Scientific Computing and Data Management

BACKGROUND: Scientific research in the 21st century is more data intensive and collaborative than in the past. It is important to study the data practices of researchers--data accessibility, discovery, re-use, preservation and, particularly, data sharing. Data sharing is a valuable part of the scientific method allowi…

• ARSEV UMUR AYDINOĞLU

Abstract Life sciences research is based on individuals, often with diverse skills, assembled into research groups. These groups use their specialist expertise to address scientific problems. The in silico experiments undertaken by these research groups can be represented as workflows involving the co‐ordinated use of…

• MAHMUT NEDİM ALPDEMİR

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations…

The emergence of large-scale replication projects yielding successful rates substantially lower than expected caused the behavioural, cognitive, and social sciences to experience a so-called 'replication crisis'. In this Perspective, we reframe this 'crisis' through the lens of a credibility revolution, focusing on po…

• NİHAN ALBAYRAK

Applications of quantum chemistry have evolved from single or a few calculations to more complicated workflows, in which a series of interrelated computational tasks is performed. In particular multiscale simulations, which combine different levels of accuracy, typically require a large number of individual calculatio…

The known challenge of underutilization of data and biological material from biorepositories as potential resources for medical research has been the focus of discussion for over a decade. Recently developed guidelines for improved data availability and reusability-entitled FAIR Principles (Findability, Accessibility,…

• OSMAN UĞUR SEZERMAN

Conventional data integration techniques employ a “top-down” design philosophy, starting by assessing requirements and defining a global schema, and then mapping data sources to that schema. This works well if the problem domain is well-understood and relatively static, as with enterprise data. However, it is fundamen…

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• NIMA JAFARI NAVIMIPOUR

Summary We propose a run‐time verification mechanism of things for self‐healing capability in the Internet of Things domain. We discuss the software architecture of the proposed verification mechanism and its prototype implementations. To identify faulty running behavior of things, we utilize a complex event processin…

We consider the problem of optimizing the execution of data-intensive scientific workflows in the Cloud. We address the problem under the following scenario. The tasks of the workflows communicate through files; the output of a task is used by another task as an input file and if these tasks are assigned on different…

Dramatic changes to the transparency, rigor, reproducibility and replicability of research practices have occurred in the last decade. Despite considerable progress towards the adoption of open science practices by researchers in many disciplines, developing pedagogy to train students in open and reproducible scholars…