Abstract:
With regard to the use and transfer of research participants’ personal information,
samples and other data nationally and internationally, it is necessary to construct a
data management plan. One of the key objectives of a data management plan is to
explain the governance of clinical, biochemical, laboratory, molecular and other
sources of data according to the regulations and policies of all relevant
stakeholders. It also seeks to describe the processes involved in protecting the
personal information of research participants, especially those from vulnerable
populations. In most data management plans, the framework therefore consists of
describing the collection, organization, use, storage, contextualization,
preservation, sharing and access of/to research data and/or samples. It may
also include a description of data management resources, including those
associated with analyzed samples, and identifies responsible parties for the
establishment, implementation and overall management of the data
management strategy. Importantly, the data management plan serves to
highlight potential problems with the collection, sharing, and preservation of
research data. However, there are different forms of data management plans
and requirements may vary due to funder guidelines and the nature of the study
under consideration. This paper leverages the detailed data management plans
constructed for the ‘NESHIE study’ and is a first attempt at providing a
comprehensive template applicable to research focused on vulnerable
populations, particularly those within LMICs, that includes a multi-omics
approach to achieve the study aims. More particularly, this template, available
for download as a supplementary document, provides a modifiable outline for
future projects that involve similar sensitivities, whether in clinical research or
clinical trials. It includes a description of the management not only of the data
generated through standard clinical practice, but also that which is generated
through the analysis of a variety of samples being collected from research
participants and analyzed using multi-omics approaches.
