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59f1ec01-e145-4373-863d-cd9214a2792a:0 | CMR | Cameroon | CZ031 | Jihočeský kraj | CZ | nuts3_2024 | 0.5652 | 0.5415 | 0.5317 | 0.5434 | 0.5581 | 0.5717 | 0.5836 | 0.594 | 0.603 | 0.6107 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:1 | CMR | Cameroon | ITG18 | Ragusa | IT | nuts3_2024 | 0.5327 | 0.4903 | 0.4465 | 0.4254 | 0.4203 | 0.4213 | 0.4247 | 0.4293 | 0.4345 | 0.4397 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:2 | CMR | Cameroon | DE136 | Schwarzwald-Baar-Kreis | DE | nuts3_2024 | 0.5623 | 0.5364 | 0.5226 | 0.5331 | 0.5475 | 0.5613 | 0.5732 | 0.5837 | 0.5919 | 0.5987 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:3 | CMR | Cameroon | FRB02 | Eure-et-Loir | FR | nuts3_2024 | 0.6426 | 0.6127 | 0.585 | 0.5762 | 0.578 | 0.582 | 0.5859 | 0.5895 | 0.5925 | 0.5951 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:4 | CMR | Cameroon | AL021 | Elbasan | AL | nuts3_2024 | 0.5048 | 0.456 | 0.4036 | 0.3696 | 0.356 | 0.3496 | 0.3464 | 0.345 | 0.3443 | 0.344 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:5 | CMR | Cameroon | CH053 | Appenzell Ausserrhoden | CH | nuts3_2024 | 0.5685 | 0.5302 | 0.5154 | 0.525 | 0.5399 | 0.555 | 0.567 | 0.5779 | 0.5874 | 0.5961 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:6 | CMR | Cameroon | NO084 | Akershus | NO | nuts3_2024 | 0.5492 | 0.5009 | 0.4679 | 0.4669 | 0.4742 | 0.483 | 0.4918 | 0.5 | 0.5076 | 0.5146 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:7 | CMR | Cameroon | FRB01 | Cher | FR | nuts3_2024 | 0.6326 | 0.6037 | 0.5788 | 0.571 | 0.5739 | 0.5785 | 0.5829 | 0.5867 | 0.5901 | 0.5931 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:8 | CMR | Cameroon | DEA12 | Duisburg, Kreisfreie Stadt | DE | nuts3_2024 | 0.5132 | 0.4844 | 0.462 | 0.4628 | 0.4716 | 0.4807 | 0.4885 | 0.4951 | 0.5003 | 0.5047 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:9 | CMR | Cameroon | HU213 | Veszprém | HU | nuts3_2024 | 0.5733 | 0.5308 | 0.4985 | 0.4948 | 0.4998 | 0.5058 | 0.5121 | 0.5186 | 0.5249 | 0.5313 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:10 | CMR | Cameroon | PL229 | Gliwicki | PL | nuts3_2024 | 0.5467 | 0.5196 | 0.506 | 0.5148 | 0.5265 | 0.5377 | 0.5481 | 0.558 | 0.5673 | 0.5757 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:11 | CMR | Cameroon | RO126 | Sibiu | RO | nuts3_2024 | 0.5768 | 0.5306 | 0.4903 | 0.4792 | 0.4832 | 0.4914 | 0.5009 | 0.5104 | 0.5196 | 0.5283 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:12 | CMR | Cameroon | ITI15 | Prato | IT | nuts3_2024 | 0.5478 | 0.5144 | 0.4927 | 0.4918 | 0.501 | 0.5127 | 0.5247 | 0.5365 | 0.5474 | 0.5576 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:13 | CMR | Cameroon | PL514 | Miasto Wrocław | PL | nuts3_2024 | 0.5522 | 0.534 | 0.5324 | 0.5479 | 0.5632 | 0.577 | 0.5892 | 0.6003 | 0.6102 | 0.6189 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:14 | CMR | Cameroon | ES620 | Murcia | ES | nuts3_2024 | 0.5579 | 0.5312 | 0.51 | 0.512 | 0.5235 | 0.5362 | 0.5478 | 0.558 | 0.5667 | 0.5741 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:15 | CMR | Cameroon | TR901 | Trabzon | TR | nuts3_2024 | 0.2928 | 0.2615 | 0.2287 | 0.2059 | 0.1947 | 0.1885 | 0.1847 | 0.1824 | 0.181 | 0.18 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:16 | CMR | Cameroon | BG332 | Dobrich | BG | nuts3_2024 | 0.515 | 0.4785 | 0.4496 | 0.444 | 0.4487 | 0.4558 | 0.4628 | 0.4695 | 0.4759 | 0.4818 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:17 | CMR | Cameroon | ITF47 | Bari | IT | nuts3_2024 | 0.5744 | 0.5348 | 0.4957 | 0.4759 | 0.4718 | 0.4738 | 0.4785 | 0.4843 | 0.4905 | 0.4967 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:18 | CMR | Cameroon | DE735 | Schwalm-Eder-Kreis | DE | nuts3_2024 | 0.5818 | 0.5521 | 0.5392 | 0.5483 | 0.5616 | 0.5739 | 0.5858 | 0.596 | 0.6053 | 0.6137 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:19 | CMR | Cameroon | MT002 | Gozo and Comino/Għawdex u Kemmuna | MT | nuts3_2024 | 0.4872 | 0.4421 | 0.3992 | 0.381 | 0.3794 | 0.3805 | 0.383 | 0.3862 | 0.3896 | 0.3936 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:20 | CMR | Cameroon | DE22A | Rottal-Inn | DE | nuts3_2024 | 0.5481 | 0.5297 | 0.5254 | 0.5417 | 0.5595 | 0.575 | 0.5889 | 0.6016 | 0.6124 | 0.6222 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:21 | CMR | Cameroon | DE244 | Hof, Kreisfreie Stadt | DE | nuts3_2024 | 0.563 | 0.5385 | 0.525 | 0.5304 | 0.5432 | 0.5551 | 0.566 | 0.5754 | 0.5834 | 0.5895 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:22 | CMR | Cameroon | FRG04 | Sarthe | FR | nuts3_2024 | 0.653 | 0.6266 | 0.604 | 0.5952 | 0.5956 | 0.5985 | 0.6021 | 0.6053 | 0.6082 | 0.6108 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:23 | CMR | Cameroon | AT122 | Niederösterreich-Süd | AT | nuts3_2024 | 0.5732 | 0.5412 | 0.5211 | 0.5283 | 0.5412 | 0.5544 | 0.5667 | 0.5777 | 0.5876 | 0.5964 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:24 | CMR | Cameroon | ITC20 | Valle d’Aosta/Vallée d’Aoste | IT | nuts3_2024 | 0.5721 | 0.5435 | 0.5228 | 0.5256 | 0.5376 | 0.551 | 0.5649 | 0.5779 | 0.5902 | 0.6013 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:25 | CMR | Cameroon | NO0B2 | Svalbard | NO | nuts3_2024 | 0.5642 | 0.5509 | 0.5985 | 0.6291 | 0.6457 | 0.6682 | 0.6749 | 0.6753 | 0.6763 | 0.6751 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:26 | CMR | Cameroon | DEA53 | Hagen, Kreisfreie Stadt | DE | nuts3_2024 | 0.5137 | 0.4845 | 0.464 | 0.4677 | 0.4787 | 0.4891 | 0.4983 | 0.5059 | 0.5124 | 0.5172 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:27 | CMR | Cameroon | DEA5B | Soest | DE | nuts3_2024 | 0.5575 | 0.5338 | 0.5214 | 0.5292 | 0.5428 | 0.5561 | 0.5676 | 0.5777 | 0.5862 | 0.593 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:28 | CMR | Cameroon | FRI21 | Corrèze | FR | nuts3_2024 | 0.6077 | 0.5809 | 0.5571 | 0.5553 | 0.5608 | 0.5678 | 0.5741 | 0.5796 | 0.5842 | 0.5882 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:29 | CMR | Cameroon | ITG11 | Trapani | IT | nuts3_2024 | 0.5747 | 0.5267 | 0.478 | 0.4514 | 0.4428 | 0.4413 | 0.4432 | 0.4465 | 0.4506 | 0.4551 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:30 | CMR | Cameroon | ES300 | Madrid | ES | nuts3_2024 | 0.6207 | 0.599 | 0.5877 | 0.6009 | 0.619 | 0.6348 | 0.6475 | 0.6573 | 0.665 | 0.671 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:31 | CMR | Cameroon | HU321 | Hajdú-Bihar | HU | nuts3_2024 | 0.5594 | 0.5205 | 0.489 | 0.4841 | 0.4882 | 0.494 | 0.5003 | 0.5068 | 0.5135 | 0.5201 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:32 | CMR | Cameroon | ES416 | Segovia | ES | nuts3_2024 | 0.5672 | 0.548 | 0.539 | 0.5505 | 0.566 | 0.5805 | 0.5937 | 0.6046 | 0.6137 | 0.6208 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:33 | CMR | Cameroon | DE234 | Amberg-Sulzbach | DE | nuts3_2024 | 0.5579 | 0.5307 | 0.5296 | 0.5448 | 0.5615 | 0.5765 | 0.5892 | 0.6006 | 0.6104 | 0.6181 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:34 | CMR | Cameroon | FI1D5 | Keski-Pohjanmaa | FI | nuts3_2024 | 0.5021 | 0.455 | 0.422 | 0.4156 | 0.419 | 0.4244 | 0.4312 | 0.4374 | 0.4438 | 0.4487 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:35 | CMR | Cameroon | DE145 | Alb-Donau-Kreis | DE | nuts3_2024 | 0.5448 | 0.521 | 0.5058 | 0.5118 | 0.525 | 0.5376 | 0.5479 | 0.5572 | 0.5646 | 0.5702 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:36 | CMR | Cameroon | DE27A | Lindau (Bodensee) | DE | nuts3_2024 | 0.5623 | 0.5443 | 0.5352 | 0.5532 | 0.5709 | 0.5873 | 0.6005 | 0.6117 | 0.6199 | 0.6267 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:37 | CMR | Cameroon | SK010 | Bratislavský kraj | SK | nuts3_2024 | 0.568 | 0.5438 | 0.5328 | 0.542 | 0.5555 | 0.5682 | 0.5796 | 0.5896 | 0.5979 | 0.6048 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:38 | CMR | Cameroon | CH021 | Bern / Berne | CH | nuts3_2024 | 0.5626 | 0.5356 | 0.5197 | 0.5288 | 0.5434 | 0.5577 | 0.5704 | 0.5813 | 0.591 | 0.5993 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:39 | CMR | Cameroon | DE271 | Augsburg, Kreisfreie Stadt | DE | nuts3_2024 | 0.5301 | 0.5091 | 0.498 | 0.5102 | 0.5263 | 0.5411 | 0.5531 | 0.563 | 0.5707 | 0.5768 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:40 | CMR | Cameroon | DEF04 | Neumünster, Kreisfreie Stadt | DE | nuts3_2024 | 0.5878 | 0.553 | 0.5332 | 0.5417 | 0.5538 | 0.5639 | 0.5732 | 0.58 | 0.5857 | 0.5899 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:41 | CMR | Cameroon | HR034 | Šibensko-kninska županija | HR | nuts3_2024 | 0.4371 | 0.4124 | 0.4007 | 0.4067 | 0.4189 | 0.4313 | 0.442 | 0.4524 | 0.4615 | 0.4699 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:42 | CMR | Cameroon | DE922 | Diepholz | DE | nuts3_2024 | 0.5625 | 0.5366 | 0.5222 | 0.5296 | 0.5429 | 0.556 | 0.5669 | 0.5761 | 0.5835 | 0.5894 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:43 | CMR | Cameroon | FI1B1 | Helsinki-Uusimaa | FI | nuts3_2024 | 0.4976 | 0.4672 | 0.4496 | 0.4522 | 0.4609 | 0.4704 | 0.4792 | 0.4872 | 0.4943 | 0.5003 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:44 | CMR | Cameroon | HU311 | Borsod-Abaúj-Zemplén | HU | nuts3_2024 | 0.5676 | 0.5284 | 0.4959 | 0.4894 | 0.4923 | 0.4975 | 0.5033 | 0.5093 | 0.5157 | 0.5222 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:45 | CMR | Cameroon | DE94A | Friesland (DE) | DE | nuts3_2024 | 0.6174 | 0.5767 | 0.5557 | 0.5584 | 0.5697 | 0.5817 | 0.5924 | 0.6021 | 0.6106 | 0.6166 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:46 | CMR | Cameroon | FRF12 | Haut-Rhin | FR | nuts3_2024 | 0.6027 | 0.5723 | 0.5454 | 0.539 | 0.5426 | 0.5482 | 0.5537 | 0.5587 | 0.5631 | 0.5668 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:47 | CMR | Cameroon | SI038 | Primorsko-notranjska | SI | nuts3_2024 | 0.4732 | 0.4508 | 0.4417 | 0.4527 | 0.4659 | 0.4794 | 0.4928 | 0.5048 | 0.5152 | 0.5253 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:48 | CMR | Cameroon | ITH54 | Modena | IT | nuts3_2024 | 0.5338 | 0.5042 | 0.4874 | 0.4907 | 0.5029 | 0.5163 | 0.5295 | 0.5416 | 0.5526 | 0.5627 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:49 | CMR | Cameroon | AL034 | Korcë | AL | nuts3_2024 | 0.4764 | 0.4277 | 0.3841 | 0.3586 | 0.3483 | 0.3441 | 0.3425 | 0.3421 | 0.3422 | 0.3428 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:50 | CMR | Cameroon | PL228 | Bytomski | PL | nuts3_2024 | 0.5482 | 0.5216 | 0.5062 | 0.5131 | 0.5251 | 0.5371 | 0.5484 | 0.5589 | 0.5688 | 0.578 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:51 | CMR | Cameroon | FRG05 | Vendée | FR | nuts3_2024 | 0.6418 | 0.6161 | 0.5985 | 0.5931 | 0.5954 | 0.5996 | 0.6037 | 0.6076 | 0.6109 | 0.6138 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:52 | CMR | Cameroon | BE329 | Arr. La Louvière | BE | nuts3_2024 | 0.6185 | 0.5752 | 0.5296 | 0.5041 | 0.4943 | 0.4903 | 0.4893 | 0.4896 | 0.4906 | 0.492 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:53 | CMR | Cameroon | AT323 | Salzburg und Umgebung | AT | nuts3_2024 | 0.5254 | 0.5039 | 0.4982 | 0.5151 | 0.5349 | 0.5527 | 0.5685 | 0.5821 | 0.5938 | 0.6038 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:54 | CMR | Cameroon | DK031 | Fyn | DK | nuts3_2024 | 0.5918 | 0.545 | 0.5094 | 0.501 | 0.5019 | 0.5057 | 0.5108 | 0.5163 | 0.5218 | 0.527 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:55 | CMR | Cameroon | FRI23 | Haute-Vienne | FR | nuts3_2024 | 0.6053 | 0.5854 | 0.5719 | 0.574 | 0.5809 | 0.5879 | 0.5943 | 0.5996 | 0.604 | 0.6079 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:56 | CMR | Cameroon | DED42 | Erzgebirgskreis | DE | nuts3_2024 | 0.614 | 0.5932 | 0.5922 | 0.6127 | 0.6333 | 0.6511 | 0.6666 | 0.6788 | 0.6893 | 0.6981 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:57 | CMR | Cameroon | HR036 | Istarska županija | HR | nuts3_2024 | 0.4576 | 0.4321 | 0.4177 | 0.426 | 0.4408 | 0.4555 | 0.4689 | 0.4811 | 0.4924 | 0.5023 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:58 | CMR | Cameroon | RO125 | Mureş | RO | nuts3_2024 | 0.5472 | 0.5086 | 0.4763 | 0.4688 | 0.4733 | 0.4809 | 0.4889 | 0.4971 | 0.5049 | 0.5124 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:59 | CMR | Cameroon | SE311 | Värmlands län | SE | nuts3_2024 | 0.5445 | 0.4961 | 0.4564 | 0.4475 | 0.4505 | 0.4567 | 0.4635 | 0.4702 | 0.4767 | 0.4827 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:60 | CMR | Cameroon | DEB36 | Neustadt an der Weinstraße, Kreisfreie Stadt | DE | nuts3_2024 | 0.5887 | 0.5657 | 0.5505 | 0.558 | 0.5705 | 0.5843 | 0.5951 | 0.6035 | 0.6121 | 0.62 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:61 | CMR | Cameroon | AT121 | Mostviertel-Eisenwurzen | AT | nuts3_2024 | 0.5543 | 0.5274 | 0.5118 | 0.5192 | 0.5323 | 0.5452 | 0.5583 | 0.5702 | 0.581 | 0.5911 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:62 | CMR | Cameroon | ITF14 | Chieti | IT | nuts3_2024 | 0.5392 | 0.5043 | 0.4743 | 0.4667 | 0.4706 | 0.4783 | 0.4873 | 0.4964 | 0.5057 | 0.5145 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:63 | CMR | Cameroon | ITC42 | Como | IT | nuts3_2024 | 0.5698 | 0.5417 | 0.5223 | 0.5256 | 0.5377 | 0.5515 | 0.5649 | 0.5775 | 0.589 | 0.5994 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:64 | CMR | Cameroon | ES111 | A Coruña | ES | nuts3_2024 | 0.5932 | 0.5667 | 0.5468 | 0.5501 | 0.5622 | 0.5743 | 0.5855 | 0.5952 | 0.6036 | 0.6107 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:65 | CMR | Cameroon | RO121 | Alba | RO | nuts3_2024 | 0.5585 | 0.5185 | 0.4834 | 0.4747 | 0.4792 | 0.487 | 0.4959 | 0.5049 | 0.5136 | 0.5221 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:66 | CMR | Cameroon | DEF02 | Kiel, Kreisfreie Stadt | DE | nuts3_2024 | 0.5373 | 0.5208 | 0.5135 | 0.524 | 0.5379 | 0.5508 | 0.561 | 0.5685 | 0.5746 | 0.5791 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:67 | CMR | Cameroon | TRB12 | Elazığ | TR | nuts3_2024 | 0.2094 | 0.1891 | 0.1673 | 0.1536 | 0.1484 | 0.1461 | 0.1453 | 0.1451 | 0.1451 | 0.1453 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:68 | CMR | Cameroon | ITG17 | Catania | IT | nuts3_2024 | 0.5777 | 0.537 | 0.4935 | 0.4704 | 0.4651 | 0.4667 | 0.4709 | 0.476 | 0.4817 | 0.4874 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:69 | CMR | Cameroon | CH031 | Basel-Stadt | CH | nuts3_2024 | 0.5247 | 0.5073 | 0.5006 | 0.5185 | 0.5366 | 0.5525 | 0.5656 | 0.5761 | 0.5844 | 0.5913 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:70 | CMR | Cameroon | ES531 | Eivissa y Formentera | ES | nuts3_2024 | 0.5725 | 0.5489 | 0.5326 | 0.5417 | 0.559 | 0.5762 | 0.5921 | 0.6051 | 0.6168 | 0.6265 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:71 | CMR | Cameroon | ITF61 | Cosenza | IT | nuts3_2024 | 0.5374 | 0.5027 | 0.4716 | 0.459 | 0.458 | 0.4611 | 0.4664 | 0.4724 | 0.4788 | 0.4852 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:72 | CMR | Cameroon | ES432 | Cáceres | ES | nuts3_2024 | 0.5653 | 0.5421 | 0.5259 | 0.5278 | 0.5374 | 0.5478 | 0.5581 | 0.5674 | 0.5759 | 0.5834 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:73 | CMR | Cameroon | BE241 | Arr. Halle-Vilvoorde | BE | nuts3_2024 | 0.5798 | 0.5452 | 0.5193 | 0.5145 | 0.5178 | 0.5228 | 0.5282 | 0.5334 | 0.5383 | 0.5429 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:74 | CMR | Cameroon | AL022 | Tiranë | AL | nuts3_2024 | 0.4483 | 0.4142 | 0.3788 | 0.3585 | 0.3533 | 0.3532 | 0.3549 | 0.357 | 0.359 | 0.3608 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:75 | CMR | Cameroon | RO214 | Neamţ | RO | nuts3_2024 | 0.5568 | 0.5126 | 0.4733 | 0.462 | 0.4646 | 0.4708 | 0.4781 | 0.4854 | 0.4927 | 0.4995 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:76 | CMR | Cameroon | PL715 | Skierniewicki | PL | nuts3_2024 | 0.5324 | 0.5105 | 0.5002 | 0.5121 | 0.527 | 0.5398 | 0.5508 | 0.5608 | 0.57 | 0.5789 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:77 | CMR | Cameroon | XK003 | Pejë | XK | nuts3_2024 | 0.3175 | 0.2937 | 0.2651 | 0.246 | 0.2381 | 0.2345 | 0.2329 | 0.2323 | 0.2322 | 0.2323 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:78 | CMR | Cameroon | ES616 | Jaén | ES | nuts3_2024 | 0.5516 | 0.5243 | 0.4986 | 0.4937 | 0.4999 | 0.5088 | 0.5182 | 0.527 | 0.5353 | 0.5428 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:79 | CMR | Cameroon | DE94H | Wittmund | DE | nuts3_2024 | 0.6125 | 0.5726 | 0.5522 | 0.5559 | 0.5675 | 0.5796 | 0.5915 | 0.6015 | 0.6101 | 0.618 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:80 | CMR | Cameroon | DE264 | Aschaffenburg, Landkreis | DE | nuts3_2024 | 0.5783 | 0.5474 | 0.5333 | 0.5428 | 0.5566 | 0.5703 | 0.5828 | 0.5931 | 0.6028 | 0.6111 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:81 | CMR | Cameroon | TR521 | Konya | TR | nuts3_2024 | 0.2124 | 0.1845 | 0.1561 | 0.1381 | 0.13 | 0.1257 | 0.1232 | 0.1217 | 0.1208 | 0.1202 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:82 | CMR | Cameroon | NO081 | Oslo | NO | nuts3_2024 | 0.471 | 0.4498 | 0.4445 | 0.4579 | 0.4723 | 0.485 | 0.4962 | 0.5064 | 0.5156 | 0.5239 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:83 | CMR | Cameroon | DE23A | Tirschenreuth | DE | nuts3_2024 | 0.5603 | 0.5374 | 0.5273 | 0.5428 | 0.5581 | 0.5722 | 0.5861 | 0.5996 | 0.6112 | 0.6222 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:84 | CMR | Cameroon | ITF31 | Caserta | IT | nuts3_2024 | 0.5525 | 0.5121 | 0.4696 | 0.4484 | 0.4445 | 0.4468 | 0.4516 | 0.4574 | 0.4637 | 0.47 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:85 | CMR | Cameroon | DEF09 | Pinneberg | DE | nuts3_2024 | 0.5717 | 0.5468 | 0.5371 | 0.5479 | 0.5615 | 0.5733 | 0.5826 | 0.59 | 0.5952 | 0.5994 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:86 | CMR | Cameroon | BE343 | Arr. Marche-en-Famenne | BE | nuts3_2024 | 0.6017 | 0.5629 | 0.5297 | 0.5177 | 0.5175 | 0.5201 | 0.5232 | 0.5267 | 0.5307 | 0.5342 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:87 | CMR | Cameroon | RS224 | Jablanička oblast | RS | nuts3_2024 | 0.4983 | 0.4698 | 0.4536 | 0.4494 | 0.4513 | 0.4559 | 0.4616 | 0.4683 | 0.4754 | 0.4824 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:88 | CMR | Cameroon | DEA22 | Bonn, Kreisfreie Stadt | DE | nuts3_2024 | 0.5344 | 0.5175 | 0.5127 | 0.5282 | 0.5435 | 0.5573 | 0.5685 | 0.5775 | 0.5847 | 0.5902 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:89 | CMR | Cameroon | DE928 | Schaumburg | DE | nuts3_2024 | 0.5659 | 0.5392 | 0.5258 | 0.5345 | 0.5475 | 0.5596 | 0.5704 | 0.5789 | 0.5863 | 0.5919 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:90 | CMR | Cameroon | NL230 | Flevoland | NL | nuts3_2024 | 0.6531 | 0.6081 | 0.5747 | 0.5643 | 0.5668 | 0.5726 | 0.5789 | 0.5849 | 0.59 | 0.5946 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:91 | CMR | Cameroon | TR221 | Balıkesir | TR | nuts3_2024 | 0.3199 | 0.277 | 0.2379 | 0.215 | 0.2051 | 0.2002 | 0.1977 | 0.1965 | 0.1961 | 0.196 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:92 | CMR | Cameroon | DE937 | Rotenburg (Wümme) | DE | nuts3_2024 | 0.5716 | 0.5448 | 0.5329 | 0.541 | 0.5531 | 0.5654 | 0.5767 | 0.5855 | 0.5935 | 0.5999 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:93 | CMR | Cameroon | ITI22 | Terni | IT | nuts3_2024 | 0.5445 | 0.5152 | 0.494 | 0.4943 | 0.5031 | 0.5152 | 0.5276 | 0.5394 | 0.5509 | 0.5617 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:94 | CMR | Cameroon | DE40A | Oberhavel | DE | nuts3_2024 | 0.6206 | 0.5946 | 0.5886 | 0.607 | 0.6235 | 0.637 | 0.6474 | 0.6559 | 0.6623 | 0.6671 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:95 | CMR | Cameroon | CH061 | Luzern | CH | nuts3_2024 | 0.5278 | 0.5024 | 0.4926 | 0.5057 | 0.5226 | 0.5383 | 0.5517 | 0.563 | 0.5727 | 0.5809 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:96 | CMR | Cameroon | DEA14 | Krefeld, Kreisfreie Stadt | DE | nuts3_2024 | 0.5565 | 0.5311 | 0.513 | 0.5177 | 0.5286 | 0.5393 | 0.5488 | 0.5562 | 0.5627 | 0.5677 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:97 | CMR | Cameroon | ES704 | Fuerteventura | ES | nuts3_2024 | 0.5776 | 0.5484 | 0.5299 | 0.5363 | 0.5512 | 0.567 | 0.5812 | 0.5932 | 0.6039 | 0.6131 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:98 | CMR | Cameroon | DE94C | Leer | DE | nuts3_2024 | 0.6024 | 0.5708 | 0.5472 | 0.5482 | 0.5578 | 0.5686 | 0.5795 | 0.5888 | 0.5972 | 0.6043 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
59f1ec01-e145-4373-863d-cd9214a2792a:99 | CMR | Cameroon | DE134 | Ortenaukreis | DE | nuts3_2024 | 0.5526 | 0.5298 | 0.5177 | 0.5267 | 0.5409 | 0.5544 | 0.5663 | 0.5765 | 0.5852 | 0.5923 | 2,024 | 2,026 | 2024-2026 | AI for Good at Meta | Cross Gender Ties | nuts3_2024_cgfr.csv | b138131c-52d0-48c0-8351-ee1c21cadf34 | 59f1ec01-e145-4373-863d-cd9214a2792a | https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv | cc-by | 2026-08-25T16:58:39Z |
Cross Gender Ties | Africa (Cameroon official open data)
1,291 rows - 1 Africa country - 2024-2026 - Repackaged by Electric Sheep Africa
TL;DR
This dataset packages one official CSV resource from Cameroon 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: nuts3_2024_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 |
|---|---|---|---|---|
CMR |
1,291 | 2024 | 2026 | Cameroon |
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. | 59f1ec01-e145-4373-863d-cd9214a2792a:0 |
country_iso3 |
category |
ISO3 country code. | CMR |
country_name |
category |
Country name. | Cameroon |
region_id |
string |
Source column. | CZ031 |
region_name |
string |
Source column. | Jihočeský kraj |
country |
string |
Source column. | CZ |
level |
string |
Source column. | nuts3_2024 |
cgfr_5 |
float64 |
Source column. | 0.5652 |
cgfr_10 |
float64 |
Source column. | 0.5415 |
cgfr_25 |
float64 |
Source column. | 0.5317 |
cgfr_50 |
float64 |
Source column. | 0.5434 |
cgfr_75 |
float64 |
Source column. | 0.5581 |
cgfr_100 |
float64 |
Source column. | 0.5717 |
cgfr_125 |
float64 |
Source column. | 0.5836 |
cgfr_150 |
float64 |
Source column. | 0.594 |
cgfr_175 |
float64 |
Source column. | 0.603 |
cgfr_200 |
float64 |
Source column. | 0.6107 |
source_period_start_year |
Int64 |
First year inferred from source resource metadata. | 2024 |
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. | 2024-2026 |
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. | nuts3_2024_cgfr.csv |
source_package_id |
category |
CKAN package UUID. | b138131c-52d0-48c0-8351-ee1c21cadf34 |
source_resource_id |
category |
CKAN resource UUID. | 59f1ec01-e145-4373-863d-cd9214a2792a |
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. | 2026-08-25T16:58:39Z |
Usage
from datasets import load_dataset
ds = load_dataset("electricsheepafrica/africa-cameroon-cross-gender-ties-3a3525eb")
df = ds["train"].to_pandas()
print(df.head())
Filter to one country
sample_country = df[df["country_iso3"] == "CMR"]
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_cameroon_cross_gender_ties_3a3525eb_2026,
title = {Cross Gender Ties | Africa (Cameroon 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-cameroon-cross-gender-ties-3a3525eb}}
}
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 2026-08-25 via the Electric Sheep pipeline. Source URL: https://data.humdata.org/dataset/b138131c-52d0-48c0-8351-ee1c21cadf34/resource/59f1ec01-e145-4373-863d-cd9214a2792a/download/nuts3_2024_cgfr.csv
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