Category: Digital Curation News

RDMkit is an open, community-driven knowledge resource providing good data management practices—in line with the FAIR principles—for the life sciences. RDMkit has guidelines, practical information, and pointers to other RDM resources, and it organizes its advice in a way that follows the life cycle of data from the start to the end of research projects. RDMkit was created and is maintained through open collaboration by the RDMkit community, composed of RDM practitioners and researchers from several domains. RDMkit is based on an open-source, reusable technological core, which allows communities, initiatives, and projects to easily deploy online guidelines.

The main goal of RDMkit is to provide information to researchers so that they can plan and execute their data management using state-of-the-art tools and current best practices. RDMkit content is intended to close the RDM guidance gap (1) by providing context to the tools, showing when to use them in a project and for which RDM activity and by whom they should be used, as well as how various communities have combined tools to provide custom data management solutions; (2) by placing itself centrally with bidirectional connections to other RDM knowledge resources, making it possible for researchers to navigate the RDM knowledge space without getting lost; and (3) by providing live content looked after by an active community using well-defined processes to ensure that the guidance stays up to date.

The key target audience of RDMkit is life science researchers, such as biologists and bioinformaticians and data stewards. For researchers, RDMkit is a one-stop shop for information, advice, and signposting to RDM know-how, resources, examples, and best practices. Simultaneously, RDMkit aims to address the needs of professionals in RDM, such as data stewards with different domain areas, responsibilities, and tasks, which directly support researchers by providing advice, training, RDM services, tools, and infrastructures. Data stewards can use RDMkit to complement their expertise, resources, and training material. Funding agencies and policymakers can benefit from the RDMkit by including it in their DMP guidelines and exemplifying how life science communities have approached implementing open-science requirements and FAIR principles with state-of-the-art tools and resources.

https://doi.org/10.1016/j.patter.2025.101345

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

Documenting the processes and practices of making and processing research data has been identified as key prerequisite of data reusability and intelligibility. A large number of methods and approaches for generating and identifying such information have been proposed, however, dispersed across the literature. Consequently, the current understanding of what types of approaches have been envisioned, how they differ and relate to each other, and what kind of paradata they produce is limited. This paper reports an initial study to increase understanding of the methods landscape through review and categorization of paradata generation and identification methods. We identified three major temporal categories of (1) prospective, (2) in situ, and (3) retrospective methods and approaches, and five categories of paradata artifacts generated: (1) structured metadata, (2) narratives, (3) snapshots, (4) diagrammatic representations, and (5) standard procedures.

https://doi.org/10.1177/09610006251342811

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |

Background

Data reusability is the driving force of the research data life cycle. However, implementing strategies to generate reusable data from the data creation to the sharing stages is still a significant challenge. Even when datasets supporting a study are publicly shared, the outputs are often incomplete and/or not reusable. The FAIR (Findable, Accessible, Interoperable, Reusable) principles were published as a general guidance to promote data reusability in research, but the practical implementation of FAIR principles in research groups is still falling behind. In biology, the lack of standard practices for a large diversity of data types, data storage and preservation issues, and the lack of familiarity among researchers are some of the main impeding factors to achieve FAIR data. Past literature describes biological curation from the perspective of data resources that aggregate data, often from publications.

Methods

Our team works alongside data-generating, experimental researchers so our perspective aligns with publication authors rather than aggregators. We detail the processes for organizing datasets for publication, showcasing practical examples from data curation to data sharing. We also recommend strategies, tools and web resources to maximize data reusability, while maintaining research productivity.

Conclusion

We propose a simple approach to address research data management challenges for experimentalists, designed to promote FAIR data sharing. This strategy not only simplifies data management, but also enhances data visibility, recognition and impact, ultimately benefiting the entire scientific community.

https://doi.org/10.12688/wellcomeopenres.22899.1

| Artificial Intelligence |
| Research Data Curation and Management Works |
| Digital Curation and Digital Preservation Works |
| Open Access Works |
| Digital Scholarship |