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The Web of Things (WoT) is rapidly growing in popularity getting the interest of not only technologist and scientific communities but industrial, system integrators and solution providers. The key aspect of the WoT to succeed is the…

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data…

In the age of big data, it is important for primary research data to follow the FAIR principles of findability, accessibility, interoperability, and reusability. Data harmonization enhances interoperability and reusability by aligning…

The concept of FAIR Digital Objects represents a foundational step towards realizing machine-actionable, interoperable data infrastructures across scientific and industrial domains. As digital spaces become increasingly heterogeneous,…

The continuous scaling of CMOS technology has significantly increased the complexity of very large-scale integrated circuits, driving interest in applying machine learning (ML) to electronic design automation (EDA). However, the limited…

Personalized chronic care requires the integration of multimodal health data to enable precise, adaptive, and preventive decision-making. Yet most current digital twin (DT) applications remain organ-specific or tied to isolated data types,…

The proliferation of sensor technologies and advancements in data collection methods have enabled the accumulation of very large amounts of data. Increasingly, these datasets are considered for scientific research. However, the design of…

An infectious kind of disease affects a huge number of human beings. A lot of investigation being conducted throughout the world. There are many interactive hardware platform packages like IoT in healthcare including smart tracking, smart…

An Intrusion Detection System (IDS) aims to alert users of incoming attacks by deploying a detector that monitors network traffic continuously. As an effort to increase detection capabilities, a set of independent IDS detectors typically…

Data Spaces are an emerging concept for the trusted implementation of data-based applications and business models, offering a high degree of flexibility and sovereignty to all stakeholders. As Data Spaces are currently emerging in different…

Motivation. Digital commons is an emerging phenomenon and of increasing importance, as we enter a digital society. Open data is one example that makes up a pivotal input and foundation for many of today's digital services and applications.…

Using Scopus data, we construct a global map of science based on aggregated journal-journal citations from 1996-2012 (N of journals = 20,554). This base map enables users to overlay downloads from Scopus interactively. Using a single year…

Therapeutics machine learning is an emerging field with incredible opportunities for innovatiaon and impact. However, advancement in this field requires formulation of meaningful learning tasks and careful curation of datasets. Here, we…

Purpose: Deep learning methods have shown promising results in the segmentation, and detection of diseases in medical images. However, most methods are trained and tested on data from a single source, modality, organ, or disease type,…

Nowadays, there are plenty of data sources generating massive amounts of information that, combined with novel data analytics frameworks, are meant to support optimisation in many application domains. Nonetheless, there are still…

Centralized electronic health record repositories are critical for advancing disease surveillance, public health research, and evidence-based policymaking. However, developing countries face persistent challenges in achieving this due to…

The two Journal Citation Reports of the Science Citation Index 2004 and the Social Science Citation Index 2004 were combined in order to analyze and map journals and specialties at the edges and in the overlap between the two databases. For…

Data heterogeneity hampers the effort to integrate and infer knowledge from vast heterogeneous data sources. An application case study is described, in which the objective was to semantically represent and integrate structured data from…

Developing artificial intelligence (AI) tools for healthcare is a collaborative effort, bringing data scientists, clinicians, patients and other disciplines together. In this paper, we explore the collaborative data practices of research…

Pedestrian accessibility is an important factor in urban transport and land use policy and critical for creating healthy, sustainable cities. Developing and evaluating indicators measuring inequalities in pedestrian accessibility can help…