The CARE Principles (Collective Benefit, Authority to Control, Responsibility, Ethics) were developed to ensure ethical stewardship of Indigenous data. However, their adaptability makes them an ideal framework for managing data related to vulnerable populations affected by armed conflicts. This essay explores the application of CARE principles to wartime contexts, with a particular focus on internally displaced persons (IDPs) and civilians living under occupation. These groups face significant risks of data misuse, ranging from privacy violations to targeted repression. By adapting CARE, data governance can prioritize safety, dignity, and empowerment while ensuring that data serves the collective welfare of affected communities. Drawing on examples from Indigenous data governance, open science initiatives, and wartime humanitarian challenges, this essay argues for extending CARE principles beyond their original scope. Such an adaptation highlights CARE’s potential as a universal standard for addressing the ethical complexities of data management in humanitarian crises and conflict-affected environments.

Recent trends within computational and data sciences show an increasing recognition and adoption of computational workflows as tools for productivity and reproducibility that also democratize access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational workflows benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. The Workflows Community Initiative’s FAIR Workflows Working Group (WCI-FW), a global and open community of researchers and developers working with computational workflows across disciplines and domains, has systematically addressed the application of both FAIR data and software principles to computational workflows. We present recommendations with commentary that reflects our discussions and justifies our choices and adaptations. These are offered to workflow users and authors, workflow management system developers, and providers of workflow services as guidelines for adoption and fodder for discussion. The FAIR recommendations for workflows that we propose in this paper will maximize their value as research assets and facilitate their adoption by the wider community.

Scientists are increasingly required by funding agencies, publishers and their institutions to produce and publish data that are Findable, Accessible, Interoperable and Reusable (FAIR). This requires curatorial activities, which are expensive in terms of both time and effort. Based on our experience of supporting a multidisciplinary research team, we provide recommendations to direct the efforts of researchers towards affordable ways to achieve a reasonable degree of “FAIRness” for their data to become reusable upon its publication. The recommendations are accompanied by concrete insights on the challenges faced when trying to implement them in an actual data-intensive reference project.

Ensuring the integrity of research data is crucial for the accuracy and reproducibility of any data-based scientific study. This can only be achieved by establishing and implementing strict rules for the handling of research data. Essential steps for achieving high-quality data involve planning what data to gather, collecting it in the correct manner, and processing it in a robust and reproducible way. Despite its importance, a comprehensive framework detailing how to achieve data quality is currently unavailable. To address this gap, our study proposes guidelines designed to establish a reliable approach to data handling. They provide clear and practical instructions for the complete research process, including an overall data collection strategy, variable definitions, and data processing recommendations. In addition to raising awareness about potential pitfalls and establishing standardization in research data usage, the proposed guidelines serve as a reference for researchers to provide a consistent standard of data quality. Furthermore, they improve the robustness and reliability of the scientific landscape by emphasising the critical role of data quality in research.

A key source of biodiversity preservation is in the ex situ storage of seed in what are known as germplasm banks (GBs). Unfortunately, wild species germplasm bank databases, often maintained by resource-limited botanical gardens, are highly disparate and capture information about their collections in a wide range of underlying data formats, storage platforms, following different standards, and with varying degrees of data accessibility. Thus, it is extremely difficult to build conservation strategies for wild species via integrating data from these GBs. Here, we envisage that the application of the FAIR Principles to wild species and crop wild relatives information, through the creation of a federated network of FAIR GB databases, would greatly facilitate cross-resource discovery and exploration, thus assisting with the design of more efficient conservation strategies for wild species, and bringing more attention to these key data providers.

The release of ChatGPT has triggered global attention on artificial intelligence (AI), and AI for science is thus becoming a hot topic in the scientific community. When we think about unleashing the power of AI to accelerate scientific research, the question coming to our mind first is whether there is a continuous supply of highly available data at a sufficiently large scale.