{"id":11423,"date":"2021-09-08T12:56:47","date_gmt":"2021-09-08T10:56:47","guid":{"rendered":"https:\/\/h3africa.org\/?page_id=11423"},"modified":"2026-07-26T16:54:41","modified_gmt":"2026-07-26T14:54:41","slug":"h3africa-dbac-approvals","status":"publish","type":"page","link":"https:\/\/h3africa.org\/index.php\/h3africa-dbac-approvals\/","title":{"rendered":"H3Africa DBAC Approvals"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text css=”.vc_custom_1631098962553{margin-top: 36px !important;}”]<\/p>\n

DBAC Approvals<\/h3>\n

Search by requester, institution, study, year, approval type, or approved resource. Select any column heading to sort the results, and use the page-length control to view more entries.<\/p>\n

\n

\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
Year<\/th>Approval type<\/th>Name<\/th>Institution \/ Research Group<\/th>Study<\/th>Approved resources<\/th>\n<\/tr>\n<\/thead>\n
2026<\/td>Dataset<\/td>Didi Tsitsi<\/td>University of the Witwatersrand<\/td>Genomic data driven approach to estimate the prevalence of autosomal recessive disorders in African populations<\/td>CAfGEN Exome \u2014 EGAD00001006224
\nH3Africa Consortium WGS VCF \u2014 EGAD00001008577<\/td>\n<\/tr>\n
2026<\/td>Dataset<\/td>Sandra Oliveira; Dang Liu<\/td>University of Zurich<\/td>Reconstructing the history of African populations to understand the impact of demography in the evolution of languages and other cultural traits<\/td>H3AChip-Trypanogen 1 \u2014 EGAD00001004393
\nH3AChip-Elsi \u2014 EGAD00001004316
\nTrypanoGEN Main \u2014 EGAD00001005076
\nH3AChip-Malsic \u2014 EGAD00001004557
\nH3AChip-Trypanogen 2 \u2014 EGAD00001004220
\nH3AChip-Needi \u2014 EGAD00001004334
\nH3AChip-Cafgen \u2014 EGAD00001004533
\nAWI-Gen Phase 1 WGS data from 100 South Africans \u2014 EGAD00001006418
\nH3AChip-Accme \u2014 EGAD00001004505
\nH3AChip-Awi-gen \u2014 EGAD00001004448
\nTrypanoGEN+ Whole Genome Sequencing \u2014 EGAD00001011666<\/td>\n<\/tr>\n
2025<\/td>Dataset<\/td>Ayoub Ksouri<\/td>University of the Witwatersrand<\/td>Clinical and Genomic Predictors of Childhood Status Epilepticus (SE) and Epilepsy in Sub-Saharan Africa: A Case-Control Study in Nigerian Children<\/td>KDRN Genotype \u2014 EGAD00001009333
\nACEGID Omni 2.5M and 5M \u2014 EGAD00010002509
\nACEGID H3Africa Array \u2014 EGAD00010002510
\nSIREN GWAS Stroke Microbiome \u2014 EGAD00010002551
\nH3Africa Consortium WGS VCF \u2014 EGAD00001008577<\/td>\n<\/tr>\n
2025<\/td>Dataset<\/td>Dr Anissah Ghoorah<\/td>University of Mauritius<\/td>Genotype\u2013phenotype data correlation using deep learning<\/td>AWI-Gen Phase 1 GWAS Genotype \u2014 EGAD00010001996
\nAWI-GEN 2 Phenotype \u2014 EGAD00001015440<\/td>\n<\/tr>\n
2025<\/td>Dataset<\/td>Dr Stephane E. Castel<\/td>Variant Bio<\/td>Mapping Germline Immune Variation in Continental African Groups<\/td>H3AChip-Trypanogen 1 \u2014 EGAD00001004393
\nH3AChip-Elsi \u2014 EGAD00001004316
\nTrypanoGEN Main \u2014 EGAD00001005076
\nH3AChip-Malsic \u2014 EGAD00001004557
\nH3AChip-Trypanogen 2 \u2014 EGAD00001004220
\nH3AChip-Needi \u2014 EGAD00001004334
\nH3AChip-Cafgen \u2014 EGAD00001004533
\nAWI-Gen Phase 1 WGS data from 100 South Africans \u2014 EGAD00001006418
\nH3AChip-Accme \u2014 EGAD00001004505
\nH3AChip-Awi-gen \u2014 EGAD00001004448
\nCAfGEN Exome \u2014 EGAD00001006224
\nTrypanoGEN+ Whole Genome Sequencing \u2014 EGAD00001011666<\/td>\n<\/tr>\n
2025<\/td>Dataset<\/td>Dr. Tope-Oke Adebusola<\/td>Afe Babalola University<\/td>Quantum-Accelerated Rare Genome Detection In African Genomes<\/td>AWI-Gen Phase 1 Phenotype \u2014 EGAD00001006425
\nAWI-Gen Phase 1 WGS data from 100 South Africans \u2014 EGAD00001006418
\nAWI-Gen Phase 1 GWAS Genotype \u2014 EGAD00010001996
\nH3AChip-Awi-gen \u2014 EGAD00001004448
\nCAfGEN Exome \u2014 EGAD00001006224
\nTrypanoGEN+ Whole Genome Sequencing \u2014 EGAD00001011666
\nH3Africa Consortium WGS VCF \u2014 EGAD00001008577<\/td>\n<\/tr>\n
2025<\/td>Dataset<\/td>Hymne Elne Spies<\/td>University of the Free State<\/td>In-silico data analysis of genes associated with preeclampsia and its comorbidities<\/td>AWI-Gen Pilot \u2014 EGAD00010001258
\nAWI-Gen Phase 1 Phenotype \u2014 EGAD00001006425
\nKDRN Genotype \u2014 EGAD00001009333
\nKDRN Phenotype \u2014 EGAD00010002365<\/td>\n<\/tr>\n
2025<\/td>Dataset<\/td>Meriem Yagoubi<\/td>American University in Cairo<\/td>Deep Learning\u2013Based Taxonomic Profiling of the African Gut Microbiome Using the AWI-Gen 2 Dataset<\/td>AWI-GEN 2 Phenotype \u2014 EGAD00001015440
\nAWI-GEN 2 Microbiome \u2014 EGAD00001015449<\/td>\n<\/tr>\n
2025<\/td>Dataset<\/td>Prof Andrea Manica<\/td>University of Cambridge<\/td>The role of culture and mobility on shaping African genetic diversity<\/td>H3AChip-Trypanogen 1 \u2014 EGAD00001004393
\nH3AChip-Elsi \u2014 EGAD00001004316
\nH3AChip-Trypanogen 2 \u2014 EGAD00001004220
\nH3AChip-Cafgen \u2014 EGAD00001004533<\/td>\n<\/tr>\n
2025<\/td>Dataset<\/td>Prof Shaohua Fan<\/td>Fudan University<\/td>Investigating Evolution, Mutation Hotspots, and Archaic Introgression in African Populations Using H3Africa Whole Genome Sequencing Data<\/td>H3Africa Consortium WGS VCF \u2014 EGAD00001008577<\/td>\n<\/tr>\n
2025<\/td>Biospecimen<\/td>Zaza M. Ndhlovu<\/td>Africa Health Research Institute<\/td>HUMAN LEUKOCYTE ANTIGEN (HLA) DIVERSITY IN SUB-SAHARA AFRICAN POPULATIONS<\/td>NEEDI
\nSIREN<\/td>\n<\/tr>\n
2024<\/td>Dataset<\/td>Dr Caitlin Uren<\/td>Stellenbosch University<\/td>A comprehensive whole genome sequence positive selection scan in southern Africa<\/td>AWI-Gen Phase 1 WGS data from 100 South Africans \u2014 EGAD00001006418
\nH3Africa Consortium WGS VCF \u2014 EGAD00001008577
\nH3AChip-Trypanogen 2 \u2014 EGAD00001004220
\nH3AChip-Cafgen \u2014 EGAD00001004533
\nAWI-Gen Phase 1 Phenotype \u2014 EGAD00001006425
\nH3AChip-Phenotype \u2014 EGAD00001005310<\/td>\n<\/tr>\n
2024<\/td>Dataset<\/td>Nicholas Martin<\/td>QIMR Berghofer<\/td>Genetics of DZ twinning in African, Asian and European ancestry populations: implications for infertility<\/td>KDRN Genotype \u2014 EGAD00001009333
\nKDRN Phenotype \u2014 EGAD00010002365<\/td>\n<\/tr>\n
2024<\/td>Dataset<\/td>Patin Etienne<\/td>Institut Pasteur<\/td>The impact of recent demographic changes on the genetic architecture of complex diseases in Central Africans<\/td>H3AChip-Phenotype \u2014 EGAD00001005310<\/td>\n<\/tr>\n
2024<\/td>Dataset<\/td>Prof Ambroise Wonkam<\/td>University of Cape Town<\/td>Genomics of Sickle Cell Disease: a model for understanding human adaptive evolution in Africa and exploring gene-based therapies<\/td>H3AChip-Trypanogen 1 \u2014 EGAD00001004393
\nTrypanoGEN Main \u2014 EGAD00001005076
\nH3AChip-Malsic \u2014 EGAD00001004557
\nH3AChip-Trypanogen 2 \u2014 EGAD00001004220
\nH3AChip-Needi \u2014 EGAD00001004334
\nH3AChip-Cafgen \u2014 EGAD00001004533
\nH3AChip-Accme \u2014 EGAD00001004505
\nH3AChip-Phenotype \u2014 EGAD00001005310
\nH3AChip-Awi-gen \u2014 EGAD00001004448
\nH3Africa Consortium WGS VCF \u2014 EGAD00001008577<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Alicia Martin, Ph.D<\/td>Broad Institute, Massachusetts, United States<\/td>Developing a population genetic and imputation resource of diverse human genomes<\/td>WGS datasets: H3AChip-Phenotype EGAD00001005310; AWI-Gen Phase 1 GWAS Genotype EGAD00010001996; AWI-Gen Phase 1 WGS data from 100 South Africans EGAD00001006418; H3AChip-Trypanogen 2 EGAD00001004220; H3AChip-Awi-gen EGAD00001004448; H3AChip-Elsi EGAD00001004316; H3AChip-Trypanogen 1 EGAD00001004393; H3AChip-Cafgen EGAD00001004533; H3AChip-Accme EGAD00001004505; H3AChip-Needi EGAD00001004334; H3AChip-Malsic EGAD00001004557<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Dr Carla Marquez-Luna<\/td>Martingale Labs, Brooklyn, United States<\/td>Evaluating the utility of current clinical genetic testing methods across diverse populations<\/td>WGS datasets: H3Africa Consortium WGS VCF EGAD00001008577<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Dr Catherine Tcheandjieu<\/td>University of California San Francisco, California, USA<\/td>Understanding the 9p21.3 coronary artery disease-risk region<\/td>WGS, SNP array, and phenotype datasets: AWI-Gen Phase 1 GWAS Genotype EGAD00010001996 AWI-Gen Phase 1 Phenotype EGAD00001006425 H3Africa Consortium WGS VCF EGAD00001008577<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Dr David Twesigomwe<\/td>University of the Witwatersrand, South Africa<\/td>Characterisation of pharmacogene allelic variation in African populations and development of a novel diplotype calling algorithm: Phase 2<\/td>WGS and WES datasets: H3AChip-Elsi EGAD00001004316 H3AChip-Malsic EGAD00001004557 H3AChip-Trypanogen 1 EGAD00001004393 H3AChip-Accme EGAD00001004505 H3AChip-Needi EGAD00001004334 CAfGEN Exome EGAD00001006224 H3AChip-Trypanogen 2 EGAD00001004220 H3AChip-Awi-gen EGAD00001004448 H3AChip-Cafgen EGAD00001004533 AWI-Gen Phase 1 WGS data from 100 South Africans EGAD00001006418<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Dr Hongsheng Gui<\/td>Michigan State University Health Sciences, United States<\/td>Pharmacoepidemiology and pharmacogenomics of opioid use and use disorder during COVID pandemic<\/td>WGS datasets: H3Africa Consortium WGS VCF EGAD00001008577<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Dr Houcemeddine Othman<\/td>University of the Witwatersrand, Johannesburg, South Africa<\/td>An integrative data-driven approach to the genomics of anti-tuberculosis and anti-malarial drug responses in African populations: The AGORA-TM project<\/td>WGS datasets: H3Africa Consortium WGS VCF EGAD00001008577<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Dr Jana Vandrovcova<\/td>University College London, United Kingdom<\/td>The International Centre for Genomic Medicine in Neuromuscular Disease (ICGNMD)<\/td>WGS\/WES datasets: AWI-Gen Phase 1 GWAS Genotype EGAD00010001996 AWI-Gen Phase 1 WGS data from 100 South Africans EGAD00001006418 H3AChip-Trypanogen 2 EGAD00001004220 H3AChip-Awi-gen EGAD00001004448 H3AChip-Elsi EGAD00001004316 H3AChip-Trypanogen 1 EGAD00001004393 H3AChip-Cafgen EGAD00001004533 H3AChip-Accme EGAD00001004505 H3AChip-Needi EGAD00001004334 NEEDI SNPs and INDELS EGAD00001006295 CAfGEN Exome EGAD00001006224 H3AChip-Malsic EGAD00001004557 TrypanoGEN Main EGAD00001005076 AWI-Gen Pilot EGAD00010001258<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Dr Mahmoud Aarabi<\/td>University of Pittsburgh, United States<\/td>Carrier frequency of autosomal recessive conditions in African population<\/td>WGS data TrypanoGEN Main EGAD00001005076<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Dr Sandra Beleza<\/td>University of Leicester, United Kingdom<\/td>Whole Genome Sequencing of Populations from Cabinda, Angola and Maputo, Mozambique<\/td>H3AChip data H3AChip-Trypanogen 2 EGAD00001004220 H3AChip-Elsi EGAD00001004316 H3AChip-Trypanogen 1 EGAD00001004393 H3AChip-Cafgen EGAD00001004533 H3AChip-Accme EGAD00001004505 H3AChip-Needi EGAD00001004334 H3AChip-Malsic EGAD00001004557 H3AChip-AWI-Gen EGAD00001004448<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Etienne Patin, PhD<\/td>Institut Pasteur, France<\/td>The impact of recent demographic changes on the genetic architecture of complex diseases in Central Africans<\/td>WGS datasets: H3AChip-Elsi EGAD00001004316 H3AChip-Malsic EGAD00001004557 H3AChip-Trypanogen 1 EGAD00001004393 H3AChip-Accme EGAD00001004505 H3AChip-Needi EGAD00001004334 H3AChip-Trypanogen 2 EGAD00001004220 H3AChip-Awi-gen EGAD00001004448 H3AChip-Cafgen EGAD00001004533<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Maria Chahrour, Ph.D.<\/td>University of Texas Southwestern Medical Center, United States<\/td>The genetics of neurodevelopmental disorders<\/td>H3AChip data: H3AChip-Trypanogen 2 EGAD00001004220; H3AChip-Awi-gen EGAD00001004448; H3AChip-Elsi EGAD00001004316; H3AChip-Trypanogen 1 EGAD00001004393; H3AChip-Cafgen EGAD00001004533; H3AChip-Accme EGAD00001004505; H3AChip-Needi EGAD00001004334; H3AChip-Malsic EGAD00001004557.<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Prof Carina Schlebusch<\/td>Uppsala University, Evolutionary Biology Centre, Sweden<\/td>A genomic perspective on the history of Africa<\/td>H3A-Baylor and TrypanoGEN WGS datasets: H3AChip-TrypanoGEN 2 EGAD00001004220 H3AChip-AWI-Gen EGAD00001004448 H3AChip-TrypanoGEN 1 EGAD00001004393 H3AChip-ELSI EGAD00001004316 H3AChip-CAfGEN EGAD00001004533 H3AChip-ACCME EGAD00001004505 H3AChip-NEEDI EGAD00001004334 H3AChip-MalSic EGAD00001004557 TrypanoGEN Main EGAD00001005076<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Prof Christof R. Hauck<\/td>Konstanz University, Germany<\/td>CEACAM polymorphisms and their role for bacterial adhesin binding<\/td>WES\/WGS: CAfGEN Exome EGAD00001006224 TrypanoGEN Main EGAD00001005076<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Prof David Curtis<\/td>University College London, United Kingdom<\/td>Distribution of genetic variants across human populations<\/td>AWI-Gen Phase 1 GWAS Genotype EGAD00010001996 H3Africa Consortium WGS VCF EGAD00001008577<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Prof Ezekiel Adebiyi<\/td>Covenant University Ota, Ogun State, Nigeria<\/td>Genome-wide characterization of complex variants and their phenotypic effects in African populations<\/td>WGS datasets: H3AChip-Elsi EGAD00001004316 NEEDI SNPs and INDELS EGAD00001006295 H3AChip-Malsic EGAD00001004557 H3AChip-Trypanogen 1 EGAD00001004393 AWI-Gen Phase 1 GWAS Genotype EGAD00010001996 H3AChip-Needi EGAD00001004334 CAfGEN Exome EGAD00001006224 AWI-Gen Pilot EGAD00010001258 TrypanoGEN Main EGAD00001005076 H3AChip-Phenotype EGAD00001005310 H3AChip-Trypanogen 2 EGAD00001004220 H3AChip-Awi-gen EGAD00001004448 H3AChip-Cafgen EGAD00001004533 AWI-Gen Phase 1 Phenotype EGAD00001006425 AWI-Gen Phase 1 Pilot Microbiome PRJEB40733 AWI-Gen Phase 1 Pilot Microbiome Phenotype EGAD00001006581 AWI-Gen Phase 1 WGS data from 100 South Africans EGAD00001006418 H3Africa Consortium WGS VCF EGAD00001008577<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Prof JM Heckmann<\/td>University of Capetown, South Africa<\/td>Investigating the genetic basis of amyotrophic lateral sclerosis (ALS) and other neuromuscular disorders in South Africans<\/td>TrypanoGEN and H3ABaylor WGS datasets: H3AChip-TrypanoGEN 2 EGAD00001004220 H3AChip-AWI-Gen EGAD00001004448 H3AChip-TrypanoGEN 1 EGAD00001004393 H3AChip-ELSI EGAD00001004316 H3AChip-CAfGEN EGAD00001004533 H3AChip-ACCME EGAD00001004505 H3AChip-NEEDI EGAD00001004334 H3AChip-MalSic EGAD00001004557 TrypanoGEN Main EGAD00001005076 AWI-Gen Phase 1 WGS data from 100 South Africans EGAD00001006418<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Prof Michael Pepper<\/td>University of Pretoria, South Africa<\/td>To determine the African variant allele frequencies and predicted variant effects of genes associated with neonatal encephalopathy with suspected hypoxic-ischemic encephalopathy (NESHIE), Coronavirus disease 2019 (COVID-19), cystic fibrosis and cardiometabolic diseases<\/td>H3AChip-Trypanogen 2 EGAD00001004220 H3AChip-Awi-gen EGAD00001004448 H3AChip-Elsi EGAD00001004316 H3AChip-Trypanogen 1 EGAD00001004393 H3AChip-Cafgen EGAD00001004533 H3AChip-Accme EGAD00001004505 H3AChip-Needi EGAD00001004334 CAfGEN Exome EGAD00001006224 H3AChip-Malsic EGAD00001004557 TrypanoGEN Main EGAD00001005076<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Prof Michael Pepper<\/td>University of Pretoria, South Africa<\/td>A multi-variate, multi-omics study on the pathogenesis of moderate-severe neonatal encephalopathy with suspected hypoxic ischemic encephalopathy<\/td>Datasets: H3AChip-Elsi EGAD00001004316 H3AChip-Malsic EGAD00001004557 H3AChip-Trypanogen 1 EGAD00001004393 H3AChip-Accme EGAD00001004505 H3AChip-Needi EGAD00001004334 CAfGEN Exome EGAD00001006224 H3AChip-Trypanogen 2 EGAD00001004220 H3AChip-Awi-gen EGAD00001004448 H3AChip-Cafgen EGAD00001004533 H3Africa Consortium WGS VCF EGAD00001008577<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Prof Vanessa Dumeaux<\/td>The University of Western Ontario, Canada<\/td>Functional Interactions and Global Diversity in Gut Microbiomes: Advancing Microbial Community Type Identification with Deep Learning Approaches<\/td>AWI-Gen Phase 1 Pilot Microbiome Phenotype EGAD00001006581<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Prof Yukinori Okada<\/td>Osaka University Graduate School of Medicine, Japan<\/td>Elucidation of disease pathology by multi-omics analysis of the homogeneity and heterogeneity of selection signatures in diverse populations<\/td>WGS datasets: H3AChip-Elsi EGAD00001004316 H3AChip-Malsic EGAD00001004557 H3AChip-Trypanogen 1 EGAD00001004393 AWI-Gen Phase 1 GWAS Genotype EGAD00010001996 H3AChip-Accme EGAD00001004505 H3AChip-Needi EGAD00001004334 AWI-Gen Pilot EGAD00010001258 TrypanoGEN Main EGAD00001005076 ACCME NIH H3Africa phs001945.v1.p1 H3AChip-Phenotype EGAD00001005310 H3AChip-Trypanogen 2 EGAD00001004220 H3AChip-Awi-gen EGAD00001004448 H3AChip-Cafgen EGAD00001004533 AWI-Gen Phase 1 Phenotype EGAD00001006425 ReMAC-Phenotype EGAD00001006244 AWI-Gen Phase 1 WGS data from 100 South Africans EGAD00001006418 H3Africa Consortium WGS VCF EGAD00001008577<\/td>\n<\/tr>\n
Earlier<\/td>Dataset<\/td>Tabitha Osler<\/td>University of the Witwatersrand, South Africa<\/td>Prevalence and consequences of variants in genes associated with breast cancer in black South African women<\/td>WGS datasets: TrypanoGEN Main EGAD00001005076 H3Africa Consortium WGS VCF EGAD00001008577<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n[\/vc_column_text][\/vc_column][\/vc_row]<\/p>\n","protected":false},"excerpt":{"rendered":"

[vc_row][vc_column][vc_column_text css=”.vc_custom_1631098962553{margin-top: 36px !important;}”] DBAC Approvals Search by requester, institution, [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","footnotes":""},"categories":[24],"tags":[],"class_list":["post-11423","page","type-page","status-publish","hentry","category-consortium-documents-consortium-documents"],"_links":{"self":[{"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/pages\/11423","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/comments?post=11423"}],"version-history":[{"count":4,"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/pages\/11423\/revisions"}],"predecessor-version":[{"id":15439,"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/pages\/11423\/revisions\/15439"}],"wp:attachment":[{"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/media?parent=11423"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/categories?post=11423"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/h3africa.org\/index.php\/wp-json\/wp\/v2\/tags?post=11423"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}