{"id":763,"date":"2023-05-18T13:21:21","date_gmt":"2023-05-18T13:21:21","guid":{"rendered":"https:\/\/deeplearningindaba.com\/2023\/?page_id=763"},"modified":"2023-08-23T06:37:55","modified_gmt":"2023-08-23T06:37:55","slug":"african-datasets","status":"publish","type":"page","link":"https:\/\/deeplearningindaba.com\/2023\/african-datasets\/","title":{"rendered":"AFRICAN DATASETS"},"content":{"rendered":"

We invite submissions of a one page abstract of an African Dataset for the Deep Learning Indaba in 2023. Submissions are limited to only selected attendees who wish to share a 15 Minutes presentation about their African Dataset during the Research in Africa Showcase Days.<\/p>\n

Submission of the African Dataset Abstract is to be done using the DLI2023 registration form that will be shared via Baobab<\/a> with participants.<\/p>\n

Submission Deadline: 7 August 2023<\/p>\n

African Datasets Talks Schedule<\/strong><\/span><\/p>\n

Date: <\/strong>6th September, 2023<\/p>\n

Time:<\/strong> 14:00 – 16:00<\/p>\n\n\n\n\n\n\n\n\n\n\n\n
\n

Time<\/b><\/p>\n<\/td>\n

\n

Speaker<\/b><\/p>\n<\/td>\n

\n

Title<\/b><\/p>\n<\/td>\n<\/tr>\n

\n

14:00<\/p>\n<\/td>\n

\n

Tejumade<\/p>\n<\/td>\n

\n

Afonja<\/p>\n<\/td>\n

\n

SautiDB: Nigerian Accent Dataset Collection<\/p>\n<\/td>\n<\/tr>\n

\n

14:15<\/p>\n<\/td>\n

\n

David<\/p>\n<\/td>\n

\n

Adelani<\/p>\n<\/td>\n

\n

MasakhaNEWS<\/p>\n<\/td>\n<\/tr>\n

\n

14:30<\/p>\n<\/td>\n

\n

Asmelash Teka<\/p>\n<\/td>\n

\n

Hadgu<\/p>\n<\/td>\n

\n

Machine Translation Benchmark Dataset for Languages in the Horn of Africa<\/p>\n<\/td>\n<\/tr>\n

\n

14:45<\/p>\n<\/td>\n

\n

Brian<\/p>\n<\/td>\n

\n

Halubanza<\/p>\n<\/td>\n

\n

African Migratory Locust and Red Locust Dataset<\/p>\n<\/td>\n<\/tr>\n

\n

15:00<\/p>\n<\/td>\n

\n

Austin<\/p>\n<\/td>\n

\n

Thope<\/p>\n<\/td>\n

\n

Chichewa parts of speech dataset<\/p>\n<\/td>\n<\/tr>\n

\n

15:15<\/p>\n<\/td>\n

\n

Nicolas<\/p>\n<\/td>\n

\n

Lopez Carranza<\/p>\n<\/td>\n

\n

African component on the 1000 Genome Dataset<\/p>\n<\/td>\n<\/tr>\n

\n

15:30<\/p>\n<\/td>\n

\n

Sekou Lionel<\/p>\n<\/td>\n

\n

Remy<\/p>\n<\/td>\n

\n

An Offline Reinforcement Learning Dataset for Global Health<\/p>\n<\/td>\n<\/tr>\n

\n

15:45<\/p>\n<\/td>\n

\n

Lillian<\/p>\n<\/td>\n

\n

Wanzare<\/p>\n<\/td>\n

\n

AI4KSL: Bridging Language Barrier using Artificial Intelligence for Kenyan Sign Language among Deaf Learners<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

List of African Datasets Poster Presentations
<\/strong><\/p>\n

Date:<\/strong> 5th September, 2023<\/p>\n

Time:<\/strong> 11:00<\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Dataset #<\/td>\nFirst Name<\/td>\nLast Name<\/td>\nDataset Title<\/td>\nDataset PDF<\/td>\n<\/tr>\n
1<\/td>\nAanuoluwapo\u00a0<\/td>\nAkinniranye\u00a0<\/td>\nAfrican Fashion<\/td>\nhttps:\/\/drive.google.com\/open?id=17QcT3FmsDBX_JRLQmp_F37Vt-NWPG3oQ<\/a><\/span><\/td>\n<\/tr>\n
2<\/td>\nAbdoulaye<\/td>\nDiallo<\/td>\nwaxal datasets<\/td>\n\u00a0<\/td>\n<\/tr>\n
4<\/td>\nAmelia<\/td>\nTaylor<\/td>\nC19 News and journal articles<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Amelia_Taylor.pdf\u00a0<\/a><\/td>\n<\/tr>\n
5<\/td>\nAsmelash Teka<\/td>\nHadgu<\/td>\nMachine Translation Benchmark Dataset for Languages in the Horn of Africa<\/td>\n\u00a0<\/td>\n<\/tr>\n
6<\/td>\nAustin<\/td>\nThope<\/td>\nChichewa parts of speech dataset<\/td>\n\u00a0<\/td>\n<\/tr>\n
7<\/td>\nBrian<\/td>\nHalubanza<\/td>\nAfrican Migratory Locust and Red Locust Dataset<\/td>\n\u00a0<\/td>\n<\/tr>\n
8<\/td>\nCYNTHIA<\/td>\nJAYNE AMOL<\/td>\nPolitiKweli<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_CYNTHIA_JAYNE AMOL.pdf\u00a0<\/a><\/td>\n<\/tr>\n
9<\/td>\nDavid<\/td>\nAdelani<\/td>\nMasakhaNEWS<\/td>\nhttps:\/\/drive.google.com\/open?id=1zojAfqmXvdQn1WJV7i3rfCVptGLihwTk<\/a><\/span><\/td>\n<\/tr>\n
10<\/td>\nDylan<\/td>\nGeldenhuys<\/td>\nImage and morphometrics dataset for tsetse fly<\/td>\n\u00a0<\/td>\n<\/tr>\n
11<\/td>\nEliette<\/td>\nNgo Tjomb Assembe<\/td>\nEwondo-French Morphological analyser<\/td>\n\u00a0<\/td>\n<\/tr>\n
12<\/td>\nEsther<\/td>\nOduntan<\/td>\nIndigenous African Fashion Dataset<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Esther_Oduntan.pdf\u00a0<\/a><\/td>\n<\/tr>\n
13<\/td>\nFalia<\/td>\nRAMANANTSOA<\/td>\nVoxMg: An Automatic Speech Recognition Dataset for Malagasy<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Falia_RAMANANTSOA.pdf\u00a0<\/a><\/td>\n<\/tr>\n
14<\/td>\nFetenech<\/td>\nMeskele<\/td>\nPrediction of malaria Diseases\u2019 High Prevalence Time and place using Machine Learning Algorithm in Wolaita Zone, Ethiopia<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Fetenech_Meskele.pdf\u00a0<\/a><\/td>\n<\/tr>\n
15<\/td>\nFortune<\/td>\nAdekogbe<\/td>\nEnglish and Nigerian Pidgin Parallel Sentences<\/td>\n\u00a0<\/td>\n<\/tr>\n
16<\/td>\nGyabeng\u00a0<\/td>\nElton Modestus<\/td>\nPAI: Palm oil Adulteration Index<\/td>\n\u00a0<\/td>\n<\/tr>\n
17<\/td>\nHalleluyah<\/td>\nAworinde<\/td>\nPoultry Fecal Imagery Dataset for Health Status Prediction<\/td>\n\u00a0<\/td>\n<\/tr>\n
18<\/td>\nIan<\/td>\nOmung’a<\/td>\nCapabilities Distribution Analysis of Cancer Screening Centres in Kenya<\/td>\n\u00a0<\/td>\n<\/tr>\n
19<\/td>\nIbukun David<\/td>\nBabatunde<\/td>\nAfrica Tenders dataset<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Ibukun David_Babatunde.pdf\u00a0<\/a><\/td>\n<\/tr>\n
20<\/td>\nJabez\u00a0<\/td>\nMagomere<\/td>\nScaling Crowdsourced Election Monitoring: Construction and Evaluation of Classification Models for Multilingual Election Reports and Cross-Domain Classification Settings<\/td>\n\u00a0<\/td>\n<\/tr>\n
21<\/td>\nJoshua<\/td>\nMuhumuza<\/td>\n\u00a0<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Joshua_Muhumuza.pdf\u00a0<\/a><\/td>\n<\/tr>\n
22<\/td>\nLillian\u00a0<\/td>\nWanzare<\/td>\nAI4KSL: Bridging Language Barrier using Artificial Intelligence for Kenyan Sign Language among Deaf Learners<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Lillian _Wanzare.pdf\u00a0<\/a><\/td>\n<\/tr>\n
23<\/td>\nMhlasakululeka<\/td>\nMvubu<\/td>\n\u00a0<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Mhlasakululeka_Mvubu.pdf\u00a0<\/a><\/td>\n<\/tr>\n
24<\/td>\nMuhammed<\/td>\nSaeed<\/td>\nEnglish to Nigerien pidgin parallel dataset\u00a0<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Muhammed_Saeed.pdf\u00a0<\/a><\/td>\n<\/tr>\n
25<\/td>\nNabil<\/td>\nBADRI<\/td>\nTun-EL: Arabic Multi-dialect Dataset<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Nabil_BADRI.pdf\u00a0<\/a><\/td>\n<\/tr>\n
26<\/td>\nNicolas\u00a0<\/td>\nLopez Carranza\u00a0<\/td>\nAfrican component on the 1000 Genome Dataset<\/td>\n\u00a0<\/td>\n<\/tr>\n
27<\/td>\nNUMFOR<\/td>\nELMER CHENEMO<\/td>\ncmr_exno_data_inondationlc_md_mt_v1.0_20221206<\/td>\n\u00a0<\/td>\n<\/tr>\n
28<\/td>\nRichard<\/td>\nSserunjogi<\/td>\nHyperlocal air quality dataset collected from spatially distributed AirQo network<\/td>\nhttps:\/\/drive.google.com\/open?id=1O-vl-_woQsyK87piBgTKF_HP9TaYA0hQ<\/a><\/span><\/td>\n<\/tr>\n
29<\/td>\nRokhaya<\/td>\nDiagne<\/td>\nFish\u00a0 dataset for underwater species\u00a0 classification and inventory\u00a0<\/td>\n\u00a0<\/td>\n<\/tr>\n
30<\/td>\nSamuel<\/td>\nRutunda<\/td>\nParralel English Kinyarwanda dataset for Education and Tourism<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Samuel_Rutunda.pdf\u00a0<\/a><\/td>\n<\/tr>\n
31<\/td>\nstephen\u00a0<\/td>\nkalyesubula<\/td>\nHMIS<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_stephen _kalyesubula.pdf\u00a0<\/a><\/td>\n<\/tr>\n
32<\/td>\nTadesse<\/td>\nKebede<\/td>\nOrosenti<\/td>\n\u00a0<\/td>\n<\/tr>\n
33<\/td>\nTage<\/td>\nAboneh<\/td>\n\u00a0<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Tage_Aboneh.pdf\u00a0<\/a><\/td>\n<\/tr>\n
34<\/td>\nTayo<\/td>\nOgundunmade<\/td>\nTerrorism dataset in Nigeria<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Tayo_Ogundunmade.pdf\u00a0<\/a><\/td>\n<\/tr>\n
35<\/td>\nTejumade<\/td>\nAfonja<\/td>\nSautiDB: Nigerian Accent Dataset Collection<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Tejumade_Afonja.pdf\u00a0<\/a><\/td>\n<\/tr>\n
36<\/td>\nUssen<\/td>\nKimanuka<\/td>\nLow-resource congolese datasets<\/td>\n\u00a0<\/td>\n<\/tr>\n
37<\/td>\nWalelign<\/td>\nSewunetie<\/td>\nAmharic Text to Speech<\/td>\n\u00a0<\/td>\n<\/tr>\n
38<\/td>\nYuda<\/td>\nMnyawami<\/td>\nEastern African Secondary School Dataset\u00a0<\/td>\nhttps:\/\/storage.googleapis.com\/indaba-public\/poster_Yuda_Mnyawami.pdf\u00a0<\/a><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"

We invite submissions of a one page abstract of an African Dataset for the Deep Learning Indaba in 2023. Submissions are limited to only selected attendees who wish to share a 15 Minutes presentation about their African Dataset during the Research in Africa Showcase Days. Submission of the African Dataset Abstract is to be done […]<\/p>\n","protected":false},"author":24,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-763","page","type-page","status-publish","hentry"],"acf":[],"yoast_head":"\nAFRICAN DATASETS - Deep Learning Indaba 2023<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/deeplearningindaba.com\/2023\/african-datasets\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AFRICAN DATASETS - Deep Learning Indaba 2023\" \/>\n<meta property=\"og:description\" content=\"We invite submissions of a one page abstract of an African Dataset for the Deep Learning Indaba in 2023. 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