id,name,description,createdAt,updatedAt,namespace,isPrivate,createdByUserId,metadataEditedAt,metadataEditedByUserId,dataEditedAt,dataEditedByUserId,nonRedistributable,isArchived,sourceChecksum,shortName,version,updatePeriodDays
6652,"COVID-19, decoupling of indicators",,2024-07-31 15:42:15,2024-07-31 15:42:16,covid,0,74,2024-07-31 15:42:17,74,2024-07-31 15:42:17,74,0,0,06601e33ab6ea9439d12d6bdecead6fb,decoupling,latest,0
6651,"Long run homicide rates (1250-2022; Eisner, WHO, UNODC)",,2024-07-31 12:15:49,2024-07-31 12:15:49,homicide,0,74,2024-07-31 12:15:50,74,2024-07-31 12:15:50,74,0,0,6a033264aad7fcb3fec97d570f537e96,homicide_long_run_omm,2024-07-30,
6650,World Bank's income classification,,2024-07-31 09:04:31,2024-07-31 09:04:32,wb,0,74,2024-07-31 09:04:32,74,2024-07-31 09:04:32,74,0,0,8a8539e4be0976b620652a22bd35ea9b,income_groups,2024-07-29,365
6649,WHO Mortality Database,,2024-07-30 17:20:39,2024-07-30 17:20:52,homicide,0,74,2024-07-30 17:20:52,74,2024-07-30 17:20:52,74,0,0,cb0e295e2b05243dbf525e3d77902f48,who_mort_db,2024-07-30,
6648,"COVID-19, sequencing",,2024-07-30 16:30:51,2024-07-31 15:42:27,covid,1,74,2024-07-31 15:42:27,74,2024-07-31 15:42:27,74,1,0,cbe7f431de5a8b0f276d73aafeaca919,sequence,latest,14
6647,Global Military Spending Dataset,,2024-07-30 12:02:33,2024-07-30 12:02:36,harvard,0,74,2024-07-30 12:02:37,74,2024-07-30 12:02:37,74,0,0,3795d8b929aab45a966880b1d0112ca3,global_military_spending_dataset,2024-07-22,365
6646,SIPRI Military Expenditure Database,,2024-07-30 12:02:09,2024-07-30 12:02:11,sipri,0,74,2024-07-30 12:02:11,74,2024-07-30 12:02:11,74,0,0,c68fe4a79605a62e7d11d6ffab521702,military_expenditure,2024-07-08,365
6645,National Material Capabilities,,2024-07-30 12:02:08,2024-07-30 12:02:11,cow,0,74,2024-07-30 12:02:11,74,2024-07-30 12:02:11,74,0,0,772b81e9c7a807bc10ec3842085f6535,national_material_capabilities,2024-07-26,365
6644,AI Index Report,,2024-07-29 17:27:33,2024-07-29 17:27:35,artificial_intelligence,0,74,2024-07-29 17:27:35,74,2024-07-29 17:27:35,74,0,0,dffff120722f2e2ffdf0e02877ed43cd,ai_adoption,2024-06-28,365
6643,Maternal mortality (OWID based on UN MMEIG & other sources) (1751-2020),,2024-07-29 15:05:54,2024-07-31 12:58:56,maternal_mortality,0,74,2024-07-31 12:58:57,74,2024-07-31 12:58:57,74,0,0,db20d6d776be42677ac7874dfef0d83e,maternal_mortality,2024-07-08,365
6642,Trends in maternal mortality,,2024-07-29 15:05:37,2024-07-29 15:05:39,un,0,74,2024-07-29 15:05:40,74,2024-07-29 15:05:40,74,0,0,232190f26808f8d40de67d8f0bea9154,maternal_mortality,2024-07-08,365
6641,ICD Codes,,2024-07-29 12:47:23,2024-07-29 12:47:24,who,0,74,2024-07-29 12:47:24,74,2024-07-29 12:47:24,74,0,0,661bf489ff17feb9807f80af5caa7dc5,icd_codes,2024-07-29,365
6640,Trends in Machine Learning Hardware,,2024-07-29 11:40:02,2024-07-29 11:40:03,artificial_intelligence,0,74,2024-07-29 11:40:04,74,2024-07-29 11:40:04,74,0,0,f774340130f8c11359d443ee3b71558b,epoch_gpus,2024-07-11,365
6639,WHO Mortality Database,,2024-07-29 10:13:40,2024-07-29 10:21:55,who,0,74,2024-07-29 10:21:55,74,2024-07-29 10:21:55,74,0,0,567ced30224e9ba526dca94182ad30f5,mortality_database,2024-07-26,365
6638,HDI per capita land use (UNDP Project),,2024-07-28 15:37:22,2024-07-28 15:37:22,fasttrack,0,74,2024-07-28 15:37:23,74,2024-07-28 15:37:23,74,0,0,d66529ae8cf2da035165387310f70d6b,hdi_per_land_use,latest,
6637,"COVID-19, confirmed cases and deaths",,2024-07-26 15:16:56,2024-07-31 15:43:12,covid,0,74,2024-07-31 15:43:12,74,2024-07-31 15:43:12,74,0,0,149ac16d95a19d9b91111ded3a752aae,cases_deaths,latest,31
6636,Tree Cover Loss by Dominant Driver,,2024-07-26 14:48:55,2024-07-26 14:48:57,forests,0,74,2024-07-26 14:48:57,74,2024-07-26 14:48:57,74,0,0,a2a5f60212ff2b951bdaa2f40a0fe4a5,tree_cover_loss_by_driver,2024-07-10,0
6635,Anthromes,,2024-07-26 14:39:27,2024-07-26 14:39:39,papers,0,74,2024-07-26 14:39:40,74,2024-07-26 14:39:40,74,0,0,1f5b22e66978f8a897572ac22ae0faa3,anthromes,2024-01-05,
6634,HDI per capita CO2 (UNDP Project),,2024-07-26 14:02:23,2024-07-28 12:50:15,fasttrack,0,74,2024-07-28 12:50:16,74,2024-07-28 12:50:16,74,0,0,e7ab2b5df826f9e01c9e1e33abdc97f7,hdi_per_co2,latest,
6633,Country Activity Tracker: Artificial Intelligence,,2024-07-25 13:03:47,2024-07-30 06:18:30,artificial_intelligence,0,74,2024-07-30 06:18:31,74,2024-07-30 06:18:31,74,0,0,177bde1cdd4709b76be7174c8e32f758,cset,2024-07-16,365
6632,International Migrant Stock,,2024-07-25 12:44:19,2024-07-25 12:44:34,un,0,74,2024-07-25 12:44:35,74,2024-07-25 12:44:35,74,0,0,3af5b039f6b23dd34cc22df7dcc1335d,migrant_stock,2024-07-16,730
6631,Robot intelligence survey,,2024-07-25 12:17:08,2024-07-25 12:17:09,artificial_intelligence,0,74,2024-07-25 12:17:10,74,2024-07-25 12:17:10,74,0,0,c9e8187433ce3681f1df1e65f752fc7e,yougov_robots,2024-07-23,180
6630,How worried are Americans about being automated out of a job?,,2024-07-25 12:17:08,2024-07-25 12:17:09,artificial_intelligence,0,74,2024-07-25 12:17:10,74,2024-07-25 12:17:10,74,0,0,a80c0f8ec0f6e37345b983756d91f21f,yougov_job_automation,2024-07-23,180
6629,AI Performance on Imagenet,,2024-07-25 11:21:52,2024-07-25 12:17:07,artificial_intelligence,0,74,2024-07-25 12:17:08,74,2024-07-25 12:17:08,74,0,0,3994b97eac65be0b1864633daf7677c3,papers_with_code_imagenet,2024-07-23,
6628,Climate Change Impacts - Monthly,,2024-07-23 10:09:46,2024-07-31 07:50:09,climate,0,74,2024-07-31 07:50:10,74,2024-07-31 07:50:10,74,0,0,9a645151da184c3053e2f989a5cc08b7,climate_change_impacts_monthly,2024-07-23,60
6627,Climate Change Impacts - Annual,,2024-07-23 10:09:46,2024-07-31 07:49:59,climate,0,74,2024-07-31 07:49:59,74,2024-07-31 07:49:59,74,0,0,95c0acaa5df6f16d48b939bae375d912,climate_change_impacts_annual,2024-07-23,60
6626,Population doubling times,,2024-07-22 15:00:52,2024-07-30 06:18:11,demography,0,74,2024-07-30 06:18:12,74,2024-07-30 06:18:12,74,0,0,6b8a73b193408d84ae06a937941e7a69,population_doubling_times,2024-07-18,730
6625,Natural Earth - Large scale data (1:10m Cultural Vectors),,2024-07-19 08:03:32,2024-07-19 10:07:35,geography,0,74,2024-07-19 10:07:36,74,2024-07-19 10:07:36,74,0,0,b7f648c81c47daf0fa527620dd47ca05,latitude,2024-07-18,0
6624,Notable AI systems by researcher affiliation,,2024-07-17 16:46:29,2024-07-17 16:46:31,artificial_intelligence,0,74,2024-07-17 16:46:31,74,2024-07-17 16:46:31,74,0,0,0ca234f23014e08c78c1c99cf3d55bb8,epoch_aggregates_affiliation,2024-07-10,31
6623,"Parameter, Compute and Data Trends in Machine Learning",,2024-07-17 16:46:28,2024-07-17 16:46:30,artificial_intelligence,0,74,2024-07-17 16:46:30,74,2024-07-17 16:46:30,74,0,0,097c404197d3953b357d48a9b2b3f97d,epoch,2024-07-10,31
6622,Notable AI systems by domain type,,2024-07-17 16:46:28,2024-07-17 21:21:38,artificial_intelligence,0,74,2024-07-17 21:21:38,74,2024-07-17 21:21:38,74,0,0,ad3f29e8f6c432a5b9265377addd8e30,epoch_aggregates_domain,2024-07-10,31
6621,Population,,2024-07-17 09:27:08,2024-07-30 06:18:03,demography,0,74,2024-07-30 06:18:04,74,2024-07-30 06:18:04,74,0,0,5d507a8fb89ccc8f44acf9dc580abd6f,population,2024-07-15,730
6620,Rock-to-metal mining ratio (Nassar et al. 2022 and Wang et al. 2024),,2024-07-16 08:49:21,2024-07-16 08:49:21,fasttrack,0,74,2024-07-16 08:49:22,74,2024-07-16 08:49:22,74,0,0,7e7f25560fa36f67e011efa98060321a,rock_metal_ratio_nassar_wang,latest,
6619,Historical World Population Prospects,,2024-07-12 17:57:23,2024-07-15 12:51:34,demography,0,74,2024-07-15 12:51:34,74,2024-07-15 12:51:34,74,0,0,94bccbe8d3c318f970bd9a23d25ca835,un_wpp_historical,2024-07-12,740
6618,World Population Prospects (projections full timeseries),,2024-07-12 03:19:48,2024-07-15 13:00:13,un,0,74,2024-07-15 13:00:14,74,2024-07-15 13:00:14,74,0,0,1d60785e408c1948c4cd307bc40ff9b7,un_wpp_full,2024-07-12,
6617,World Population Prospects,,2024-07-12 03:18:25,2024-07-15 12:56:26,un,0,74,2024-07-15 12:56:26,74,2024-07-15 12:56:26,74,0,0,bf2c33e4dfdb590098e9b99e52631789,un_wpp,2024-07-12,
6616,World Population Prospects (projections full timeseries),,2024-07-11 17:26:52,2024-07-15 09:57:33,un,1,74,2024-07-11 17:28:27,74,2024-07-11 17:28:27,74,0,1,de0fd180660dbfa95b44b62b38597d00,un_wpp_full,2024-07-11,
6615,World Population Prospects,,2024-07-11 17:26:51,2024-07-15 09:45:30,un,1,74,2024-07-11 17:29:11,74,2024-07-11 17:29:11,74,0,1,b6e7eae1f20b7d0402d3ffbe93a70296,un_wpp,2024-07-11,
6614,AI Index Report,,2024-07-10 09:35:46,2024-07-10 09:35:49,artificial_intelligence,0,74,2024-07-10 09:35:49,74,2024-07-10 09:35:49,74,0,0,4defd7e3dca39c9f613e1d9b937091a1,ai_phds,2024-06-28,365
6613,AI Index Report,,2024-07-10 09:35:46,2024-07-25 22:50:45,artificial_intelligence,0,74,2024-07-25 22:50:46,74,2024-07-25 22:50:46,74,0,0,5117851dd34bc2802e26714be86a95a2,ai_strategies,2024-06-28,365
6612,AI Index Report,,2024-07-10 09:35:45,2024-07-10 09:35:47,artificial_intelligence,0,74,2024-07-10 09:35:48,74,2024-07-10 09:35:48,74,0,0,6dbd6ba8c017d67bd012fe1414a003a9,ai_bills,2024-06-28,365
6611,UNESCO Institute for Statistics (UIS) - Education,,2024-07-09 16:17:39,2024-07-09 16:21:15,unesco,0,74,2024-07-09 16:21:15,74,2024-07-09 16:21:15,74,0,0,493b1248cfb9ff30df4dafaaf93fc5db,education_sdgs,2024-06-25,365
6610,Homelessness - Better Data Project,,2024-07-08 16:14:04,2024-07-22 14:46:04,igh,0,74,2024-07-22 14:46:04,74,2024-07-22 14:46:04,74,0,0,98689908cc375b9dc67e2b0c1336e0bf,better_data_homelessness,2024-07-05,365
6609,"Annual road fatalities, injured, injury crashes",,2024-07-05 10:47:01,2024-07-25 23:10:19,oecd,0,74,2024-07-25 23:10:20,74,2024-07-25 23:10:20,74,0,0,d07bbfeb7cbb5466578a84c972c3e512,road_accidents,2024-07-01,365
6608,AI Index Report,,2024-07-03 10:48:31,2024-07-10 09:35:48,artificial_intelligence,0,74,2024-07-10 09:35:48,74,2024-07-10 09:35:48,74,0,0,a233a943d228b8b6e4100619b2d53c66,ai_robots,2024-06-28,365
6607,Country level monthly temperature anomalies,,2024-07-03 09:35:54,2024-07-17 12:26:42,climate,0,74,2024-07-17 12:26:43,74,2024-07-17 12:26:43,74,0,0,8d8acbd11cd09db63c41dc1d442905d6,surface_country_level_monthly_anomaly,2023-12-20,
6606,Global monthly temperature anomalies for all countries,,2024-07-03 09:35:54,2024-07-17 12:26:40,climate,0,74,2024-07-17 12:26:40,74,2024-07-17 12:26:40,74,0,0,4532c863637b17cb0d3c560cca471abb,surface_global_monthly_anomaly_all_countries,2023-12-20,
6605,AI Index Report,,2024-07-02 19:13:37,2024-07-22 08:46:10,artificial_intelligence,0,74,2024-07-22 08:46:11,74,2024-07-22 08:46:11,74,0,0,78ce9fe5dda33ad31e15799586b18d80,ai_private_investment,2024-06-28,365
6604,AI Index Report,,2024-07-02 19:13:37,2024-07-10 11:55:24,artificial_intelligence,0,74,2024-07-10 11:55:25,74,2024-07-10 11:55:25,74,0,0,2e65b3f23908e0b1231985c5f9f21710,ai_corporate_investment,2024-06-28,365
6603,AI Index Report,,2024-07-02 19:13:36,2024-07-10 09:35:46,artificial_intelligence,0,74,2024-07-10 09:35:46,74,2024-07-10 09:35:46,74,0,0,9cb8f9d01372d83c921b2ed56b4c1fc4,ai_conferences,2024-06-28,365
6602,AI Index Report,,2024-07-02 19:13:36,2024-07-10 09:35:46,artificial_intelligence,0,74,2024-07-10 09:35:46,74,2024-07-10 09:35:46,74,0,0,95c6c3d444d50cd19494ed55195ea41f,ai_jobs,2024-06-28,365
6601,AI Index Report,,2024-07-02 19:13:36,2024-07-22 08:46:10,artificial_intelligence,0,74,2024-07-22 08:46:11,74,2024-07-22 08:46:11,74,0,0,725a6fd128dbea3ff9d9d21b2a4e2f61,ai_investment_generative_companies,2024-06-28,365
6600,AI Index Report,,2024-07-02 19:13:36,2024-07-10 09:35:46,artificial_intelligence,0,74,2024-07-10 09:35:47,74,2024-07-10 09:35:47,74,0,0,2fb14ff4a85da4078222833bf972a127,ai_incidents,2024-06-28,365
6599,Global Burden of Disease - Health-adjusted life expectancy,,2024-07-02 16:03:20,2024-07-02 16:03:21,ihme_gbd,1,74,2024-07-02 16:03:22,74,2024-07-02 16:03:22,74,1,0,f9a57693d27e705d2f6c42dcf9d59326,gbd_healthy_life_expectancy,2024-07-02,
6598,Extreme poverty projections up to 2030,,2024-07-02 16:03:20,2024-07-08 18:01:50,wb,0,74,2024-07-08 18:01:50,74,2024-07-08 18:01:50,74,0,0,d9d4b31fc5256e47cb212f273daf111f,poverty_projections,2024-06-26,365
6597,Global Burden of Disease - Mental Health Burden,,2024-07-01 16:11:41,2024-07-01 16:11:55,ihme_gbd,1,74,2024-07-01 16:11:55,74,2024-07-01 16:11:55,74,1,0,63fa26117ae65b1decad4db6c498a621,gbd_mental_health_burden_dalys,2024-05-20,1460
6596,Breakdown of material footprint of electricity (Wang et al. 2024),,2024-07-01 13:39:32,2024-07-24 11:36:37,fasttrack,0,74,2024-07-24 11:36:38,74,2024-07-24 11:36:38,74,0,0,85df786b96aad8ba2f199e332d090335,breakdown_material_footprint_wang,latest,
6595,Material footprint of electricity (Wang et al. 2024),,2024-07-01 13:30:46,2024-07-24 11:36:40,fasttrack,0,74,2024-07-24 11:36:41,74,2024-07-24 11:36:41,74,0,0,5c77e598f3cc907ad06173d0ca2c1348,electricity_material_footprint_wang,latest,
6594,Rock-to-metal mining ratio (Nassar et al. 2022),,2024-07-01 11:50:31,2024-07-08 18:06:14,fasttrack,0,74,2024-07-08 18:06:14,74,2024-07-08 18:06:14,74,0,0,83376a09eb035b5c73fff4ea517eef8c,rock_metal_ratio_nassar,latest,
6593,Global Burden of Disease - Child Mortality,,2024-06-28 16:35:07,2024-07-25 23:25:34,ihme_gbd,1,74,2024-07-25 23:25:34,74,2024-07-25 23:25:34,74,1,0,f866f94158f256cb16ebb395c97ca3af,gbd_child_mortality,2024-05-20,1460
6592,DRAFT Voter turnout by age - Sheet1,,2024-06-25 15:45:14,2024-06-26 09:44:11,fasttrack,1,74,2024-06-26 09:44:12,74,2024-06-26 09:44:12,74,0,0,91600f0f07d61c6c096196b8d8cdb8db,voter_turnout_by_age__sheet1,latest,
6591,Global Carbon Budget,,2024-06-25 14:50:41,2024-07-25 23:15:33,gcp,0,74,2024-07-25 23:15:34,74,2024-07-25 23:15:34,74,0,0,458acf1025183ccba501835e05639745,global_carbon_budget,2024-06-20,365
6590,Energy mix,,2024-06-25 14:50:29,2024-07-25 23:09:01,energy,0,74,2024-07-25 23:09:02,74,2024-07-25 23:09:02,74,0,0,ad67a06d0cc70c0953315de4ae2ecf7d,energy_mix,2024-06-20,365
6589,Electricity mix,,2024-06-25 14:50:26,2024-07-25 23:15:33,energy,0,74,2024-07-25 23:15:33,74,2024-07-25 23:15:33,74,0,0,e2bcffe7f7bfd26ad748f193bc2d7df8,electricity_mix,2024-06-20,365
6588,UK historical electricity,,2024-06-25 14:50:26,2024-07-25 23:20:11,energy,0,74,2024-07-25 23:20:11,74,2024-07-25 23:20:11,74,0,0,3b8f8db6da2250085096e61ada26d821,uk_historical_electricity,2024-06-20,365
6587,Global Primary Energy,,2024-06-25 14:50:26,2024-07-25 23:07:09,energy,0,74,2024-07-25 23:07:09,74,2024-07-25 23:07:09,74,0,0,763c2509de75fd37b6ee215a60a5e80f,global_primary_energy,2024-06-20,365
6586,Statistical Review of World Energy,,2024-06-25 14:50:25,2024-07-25 22:54:44,energy_institute,0,74,2024-07-25 22:54:44,74,2024-07-25 22:54:44,74,0,0,ec180a36fc9a5a668e77b2a959ff0e2f,statistical_review_of_world_energy,2024-06-20,365
6585,Fossil fuel production,,2024-06-25 14:50:24,2024-07-25 23:09:03,energy,0,74,2024-07-25 23:09:03,74,2024-07-25 23:09:03,74,0,0,a62ca1ef6e233c704c7e78c8e55a8627,fossil_fuel_production,2024-06-20,365
6584,Fossil fuel reserves/production ratio,,2024-06-25 14:50:23,2024-07-25 23:06:58,energy,0,74,2024-07-25 23:06:58,74,2024-07-25 23:06:58,74,0,0,0f3cead34b959e78824b0ea7c04d9e70,fossil_fuel_reserves_production_ratio,2024-06-20,365
6583,Primary energy consumption,,2024-06-25 14:50:22,2024-07-25 23:09:03,energy,0,74,2024-07-25 23:09:04,74,2024-07-25 23:09:04,74,0,0,743b81cd328a7faa659f5c3be7f1f381,primary_energy_consumption,2024-06-20,365
6582,Luxembourg Income Study (LIS),,2024-06-25 14:16:15,2024-07-25 22:56:13,lis,0,74,2024-07-25 22:56:13,74,2024-07-25 22:56:13,74,0,0,d8a3298a485b9af1908396691a7e4cdf,luxembourg_income_study,2024-06-13,365
6581,Guinea worm reported cases and certification (WHO),,2024-06-25 09:06:35,2024-07-08 16:38:18,who,0,74,2024-07-08 16:38:19,74,2024-07-08 16:38:19,74,0,0,98d7e1e402c5b584cc3510c26ec5edaf,guinea_worm,2024-06-17,365
6580,"Guinea worm cases (WHO, 2024)",,2024-06-25 09:06:28,2024-07-08 16:38:17,fasttrack,0,74,2024-07-08 16:38:18,74,2024-07-08 16:38:18,74,0,0,2e67451df240fb18e36f8b171d1fbe3e,guinea_worm,2024-06-17,
6579,UNESCO Institute for Statistics (UIS) - Education,,2024-06-25 08:11:24,2024-07-09 16:16:53,unesco,0,74,2024-07-09 16:16:53,74,2024-07-09 16:16:53,74,0,0,2309423a8373f07c8484fe6c01fa043f,education_opri,2024-06-16,365
6578,Standard country or area codes for statistical use (M49),,2024-06-24 10:15:49,2024-07-08 17:24:45,un,0,74,2024-07-08 17:24:45,74,2024-07-08 17:24:45,74,0,0,e66e081f0cb6d21baac348faeb8e0cdb,sdg_regions,2024-06-24,365
6577,Global Burden of Disease - Risk Factors,,2024-06-21 17:24:29,2024-07-25 23:34:38,ihme_gbd,1,74,2024-07-25 23:34:38,74,2024-07-25 23:34:38,74,1,0,565125284f09ae76fec8422091fb5148,gbd_risk,2024-05-20,1460
6576,"Emissions and energy demand from cooling and heating (IEA, 2023)",,2024-06-21 13:55:37,2024-07-08 17:24:04,fasttrack,0,74,2024-07-08 17:24:05,74,2024-07-08 17:24:05,74,0,0,fc79164768b0c77df72b958d4ad586d4,emissions_energy_heating_cooling_iea,latest,
6575,World Happiness Report,,2024-06-20 15:49:57,2024-07-25 23:07:59,happiness,0,74,2024-07-25 23:08:00,74,2024-07-25 23:08:00,74,0,0,552fc45c05bc72e08f9556c289d5530b,happiness,2024-06-09,365
6574,Large-scale AI systems by domain type,,2024-06-19 14:36:00,2024-07-08 16:32:09,artificial_intelligence,0,74,2024-07-08 16:32:09,74,2024-07-08 16:32:09,74,0,0,2225f650ebbf53a12faf92cdadf4da8a,epoch_compute_intensive_domain,2024-06-19,180
6573,Large-scale AI systems by country,,2024-06-19 14:36:00,2024-07-08 16:47:58,artificial_intelligence,0,74,2024-07-08 16:47:58,74,2024-07-08 16:47:58,74,0,0,012349fa04ec5c76355a661d9ea0423c,epoch_compute_intensive_countries,2024-06-19,180
6572,Large-scale AI models by organization,,2024-06-19 14:36:00,2024-06-20 15:08:13,artificial_intelligence,1,74,2024-06-19 14:36:01,74,2024-06-19 14:36:01,74,0,1,04d909b2020d5ec2a346f3907e91b0d7,epoch_compute_intensive_organizations,2024-06-19,180
6571,Tracking Compute-Intensive AI Models,,2024-06-19 14:35:59,2024-07-08 16:38:43,artificial_intelligence,0,74,2024-07-08 16:38:43,74,2024-07-08 16:38:43,74,0,0,2ecf9855a8eb26669d58a3964166f7fe,epoch_compute_intensive,2024-06-19,180
6570,Public Finances in Modern History,,2024-06-17 16:35:31,2024-07-08 16:38:04,imf,0,74,2024-07-08 16:38:04,74,2024-07-08 16:38:04,74,0,0,70eb24f3a09b6a5c1df80da9f382a2ac,public_finances_modern_history,2024-06-12,365
6569,Same-sex marriage around the world - Pew Research Center,,2024-06-17 15:46:03,2024-07-25 23:08:49,pew,0,74,2024-07-25 23:08:49,74,2024-07-25 23:08:49,74,0,0,a45ae83bd3d79c86057d6d8b0d2a32e7,same_sex_marriage,2024-06-03,365
6568,"Draft: Joe – Difference in top 1 percent share, 1980-2018, PIP vs WID data (take2)",,2024-06-15 11:19:33,2024-06-15 11:19:34,fasttrack,1,74,2024-06-15 11:19:34,74,2024-06-15 11:19:34,74,0,0,d48e5569034a32b28d0a3856338f3016,draft_joe_top1share_diff_1980_2018,latest,
6567,"Draft: Joe – Difference in Gini, 1980-2018, PIP vs WID data (take2)",,2024-06-15 11:03:45,2024-06-17 12:19:29,fasttrack,1,74,2024-06-17 12:19:29,74,2024-06-17 12:19:29,74,0,0,8db434faed17c7339573bd66261f0178,draft_joe_gini_diff_1980_2018_take2,latest,
6566,"Draft: Joe – Difference in Gini, 1980-2018, PIP vs WID data",,2024-06-15 10:58:15,2024-06-17 12:19:28,fasttrack,1,74,2024-06-17 12:19:29,74,2024-06-17 12:19:29,74,0,0,346f55d075c7c6168f301b0f960f6d41,draft_joe_gini_diff_1980_2018,latest,
6565,Homosexuality criminalization data from Mignot,,2024-06-14 15:10:41,2024-07-25 23:08:49,lgbt_rights,0,74,2024-07-25 23:08:50,74,2024-07-25 23:08:50,74,0,0,1ed6592be54be88717ce8a78318b55bb,criminalization_mignot,2024-06-11,365
6564,"Draft: Joe – Difference in Top 1 percent share, 1993-2018, PIP vs WID data",,2024-06-14 07:52:32,2024-06-16 06:41:52,fasttrack,1,74,2024-06-16 06:41:52,74,2024-06-16 06:41:52,74,0,0,19c698c2c28cb2d48eeeccf24fbd9255,draft_joe_top1_diff_1993_2018,latest,
6563,ISAPS Global Survey,,2024-06-12 20:42:54,2024-07-08 16:30:42,health,0,74,2024-07-08 16:30:42,74,2024-07-08 16:30:42,74,0,0,60e52f1bdd5c205d9cc7e9c9b71e0e86,isaps_plastic_surgery,2024-06-11,365
6562,"Global Burden of Disease, Cause Hierarchy",,2024-06-12 19:07:37,2024-07-25 23:35:36,ihme_gbd,1,74,2024-07-25 23:35:36,74,2024-07-25 23:35:36,74,1,0,34c13d401ba0f084dc49b851d3474ca0,leading_causes_deaths,2024-06-10,365
6561,Notable AI systems by domain type,,2024-06-11 19:07:25,2024-07-08 16:31:12,artificial_intelligence,0,74,2024-07-08 16:31:12,74,2024-07-08 16:31:12,74,0,0,905d5f4a1c8d05ebe839b68d4104535e,epoch_aggregates_domain,2024-06-03,31
6560,Notable AI systems by organization,,2024-06-11 19:07:25,2024-06-20 15:12:41,artificial_intelligence,1,74,2024-06-13 09:20:51,74,2024-06-13 09:20:51,74,0,1,6361d13a3bcae93281526cf9947bce45,epoch_aggregates_organizations,2024-06-03,31
6559,Notable AI systems by country,,2024-06-11 19:07:25,2024-06-20 15:12:27,artificial_intelligence,1,74,2024-06-13 09:20:52,74,2024-06-13 09:20:52,74,0,1,b2771dc84b08be1bf8585b57909ae472,epoch_aggregates_countries,2024-06-03,31
6558,Notable AI systems by researcher affiliation,,2024-06-11 19:07:24,2024-07-08 16:31:08,artificial_intelligence,0,74,2024-07-08 16:31:08,74,2024-07-08 16:31:08,74,0,0,6410245d0da2c017a1b0f04d4087eaf7,epoch_aggregates_affiliation,2024-06-03,31
6557,"Parameter, Compute and Data Trends in Machine Learning",,2024-06-11 19:07:24,2024-07-08 17:29:30,artificial_intelligence,0,74,2024-07-08 17:29:30,74,2024-07-08 17:29:30,74,0,0,e842f7545f3850c8413479bacccde786,epoch,2024-06-03,31
6556,Equaldex,,2024-06-11 18:31:55,2024-07-25 23:14:45,lgbt_rights,0,74,2024-07-25 23:14:46,74,2024-07-25 23:14:46,74,0,0,ef5aa4c559298d02c544501cdc822327,equaldex,2024-06-03,365
6555,International Smoking Statistics,,2024-06-11 18:05:08,2024-07-25 23:11:33,smoking,0,74,2024-07-25 23:11:33,74,2024-07-25 23:11:33,74,0,0,97c79763b2c21c51f55704d8f4f223fd,cigarette_sales,2024-05-30,0
6554,World Bank Gender Statistics,,2024-06-11 17:33:11,2024-07-08 17:30:08,wb,0,74,2024-07-08 17:30:09,74,2024-07-08 17:30:09,74,0,0,a582d69123dfd06ef455dab08058de14,gender_statistics,2024-06-10,365
6553,The rising costs of training frontier AI models,,2024-06-10 11:03:07,2024-07-08 15:47:05,artificial_intelligence,0,74,2024-07-08 15:47:05,74,2024-07-08 15:47:05,74,0,0,5dfa81a49594e45956e0756ed1a957d4,epoch_compute_cost,2024-06-06,
6552,"Draft: Joe – Difference in Gini, 1993-2018, PIP vs WID data",,2024-06-09 16:04:27,2024-06-17 11:37:58,fasttrack,1,74,2024-06-17 11:37:58,74,2024-06-17 11:37:58,74,0,0,4a8eec0c9396a6b22cdd4119d9c5a5a2,draft_joe_gini_diff_1993_2018,latest,