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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…
In the rapidly evolving field of bioimaging, the integration and orchestration of Findable, Accessible, Interoperable, and Reusable (FAIR) image analysis workflows remains a challenge. We introduce BIOMERO, a bridge connecting OMERO, a…
For autonomous vehicles to be fully aware of its environment, it needs to collect data consistently from other vehicles and Road Side Units (RSU) in the surroundings. This heavy exchange increases latency and cybersecurity threats. This…
We present a novel multi-modal bio-sensing platform capable of integrating multiple data streams for use in real-time applications. The system is composed of a central compute module and a companion headset. The compute node collects,…
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…
The expansive production of data in materials science, their widespread sharing and repurposing requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of…
Good research data management is essential in modern-day lab work. Various solutions exist that are either highly specific or need a significant effort to be customized appropriately. This paper presents an integrated solution for…
Reproducibility and replicability of research findings are central to the scientific integrity of epidemiology. In addition, many research questions require combiningdata from multiple sources to achieve adequate statistical power. However,…
Software engineering for digital health applications entails several challenges, including heterogeneous data acquisition, data standardization, software reuse, security, and privacy considerations. We explore these challenges and how our…
In the digital health domain, ethical data collection practices are crucial for ensuring the availability of quality datasets that drive medical advancement. Data donation, allowing patients to share their medical data for secondary…
The latency issue of the cloud-centric IoT management system has motivated Fog and Edge Computing (FEC) architecture, which distributes the tasks from the cloud to the edge resources such as routers, switches or the IoT devices themselves.…
Cloud computing provides a great opportunity for scientists, as it enables large-scale experiments that cannot are too long to run on local desktop machines. Cloud-based computations can be highly parallel, long running and data-intensive,…
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…
The Center for Expanded Data Annotation and Retrieval (CEDAR) aims to revolutionize the way that metadata describing scientific experiments are authored. The software we have developed--the CEDAR Workbench--is a suite of Web-based tools and…
Neuroscience has been carried into the domain of big data and high performance computing (HPC) on the backs of initiatives in data collection and an increasingly compute-intensive tools. While managing HPC experiments requires considerable…
The Central Luzon Agriculture, Aquatic and Natural Resources Research and Development Consortium (CLAARRDEC), comprising 29 member institutions, faces challenges in effectively monitoring and evaluating their R&D activities. To address…
De-identified health data are frequently used in research. As AI advances heighten the risk of re-identification, it is important to respond to concerns about transparency, data privacy, and patient preferences. However, few practical and…
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…
Clinical trial data cleaning represents a critical bottleneck in drug development, with manual review processes struggling to manage exponentially increasing data volumes and complexity. This paper presents Octozi, an artificial…
A recent study showed that more than 70% of researchers fail to reproduce their peers's experiments and more than half fail to reproduce their own experiments. Obviously, from a perspective of scientific quality this is a more than…