Current data science capacity building initiatives for health researchers in LMICs: global & regional efforts
Background: Data science approaches have proved crucial for generating major insights to address public health challenges. While such approaches have played significant roles during the COVID-19 pandemic, there has been limited investment in capacity building in data science skills and infrastructur...
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Frontiers Media
2024
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| _version_ | 1826316808548777984 |
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| author | Kiosia, A Boylan, S Retford, M Marques, LP Bueno, FTC Kirima, C Islam, MS Naheed, A Wozencraft, A |
| author_facet | Kiosia, A Boylan, S Retford, M Marques, LP Bueno, FTC Kirima, C Islam, MS Naheed, A Wozencraft, A |
| author_sort | Kiosia, A |
| collection | OXFORD |
| description | Background: Data science approaches have proved crucial for generating major insights to address public health challenges. While such approaches have played significant roles during the COVID-19 pandemic, there has been limited investment in capacity building in data science skills and infrastructure for health researchers in LMICs. Objectives: This review aims to identify current health data science capacity building initiatives and gaps in Africa, Asia, and Latin America and the Caribbean (LAC), to support knowledge sharing and collaborations, and inform future initiatives and associated investment. Methods: We conducted a literature review using PubMed and Scopus, supplemented by a grey literature search on Google to identify relevant initiatives. Articles were screened based on inclusion criteria. Findings: From 212 records, 85 met inclusion criteria, with 20 from PubMed and Scopus, and 65 from grey literature. The majority of programmes are tailored to specific disease areas, varying by region. Despite these efforts, there are limited initiatives with a clear, documented strategy on data science capacity building to accelerate global research insights, with the majority adopting a fragmented approach. Conclusion and future directions: Despite the integration of data science approaches into health research initiatives in LMICs, there is a need for a standardised framework on data science capacity building to facilitate multidisciplinary and global collaboration. Structured approaches, inter-disciplinary, inter-regional connections and robust impact measurement will all be vital for advancing health research insights in these settings. |
| first_indexed | 2025-02-19T04:28:41Z |
| format | Journal article |
| id | oxford-uuid:a118ffe0-c0bc-45d0-bad2-dc70fb65a3fd |
| institution | University of Oxford |
| language | English |
| last_indexed | 2025-02-19T04:28:41Z |
| publishDate | 2024 |
| publisher | Frontiers Media |
| record_format | dspace |
| spelling | oxford-uuid:a118ffe0-c0bc-45d0-bad2-dc70fb65a3fd2024-12-11T20:07:57ZCurrent data science capacity building initiatives for health researchers in LMICs: global & regional effortsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a118ffe0-c0bc-45d0-bad2-dc70fb65a3fdEnglishJisc Publications RouterFrontiers Media2024Kiosia, ABoylan, SRetford, MMarques, LPBueno, FTCKirima, CIslam, MSNaheed, AWozencraft, ABackground: Data science approaches have proved crucial for generating major insights to address public health challenges. While such approaches have played significant roles during the COVID-19 pandemic, there has been limited investment in capacity building in data science skills and infrastructure for health researchers in LMICs. Objectives: This review aims to identify current health data science capacity building initiatives and gaps in Africa, Asia, and Latin America and the Caribbean (LAC), to support knowledge sharing and collaborations, and inform future initiatives and associated investment. Methods: We conducted a literature review using PubMed and Scopus, supplemented by a grey literature search on Google to identify relevant initiatives. Articles were screened based on inclusion criteria. Findings: From 212 records, 85 met inclusion criteria, with 20 from PubMed and Scopus, and 65 from grey literature. The majority of programmes are tailored to specific disease areas, varying by region. Despite these efforts, there are limited initiatives with a clear, documented strategy on data science capacity building to accelerate global research insights, with the majority adopting a fragmented approach. Conclusion and future directions: Despite the integration of data science approaches into health research initiatives in LMICs, there is a need for a standardised framework on data science capacity building to facilitate multidisciplinary and global collaboration. Structured approaches, inter-disciplinary, inter-regional connections and robust impact measurement will all be vital for advancing health research insights in these settings. |
| spellingShingle | Kiosia, A Boylan, S Retford, M Marques, LP Bueno, FTC Kirima, C Islam, MS Naheed, A Wozencraft, A Current data science capacity building initiatives for health researchers in LMICs: global & regional efforts |
| title | Current data science capacity building initiatives for health researchers in LMICs: global & regional efforts |
| title_full | Current data science capacity building initiatives for health researchers in LMICs: global & regional efforts |
| title_fullStr | Current data science capacity building initiatives for health researchers in LMICs: global & regional efforts |
| title_full_unstemmed | Current data science capacity building initiatives for health researchers in LMICs: global & regional efforts |
| title_short | Current data science capacity building initiatives for health researchers in LMICs: global & regional efforts |
| title_sort | current data science capacity building initiatives for health researchers in lmics global regional efforts |
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