-- DBMS: bigquery
CREATE TABLE `bigquery-public-data.dimensions_ai_covid19.datasets`
(
  id STRING,
  title STRING,
  description STRING,
  created_date STRING,
  embargo_date STRING,
  repository_url STRING,
  doi STRING,
  repository_id STRING,
  repository_name STRING,
  associated_publication_doi STRING,
  associated_publication_pmid STRING,
  associated_publication_arxiv_id STRING,
  associated_publication_id STRING,
  citation_string STRING,
  authors ARRAY<STRUCT<name STRING, orcid STRING, role STRING>>,
  license STRUCT<name STRING, url STRING>,
  category_bra STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>,
  category_for STRUCT<first_level STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>, second_level STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>>,
  category_hra STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>,
  category_hrcs_hc STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>,
  category_hrcs_rac STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>,
  category_icrp_cso STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>,
  category_icrp_ct STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>,
  category_rcdc STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>,
  category_sdg STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>,
  category_uoa STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>,
  categories STRUCT<bra_v1 STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>, for_v1 STRUCT<first_level STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>, second_level STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>>, hra_v1 STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>, hrcs_hc_v1 STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>, hrcs_rac_v1 STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>, icrp_cso_v1 STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>, icrp_ct_v1 STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>, rcdc_v1 STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>, sdg_v1 STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>, uoa_v1 STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>, bra_v2020 STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>, for_v2020 STRUCT<first_level STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>, second_level STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>>, hrcs_hc_v2020 STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>, hrcs_rac_v2020 STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>, icrp_cso_v2020 STRUCT<codes ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, code STRING, name STRING>>>, icrp_ct_v2020 STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>, rcdc_v2020 STRUCT<values ARRAY<STRING>, `full` ARRAY<STRUCT<id STRING, value STRING>>>>,
  date TIMESTAMP,
  date_modified TIMESTAMP,
  date_imported_gbq TIMESTAMP,
  research_orgs ARRAY<STRING>,
  researcher_ids ARRAY<STRING>,
  associated_grant_ids ARRAY<STRING>,
  research_org_cities ARRAY<INT64>,
  research_org_countries ARRAY<STRING>,
  research_org_state_codes ARRAY<STRING>,
  year INT64
);

INSERT INTO `bigquery-public-data.dimensions_ai_covid19.datasets` (id, title, description, created_date, embargo_date, repository_url, doi, repository_id, repository_name, associated_publication_doi, associated_publication_pmid, associated_publication_arxiv_id, associated_publication_id, citation_string, authors, license, category_bra, category_for, category_hra, category_hrcs_hc, category_hrcs_rac, category_icrp_cso, category_icrp_ct, category_rcdc, category_sdg, category_uoa, categories, date, date_modified, date_imported_gbq, research_orgs, researcher_ids, associated_grant_ids, research_org_cities, research_org_countries, research_org_state_codes, year) VALUES ('dataset.9237851', 'Skin is a potential host of SARS-CoV-2', 'Mendeley Supplemental Tables and Methods for Sun et al. Skin is a potential host of SARS-CoV-2: a clinical, single-cell transcriptome-profiling and histological study. J Am Acad Dermatol. 2020', NULL, NULL, 'https://data.mendeley.com/datasets/scvph5w5jr/1', '10.17632/scvph5w5jr.1', 'bl.mendeley', 'Mendeley Data', NULL, NULL, NULL, NULL, NULL, '[{''name'': ''Liang, Lyimin'', ''orcid'': None, ''role'': ''Creator''}]', {'name': 'cc-by-4.0', 'url': 'https://creativecommons.org/licenses/by/4.0/legalcode'}, {'values': "['Basic Science']", 'full': "[{'id': '4000', 'value': 'Basic Science'}]"}, NULL, NULL, {'values': "['Infection']", 'full': "[{'id': '898', 'value': 'Infection'}]"}, {'codes': "['2.1']", 'full': "[{'id': '10201', 'code': '2.1', 'name': 'Biological and endogenous factors'}]"}, NULL, {'values': "['Skin Cancer']", 'full': "[{'id': '3833', 'value': 'Skin Cancer'}]"}, NULL, NULL, NULL, {'bra_v1': None, 'for_v1': None, 'hra_v1': None, 'hrcs_hc_v1': {'values': "['Skin']", 'full': "[{'id': '891', 'value': 'Skin'}]"}, 'hrcs_rac_v1': None, 'icrp_cso_v1': None, 'icrp_ct_v1': {'values': "['Skin Cancer']", 'full': "[{'id': '3833', 'value': 'Skin Cancer'}]"}, 'rcdc_v1': None, 'sdg_v1': None, 'uoa_v1': None, 'bra_v2020': {'values': "['Basic Science']", 'full': "[{'id': '4000', 'value': 'Basic Science'}]"}, 'for_v2020': {'first_level': {'codes': "['11']", 'full': "[{'id': '2211', 'code': '11', 'name': 'Medical and Health Sciences'}]"}, 'second_level': {'codes': "['1108']", 'full': "[{'id': '3114', 'code': '1108', 'name': 'Medical Microbiology'}]"}}, 'hrcs_hc_v2020': {'values': "['Infection']", 'full': "[{'id': '898', 'value': 'Infection'}]"}, 'hrcs_rac_v2020': {'codes': "['2.1']", 'full': "[{'id': '10201', 'code': '2.1', 'name': 'Biological and endogenous factors'}]"}, 'icrp_cso_v2020': None, 'icrp_ct_v2020': {'values': "['Skin Cancer']", 'full': "[{'id': '3833', 'value': 'Skin Cancer'}]"}, 'rcdc_v2020': {'values': "['Biodefense' 'Prevention' 'Infectious Diseases' 'Lung'\n 'Emerging Infectious Diseases' 'Pneumonia & Influenza' 'Immunization'\n 'Pneumonia' 'Vaccine Related']", 'full': "[{'id': '499', 'value': 'Biodefense'} {'id': '558', 'value': 'Prevention'}\n {'id': '533', 'value': 'Infectious Diseases'}\n {'id': '410', 'value': 'Lung'}\n {'id': '520', 'value': 'Emerging Infectious Diseases'}\n {'id': '556', 'value': 'Pneumonia & Influenza'}\n {'id': '531', 'value': 'Immunization'}\n {'id': '426', 'value': 'Pneumonia'}\n {'id': '583', 'value': 'Vaccine Related'}]"}}, '2020-08-08 00:00:00+00:00', 'NaT', '2022-07-27 03:15:58+00:00', '[]', '[]', '[]', '[]', '[]', '[]', 2020), ('dataset.291263', 'COVID-19: Time Series Datasets India versus World', 'This dataset consists of COVID-19 time series data of India since 24th March 2020. The data set is for all the States and Union Territories of India and is divided into five parts, including i) Confirmed cases; ii) Death Count; iii) Recovered Cases; iv) Temperature of that place; and v) Percentage humidity in the region. The data set also provides basic details of confirmed cases and death count for all the countries of the world updated daily since 30 January 2020. The end user can contact the corresponding author (Rohit Salgotra : nicresearchgroup@gmail.com) for more details. . [Dataset is updated Thrice a Week]', NULL, NULL, 'https://data.mendeley.com/datasets/tmrs92j7pv/14', '10.17632/tmrs92j7pv.14', 'bl.mendeley', 'Mendeley Data', NULL, NULL, NULL, NULL, NULL, '[{''name'': ''Salgotra, Rohit'', ''orcid'': None, ''role'': ''Creator''}
 {''name'': ''Singh, Supreet'', ''orcid'': None, ''role'': ''Other''}
 {''name'': ''Singh, Urvinder'', ''orcid'': None, ''role'': ''Other''}
 {''name'': ''Saha, Sriparna'', ''orcid'': None, ''role'': ''Other''}
 {''name'': ''Gandomi, Amir H'', ''orcid'': None, ''role'': ''Other''}]', {'name': 'CC-BY-4.0', 'url': 'https://creativecommons.org/licenses/by/4.0/legalcode'}, NULL, {'first_level': {'codes': "['14']", 'full': "[{'id': '2214', 'code': '14', 'name': 'Economics'}]"}, 'second_level': {'codes': "['1403']", 'full': "[{'id': '3313', 'code': '1403', 'name': 'Econometrics'}]"}}, NULL, NULL, NULL, NULL, NULL, NULL, NULL, {'codes': "['B11']", 'full': "[{'id': '30011', 'code': 'B11', 'name': 'Computer Science and Informatics'}]"}, {'bra_v1': None, 'for_v1': {'first_level': {'codes': "['14']", 'full': "[{'id': '2214', 'code': '14', 'name': 'Economics'}]"}, 'second_level': {'codes': "['1403']", 'full': "[{'id': '3313', 'code': '1403', 'name': 'Econometrics'}]"}}, 'hra_v1': None, 'hrcs_hc_v1': None, 'hrcs_rac_v1': None, 'icrp_cso_v1': None, 'icrp_ct_v1': None, 'rcdc_v1': None, 'sdg_v1': None, 'uoa_v1': {'codes': "['B11']", 'full': "[{'id': '30011', 'code': 'B11', 'name': 'Computer Science and Informatics'}]"}, 'bra_v2020': None, 'for_v2020': {'first_level': {'codes': "['14']", 'full': "[{'id': '2214', 'code': '14', 'name': 'Economics'}]"}, 'second_level': {'codes': "['1403']", 'full': "[{'id': '3313', 'code': '1403', 'name': 'Econometrics'}]"}}, 'hrcs_hc_v2020': None, 'hrcs_rac_v2020': None, 'icrp_cso_v2020': None, 'icrp_ct_v2020': None, 'rcdc_v2020': None}, '2020-06-24 00:00:00+00:00', 'NaT', '2022-07-27 03:15:58+00:00', '[]', '[]', '[]', '[]', '[]', '[]', 2020), ('dataset.58241872', 'Spremljanje epidemije covid-19: Sledilnik in podatkovna infrastruktura v Sloveniji, 2021', NULL, NULL, NULL, 'http://www.adp.fdv.uni-lj.si/opisi/sled21/', '10.17898/adp_sled21_v1', 'gesis.adp', 'Social Science Data Archives at UL-FDV (ADP)', NULL, NULL, NULL, NULL, NULL, '[{''name'': ''Kostanjevec, Jan'', ''orcid'': None, ''role'': ''Creator''}]', {'name': 'cc-by-nc-4.0', 'url': 'https://creativecommons.org/licenses/by-nc/4.0/legalcode'}, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, {'bra_v1': None, 'for_v1': None, 'hra_v1': None, 'hrcs_hc_v1': None, 'hrcs_rac_v1': None, 'icrp_cso_v1': None, 'icrp_ct_v1': None, 'rcdc_v1': None, 'sdg_v1': None, 'uoa_v1': None, 'bra_v2020': None, 'for_v2020': None, 'hrcs_hc_v2020': None, 'hrcs_rac_v2020': None, 'icrp_cso_v2020': None, 'icrp_ct_v2020': None, 'rcdc_v2020': None}, '2021-01-01 00:00:00+00:00', 'NaT', '2022-07-27 03:15:58+00:00', '[]', '[]', '[]', '[]', '[]', '[]', 2021), ('dataset.25222165', 'SARS-CoV-2 Infektionen in Deutschland', 'Im Datensatz ''SARS-CoV-2 Infektionen in Deutschland'' werden die tagesaktuellen Fallzahlen, der nach den Vorgaben des Infektionsschutzgesetzes - IfSG - von den Gesundheitsämtern in Deutschand gemeldeten positiven SARS-Cov-2 Infektionen, Todes- und Genesungsfälle bereitgestellt.', NULL, NULL, 'https://zenodo.org/record/4898452', '10.5281/zenodo.4898452', 'cern.zenodo', 'Zenodo', NULL, NULL, NULL, NULL, NULL, '[{''name'': ''Koch-Institut, Robert'', ''orcid'': None, ''role'': ''Creator''}]', {'name': 'cc-by-4.0', 'url': 'info:eu-repo/semantics/openAccess'}, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, {'bra_v1': None, 'for_v1': None, 'hra_v1': None, 'hrcs_hc_v1': None, 'hrcs_rac_v1': None, 'icrp_cso_v1': None, 'icrp_ct_v1': None, 'rcdc_v1': None, 'sdg_v1': None, 'uoa_v1': None, 'bra_v2020': None, 'for_v2020': None, 'hrcs_hc_v2020': None, 'hrcs_rac_v2020': None, 'icrp_cso_v2020': None, 'icrp_ct_v2020': None, 'rcdc_v2020': None}, '2021-06-04 00:00:00+00:00', '2021-06-04 00:00:00+00:00', '2022-07-27 03:15:58+00:00', '[]', '[]', '[]', '[]', '[]', '[]', 2021), ('dataset.25717468', 'Data_Sheet_1_Who Is Next? A Study on Victims of Financial Fraud in Japan.PDF', 'Japan has seen an increase in the incidents of financial frauds over the last couple of decades. Although authorities are aware of the problem, an effective solution eludes them as fraudsters use innovative swindling methods and continually change the target group. Using a nationwide survey conducted by Hiroshima University, Japan, in 2020, this study investigated the socioeconomic and psychological profiles of victims of trending and special financial fraud such as fictitious billing fraud, loan guarantee fraud, and refund fraud. It was found that financial fraud victims'' profiles are dissimilar at the aggregate and specific levels. At the specific level, victim profiles were diverse, that is, in fictitious billing fraud, loan guarantee fraud, and refund fraud cases. Males, married, and financially less satisfied people were more often victims of fictitious billing fraud; less anxious people were more likely victims of loan guarantee fraud; and older, asset-holding, and less-income-generating respondents were found to be victims of refund fraud. Our results also show some commonalities in the victims'' profiles. For example, financially less-literate people were found to be more likely victims of fictitious billing fraud and loan guarantee fraud. Finally, respondents who lived with their family, those who did not have careful buying habits, and those who suffer from bouts of loneliness were found to be common victims of all types of special financial fraud. The results of our study suggest that a one-size-fits-all policy cannot effectively combat financial fraud.', '2021-07-02', NULL, 'https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Who_Is_Next_A_Study_on_Victims_of_Financial_Fraud_in_Japan_PDF/14897949', '10.3389/fpsyg.2021.649565.s001', 'portal_316', 'Frontiers', '10.3389/fpsyg.2021.649565', NULL, NULL, 'pub.1139315931', 'Kadoya, Yoshihiko; Khan, Mostafa Saidur Rahim; Narumoto, Jin; Watanabe, Satoshi (2021): Data_Sheet_1_Who Is Next? A Study on Victims of Financial Fraud in Japan.PDF. Frontiers. Dataset. https://doi.org/10.3389/fpsyg.2021.649565.s001', '[{''name'': ''Yoshihiko Kadoya'', ''orcid'': None, ''role'': None}
 {''name'': ''Mostafa Saidur Rahim Khan'', ''orcid'': None, ''role'': None}
 {''name'': ''Jin Narumoto'', ''orcid'': None, ''role'': None}
 {''name'': ''Satoshi Watanabe'', ''orcid'': None, ''role'': None}]', {'name': 'CC BY 4.0', 'url': 'https://creativecommons.org/licenses/by/4.0/'}, NULL, {'first_level': {'codes': "['17']", 'full': "[{'id': '2217', 'code': '17', 'name': 'Psychology and Cognitive Sciences'}]"}, 'second_level': {'codes': "['1701']", 'full': "[{'id': '3468', 'code': '1701', 'name': 'Psychology'}]"}}, NULL, NULL, NULL, NULL, NULL, NULL, NULL, {'codes': "['C17']", 'full': "[{'id': '30017', 'code': 'C17', 'name': 'Business and Management Studies'}]"}, {'bra_v1': None, 'for_v1': {'first_level': {'codes': "['17']", 'full': "[{'id': '2217', 'code': '17', 'name': 'Psychology and Cognitive Sciences'}]"}, 'second_level': {'codes': "['1701']", 'full': "[{'id': '3468', 'code': '1701', 'name': 'Psychology'}]"}}, 'hra_v1': None, 'hrcs_hc_v1': None, 'hrcs_rac_v1': None, 'icrp_cso_v1': None, 'icrp_ct_v1': None, 'rcdc_v1': None, 'sdg_v1': None, 'uoa_v1': {'codes': "['C17']", 'full': "[{'id': '30017', 'code': 'C17', 'name': 'Business and Management Studies'}]"}, 'bra_v2020': None, 'for_v2020': {'first_level': {'codes': "['14']", 'full': "[{'id': '2214', 'code': '14', 'name': 'Economics'}]"}, 'second_level': {'codes': "['1402']", 'full': "[{'id': '3292', 'code': '1402', 'name': 'Applied Economics'}]"}}, 'hrcs_hc_v2020': None, 'hrcs_rac_v2020': None, 'icrp_cso_v2020': None, 'icrp_ct_v2020': None, 'rcdc_v2020': None}, '2021-07-02 00:00:00+00:00', '2021-07-02 00:00:00+00:00', '2022-07-27 03:15:58+00:00', '[''grid.272458.e'' ''grid.257022.0'' ''grid.411285.b'']', '[''ur.01113460323.38'' ''ur.07710252001.77'' ''ur.01245160070.29'']', '[]', '[2113126 1857910 1862415]', '[''JP'']', '[]', 2021);