A minimal metadata set (MNMS) to repurpose nonclinical in vivo data for biomedical research

Abstract

Although biomedical research is experiencing a data explosion, the accumulation of vast quantities of data alone does not guarantee a primary objective for science: building upon existing knowledge. Data collected that lack appropriate metadata cannot be fully interrogated or integrated into new research projects, leading to wasted resources and missed opportunities for data repurposing. This issue is particularly acute for research using animals, where concerns regarding data reproducibility and ensuring animal welfare are paramount. Here, to address this problem, we propose a minimal metadata set (MNMS) designed to enable the repurposing of in vivo data. MNMS aligns with an existing validated guideline for reporting in vivo data (ARRIVE 2.0) and contributes to making in vivo data FAIR-compliant. Scenarios where MNMS should be implemented in diverse research environments are presented, highlighting opportunities and challenges for data repurposing at different scales. We conclude with a ‘call for action’ to key stakeholders in biomedical research to adopt and apply MNMS to accelerate both the advancement of knowledge and the betterment of animal welfare.

Big data and its impact on the 3Rs: a home cage monitoring oriented review

“…Metadata is essential for ensuring that raw or primary data stored in data repositories is FAIR (Findable, Accessible, Interoperable, Reusable) and enables researchers to interrogate data and understand the potential for repurposing. A “minimal metadata set” (referred to as “MNMS”) has recently been proposed to describe data generated from in vivo biomedical research experiments, with a view to enabling data repurposing (Moresis et al, 2024 ). To realize the full potential of HCM data to be used in scenarios of Replacement, as we have outlined, it will be essential that systems capture metadata in a way that minimizes the burden to the experimenter and that metadata follows raw/primary data into databases where legal considerations for data sharing have been established (Moresis et al, 2024 ).…”

“…A “minimal metadata set” (referred to as “MNMS”) has recently been proposed to describe data generated from in vivo biomedical research experiments, with a view to enabling data repurposing (Moresis et al, 2024 ). To realize the full potential of HCM data to be used in scenarios of Replacement, as we have outlined, it will be essential that systems capture metadata in a way that minimizes the burden to the experimenter and that metadata follows raw/primary data into databases where legal considerations for data sharing have been established (Moresis et al, 2024 ). A key objective for Replacement is to increase the relevance and applicability of scientific studies to human health and disease by deploying more predictive models.…”

“…By replacing control group animals with existing randomized datasets, VCGs could reduce animal use by an estimated 25%. VCGs are also argued to support improvements in experiment repeatability and reproducibility, in part by ensuring transparent reporting of data (Moresis et al, 2024 ). The use of VCGs in non-clinical research is at an early stage, and the adoption of VCGs requires careful validation, considering both biological and statistical factors (Moresis et al, 2024 ).…”

“…VCGs are also argued to support improvements in experiment repeatability and reproducibility, in part by ensuring transparent reporting of data (Moresis et al, 2024 ). The use of VCGs in non-clinical research is at an early stage, and the adoption of VCGs requires careful validation, considering both biological and statistical factors (Moresis et al, 2024 ). However, with a standardized approach to data capture and storage and highly scalable infrastructure, HCM represents an ideal context in which to further research and advance the use of VCGs.…”