Datasets:

thanatchon36
/

underwater-collateral-data

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 4 new columns ({'index_2011_100', 'region', 'flag', 'type'}) and 7 missing columns ({'level', 'deposits_mthb', 'loans_mthb', 'province_th', 'flags', 'series_id', 'province_en'}).

This happened while the csv dataset builder was generating data using

hf://datasets/thanatchon36/underwater-collateral-data/processed/bot_rppi_monthly.csv (at revision 1276cf98cb065c78ac42f887de4116d29ca5ec00), ['hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/bot_provincial_credit_monthly.csv', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/bot_rppi_monthly.csv', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_buf10_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_buf2_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_nocap_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_nocrop_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_prov20_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_prov30_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_ref2123_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_thr10_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_thr50_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sheet_admin.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sheet_flood_2026.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_unosat_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2025_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_admin_units.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/s2_sheet_keys_rq1_2024.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/s2_sheet_water_rq1_2024.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_flood2024_north.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_flood_s1.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_landcover_basin.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_north_covariates.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_panel.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/registration_hashes.csv', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/synthetic/loan_panel_synth.csv.gz']

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 1856, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, 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 822, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              date: string
              type: string
              region: string
              index_2011_100: double
              flag: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 846
              to
              {'province_th': Value('string'), 'province_en': Value('string'), 'date': Value('string'), 'loans_mthb': Value('float64'), 'deposits_mthb': Value('float64'), 'series_id': Value('string'), 'level': Value('string'), 'flags': 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 1379, 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 956, 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 1702, 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 1858, 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 4 new columns ({'index_2011_100', 'region', 'flag', 'type'}) and 7 missing columns ({'level', 'deposits_mthb', 'loans_mthb', 'province_th', 'flags', 'series_id', 'province_en'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/thanatchon36/underwater-collateral-data/processed/bot_rppi_monthly.csv (at revision 1276cf98cb065c78ac42f887de4116d29ca5ec00), ['hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/bot_provincial_credit_monthly.csv', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/bot_rppi_monthly.csv', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_buf10_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_buf2_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_nocap_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_nocrop_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_prov20_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_prov30_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_ref2123_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_thr10_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sens_thr50_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sheet_admin.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_sheet_flood_2026.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2024_unosat_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_2025_sample_sheets.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/rq1_admin_units.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/s2_sheet_keys_rq1_2024.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/s2_sheet_water_rq1_2024.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_flood2024_north.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_flood_s1.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_landcover_basin.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_north_covariates.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/processed/sheet_panel.csv.gz', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/registration_hashes.csv', 'hf://datasets/thanatchon36/underwater-collateral-data@1276cf98cb065c78ac42f887de4116d29ca5ec00/synthetic/loan_panel_synth.csv.gz']
              
              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.

province_th string	province_en string	date string	loans_mthb float64	deposits_mthb float64	series_id string	level string	flags string
กรุงเทพมหานคร (สาขา)	Bangkok (other offices)	2005-07-01	1,333,650	2,882,423	FI_CB_011_S2	bkk_branches	no_ibf_from_2006_10
กรุงเทพมหานคร (สำนักงานใหญ่)	Bangkok (head office)	2005-07-01	2,618,315	998,198	FI_CB_011_S2	bkk_head_office	no_ibf_from_2006_10
กรุงเทพมหานคร	Bangkok	2005-07-01	3,951,965	3,880,621	FI_CB_011_S2	bkk_total	no_ibf_from_2006_10
หนองคาย+บึงกาฬ	Nong Khai + Bueng Kan	2005-07-01	7,233	13,713	FI_CB_011_S2	harmonised	no_ibf_from_2006_10;bueng_kan_inside_nong_khai
รวมทั้งสิ้น	Whole kingdom	2005-07-01	5,308,574	5,936,306	FI_CB_011_S2	national	no_ibf_from_2006_10
กระบี่	Krabi	2005-07-01	8,881	10,734	FI_CB_011_S2	province	no_ibf_from_2006_10
กาญจนบุรี	Kanchanaburi	2005-07-01	12,965	21,952	FI_CB_011_S2	province	no_ibf_from_2006_10
กาฬสินธุ์	Kalasin	2005-07-01	7,441	8,009	FI_CB_011_S2	province	no_ibf_from_2006_10
กำแพงเพชร	Kamphaeng Phet	2005-07-01	9,893	10,219	FI_CB_011_S2	province	no_ibf_from_2006_10
ขอนแก่น	Khon Kaen	2005-07-01	41,217	45,520	FI_CB_011_S2	province	no_ibf_from_2006_10
จันทบุรี	Chanthaburi	2005-07-01	14,031	24,915	FI_CB_011_S2	province	no_ibf_from_2006_10
ฉะเชิงเทรา	Chachoengsao	2005-07-01	20,793	35,423	FI_CB_011_S2	province	no_ibf_from_2006_10
ชลบุรี	Chon Buri	2005-07-01	103,635	143,162	FI_CB_011_S2	province	no_ibf_from_2006_10
ชัยนาท	Chai Nat	2005-07-01	7,432	6,933	FI_CB_011_S2	province	no_ibf_from_2006_10
ชัยภูมิ	Chaiyaphum	2005-07-01	6,532	9,524	FI_CB_011_S2	province	no_ibf_from_2006_10
ชุมพร	Chumphon	2005-07-01	11,787	15,320	FI_CB_011_S2	province	no_ibf_from_2006_10
ตรัง	Trang	2005-07-01	11,569	21,500	FI_CB_011_S2	province	no_ibf_from_2006_10
ตราด	Trat	2005-07-01	4,179	9,150	FI_CB_011_S2	province	no_ibf_from_2006_10
ตาก	Tak	2005-07-01	6,251	10,878	FI_CB_011_S2	province	no_ibf_from_2006_10
นครนายก	Nakhon Nayok	2005-07-01	3,999	7,046	FI_CB_011_S2	province	no_ibf_from_2006_10
นครปฐม	Nakhon Pathom	2005-07-01	41,037	75,422	FI_CB_011_S2	province	no_ibf_from_2006_10
นครพนม	Nakhon Phanom	2005-07-01	5,200	7,015	FI_CB_011_S2	province	no_ibf_from_2006_10
นครราชสีมา	Nakhon Ratchasima	2005-07-01	47,140	59,642	FI_CB_011_S2	province	no_ibf_from_2006_10
นครศรีธรรมราช	Nakhon Si Thammarat	2005-07-01	21,112	34,213	FI_CB_011_S2	province	no_ibf_from_2006_10
นครสวรรค์	Nakhon Sawan	2005-07-01	28,477	32,712	FI_CB_011_S2	province	no_ibf_from_2006_10
นนทบุรี	Nonthaburi	2005-07-01	60,320	160,932	FI_CB_011_S2	province	no_ibf_from_2006_10
นราธิวาส	Narathiwat	2005-07-01	5,131	12,161	FI_CB_011_S2	province	no_ibf_from_2006_10
น่าน	Nan	2005-07-01	3,150	4,427	FI_CB_011_S2	province	no_ibf_from_2006_10
บุรีรัมย์	Buri Ram	2005-07-01	13,795	14,483	FI_CB_011_S2	province	no_ibf_from_2006_10
ปทุมธานี	Pathum Thani	2005-07-01	47,707	93,299	FI_CB_011_S2	province	no_ibf_from_2006_10
ประจวบคีรีขันธ์	Prachuap Khiri Khan	2005-07-01	14,197	19,052	FI_CB_011_S2	province	no_ibf_from_2006_10
ปราจีนบุรี	Prachin Buri	2005-07-01	8,185	11,661	FI_CB_011_S2	province	no_ibf_from_2006_10
ปัตตานี	Pattani	2005-07-01	6,933	11,819	FI_CB_011_S2	province	no_ibf_from_2006_10
พระนครศรีอยุธยา	Phra Nakhon Si Ayutthaya	2005-07-01	25,953	45,392	FI_CB_011_S2	province	no_ibf_from_2006_10
พะเยา	Phayao	2005-07-01	5,529	7,544	FI_CB_011_S2	province	no_ibf_from_2006_10
พังงา	Phangnga	2005-07-01	6,734	10,021	FI_CB_011_S2	province	no_ibf_from_2006_10
พัทลุง	Phatthalung	2005-07-01	3,802	7,615	FI_CB_011_S2	province	no_ibf_from_2006_10
พิจิตร	Phichit	2005-07-01	10,593	13,975	FI_CB_011_S2	province	no_ibf_from_2006_10
พิษณุโลก	Phitsanulok	2005-07-01	19,075	22,980	FI_CB_011_S2	province	no_ibf_from_2006_10
ภูเก็ต	Phuket	2005-07-01	46,634	51,985	FI_CB_011_S2	province	no_ibf_from_2006_10
มหาสารคาม	Maha Sarakham	2005-07-01	7,685	8,500	FI_CB_011_S2	province	no_ibf_from_2006_10
มุกดาหาร	Mukdahan	2005-07-01	4,330	5,024	FI_CB_011_S2	province	no_ibf_from_2006_10
ยะลา	Yala	2005-07-01	7,215	14,046	FI_CB_011_S2	province	no_ibf_from_2006_10
ยโสธร	Yasothon	2005-07-01	6,611	5,713	FI_CB_011_S2	province	no_ibf_from_2006_10
ระนอง	Ranong	2005-07-01	3,897	10,750	FI_CB_011_S2	province	no_ibf_from_2006_10
ระยอง	Rayong	2005-07-01	27,846	45,159	FI_CB_011_S2	province	no_ibf_from_2006_10
ราชบุรี	Ratchaburi	2005-07-01	26,241	42,438	FI_CB_011_S2	province	no_ibf_from_2006_10
ร้อยเอ็ด	Roi Et	2005-07-01	12,592	12,003	FI_CB_011_S2	province	no_ibf_from_2006_10
ลพบุรี	Lop Buri	2005-07-01	14,205	21,251	FI_CB_011_S2	province	no_ibf_from_2006_10
ลำปาง	Lampang	2005-07-01	12,305	20,485	FI_CB_011_S2	province	no_ibf_from_2006_10
ลำพูน	Lamphun	2005-07-01	7,147	8,999	FI_CB_011_S2	province	no_ibf_from_2006_10
ศรีสะเกษ	Si Sa Ket	2005-07-01	9,192	10,531	FI_CB_011_S2	province	no_ibf_from_2006_10
สกลนคร	Sakon Nakhon	2005-07-01	10,458	11,122	FI_CB_011_S2	province	no_ibf_from_2006_10
สงขลา	Songkhla	2005-07-01	58,220	77,778	FI_CB_011_S2	province	no_ibf_from_2006_10
สตูล	Satun	2005-07-01	2,566	4,579	FI_CB_011_S2	province	no_ibf_from_2006_10
สมุทรปราการ	Samut Prakan	2005-07-01	97,838	196,399	FI_CB_011_S2	province	no_ibf_from_2006_10
สมุทรสงคราม	Samut Songkhram	2005-07-01	7,079	10,075	FI_CB_011_S2	province	no_ibf_from_2006_10
สมุทรสาคร	Samut Sakhon	2005-07-01	35,851	65,071	FI_CB_011_S2	province	no_ibf_from_2006_10
สระบุรี	Saraburi	2005-07-01	23,619	30,560	FI_CB_011_S2	province	no_ibf_from_2006_10
สระแก้ว	Sa Kaeo	2005-07-01	5,009	7,364	FI_CB_011_S2	province	no_ibf_from_2006_10
สิงห์บุรี	Sing Buri	2005-07-01	6,594	9,351	FI_CB_011_S2	province	no_ibf_from_2006_10
สุพรรณบุรี	Suphan Buri	2005-07-01	21,671	25,575	FI_CB_011_S2	province	no_ibf_from_2006_10
สุราษฎร์ธานี	Surat Thani	2005-07-01	34,320	41,531	FI_CB_011_S2	province	no_ibf_from_2006_10
สุรินทร์	Surin	2005-07-01	12,591	13,658	FI_CB_011_S2	province	no_ibf_from_2006_10
สุโขทัย	Sukhothai	2005-07-01	7,455	11,784	FI_CB_011_S2	province	no_ibf_from_2006_10
หนองคาย	Nong Khai	2005-07-01	7,233	13,713	FI_CB_011_S2	province	no_ibf_from_2006_10;bueng_kan_inside_nong_khai
หนองบัวลำภู	Nong Bua Lam Phu	2005-07-01	2,812	3,484	FI_CB_011_S2	province	no_ibf_from_2006_10
อำนาจเจริญ	Amnat Charoen	2005-07-01	3,902	3,268	FI_CB_011_S2	province	no_ibf_from_2006_10
อุดรธานี	Udon Thani	2005-07-01	23,967	32,304	FI_CB_011_S2	province	no_ibf_from_2006_10
อุตรดิตถ์	Uttaradit	2005-07-01	6,678	8,762	FI_CB_011_S2	province	no_ibf_from_2006_10
อุทัยธานี	Uthai Thani	2005-07-01	3,703	5,185	FI_CB_011_S2	province	no_ibf_from_2006_10
อุบลราชธานี	Ubon Ratchathani	2005-07-01	21,993	25,145	FI_CB_011_S2	province	no_ibf_from_2006_10
อ่างทอง	Ang Thong	2005-07-01	4,047	8,256	FI_CB_011_S2	province	no_ibf_from_2006_10
เชียงราย	Chiang Rai	2005-07-01	19,085	27,643	FI_CB_011_S2	province	no_ibf_from_2006_10
เชียงใหม่	Chiang Mai	2005-07-01	75,688	92,560	FI_CB_011_S2	province	no_ibf_from_2006_10
เพชรบุรี	Phetchaburi	2005-07-01	10,448	18,195	FI_CB_011_S2	province	no_ibf_from_2006_10
เพชรบูรณ์	Phetchabun	2005-07-01	10,704	13,807	FI_CB_011_S2	province	no_ibf_from_2006_10
เลย	Loei	2005-07-01	5,807	6,898	FI_CB_011_S2	province	no_ibf_from_2006_10
แพร่	Phrae	2005-07-01	5,523	7,755	FI_CB_011_S2	province	no_ibf_from_2006_10
แม่ฮ่องสอน	Mae Hong Son	2005-07-01	1,173	2,329	FI_CB_011_S2	province	no_ibf_from_2006_10
กรุงเทพมหานคร (สาขา)	Bangkok (other offices)	2005-08-01	1,345,443	2,900,035	FI_CB_011_S2	bkk_branches	no_ibf_from_2006_10
กรุงเทพมหานคร (สำนักงานใหญ่)	Bangkok (head office)	2005-08-01	2,683,785	1,052,235	FI_CB_011_S2	bkk_head_office	no_ibf_from_2006_10
กรุงเทพมหานคร	Bangkok	2005-08-01	4,029,228	3,952,270	FI_CB_011_S2	bkk_total	no_ibf_from_2006_10
หนองคาย+บึงกาฬ	Nong Khai + Bueng Kan	2005-08-01	7,254	13,888	FI_CB_011_S2	harmonised	no_ibf_from_2006_10;bueng_kan_inside_nong_khai
รวมทั้งสิ้น	Whole kingdom	2005-08-01	5,402,740	6,046,741	FI_CB_011_S2	national	no_ibf_from_2006_10
กระบี่	Krabi	2005-08-01	8,911	10,971	FI_CB_011_S2	province	no_ibf_from_2006_10
กาญจนบุรี	Kanchanaburi	2005-08-01	13,092	22,723	FI_CB_011_S2	province	no_ibf_from_2006_10
กาฬสินธุ์	Kalasin	2005-08-01	7,547	8,084	FI_CB_011_S2	province	no_ibf_from_2006_10
กำแพงเพชร	Kamphaeng Phet	2005-08-01	10,203	10,279	FI_CB_011_S2	province	no_ibf_from_2006_10
ขอนแก่น	Khon Kaen	2005-08-01	41,822	46,001	FI_CB_011_S2	province	no_ibf_from_2006_10
จันทบุรี	Chanthaburi	2005-08-01	14,099	25,049	FI_CB_011_S2	province	no_ibf_from_2006_10
ฉะเชิงเทรา	Chachoengsao	2005-08-01	21,095	35,881	FI_CB_011_S2	province	no_ibf_from_2006_10
ชลบุรี	Chon Buri	2005-08-01	104,995	145,752	FI_CB_011_S2	province	no_ibf_from_2006_10
ชัยนาท	Chai Nat	2005-08-01	7,440	7,008	FI_CB_011_S2	province	no_ibf_from_2006_10
ชัยภูมิ	Chaiyaphum	2005-08-01	6,549	9,611	FI_CB_011_S2	province	no_ibf_from_2006_10
ชุมพร	Chumphon	2005-08-01	11,777	15,495	FI_CB_011_S2	province	no_ibf_from_2006_10
ตรัง	Trang	2005-08-01	11,518	21,994	FI_CB_011_S2	province	no_ibf_from_2006_10
ตราด	Trat	2005-08-01	4,176	9,057	FI_CB_011_S2	province	no_ibf_from_2006_10
ตาก	Tak	2005-08-01	6,309	10,909	FI_CB_011_S2	province	no_ibf_from_2006_10
นครนายก	Nakhon Nayok	2005-08-01	3,992	7,191	FI_CB_011_S2	province	no_ibf_from_2006_10

End of preview.

Underwater Collateral: derived data

Derived data behind the working paper and pilot in https://github.com/thanatchon36/underwater-collateral (archived at https://doi.org/10.5281/zenodo.23949061): how flood risk reaches bank capital in Thailand, through official land values, credit under regulatory relief and a reverse stress test. Everything here is built from public data only by the scripts in that repository. Views and errors are the author's own.

This upload is tier 1: the processed and synthetic data. The raw downloads (Treasury land values, Copernicus Sentinel-1 and Sentinel-2 scenes, Copernicus DEM, ESA WorldCover, JRC Global Surface Water, UNOSAT, OCHA boundaries, Bank of Thailand series) are not included; 03_data/README.md in the repository logs each one's URL, publisher, access date, licence and SHA-256, and 04_analysis/run_all.py rebuilds every file here from them.

Folders

Path	What it is	Built from	Licence of the derived file
`processed/rq1_2024_sample_sheets.csv.gz`, `rq1_2024_unosat_sample_sheets.csv.gz`, `rq1_2025_sample_sheets.csv.gz`, `rq1_2024_sens_*_sample_sheets.csv.gz`	The registered treatment and control samples for Question 1 (cadastral map sheets with flood shares, height above the valley floor, land cover, 2023–26 mean log value) and their pre-registered sensitivity variants	Treasury Department official land values (data.go.th, Open Data Common), Sentinel-1 flood maps, Copernicus DEM, ESA WorldCover, UNOSAT	Attribution to each source; the UNOSAT sample additionally CC BY-SA
`processed/sheet_.csv.gz`, `rq1_admin_units.csv.gz`, `rq1_2024_sheet_admin.csv.gz`, `rq1_2024_sheet_flood_2026.csv.gz`, `s2_sheet_.csv.gz`	Sheet-level covariates, flood shares by year, administrative units, Sentinel-2 optical check	As above, plus OCHA COD-AB boundaries (CC BY-IGO) and Sentinel-2	Attribution to each source
`processed/flood_s1/*.tif`	Flood and reference-water rasters from Sentinel-1 GRD by a fixed rule (0.001° grid) and a WorldCover cropland-share raster	Copernicus Sentinel-1 (2021–2026), ESA WorldCover 2021 (CC BY 4.0)	"Contains modified Copernicus Sentinel data [2021–2026]"; WorldCover CC BY 4.0
`processed/unosat_fl20240912tha/`	UNOSAT satellite-detected water extents, Chiang Rai, activation FL20240912THA, as downloaded from HDX	UNOSAT (UNITAR) with GISTDA	CC BY-SA (HDX dataset licence)
`processed/bot_provincial_credit_monthly.csv`, `bot_rppi_monthly.csv`	Monthly commercial-bank loans by province (from data.go.th, CC BY) and the Bank of Thailand residential property price index, tidied	Bank of Thailand	Attribution to the Bank of Thailand; the index under the BOT website terms (general permission to use, download and copy)
`synthetic/loan_panel_synth.csv.gz`, `loan_panel_synth_true_params.json`	Seeded synthetic loan panel with known parameters, used to test the Question 2 estimator; not data about any borrower or bank	Generated by `04_analysis/src/synth_tier2.py`	Same terms as the repository
`registration_hashes.csv`	SHA-256 of each registered sample file (decompressed), as printed in the paper's appendix

Attribution lines to carry with any reuse: "Contains modified Copernicus Sentinel data [2021–2026]"; ESA WorldCover 10 m 2021 v200 © ESA WorldCover project / Zanaga et al. (2022), CC BY 4.0; JRC Global Surface Water (Pekel et al. 2016), © EU, CC BY 4.0; Copernicus DEM © DLR e.V. (2010–2014) and © Airbus Defence and Space GmbH (2014–2018), provided under COPERNICUS by the European Union and ESA; UNOSAT/UNITAR, CC BY-SA; OCHA COD-AB Thailand, CC BY-IGO; กรมธนารักษ์ (Treasury Department) official land appraisal prices 2023–2026 via data.go.th; ธนาคารแห่งประเทศไทย (Bank of Thailand) statistics.

Registered sample files

File	Rows	SHA-256 of the decompressed CSV
`rq1_2024_sample_sheets.csv.gz`	2,089	`3ad6cad66ec12a2cb6e3be3636d9b0ef6393dca8817d23819dcee454fc8af115`
`rq1_2024_unosat_sample_sheets.csv.gz`	1,793	`200cebb02bdb4b588f8ae54c4290766061e692ca10bc6147f215976f721fb1d7`
`rq1_2025_sample_sheets.csv.gz`	8,101	`94811c17b57c881a94199f0f2eee818721e8bdfe676c691a896e54df1499950a`
`s2_sheet_water_rq1_2024.csv.gz`	128,092	`7610b2bb9035852d19a46bf53020b882c2f8a51c340f32dfc52e757047fcddd5`
`s2_sheet_keys_rq1_2024.csv.gz`	2,089	`ea7d341d0d87652ffd9fbe957dfa776dc3d5d5dc9d977e395d9233668b74be57`
`rq1_2024_sheet_admin.csv.gz`	2,089	`fd244e7a5175f6e5ffbb6382d4557f3e1fd82343ceb442a7025f50ce40ba7b6f`
`rq1_admin_units.csv.gz`	353	`e1f0fdddb60487fd250cde5b83e30c35c7c1f1e42981cdb9b559d87000a5c3cb`
`rq1_2024_sheet_flood_2026.csv.gz`	2,089	`a130e02b1dafe5c151fb238dc4fc33005995ac6eebc1f35ab55806f8e17be85f`
`rq1_2024_sens_thr10_sample_sheets.csv.gz`	2,857	`1ca557168595e18a0aca6dc954565109ca05615174129d3e9580d1db8fbd9fa4`
`rq1_2024_sens_thr50_sample_sheets.csv.gz`	1,883	`749f9164577a4ec8d146da468aa70e91b412c7a8b34c7ec1cea9d86684b996f8`
`rq1_2024_sens_prov20_sample_sheets.csv.gz`	4,131	`5cbdea680777101aa4153f1bb3e76547022bf36a78f38b3b779732e1dcfa47be`
`rq1_2024_sens_prov30_sample_sheets.csv.gz`	750	`cfd29303dc2e887c2e7ffa9b1e195090e8885b6b397442614dceb2448d33af78`
`rq1_2024_sens_buf2_sample_sheets.csv.gz`	2,592	`2032fccbe1c012b72f67f22261e7a17c247a1000f702e6a8bcde9fad455c4889`
`rq1_2024_sens_buf10_sample_sheets.csv.gz`	1,714	`01d43d35421ab5077e1c8c6b509068d6211e86932af17a016032318f5306dc29`
`rq1_2024_sens_nocap_sample_sheets.csv.gz`	8,789	`a4ce7011c9379e9185c96df5ad3e831c936949c501620c1565e423f3aa93b2e8`
`rq1_2024_sens_ref2123_sample_sheets.csv.gz`	2,050	`b34de6edc96a30a4f3b0e951b93c5d227a3f43c45fb67c95bde0a44d622a3e80`
`rq1_2024_sens_nocrop_sample_sheets.csv.gz`	1,923	`c7a93c82d388f1de231a8d0731943aa346fa5312290df3062fa57b3a112c3203`

To check a hash: download the file, decompress it, and compute SHA-256 of the decompressed CSV. The same hashes are in the paper's appendix and will be in the OSF registration of the pre-analysis plan.

Citation

Cite the repository's Zenodo DOI (https://doi.org/10.5281/zenodo.23949061) and this dataset page. Working draft: not yet peer reviewed.

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