Related papers: Knowledge engineering for open science: Building a…
The FAIR principles define a number of expected behaviours for the data and services ecosystem with the goal of improving the findability, accessibility, interoperability, and reusability of digital objects. A key aspiration of the…
As the amount of scientific data continues to grow at ever faster rates, the research community is increasingly in need of flexible computational infrastructure that can support the entirety of the data science lifecycle, including…
FAIR metadata is critical to supporting FAIR data overall. Transparency, community engagement, and flexibility are key aspects of FAIR that apply to metadata. This paper presents YAMZ (Yet Another Metadata Zoo), a community-driven…
Across many domains, large swaths of digital assets are being stored across distributed data repositories, e.g., the DANDI Archive [8]. The distribution and diversity of these repositories impede researchers from formally defining…
Metadata play a crucial role in adopting the FAIR principles for research software and enables findability and reusability. However, creating high-quality metadata can be resource-intensive for researchers and research software engineers.…
While scientists increasingly recognize the importance of metadata in describing their data, spreadsheets remain the preferred tool for supplying this information despite their limitations in ensuring compliance and quality. Various tools…
Reproducibility is a fundamental requirement of the scientific process since it enables outcomes to be replicated and verified. Computational scientific experiments can benefit from improved reproducibility for many reasons, including…
The global influence of Big Data is not only growing but seemingly endless. The trend is leaning towards knowledge that is attained easily and quickly from massive pools of Big Data. Today we are living in the technological world that Dr.…
In this article, we focus on the importance of open research information as the foundation for transparent and responsible research assessment and discovery of research outputs. We introduce work in which we support the open research…
Data sharing is fundamental to scientific progress, enhancing transparency, reproducibility, and innovation across disciplines. Despite its growing significance, the variability of data-sharing practices across research fields remains…
The Virtual Research Environment is an analysis platform developed at CERN serving the needs of scientific communities involved in European Projects. Its scope is to facilitate the development of end-to-end physics workflows, providing…
Linked Data (LD) as a web--based technology enables in principle the seamless, machine--supported integration, interplay and augmentation of all kinds of knowledge, into what has been labeled a huge knowledge graph. Despite decades of web…
Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data…
Research is an incremental, iterative process, with new results relying and building upon previous ones. Scientists need to find, retrieve, understand, and verify results in order to confidently extend them, even when the results are their…
The OpenCitations organization is working on ingesting citation data and bibliographic metadata directly provided by the community (e.g., scholars and publishers). The aim is to improve the general coverage of open citations, which is still…
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…
Domain-specific named entity recognition (NER) on Computer Science (CS) scholarly articles is an information extraction task that is arguably more challenging for the various annotation aims that can beset the task and has been less studied…
The materials science community seeks to support the FAIR principles for computational simulation research. The MatCore Project was recently launched to address this need, with the goal of developing an overall metadata framework and…
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…
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…