Related papers: Enabling FAIR Research in Earth Science through Re…
Work in the Open Archives Initiative - Object Reuse and Exchange (OAI-ORE) focuses on an important aspect of infrastructure for eScience: the specification of the data model and a suite of implementation standards to identify and describe…
This article reports the outcomes of the FAIRNESS COST Action (CA20108), a coordinated European initiative aimed at advancing micrometeorological data toward compliance with the FAIR (Findable, Accessible, Interoperable, Reusable)…
The Facility for Antiproton and Ion Research (FAIR) will be the accelerator-based flagship research facility in many basic sciences and their applications in Europe for the coming decades. FAIR will open up unprecedented research…
The rapid evolution of Large Language Models (LLMs) highlights the necessity for ethical considerations and data integrity in AI development, particularly emphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable) data…
Recently the International Virtual Observatory Alliance (IVOA) released a standard to structure provenance metadata, and several implementations are in development in order to capture, store, access and visualize the provenance of astronomy…
Sharing and reusing research data can effectively reduce redundant efforts in data collection and curation, especially for small labs and research teams conducting human-centered system research, and enhance the replicability of evaluation…
Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the…
Modern technologies are enabling scientists to collect extraordinary amounts of complex and sophisticated data across a huge range of scales like never before. With this onslaught of data, we can allow the focal point to shift towards…
The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps tools and platforms, and the unique requirements of modern and Open Science, particularly…
The emergence of "big data" offers unprecedented opportunities for not only accelerating scientific advances but also enabling new modes of discovery. Scientific progress in many disciplines is increasingly enabled by our ability to examine…
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…
Neuroscientists are now able to acquire data at staggering rates across spatiotemporal scales. However, our ability to capitalize on existing datasets, tools, and intellectual capacities is hampered by technical challenges. The key barriers…
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…
Research Objects (ROs) are semantically enhanced aggregations of resources associated to scientific experiments, such as data, provenance of these data, the scientific workflow used to run the experiment, intermediate results, logs and the…
Throughout the food supply chain, between production, transportation, packaging, and green employment, a plethora of indicators cover the environmental footprint and resource use. By defining and tracking the more inefficient practices of…
OpenCitations is an independent not-for-profit infrastructure organization for open scholarship dedicated to the publication of open bibliographic and citation data by the use of Semantic Web (Linked Data) technologies. OpenCitations…
Quality assessment of Research Software Engineering (RSE) plays an important role in all scientific fields. From the canonical three criteria (reliability, validity, and objectivity) previous research has focussed on reliability and the…
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, predict, and design. In this roadmap we provide a…
The FAIR Principles aim to make data and knowledge Findable, Accessible, Interoperable, and Reusable, yet current digital infrastructures often lack a unifying semantic framework that bridges human cognition and machine-actionability. In…
Reviewing scientific literature is a cumbersome, time consuming but crucial activity in research. Leveraging a scholarly knowledge graph, we present a methodology and a system for comparing scholarly literature, in particular research…