt％)	1	[4]
Sr0.7Bi0.3Ti0.7Fe0.3O3	25	null	Hydrothermal	473	48	Hg lamp (500 W),λ ≥ 400 nm	0.05M Na2SO3	Pt(1 wt％)	1	[4]
Sr0.6Bi0.4Ti0.6Fe0.4O3	50	null	Hydrothermal	473	48	Hg lamp (500 W),λ ≥ 400 nm	0.05M Na2SO3	Pt(1 wt％)	1	[4]
Sr0.5Bi0.5Ti0.5Fe0.5O3	20	null	Hydrothermal	473	48	Hg lamp (500 W),λ ≥ 400 nm	0.05M Na2SO3	Pt(1 wt％)	1	[4]
SrTiO3	202.6	3.25	Hydrothermal	423	10	Xe lamp(300W),320 nm <λ<780 nm	20% v/v CH3OH	Pt(1 wt％)	0.2	[5]
SrTiO3	106.8	3.25	Hydrothermal	453	10	Xe lamp(300W),320 nm <λ<780 nm	20% v/v CH3OH	Pt(1 wt％)	0.2	[5]
SrTiO3	38.4	3.25	Solid state reaction	1,173	12	Xe lamp(300W),320 nm <λ<780 nm	20% v/v CH3OH	Pt(1 wt％)	0.2	[5]
SrTiO3	null	3.2	Polymerized complex	773	null	null	null	null	null	[6]
SrTi0.99Rh0.01O3	962	null	Polymerized complex	973	null	Xe lamp (300 W), 420 nm<λ<800 nm	20vol％ CH3OH	Pt(0.5 wt％)	1	[6]
AgTaO3	null	3.4	Hydrothermal	1,123	24	null	null	null	null	[7]
AgTa0.8Nb0.2O3	null	3.1	Hydrothermal	1,123	24	null	null	null	null	[7]
AgTa0.7Nb0.3O3	null	2.9	Hydrothermal	1,123	24	null	null	null	null	[7]
AgTa0.6Nb0.4O3	null	2.9	Hydrothermal	1,123	24	null	null	null	null	[7]
AgNbO3	null	2.8	Hydrothermal	1,123	24	null	null	null	null	[7]
CaTiO3	null	3.6	Sol-gel	1,123	10	null	null	null	null	[8]
Ca0.98Ag0.01La0.01TiO3	2.6	null	Sol-gel	1,123	10	Xe lamp (350 W), λ > 400 nm	5% v/v CH3OH	none	0.24	[8]
Ca0.96Ag0.02La0.02TiO3	2.9	null	Sol-gel	1,123	10	Xe lamp (350 W), λ > 400 nm	5% v/v CH3OH	none	0.24	[8]
Ca0.94Ag0.03La0.03TiO3	10.1	null	Sol-gel	1,123	10	Xe lamp (350 W), λ > 400 nm	5% v/v CH3OH	none	0.24	[8]
Ca0.92Ag0.04La0.04TiO3	4.1	null	Sol-gel	1,123	10	Xe lamp (350 W), λ > 400 nm	5% v/v CH3OH	none	0.24	[8]
BaTiO3	8	3	Polymerized complex	823	5	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.25 wt％)	1	[9]
BaTi0.999Rh0.001O3	10	null	Polymerized complex	823	5	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.25 wt％)	1	[9]
BaTi0.995Rh0.005O3	64	null	Polymerized complex	823	5	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.25 wt％)	1	[9]
BaTi0.99Rh0.01O3	308	null	Polymerized complex	823	5	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.25 wt％)	1	[9]
BaTi0.98Rh0.02O3	241	null	Polymerized complex	823	5	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.25 wt％)	1	[9]
BaTi0.95Rh0.05O3	199	null	Polymerized complex	823	5	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.25 wt％)	1	[9]
BaTi0.99Rh0.01O3	48	null	Solid state reaction	1,273	10	Xe lamp (300 W), λ > 420 nm	10vol％ CH3OH	Pt(0.25 wt％)	1	[9]
SrSnO3	null	4.01	Solid state reaction	1,373	6	null	null	null	null	[10]
SrSnO3	null	4.04	Wet chemical reaction	973	6	null	null	null	null	[10]
CaTi0.99Cu0.01O3	6.2	null	Sol-gel	1,123	7	Xe lamp (350 W), λ > 400 nm	5% v/v CH3OH	NiOx	0.24	[11]
CaTi0.98Cu0.02O3	22.7	null	Sol-gel	1,123	7	Xe lamp (350 W), λ > 400 nm	5% v/v CH3OH	NiOx	0.24	[11]
CaTi0.97Cu0.03O3	12.3	null	Sol-gel	1,123	7	Xe lamp (350 W), λ > 400 nm	5% v/v CH3OH	NiOx	0.24	[11]
CaTi0.96Cu0.04O3	8.1	null	Sol-gel	1,123	7	Xe lamp (350 W), λ > 400 nm	5% v/v CH3OH	NiOx	0.24	[11]
Sr2/3Zn1/3TiO3	null	3.15	Sol-gel	1,173	5	null	null	null	null	[12]
Ba5/6Zn1/6TiO3	null	3.2	Polymerized complex Sol-gel method	1,173	5	null	null	null	null	[12]
NaTaO3	null	3.94	Solvo-combustion	453	2	null	null	null	null	[13]
NaTaO3	null	3.98	Solvo-combustion	673	2	null	null	null	null	[13]
NaTaO3	null	4.01	Solvo-combustion	873	2	null	null	null	null	[13]
NaTaO3	null	4	Solvo-combustion	973	2	null	null	null	null	[13]
SrTiO3	null	3.22	Sol-gel	1,173	4	null	null	null	null	[14]
NaTaO3	null	3.97	Molten salt method	1,023	2	null	null	null	null	[15]
NaNb0.5Ta0.5O3	null	3.43	Molten salt method	1,023	2	null	null	null	null	[15]
Ca0.9La0.1Ti0.9Cr0.1O3	null	2.49	Hydrothermal	473	48	null	null	null	null	[16]
Sr0.9La0.1Ti0.9Cr0.1O3	28.8	2.31	Hydrothermal	473	48	Xe lamp (300 W),λ ≥ 400 nm	10%v/v CH3OH+4g NaOH	Pt(1 wt％)	1	[16]
Ba0.9La0.1Ti0.9Cr0.1O3	null	2.52	Hydrothermal	473	48	null	null	null	null	[16]
c-NaNbO3	423.3	3.29	Furfural alcohol derived polymerization-oxidation	873	5	Xe lamp (300 W), λ > 300 nm	5/22 v/v CH3OH	Pt(1 wt％)	1.1	[17]
o-NaNbO3	241	3.45	Polymerized complex	873	2	Xe lamp (300 W), λ > 300 nm	5/22 v/v CH3OH	Pt(1 wt％)	1.1	[17]
NaTaO3	null	4.01	Solid state reaction	1,173	10	null	null	null	null	[18]
NaTa0.975Bi0.025O3	null	3.65	Solid state reaction	1,173	10	null	null	null	null	[18]
NaTa0.95Bi0.05O3	null	3.05	Solid state reaction	1,173	10	null	null	null	null	[18]
NaTa0.925Bi0.075O3	null	2.95	Solid state reaction	1,173	10	null	null	null	null	[18]
Na0.975Bi0.025TaO3	null	3.75	Solid state reaction	1,173	10	null	null	null	null	[18]
Na0.95Bi0.05TaO3	null	3.65	Solid state reaction	1,173	10	null	null	null	null	[18]
Na0.925Bi0.075TaO3	null	3.65	Solid state reaction	1,173	10	null	null	null	null	[18]
Sr0.95Cr0.05TiO3	84	2.3	Solid state reaction	1,373	24	Xe lamp (300 W), λ≥420 nm	5/22 v/v CH3OH	Pt(0.6 wt％)	0.93	[19]
Sr0.95Cr0.05TiO3	26.8	2.3	Sol-gel hydrothermal method	353	24	Xe lamp (300 W), λ≥420 nm	5/22 v/v CH3OH	Pt(0.6 wt％)	0.93	[19]
Sr0.95Cr0.05TiO3	101.6	2.3	Sol-gel hydrothermal method	393	24	Xe lamp (300 W), λ≥420 nm	5/22 v/v CH3OH	Pt(0.6 wt％)	0.93	[19]
Sr0.95Cr0.05TiO3	137.2	2.3	Sol-gel hydrothermal method	433	24	Xe lamp (300 W), λ≥420 nm	5/22 v/v CH3OH	Pt(0.6 wt％)	0.93	[19]
Sr0.95Cr0.05TiO3	160.4	2.3	Sol-gel hydrothermal method	473	24	Xe lamp (300 W), λ≥420 nm	5/22 v/v CH3OH	Pt(0.6 wt％)	0.93	[19]
Sr0.95Cr0.05TiO3	330.4	2.3	Sol-gel hydrothermal method	473	24	Xe lamp (300 W), λ≥420 nm	5/22 v/v CH3OH	Pt(0.6 wt％)	0.93	[19]
SrTiO3	null	3.23	Sol-gel	973	4	null	null	null	null	[20]
SrTiO3	null	3.1	Polymerized complex	773	5	null	null	null	null	[21]
SrTiO3	null	3.1	Polymerized complex	873	5	null	null	null	null	[21]
SrTiO3	null	3.1	Polymerized complex	973	5	null	null	null	null	[21]
SrTiO3	null	3.1	Polymerized complex	1,073	5	null	null	null	null	[21]
SrTiO3	null	3.1	Polymerized complex	1,273	5	null	null	null	null	[21]
SrTiO3	null	3.1	Solid state reaction	1,073	5	null	null	null	null	[21]
SrTiO3	null	2	Milling assistant method	1,073	2	null	null	null	null	[21]
SrTi0.99Al0.01O3	347	3.45	Solid state reaction	1,273	10	Xe lamp (150 W), UV visible	30％vol CH3CHOHCH3	Au(0.25％)	2.5	[22]
SrTiO3	null	3.3	Solid state reaction	1,273	10	null	null	null	null	[22]
SrTi0.995Al0.005O3	null	3.4	Solid state reaction	1,273	10	null	null	null	null	[22]
SrTi0.985Al0.015O3	null	3.45	Solid state reaction	1,273	10	null	null	null	null	[22]
SrTiO3	188	3.16	Sol-gel	973	4	Xe lamp (300 W), λ≥400 nm	50vol％ CH3OH	Pt(0.5 wt％)	1	[23]
SrTi0.9Cr0.1O3−δ	null	2.31	Solid state reaction	1,573	20	null	null	null	null	[24]
La0.1Sr0.9Ti0.9Cr0.1O3	null	2.11	Solid state reaction	1,573	20	null	null	null	null	[24]
SrTiO3	null	3.25	Solid state reaction	1,573	20	null	null	null	null	[24]
LaFeO3	8,600	2.11	Sol-gel auto-combustion	773	2	Hg visible lamp(120W),(λ >> 420 nm)	10vol％ CH3OH	null	2.5	[25]
LaFeO3	7,866.7	2.1	Sol-gel auto-combustion	873	2	Hg visible lamp(120W),(λ >> 420 nm)	10vol％ CH3OH	null	2.5	[25]
LaFeO3	6,933.3	2.09	Sol-gel auto-combustion	973	2	Hg visible lamp(120W),(λ >> 420 nm)	10vol％ CH3OH	null	2.5	[25]
LaFeO3	6,066.7	2.08	Sol-gel auto-combustion	1,073	2	Hg visible lamp(120W),(λ >> 420 nm)	10vol％ CH3OH	null	2.5	[25]
LaFeO3	5,466.7	2.07	Sol-gel auto-combustion	1,173	2	Hg visible lamp(120W),(λ >> 420 nm)	10vol％ CH3OH	null	2.5	[25]
CaZrO3	12.4	4	Polymerized complex	923	6	Xe lamp (300 W), λ > 420 nm	12.5%v/v HCOOH	Pt(1 wt％)	0.56	[26]
CaTiO3	null	3.52	Template-free hydrothermal method	453	15	null	null	null	null	[27]
Ca0.95La0.05Ti0.95Cr0.05O3	98	2.49	Template-free hydrothermal method	453	15	Hg lamp (500 W),λ ≥ 400 nm	0.05M Na2SO3	Pt(1 wt％)	1	[27]
Ca0.9La0.1Ti0.9Cr0.1O3	110	2.48	Template-free hydrothermal method	453	15	Hg lamp (500 W),λ ≥ 400 nm	0.05M Na2SO3	Pt(1 wt％)	1	[27]
Ca0.8La0.2Ti0.8Cr0.2O3	164	2.5	Template-free hydrothermal method	453	15	Hg lamp (500 W),λ ≥ 400 nm	0.05M Na2SO3	Pt(1 wt％)	1	[27]
NaTaO3	null	3.96	Spray pyrolysis	1,173	null	null	null	null	null	[28]
NaBi0.06Ta0.94O3	null	2.96	Spray pyrolysis	1,173	null	null	null	null	null	[28]

End of preview.

CataTQA metadata info

Domain	Dataset or Paper Name	Access URL	DOI	Rename in Our Dataset
photocatalysis	Machine learning aided design of perovskite oxide materials for photocatalytic water splitting	https://www.sciencedirect.com/science/article/pii/S2095495621000644#s0090	10.1016/j.jechem.2021.01.035	table1 table2 table3
photocatalysis	Data mining in photocatalytic water splitting over perovskites literature for higher hydrogen production	https://www.sciencedirect.com/science/article/pii/S0926337318309470#sec0130	10.1016/j.apcatb.2018.09.104	table4 table5
photocatalysis	An insight into tetracycline photocatalytic degradation by MOFs using the artificial intelligence technique	https://www.nature.com/articles/s41598-022-10563-8#Sec10	10.1038/s41598-022-10563-8	table6
photocatalysis	Analysis of photocatalytic CO2 reduction over MOFs using machine learning	https://pubs.rsc.org/en/content/articlelanding/2024/ta/d3ta07001h	10.1039/D3TA07001H	table7
photocatalysis	Data-driven for accelerated design strategy of photocatalytic degradation activity prediction of doped TiO2 photocatalyst	https://www.sciencedirect.com/science/article/pii/S2214714422005700#s0055	10.1016/j.jwpe.2022.103126	table8
photocatalysis	A generalized predictive model for TiO2–Catalyzed photo-degradation rate constants of water contaminants through ANN	https://www.sciencedirect.com/science/article/pii/S0013935120305909	10.1016/j.envres.2020.109697	table9
photocatalysis	Statistical information review of CO2 photocatalytic reduction via bismuth-based photocatalysts using ANN	https://www.sciencedirect.com/science/article/pii/S1110016824008640?via%3Dihub	10.1016/j.aej.2024.07.120	table10
photocatalysis	Accelerated Design for Perovskite-Oxide-Based Photocatalysts Using Machine Learning Techniques	https://www.mdpi.com/1996-1944/17/12/3026	10.3390/ma17123026	table11
electrocatalysis	Building Blocks for High Performance in Electrocatalytic CO2 Reduction	https://acs.figshare.com/articles/dataset/Building_Blocks_for_High_Performance_in_Electrocatalytic_CO_sub_2_sub_Reduction/5293804	10.1021/acs.jpclett.7b01380	table12
electrocatalysis	Unlocking New Insights for Electrocatalyst Design: A Unique Data Science Workflow	https://github.com/ruiding-uchicago/InCrEDible-MaT-GO	10.1021/acscatal.3c01914	table13
electrocatalysis	Perovskite-based electrocatalyst discovery and design using word embeddings from retrained SciBERT	https://github.com/arunm917/Perovskite-based-electrocatalyst-design-and-discovery	-	table14
electrocatalysis	Exploring the Composition Space of High-Entropy Alloy Nanoparticles with Bayesian Optimization	https://github.com/vamints/Scripts_BayesOpt_PtRuPdRhAu_paper	10.1021/acscatal.2c02563	table15
electrocatalysis	High Throughput Discovery of Complex Metal Oxide Electrocatalysts for Oxygen Reduction Reaction	https://data.caltech.edu/records/1km87-52j70	10.1007/s12678-021-00694-3	table16
photoelectrocatalysis	High-thoughput OCM data	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/21010bbe-0a5c-4d12-a5fa-84eea540e4be/	10.1021/acscatal.9b04293	table17
photoelectrocatalysis	CatApp Data	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/20de069b-53cf-4310-9090-1738f53231e2/	10.1002/anie.201107947	table18
photoelectrocatalysis	Oxidative Coupling of Methane	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/9436f770-a7e2-4e87-989b-c5a9ce2312bf/	10.1002/cctc.202001032	table19
photoelectrocatalysis	ChemCatChem	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/224dd7ad-7677-4161-b744-a0c796bf5347/	10.1002/cctc.201100186	table20
photoelectrocatalysis	HTP OCM data obtained with catalysts designed on the basis of heuristics derived from random catalyst data	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/92200ba4-7644-44ca-9801-ed3cc52fc32f/	10.1002/cctc.202100460	table21
photoelectrocatalysis	Perovskite Data	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/f1b42c58-a423-4ec2-8bcf-e66c6470ff7d/	10.1039/C2EE22341D	table22
photoelectrocatalysis	Random catalyst OCM data by HTE	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/f7e30001-e440-4c1a-be64-ea866b2f77cb/	10.1021/acscatal.0c04629	table23
photoelectrocatalysis	Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/f2a6d4f2-91be-48ba-bf13-ffebbd90f6ee/	10.1021/jacs.2c06143	table24
photoelectrocatalysis	Multi-component La2O3- based catalysts in OCM	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/d6347fc1-e4d7-412e-aed5-a8ffa415a703/	10.1039/D1CY02206G	table25
photoelectrocatalysis	Catalyst Modification in OCM via Manganese Promoter	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/32dbec2c-c3d5-43ec-962a-90dba719bb44/	10.1021/acs.iecr.1c05079	table26
photoelectrocatalysis	Leveraging Machine Learning Engineering to Uncover Insights in Heterogeneous Catalyst Design for OCM	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/d84c1e22-ceb9-488a-8d45-4c7cf1c603b5/	10.1039/D3CY00596H	table27
photoelectrocatalysis	Oxidative of Coupling Literature and Highthroughput Data	https://cads.eng.hokudai.ac.jp/datamanagement/datasources/adb27910-d0e5-4a22-9415-580bf597035a/	10.1021/acscatal.0c04629, 10.1002/cctc.201100186	table28
photoelectrocatalysis	Catalytic Material Database	http://cmd.us.edu.pl/catalog/	-	table29
photoelectrocatalysis	Catalyst Hub	http://www.catalysthub.net/	-	table31
magnetic material	Magnetic Database	https://doi.org/10.15131/shef.data.24008055.v1	10.1063/9.0000657	table32
magnetic material	Materials database of Curie and Néel magnetic phase transition temperatures	https://doi.org/10.6084/m9.figshare.5702740.v1	10.1038/sdata.2018.111	table33
magnetic material	Data-driven design of molecular nanomagnets	https://go.uv.es/rosaleny/SIMDAVIS	10.1038/s41467-022-35336-9	table34
perovskite	Predicting the thermodynamic stability of perovskite oxides using machine learning models	https://doi.org/10.1016/j.dib.2018.05.007	-	table35 table36 table37
others	Crystallography Open Database(COD)	http://www.crystallography.net/cod/	-	table38
others	Alloy synthesis and processing by semi-supervised text mining	https://www.nature.com/articles/s41524-023-01138-w	10.1038/s41524-023-01138-w	table39
others	A Machine Learning Approach to Zeolite Synthesis Enabled by Automatic Literature Data Extraction	https://github.com/olivettigroup/table_extractor	10.1021/acscentsci.9b00193	table40
others	ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters	https://github.com/eltonpan/zeosyn_dataset	10.1021/acscentsci.3c01615	table41
others	Unveiling the Potential of AI for Nanomaterial Morphology Prediction	https://github.com/acid-design-lab/Nanomaterial_Morphology_Prediction	预印本：10.48550/arXiv.2406.02591	table42
others	AFLOW-2 CFID dataset 400k	https://doi.org/10.1016/j.commatsci.2012.02.005	10.1016/j.commatsci.2012.02.005	table43
others	Alexandria_DB PBE 3D all 5 million	https://alexandria.icams.rub.de/	-	table44
others	arXiv dataset 1.8 million	https://www.kaggle.com/Cornell-University/arxiv	-	table45
others	CCCBDB dataset 1333	https://cccbdb.nist.gov/	-	table46
others	3D dataset 55k	https://www.nature.com/articles/s41524-020-00440-1	10.1038/s41524-020-00440-1	table47
others	2D dataset 1.1k	https://www.nature.com/articles/s41524-020-00440-1	10.1038/s41524-020-00440-1	table48
others	halide perovskite dataset229	https://doi.org/10.1039/D1EE02971A	10.1039/D1EE02971A	table49
others	hMOF dataset 137k	https://doi.org/10.1021/acs.jpcc.6b08729	10.1021/acs.jpcc.6b08729	table50
others	HOPV15 dataset 4.5k	https://www.nature.com/articles/sdata201686	10.1038/sdata.2016.86	table51
others	Surface property dataset 607	https://doi.org/10.1039/D4DD00031E	10.1039/D4DD00031E	table52
others	JARVIS-FF 2k	https://www.nature.com/articles/s41524-020-00440-1	10.1038/s41524-020-00440-1	table53
others	MEGNET-3D CFID dataset 69k	-	-	table54
others	Materials Project-3D CFID dataset 127k	https://next-gen.materialsproject.org/	10.1063/1.4812323	table55
others	Materials Project-3D CFID dataset 84k	-	-	table56
others	OQMD-3D dataset 800k	https://www.oqmd.org/download/	10.1038/npjcompumats.2015.10	table57
others	Polymer genome 1k	https://datadryad.org/dataset/doi:10.5061/dryad.5ht3n	10.1038/sdata.2016.12	table58
others	QETB dataset 860k	https://arxiv.org/abs/2112.11585	预印本：10.48550/arXiv.2112.11585	table59
others	QM9 dataset 130k, from DGL	https://www.nature.com/articles/sdata201422	10.1038/sdata.2014.22	table60
others	QM9 standardized dataset 130k	-	-	table61
others	QMOF dataset 20k	https://www.cell.com/matter/fulltext/S2590-2385(21)00070-9	10.1016/j.matt.2021.02.015	table62
others	SNUMAT Hybrid functional dataset 10k	https://www.nature.com/articles/s41597-020-00723-8	10.1038/s41597-020-00723-8	table63
others	SSUB dataset 1726	https://github.com/wolverton-research-group/qmpy	-	table64
others	chem dataset 16414	https://www.nature.com/articles/s41524-018-0085-8	10.1038/s41524-018-0085-8	table65
others	dataset 607	https://doi.org/10.1039/D4DD00031E	10.1039/D4DD00031E	table66
others	2DMatPedia dataset 6k	http://www.2dmatpedia.org/	10.1038/s41597-019-0097-3	table67
others	vacancy dataset 464	https://doi.org/10.1063/5.0135382	10.1063/5.0135382	table68

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