Data Management Plans in the genomics research revolution of Africa: Challenges and recommendations

Fadlelmola, Faisal M.; Zass, Lyndon; Chaouch, Melek; Samtal, Chaimae; Ras, Verena; Kumuthini, Judit; Panji, Sumir; Mulder, Nicola

doi:10.1016/j.jbi.2021.103900

Cited by 11 publications

(17 citation statements)

References 24 publications

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“…The under representation of some African regions in public databases has been extensively discussed in several papers [2,3,27,28]. Very few entries have been assigned to the central-African region compared with the other African regions.…”

Section: Discussionmentioning

confidence: 99%

African Genomic Medicine Portal: A Web Portal for Biomedical Applications

Othman

Zass

Rocha

et al. 2022

JPM

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Genomics data are currently being produced at unprecedented rates, resulting in increased knowledge discovery and submission to public data repositories. Despite these advances, genomic information on African-ancestry populations remains significantly low compared with European- and Asian-ancestry populations. This information is typically segmented across several different biomedical data repositories, which often lack sufficient fine-grained structure and annotation to account for the diversity of African populations, leading to many challenges related to the retrieval, representation and findability of such information. To overcome these challenges, we developed the African Genomic Medicine Portal (AGMP), a database that contains metadata on genomic medicine studies conducted on African-ancestry populations. The metadata is curated from two public databases related to genomic medicine, PharmGKB and DisGeNET. The metadata retrieved from these source databases were limited to genomic variants that were associated with disease aetiology or treatment in the context of African-ancestry populations. Over 2000 variants relevant to populations of African ancestry were retrieved. Subsequently, domain experts curated and annotated additional information associated with the studies that reported the variants, including geographical origin, ethnolinguistic group, level of association significance and other relevant study information, such as study design and sample size, where available. The AGMP functions as a dedicated resource through which to access African-specific information on genomics as applied to health research, through querying variants, genes, diseases and drugs. The portal and its corresponding technical documentation, implementation code and content are publicly available.

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Section: Discussionmentioning

confidence: 99%

African Genomic Medicine Portal: A Web Portal for Biomedical Applications

Othman

Zass

Rocha

et al. 2022

JPM

Self Cite

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“…Furthermore, the Bill and Melinda Gates Foundation (United States) provided funding for 55 AML4H publications. As a non-governmental charity organization, this foundation is interested in encouraging research projects that translate health-related knowledge into life-saving interventions, particularly in developing countries where access to clinical information and consistent health infrastructure is very limited [51]. Moreover, the National Institute of Allergy and Infectious Diseases from the United States of America financially contributed to the development of 43 AML4H publications.…”

Section: Publishing Patternsmentioning

confidence: 99%

Machine Learning for Healthcare: A Bibliometric Study of Contributions from Africa

Turki¹,

Pouris²,

Ifeanyichukwu³

et al. 2023

Preprint

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Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020. North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

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“…DMPs are key tools for mitigating the risks of data loss and misuse. Where they do not already exist, we anticipate a widespread shift towards the establishment of data management policies within institutions and by research funding organisations (including the requirement of DMPs in research funding applications) in the near future (Bloemers & Montesanti, 2020;Fadlelmola et al, 2021;Jorgenson et al, 2021). Indeed, the primary research funding body in Aotearoa New Zealand, the Ministry of Business, Innovation and Employment, is shifting towards an open research policy (https://www.mbie.govt.nz/science-and-technology/science-andinnovation/agencies-policies-and-budget-initiatives/open-research-policy/) as many of its contemporaries have done (e.g., the Australian Research Council, the European Research Council, the National Institutes of Health), which may come to include a requirement for DMPs.…”

Section: Develop Data Management Plansmentioning

confidence: 99%

“…Contributing factors include the sheer volume of these information-rich datasets and the associated resource requirements (i.e., the time and financial costs of data curation, maintenance, and processing; Batley & Edwards, 2009;Chiang et al, 2011;Grigoriev et al, 2012;Schadt et al, 2010), as well as the inability of existing data standards, infrastructures, and repositories to keep pace with the needs of this research community (e.g., Crandall et al, 2023;Liggins et al, 2021). Best practices for biodiversity genomic data management are an active area of discussion among the biodiversity genomics community Fadlelmola et al, 2021;Field et al, 2008;Liggins et al, 2021;Yilmaz et al, 2011). However, these initiatives can be easily missed by biodiversity genomics researchers because they are often disseminated as discipline-specific outputs (e.g., publications, conference presentations, and blogs) or institution-specific internal documents.…”

Section: Introductionmentioning

confidence: 99%

Journeying towards best practice data management in biodiversity genomics

Forsdick

Wold

Angelo

et al. 2023

Preprint

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Advances in sequencing technologies and declining costs are increasing the accessibility of large-scale biodiversity genomic data sets. To maximise the impact of these data, a careful, considered approach to data management is essential. However, challenges associated with the management of such data sets remain, exacerbated by uncertainty among the research community as to what constitutes best practices. As an interdisciplinary team with diverse data management experience, we recognise the growing need for guidance on comprehensive data management practices that minimise the risks of data loss, maximise efficiency for stand-alone projects, enhance opportunities for data reuse, facilitate Indigenous data sovereignty and uphold the FAIR and CARE Guiding Principles. Here, we describe four personas reflecting user experiences with data management to identify data management challenges across the biodiversity genomics research ecosystem. We then use these personas to demonstrate realistic considerations, compromises, and actions for biodiversity genomic data management. We also launch the Biodiversity Genomics Data Management Hub (https://genomicsaotearoa.github.io/data-management-resources/), containing tips, tricks and resources to support biodiversity genomics researchers, especially those new to data management, in adopting best practice. We aim to support the biodiversity genomics community in embedding data management throughout the research life cycle to maximise research impacts and outcomes.

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Data Management Plans in the genomics research revolution of Africa: Challenges and recommendations

Cited by 11 publications

References 24 publications

African Genomic Medicine Portal: A Web Portal for Biomedical Applications

African Genomic Medicine Portal: A Web Portal for Biomedical Applications

Machine Learning for Healthcare: A Bibliometric Study of Contributions from Africa

Journeying towards best practice data management in biodiversity genomics

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