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2142541d-1b92-4fe5-accb-c3c3ec6bca04:0 | MLI | Mali | ERI.2_1 | Semenawi Keyih Bahri | ER | gadm1 | 0.4441 | 0.4828 | 0.505 | 0.5323 | 0.5392 | 0.5471 | 0.5475 | 0.5491 | 0.5463 | 0.5485 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:1 | MLI | Mali | TCD.1_1 | Barh el Ghazel | TD | gadm1 | 0.2557 | 0.2759 | 0.3063 | 0.3389 | 0.3606 | 0.374 | 0.3771 | 0.3852 | 0.3865 | 0.3918 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:2 | MLI | Mali | VUT.5_1 | Tafea | VU | gadm1 | 0.6435 | 0.6522 | 0.6782 | 0.731 | 0.7706 | 0.7946 | 0.8218 | 0.8362 | 0.8531 | 0.8642 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:3 | MLI | Mali | GBR.1_1 | null | GB | gadm1 | 0.5823 | 0.5386 | 0.5081 | 0.5044 | 0.5099 | 0.5177 | 0.5258 | 0.5336 | 0.5408 | 0.5473 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:4 | MLI | Mali | SUR.8_1 | Saramacca | SR | gadm1 | 0.6696 | 0.6413 | 0.6322 | 0.6472 | 0.6672 | 0.6887 | 0.7092 | 0.7287 | 0.7471 | 0.7625 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:5 | MLI | Mali | POL.6_1 | Małopolskie | PL | gadm1 | 0.5304 | 0.5099 | 0.4997 | 0.5105 | 0.5245 | 0.537 | 0.5484 | 0.5587 | 0.5684 | 0.5773 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:6 | MLI | Mali | BOL.3_1 | Beni | BO | gadm1 | 0.7737 | 0.7551 | 0.7557 | 0.7802 | 0.808 | 0.8339 | 0.8569 | 0.878 | 0.8971 | 0.9143 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:7 | MLI | Mali | LCA.10_1 | Vieux Fort | LC | gadm1 | 0.6877 | 0.6829 | 0.705 | 0.7518 | 0.7919 | 0.8245 | 0.8505 | 0.8714 | 0.89 | 0.9068 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:8 | MLI | Mali | ROU.40_1 | Vâlcea | RO | gadm1 | 0.5541 | 0.5117 | 0.4717 | 0.4584 | 0.4602 | 0.4665 | 0.474 | 0.4816 | 0.4891 | 0.4964 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:9 | MLI | Mali | KEN.47_1 | West Pokot | KE | gadm1 | 0.6551 | 0.6386 | 0.6429 | 0.6683 | 0.6924 | 0.7142 | 0.7329 | 0.7495 | 0.7643 | 0.777 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:10 | MLI | Mali | JPN.19_1 | Kanagawa | JP | gadm1 | 0.3627 | 0.3759 | 0.4133 | 0.4519 | 0.4728 | 0.4857 | 0.4944 | 0.5005 | 0.5048 | 0.508 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:11 | MLI | Mali | FIN.4_1 | Southern Finland | FI | gadm1 | 0.4962 | 0.465 | 0.4465 | 0.4485 | 0.4568 | 0.466 | 0.4746 | 0.4823 | 0.4892 | 0.4951 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:12 | MLI | Mali | CAN.2_1 | British Columbia | CA | gadm1 | 0.553 | 0.5199 | 0.5143 | 0.5229 | 0.5316 | 0.5402 | 0.5483 | 0.5557 | 0.5623 | 0.5682 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:13 | MLI | Mali | MTQ.3_1 | Le Trinité | FR | gadm1 | 0.597 | 0.5943 | 0.6184 | 0.6635 | 0.6983 | 0.7243 | 0.7449 | 0.7618 | 0.7751 | 0.7866 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:14 | MLI | Mali | URY.16_2 | San José | UY | gadm1 | 0.587 | 0.5504 | 0.514 | 0.5011 | 0.5029 | 0.5096 | 0.5176 | 0.5268 | 0.5357 | 0.5444 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:15 | MLI | Mali | BGR.8_1 | Kyustendil | BG | gadm1 | 0.5148 | 0.4794 | 0.4506 | 0.4439 | 0.448 | 0.4553 | 0.4623 | 0.4696 | 0.4766 | 0.4835 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:16 | MLI | Mali | LAO.9_1 | Oudômxai | LA | gadm1 | 0.5121 | 0.5001 | 0.5201 | 0.5615 | 0.5955 | 0.624 | 0.6486 | 0.6708 | 0.6908 | 0.7094 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:17 | MLI | Mali | GNB.8_1 | Quinara | GW | gadm1 | 0.4448 | 0.458 | 0.4975 | 0.5506 | 0.5798 | 0.5993 | 0.6101 | 0.6172 | 0.6215 | 0.6377 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:18 | MLI | Mali | BRA.14_1 | Pará | BR | gadm1 | 0.7126 | 0.6876 | 0.6826 | 0.6962 | 0.713 | 0.7299 | 0.7462 | 0.7616 | 0.776 | 0.7893 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:19 | MLI | Mali | PER.7_1 | Callao | PE | gadm1 | 0.7297 | 0.7123 | 0.7041 | 0.7145 | 0.7316 | 0.7497 | 0.7669 | 0.7825 | 0.7966 | 0.8091 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:20 | MLI | Mali | LTU.10_1 | Vilniaus | LT | gadm1 | 0.5331 | 0.5008 | 0.4793 | 0.482 | 0.4916 | 0.5018 | 0.5114 | 0.5204 | 0.5284 | 0.5356 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:21 | MLI | Mali | LBR.9_1 | Margibi | LR | gadm1 | 0.6563 | 0.6611 | 0.6861 | 0.7321 | 0.7618 | 0.7854 | 0.8061 | 0.8236 | 0.8375 | 0.8517 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:22 | MLI | Mali | PER.2_1 | Ancash | PE | gadm1 | 0.7293 | 0.7134 | 0.7152 | 0.7414 | 0.7707 | 0.798 | 0.8227 | 0.8447 | 0.8644 | 0.8821 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:23 | MLI | Mali | HND.15_1 | Olancho | HN | gadm1 | 0.7223 | 0.7082 | 0.7159 | 0.7513 | 0.7845 | 0.813 | 0.8374 | 0.8582 | 0.8766 | 0.8927 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:24 | MLI | Mali | DOM.8_1 | Espaillat | DO | gadm1 | 0.6719 | 0.6553 | 0.6559 | 0.6845 | 0.7147 | 0.7429 | 0.7678 | 0.7899 | 0.8095 | 0.8271 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:25 | MLI | Mali | HRV.1_1 | Bjelovarska-Bilogorska | HR | gadm1 | 0.4749 | 0.4432 | 0.4188 | 0.4134 | 0.4186 | 0.4269 | 0.4353 | 0.4443 | 0.4519 | 0.4596 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:26 | MLI | Mali | ROU.38_1 | Timiș | RO | gadm1 | 0.5805 | 0.5351 | 0.4964 | 0.4865 | 0.4911 | 0.4995 | 0.5089 | 0.5185 | 0.5278 | 0.5364 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:27 | MLI | Mali | UGA.2_1 | Apac | UG | gadm1 | 0.6081 | 0.624 | 0.6833 | 0.7669 | 0.8239 | 0.8686 | 0.9053 | 0.9372 | 0.9662 | 0.9931 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:28 | MLI | Mali | MNG.6_1 | Dornod | MN | gadm1 | 0.6252 | 0.5944 | 0.5869 | 0.5966 | 0.6057 | 0.6136 | 0.621 | 0.6272 | 0.6334 | 0.6383 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:29 | MLI | Mali | LKA.11_1 | Kandy | LK | gadm1 | 0.5096 | 0.478 | 0.4598 | 0.4588 | 0.462 | 0.4661 | 0.4702 | 0.4744 | 0.4784 | 0.4823 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:30 | MLI | Mali | VUT.2_1 | Penama | VU | gadm1 | 0.6479 | 0.5944 | 0.6164 | 0.6484 | 0.6897 | 0.7125 | 0.7256 | 0.7353 | 0.7591 | 0.7716 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:31 | MLI | Mali | CHE.1_1 | Aargau | CH | gadm1 | 0.5582 | 0.5239 | 0.5017 | 0.5072 | 0.5195 | 0.5317 | 0.5427 | 0.5522 | 0.56 | 0.5666 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:32 | MLI | Mali | BFA.4_1 | Centre-Nord | BF | gadm1 | 0.4269 | 0.4281 | 0.4515 | 0.4887 | 0.5127 | 0.5304 | 0.5436 | 0.5545 | 0.5633 | 0.5707 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:33 | MLI | Mali | PHL.25_1 | Cebu | PH | gadm1 | 0.6413 | 0.6034 | 0.5925 | 0.6111 | 0.6299 | 0.6466 | 0.6617 | 0.6756 | 0.6887 | 0.7011 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:34 | MLI | Mali | BFA.9_1 | Haut-Bassins | BF | gadm1 | 0.4242 | 0.4257 | 0.4502 | 0.4911 | 0.519 | 0.5396 | 0.5553 | 0.5677 | 0.5781 | 0.5868 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:35 | MLI | Mali | MAR.6_1 | Guelmim - Es-Semara | MA | gadm1 | 0.3519 | 0.35 | 0.3552 | 0.3685 | 0.3802 | 0.3897 | 0.3979 | 0.4048 | 0.4108 | 0.4159 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:36 | MLI | Mali | JAM.3_1 | Kingston | JM | gadm1 | 0.7096 | 0.7394 | 0.8106 | 0.9062 | 0.9465 | 0.9969 | 1.0387 | 1.0672 | 1.0761 | 1.0892 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:37 | MLI | Mali | KOR.3_1 | Chungcheongnam-do | KR | gadm1 | 0.2892 | 0.2909 | 0.3211 | 0.3651 | 0.3932 | 0.4119 | 0.4251 | 0.4348 | 0.442 | 0.4475 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:38 | MLI | Mali | TZA.26_1 | Tabora | TZ | gadm1 | 0.6491 | 0.6585 | 0.6941 | 0.7474 | 0.7836 | 0.812 | 0.8337 | 0.852 | 0.8678 | 0.8816 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:39 | MLI | Mali | PHL.67_1 | Sarangani | PH | gadm1 | 0.6854 | 0.666 | 0.6741 | 0.7053 | 0.7311 | 0.7525 | 0.7709 | 0.7876 | 0.8027 | 0.8167 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:40 | MLI | Mali | TUR.6_1 | Amasya | TR | gadm1 | 0.2904 | 0.2551 | 0.2189 | 0.1954 | 0.1842 | 0.178 | 0.1745 | 0.1723 | 0.1712 | 0.1705 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:41 | MLI | Mali | GEO.12_1 | Tbilisi | GE | gadm1 | 0.5563 | 0.5232 | 0.5054 | 0.498 | 0.4953 | 0.4952 | 0.4966 | 0.4988 | 0.5014 | 0.504 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:42 | MLI | Mali | DZA.9_1 | Biskra | DZ | gadm1 | 0.2356 | 0.211 | 0.1805 | 0.1638 | 0.1574 | 0.1542 | 0.1524 | 0.1513 | 0.1505 | 0.15 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:43 | MLI | Mali | RUS.58_1 | Rostov | RU | gadm1 | 0.5048 | 0.499 | 0.5342 | 0.5911 | 0.6182 | 0.6344 | 0.6431 | 0.6503 | 0.6552 | 0.6578 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:44 | MLI | Mali | IMN.15_2 | Onchan | GB | gadm1 | 0.5952 | 0.5282 | 0.4933 | 0.4971 | 0.5006 | 0.5102 | 0.5155 | 0.522 | 0.5279 | 0.5361 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:45 | MLI | Mali | SWE.17_1 | Värmland | SE | gadm1 | 0.5444 | 0.496 | 0.4564 | 0.4475 | 0.4505 | 0.4566 | 0.4635 | 0.4702 | 0.4766 | 0.4827 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:46 | MLI | Mali | MNE.5_1 | Budva | ME | gadm1 | 0.462 | 0.4573 | 0.4609 | 0.4758 | 0.4913 | 0.5069 | 0.5201 | 0.5344 | 0.5449 | 0.5554 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:47 | MLI | Mali | AUS.9_1 | Tasmania | AU | gadm1 | 0.5547 | 0.5072 | 0.4851 | 0.484 | 0.4872 | 0.4918 | 0.4972 | 0.5026 | 0.508 | 0.5131 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:48 | MLI | Mali | PRI.62_1 | Sabana Grande | US | gadm1 | 0.6495 | 0.6008 | 0.57 | 0.5599 | 0.5581 | 0.5626 | 0.5699 | 0.575 | 0.5814 | 0.5889 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:49 | MLI | Mali | YEM.7_1 | Al Mahrah | YE | gadm1 | 0.0951 | 0.0962 | 0.1053 | 0.1121 | 0.1164 | 0.1185 | 0.1204 | 0.1236 | 0.1251 | 0.1275 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:50 | MLI | Mali | PRI.42_1 | Lares | US | gadm1 | 0.6161 | 0.5676 | 0.5331 | 0.5204 | 0.5176 | 0.5217 | 0.526 | 0.5313 | 0.5368 | 0.5445 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:51 | MLI | Mali | KOR.10_1 | Gyeongsangnam-do | KR | gadm1 | 0.2784 | 0.2807 | 0.3101 | 0.3532 | 0.381 | 0.3998 | 0.4128 | 0.4223 | 0.4292 | 0.4346 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:52 | MLI | Mali | PRT.19_1 | Vila Real | PT | gadm1 | 0.5644 | 0.5282 | 0.4983 | 0.4932 | 0.4999 | 0.5086 | 0.5183 | 0.5279 | 0.5369 | 0.5455 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:53 | MLI | Mali | QAT.5_1 | Ar Rayyan | QA | gadm1 | 0.1487 | 0.1408 | 0.1413 | 0.1464 | 0.1499 | 0.1528 | 0.1544 | 0.1562 | 0.1569 | 0.1575 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:54 | MLI | Mali | CHL.7_1 | Coquimbo | CL | gadm1 | 0.5907 | 0.569 | 0.5624 | 0.5697 | 0.5811 | 0.594 | 0.6064 | 0.6178 | 0.6283 | 0.6377 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:55 | MLI | Mali | PER.17_1 | Loreto | PE | gadm1 | 0.7387 | 0.7263 | 0.7399 | 0.7797 | 0.8167 | 0.8486 | 0.8762 | 0.9001 | 0.9213 | 0.9401 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:56 | MLI | Mali | MKD.65_1 | Star Dojran | MK | gadm1 | 0.4959 | 0.4531 | 0.4326 | 0.4237 | 0.4217 | 0.424 | 0.4278 | 0.4339 | 0.4384 | 0.4406 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:57 | MLI | Mali | CIV.5_1 | Gôh-Djiboua | CI | gadm1 | 0.6202 | 0.6369 | 0.6929 | 0.7633 | 0.8086 | 0.8411 | 0.8654 | 0.8841 | 0.8999 | 0.9132 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:58 | MLI | Mali | TTO.11_1 | San Juan-Laventille | TT | gadm1 | 0.697 | 0.673 | 0.6683 | 0.6911 | 0.717 | 0.7406 | 0.7621 | 0.7817 | 0.7987 | 0.8141 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:59 | MLI | Mali | UGA.25_1 | Kiboga | UG | gadm1 | 0.6813 | 0.7161 | 0.7797 | 0.8973 | 0.9605 | 1.0065 | 1.0555 | 1.0899 | 1.1193 | 1.1487 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:60 | MLI | Mali | GUM.5_1 | Chalan-Pago-Ordot | US | gadm1 | 0.6419 | 0.6103 | 0.5921 | 0.5962 | 0.6121 | 0.6226 | 0.6281 | 0.6393 | 0.6446 | 0.6499 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:61 | MLI | Mali | NIC.4_1 | Carazo | NI | gadm1 | 0.7503 | 0.7384 | 0.7493 | 0.7856 | 0.8177 | 0.8447 | 0.8679 | 0.8876 | 0.9047 | 0.9196 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:62 | MLI | Mali | SLV.12_1 | Santa Ana | SV | gadm1 | 0.7256 | 0.6989 | 0.6931 | 0.714 | 0.7359 | 0.7555 | 0.7725 | 0.7874 | 0.8003 | 0.8117 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:63 | MLI | Mali | LBN.7_1 | North | LB | gadm1 | 0.3895 | 0.3578 | 0.317 | 0.2906 | 0.2788 | 0.2725 | 0.2687 | 0.2663 | 0.2649 | 0.2639 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:64 | MLI | Mali | PRI.20_1 | Ciales | US | gadm1 | 0.6581 | 0.6223 | 0.5927 | 0.5824 | 0.5815 | 0.5843 | 0.5924 | 0.5995 | 0.6071 | 0.6174 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:65 | MLI | Mali | MNE.4_1 | Bijelo Polje | ME | gadm1 | 0.4426 | 0.4175 | 0.4008 | 0.394 | 0.3948 | 0.4005 | 0.4063 | 0.4135 | 0.4206 | 0.4287 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:66 | MLI | Mali | PHL.46_1 | Masbate | PH | gadm1 | 0.6878 | 0.662 | 0.6618 | 0.6901 | 0.716 | 0.7382 | 0.7579 | 0.7761 | 0.7928 | 0.8085 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:67 | MLI | Mali | GUY.10_1 | Upper Takutu-Upper Essequibo | GY | gadm1 | 0.7463 | 0.7389 | 0.7566 | 0.8044 | 0.8446 | 0.8762 | 0.904 | 0.9257 | 0.9437 | 0.9587 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:68 | MLI | Mali | ROU.6_1 | Bistrița-Năsăud | RO | gadm1 | 0.558 | 0.5156 | 0.4787 | 0.4679 | 0.4709 | 0.4779 | 0.4859 | 0.4941 | 0.5024 | 0.5104 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:69 | MLI | Mali | HUN.4_1 | Borsod-Abaúj-Zemplén | HU | gadm1 | 0.5677 | 0.5285 | 0.4959 | 0.4894 | 0.4923 | 0.4974 | 0.5032 | 0.5093 | 0.5157 | 0.5221 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:70 | MLI | Mali | IND.28_1 | Punjab | IN | gadm1 | 0.3607 | 0.3366 | 0.3191 | 0.3138 | 0.3127 | 0.3128 | 0.3135 | 0.3143 | 0.3152 | 0.316 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:71 | MLI | Mali | THA.36_1 | Nonthaburi | TH | gadm1 | 0.5756 | 0.5458 | 0.5448 | 0.5662 | 0.5857 | 0.6024 | 0.6172 | 0.6306 | 0.6426 | 0.6535 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:72 | MLI | Mali | EST.3_1 | Ida-Viru | EE | gadm1 | 0.4786 | 0.4438 | 0.4193 | 0.4223 | 0.4312 | 0.4393 | 0.446 | 0.4516 | 0.4565 | 0.4601 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:73 | MLI | Mali | MLI.1_1 | Bamako | ML | gadm1 | 0.356 | 0.3771 | 0.4321 | 0.5038 | 0.5476 | 0.5785 | 0.6021 | 0.6212 | 0.6373 | 0.6512 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:74 | MLI | Mali | TGO.2_1 | Kara | TG | gadm1 | 0.522 | 0.5653 | 0.6781 | 0.8126 | 0.8901 | 0.941 | 0.9788 | 1.0055 | 1.0266 | 1.0443 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:75 | MLI | Mali | FSM.1_1 | Chuuk | FM | gadm1 | 0.6304 | 0.6356 | 0.6698 | 0.7196 | 0.755 | 0.7811 | 0.8001 | 0.8152 | 0.8276 | 0.8377 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:76 | MLI | Mali | IND.21_1 | Manipur | IN | gadm1 | 0.4643 | 0.4589 | 0.4775 | 0.5149 | 0.5463 | 0.5736 | 0.5977 | 0.6193 | 0.639 | 0.6572 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:77 | MLI | Mali | ZAF.5_1 | Limpopo | ZA | gadm1 | 0.6413 | 0.6608 | 0.7233 | 0.8046 | 0.8645 | 0.9114 | 0.9499 | 0.9825 | 1.0108 | 1.0359 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:78 | MLI | Mali | COL.16_2 | Guainía | CO | gadm1 | 0.7031 | 0.7034 | 0.749 | 0.8257 | 0.8846 | 0.9325 | 0.9729 | 1.0068 | 1.0362 | 1.0618 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:79 | MLI | Mali | IND.14_1 | Jammu and Kashmir | IN | gadm1 | 0.3495 | 0.3352 | 0.3292 | 0.3342 | 0.3402 | 0.3459 | 0.3511 | 0.3558 | 0.36 | 0.3638 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:80 | MLI | Mali | CHE.10_1 | Graubünden | CH | gadm1 | 0.554 | 0.5228 | 0.5078 | 0.5211 | 0.5395 | 0.5561 | 0.5708 | 0.5837 | 0.5948 | 0.6044 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:81 | MLI | Mali | RUS.1_1 | Adygey | RU | gadm1 | 0.4704 | 0.4706 | 0.5068 | 0.5604 | 0.5812 | 0.593 | 0.5977 | 0.6057 | 0.6079 | 0.6113 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:82 | MLI | Mali | VNM.43_1 | Ninh Thuận | VN | gadm1 | 0.525 | 0.5033 | 0.5044 | 0.5287 | 0.5521 | 0.5719 | 0.5889 | 0.6034 | 0.6161 | 0.6271 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:83 | MLI | Mali | LBY.19_1 | Surt | LY | gadm1 | 0.1572 | 0.1445 | 0.1269 | 0.1148 | 0.109 | 0.1057 | 0.1035 | 0.1021 | 0.1009 | 0.1002 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:84 | MLI | Mali | IND.36_1 | West Bengal | IN | gadm1 | 0.5309 | 0.5069 | 0.4913 | 0.4922 | 0.4982 | 0.5052 | 0.5124 | 0.5194 | 0.5262 | 0.5326 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:85 | MLI | Mali | SSD.7_1 | Upper Nile | SS | gadm1 | 0.4961 | 0.4762 | 0.488 | 0.5122 | 0.534 | 0.5522 | 0.5641 | 0.5751 | 0.5856 | 0.5947 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:86 | MLI | Mali | BIH.3_1 | Repuplika Srpska | BA | gadm1 | 0.4621 | 0.4309 | 0.4102 | 0.4044 | 0.4066 | 0.4117 | 0.418 | 0.4247 | 0.4313 | 0.4377 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:87 | MLI | Mali | SOM.12_1 | Nugaal | SO | gadm1 | 0.848 | 0.9154 | 1.026 | 1.1621 | 1.2328 | 1.2905 | 1.327 | 1.3537 | 1.3785 | 1.384 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:88 | MLI | Mali | ARG.7_1 | Corrientes | AR | gadm1 | 0.6883 | 0.668 | 0.6627 | 0.6734 | 0.6879 | 0.7029 | 0.7178 | 0.7321 | 0.7456 | 0.7582 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:89 | MLI | Mali | FRA.4_1 | Centre-Val de Loire | FR | gadm1 | 0.6327 | 0.6058 | 0.5828 | 0.5766 | 0.5795 | 0.5843 | 0.5889 | 0.593 | 0.5965 | 0.5994 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:90 | MLI | Mali | MNE.9_1 | Kolašin | ME | gadm1 | 0.4476 | 0.4464 | 0.4533 | 0.4664 | 0.4814 | 0.4898 | 0.5062 | 0.5145 | 0.5257 | 0.5303 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:91 | MLI | Mali | PRT.2_1 | Azores | PT | gadm1 | 0.5808 | 0.5333 | 0.4856 | 0.4645 | 0.4612 | 0.4632 | 0.4676 | 0.4728 | 0.4785 | 0.4841 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:92 | MLI | Mali | COL.30_2 | Tolima | CO | gadm1 | 0.753 | 0.7309 | 0.7233 | 0.7407 | 0.7646 | 0.7884 | 0.8107 | 0.831 | 0.8492 | 0.8658 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:93 | MLI | Mali | IMN.6_2 | Castletown | GB | gadm1 | 0.6206 | 0.5721 | 0.5295 | 0.5245 | 0.5372 | 0.5409 | 0.5511 | 0.5576 | 0.5657 | 0.5722 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:94 | MLI | Mali | USA.17_1 | Kansas | US | gadm1 | 0.6401 | 0.5905 | 0.5638 | 0.5597 | 0.5625 | 0.5677 | 0.5739 | 0.5805 | 0.5872 | 0.5937 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:95 | MLI | Mali | UGA.26_1 | Kisoro | UG | gadm1 | 0.5452 | 0.5663 | 0.631 | 0.7133 | 0.7682 | 0.8072 | 0.8414 | 0.8706 | 0.894 | 0.9183 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:96 | MLI | Mali | RUS.10_1 | Chechnya | RU | gadm1 | 0.7537 | 0.83 | 0.95 | 1.0455 | 1.0721 | 1.0893 | 1.1107 | 1.1179 | 1.1165 | 1.1247 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:97 | MLI | Mali | DEU.11_1 | Rheinland-Pfalz | DE | gadm1 | 0.5747 | 0.5466 | 0.5332 | 0.542 | 0.5547 | 0.5669 | 0.5781 | 0.5881 | 0.597 | 0.6049 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:98 | MLI | Mali | ETH.1_1 | Addis Abeba | ET | gadm1 | 0.54 | 0.5675 | 0.6454 | 0.7485 | 0.8215 | 0.8788 | 0.9261 | 0.9665 | 1.002 | 1.0335 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
2142541d-1b92-4fe5-accb-c3c3ec6bca04:99 | MLI | Mali | GNQ.2_1 | Bioko Norte | GQ | gadm1 | 0.8144 | 0.8327 | 0.9133 | 1.0546 | 1.164 | 1.2505 | 1.3208 | 1.3782 | 1.4267 | 1.4675 | 2,025 | 2,026 | 2025-2026 | AI for Good at Meta | Cross Gender Ties | gadm1_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv | cc-by | 2026-08-12T23:12:09Z |
Cross Gender Ties | Africa (Mali official open data)
3,135 rows - 1 Africa country - 2025-2026 - Repackaged by Electric Sheep Africa
TL;DR
This dataset packages one official CSV resource from Mali as
ML-ready Parquet. The source file is the provenance boundary; all usable
indicators or tabular columns from the resource stay together in this repo.
About the source
- Source: Cross Gender Ties
- Publisher: AI for Good at Meta
- Resource: gadm1_cgfr.csv
- Format:
CSV - License: CC BY 4.0
- Packaging mode:
tabular_resource
Geographic coverage
1 Africa country:
| Country | Rows | First year | Last year | Name |
|---|---|---|---|---|
MLI |
3,135 | 2025 | 2026 | Mali |
Indicators or Resource Contents
- This source file is packaged as a normalized tabular resource.
Schema
| Column | Type | Description | Example |
|---|---|---|---|
source_record_id |
string |
Stable row identifier for tabular resources. | 2142541d-1b92-4fe5-accb-c3c3ec6bca04:0 |
country_iso3 |
category |
ISO3 country code. | MLI |
country_name |
category |
Country name. | Mali |
region_id |
string |
Source column. | ERI.2_1 |
region_name |
string |
Source column. | Semenawi Keyih Bahri |
country |
string |
Source column. | ER |
level |
string |
Source column. | gadm1 |
cgfr_5 |
float64 |
Source column. | 0.4441 |
cgfr_10 |
float64 |
Source column. | 0.4828 |
cgfr_25 |
float64 |
Source column. | 0.505 |
cgfr_50 |
float64 |
Source column. | 0.5323 |
cgfr_75 |
float64 |
Source column. | 0.5392 |
cgfr_100 |
float64 |
Source column. | 0.5471 |
cgfr_125 |
float64 |
Source column. | 0.5475 |
cgfr_150 |
float64 |
Source column. | 0.5491 |
cgfr_175 |
float64 |
Source column. | 0.5463 |
cgfr_200 |
float64 |
Source column. | 0.5485 |
source_period_start_year |
Int64 |
First year inferred from source resource metadata. | 2025 |
source_period_end_year |
Int64 |
Last year inferred from source resource metadata. | 2026 |
source_period_label |
category |
Human-readable period inferred from source resource metadata. | 2025-2026 |
source_provider |
category |
Publishing organization. | AI for Good at Meta |
source_dataset |
category |
Source package title. | Cross Gender Ties |
source_resource |
category |
Source resource title. | gadm1_cgfr.csv |
source_package_id |
category |
CKAN package UUID. | b138131c-52d0-48c0-8351-ee1c21cadf34 |
source_resource_id |
category |
CKAN resource UUID. | 2142541d-1b92-4fe5-accb-c3c3ec6bca04 |
source_url |
category |
Original source resource URL. | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/re |
license_id |
category |
Source license identifier. | cc-by |
retrieved_at |
category |
UTC retrieval timestamp. | 2026-08-12T23:12:09Z |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-mali-cross-gender-ties-eb337aa0")
df = ds["train"].to_pandas()
print(df.head())
Filter to one country
sample_country = df[df["country_iso3"] == "MLI"]
Work with indicators
if "indicator_id" in df.columns:
print(df["indicator_id"].value_counts().head())
sample = df.sort_values([c for c in ["indicator_id", "year"] if c in df.columns])
Citation
@misc{electric_sheep_africa_africa_mali_cross_gender_ties_eb337aa0_2026,
title = {Cross Gender Ties | Africa (Mali official open data)},
author = {AI for Good at Meta},
year = {2026},
url = {https://data.humdata.org/dataset/cross-gender-ties},
publisher = {HuggingFace Datasets, repackaged by Electric Sheep Africa},
howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-mali-cross-gender-ties-eb337aa0}}
}
License
Released under CC BY 4.0.
Original data (c) AI for Good at Meta. When using this dataset, please cite both the original source above and the Electric Sheep Africa repackaging.
About Electric Sheep
Electric Sheep Africa is part of the Electric Sheep mission: a unified,
ML-ready data layer for Africa on Hugging Face. We pull data from authoritative
open sources, normalize the schemas, package as Parquet, and publish with
consistent dataset cards so researchers and developers can use load_dataset()
to start working in seconds.
Browse the full collection: huggingface.co/electricsheepafrica
Provenance: ingested 2026-08-13 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/2142541d-1b92-4fe5-accb-c3c3ec6bca04/download/gadm1_cgfr.csv
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