Related papers: User-friendly Composition of FAIR Workflows in a N…
The way in which data are shared can affect their utility and reusability. Here, we demonstrate how data that we had previously shared in bulk can be mobilized further through a knowledge graph that allows for much more granular exploration…
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…
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…
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…
Open Science, Reproducible Research, Findable, Accessible, Interoperable and Reusable (FAIR) data principles are long term goals for scientific dissemination. However, the implementation of these principles calls for a reinspection of our…
Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific…
Just like the scientific data they generate, simulation workflows for research should be findable, accessible, interoperable, and reusable (FAIR). However, while significant progress has been made towards FAIR data, the majority of science…
Computational workflows represent major investments of effort and expertise. As first-class, publishable research objects of their own, they are key to sharing methodological know-how for reuse, reproducibility, and transparency. Thus, the…
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 document captures the discussion and deliberation of the FAIR for Research Software (FAIR4RS) subgroup that took a fresh look at the applicability of the FAIR Guiding Principles for scientific data management and stewardship for…
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…
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The…
As scientific work becomes more computational and data intensive, research processes and results become more difficult to interpret and reproduce. In this poster, we show how the Jupyter notebook, a tool originally designed as a free…
In recent years, the importance of well-documented metadata has been discussed increasingly in many research fields. Making all metadata generated during scientific research available in a findable, accessible, interoperable, and reusable…
The broad sharing of research data is widely viewed as of critical importance for the speed, quality, accessibility, and integrity of science. Despite increasing efforts to encourage data sharing, both the quality of shared data, and the…
A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable) principles for scientific data is transforming the state-of-practice for data management and stewardship, supporting and enabling discovery and…
Reproducibility is a cornerstone of science. FAIR (findable, accessible, interoperable, and reusable) data is often a vital step towards testing the reproducibility of results. The implementation of FAIR principles in the astrophysical…
It is challenging to determine whether datasets are findable, accessible, interoperable, and reusable (FAIR) because the FAIR Guiding Principles refer to highly idiosyncratic criteria regarding the metadata used to annotate datasets.…
Citing data and software is a means to give scholarly credit and to facilitate access to research objects. Citation principles encourage authors to provide full descriptions of objects, with stable links, in their papers. As Jupyter…
Jupyter notebooks facilitate the bundling of executable code with its documentation and output in one interactive environment, and they represent a popular mechanism to document and share computational workflows. The reproducibility of…