Legal implications of data sharing in biobanking research in low-income settings: The Nigerian experience

Abstract

It is increasingly recognised that effective and appropriate data sharing in biobanking research requires the development of models of good data-sharing policy capable of ensuring that the rights and privacy interests of participants are protected. However, the effectiveness and efficacy of biobank research depend on data and samples. In the same vein, making such data available to the research community generates tension between two important goals: advancing scientific goals, and protecting the individual privacy interests of the tissue source. More critically, data sharing requires the development of models that promote an environment in which privacy rights and interests of participants are protected throughout the lifecycle of biobank initiatives. Many ethical issues are raised when genomics research is conducted on populations characterised by lower average income and literacy levels, such as populations included in lower-middle income countries (LMICs). These issues are further exacerbated in Nigeria by cultural and religious inflections. In this paper, to analyse the implications of data sharing within the legal framework of an LMIC, an analysis of existing laws in Nigeria was conducted. It was discovered that there were no provisions relating directly to data sharing, and its governance framework could only be gleaned from the patchwork of laws on privacy and confidentiality in Nigeria. There is a need for ethical guidelines in Nigeria to be adapted to the changing landscape of science, which increasingly involves storage and secondary use of samples and data. Current laws are inadequate for the challenges presented by biobanking.

What constitutes equitable data sharing in global health research? A scoping review of the literature on low-income and middle-income country stakeholders’ perspectives

“…The first included unequal research system capacity between HICs and LMICs across technological and human resources, including expertise in data curation, management and analysis 31 60. Significant investment is required to build both technological and human capacity in LMICs to enable equitable data sharing 31 51 53 63 65. Dedicated funding for building capacity for data sharing at institutional or national levels, rather than at the project level, resources for sustainable and long-term data sharing (such as funding data access committees and data managers) as well as flexible microfinancing programmes (Microfinancing programs provide very small sums, are easily applied for, and flexible in how they might be spent.)…”

Incentivising research data sharing: a scoping review

“…(2016) ; Kim (2017) ; Kim & Nah (2018) ; Kim & Burns (2016) ; Kim & Stanton (2016) ; Kim & Yoon (2017) ; Murray (2016) ; Staley et al ., 2019 ; Yoon & Kim, (2020) . Likewise references that discussed the challenges of open data practices in particular geographical settings Akintola (2018) ; Anane-Sarpong et al (2020) ; Jule et al . (2018) ; Kaewkungwal et al ., 2020 ; Mahomed & Staunton (2021) ; Rappert & Bezuidenhout (2016) ; Sa & Grieco (2016) ; Slavnic (2017) ; focussed on a specific disciplinary context Barabucci et al (2018) ; Bowman & Spence (2020) ; Curty et al .…”

Invited Peer Commentary: Research Site Anonymity in Context

“…From a shared African perspective, a person is defined through their community (Jegede, 2009), thus reiterating the philosophy of Ubuntu, as pointed out by the authors. Therefore, individual decision-making in research could be informed and influenced by family members, the community more broadly, spiritual leaders, and possibly even ancestral spirits (Akintola, 2018; Jegede, 2009). Such beliefs linking the triad of mind, body, and soul are also documented in other indigenous communities and first nations around the world (Aramoana et al, 2019; Fong et al, 2004).…”