Related papers: FAIR Metadata: A Community-driven Vocabulary Appli…
The explosive growth of data fuels data-driven research, facilitating progress across diverse domains. The FAIR principles emerge as a guiding standard, aiming to enhance the findability, accessibility, interoperability, and reusability of…
The FAIR principles for scientific data (Findable, Accessible, Interoperable, Reusable) are also relevant to other digital objects such as research software and scientific workflows that operate on scientific data. The FAIR principles can…
It is essential for the advancement of science that scientists and researchers share, reuse and reproduce workflows and protocols used by others. The FAIR principles are a set of guidelines that aim to maximize the value and usefulness of…
We present ten simple rules that support converting a legacy vocabulary -- a list of terms available in a print-based glossary or table not accessible using web standards -- into a FAIR vocabulary. Various pathways may be followed to…
Data-intensive science communities are progressively adopting FAIR practices that enhance the visibility of scientific breakthroughs and enable reuse. At the core of this movement, research objects contain and describe scientific…
To enable materials databases supporting computational and experimental research, it is critical to develop platforms that both facilitate access to the data and provide the tools used to generate/analyze it - all while considering the…
Modern workflows run on increasingly heterogeneous computing architectures and with this heterogeneity comes additional complexity. We aim to apply the FAIR principles for research reproducibility by developing software to collect metadata…
The FAIR Principles are a set of good practices to improve the reproducibility and quality of data in an Open Science context. Different sets of indicators have been proposed to evaluate the FAIRness of digital objects, including datasets…
While the FAIR principles are well accepted in the scientific community, the implementation of appropriate metadata editing and transfer to ensure FAIR research data in practice is significantly lagging behind. On the one hand, it strongly…
The increasing complexity and volume of data generated by high-throughput computational materials science require robust tools to ensure their accessibility, reproducibility, and reuse. In particular, integrating the FAIR Guiding Principles…
This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed. For furthering the field, Open Data and an…
The FAIR principles are globally accepted guidelines for improved data management practices with the potential to align data spaces on a global scale. In practice, this is only marginally achieved through the different ways in which…
Ecological research increasingly relies on integrating heterogeneous datasets and knowledge to explain and predict complex phenomena. Yet, differences in data types, terminology, and documentation often hinder interoperability, reuse, and…
Data - arguably the most important product of worldwide materials research investment - are rarely shared. The small and biased proportion of results published are buried in plots and text licensed by journals. This situation wastes…
The ability to find data is central to the FAIR principles underlying research data stewardship. As with the ability to reuse data, efforts to ensure and enhance findability have historically focused on discoverability of data by other…
A large number of services for research data management strive to adhere to the FAIR guiding principles for scientific data management and stewardship. To evaluate these services and to indicate possible improvements, use-case-centric…
There has been a large focus in recent years on making assets in scientific research findable, accessible, interoperable and reusable, collectively known as the FAIR principles. A particular area of focus lies in applying these principles…
The FAIR (Findable, Accessible, Interoperable, and Reusable) data principles [1] promote the interoperability of scientific data by encouraging the use of persistent identifiers, standardized vocabularies, and formal metadata structures.…
The reuse of research software is central to research efficiency and academic exchange. The application of software enables researchers with varied backgrounds to reproduce, validate, and expand upon study findings. Furthermore, the…
The FAIR (Findable, Accessible, Interoperable, Reusable) data principles are fundamental for climate researchers and all stakeholders in the current digital ecosystem. In this paper, we demonstrate how relational climate data can be "FAIR"…