Related papers: Provenance of astronomical data
Data makes science possible. Sharing data improves visibility, and makes the research process transparent. This increases trust in the work, and allows for independent reproduction of results. However, a large proportion of data from…
Access to astronomical data through archives and VO is essential but does not solve all problems. Availability of appropriate software for analyzing the data is often equally important for the efficiency with which a researcher can publish…
Astronomy looks after its data better than most disciplines, and it is no coincidence that the consensus standard for the archival preservation of all types of digital assets -- the OAIS Reference Model -- emerged originally from the space…
Despite much creative work on methods and tools, reproducibility -- the ability to repeat the computational steps used to obtain a research result -- remains elusive. One reason for these difficulties is that extant tools for capturing…
To meet the standards of the Open Science movement, the FAIR Principles emphasize the importance of making scientific data Findable, Accessible, Interoperable, and Reusable. Yet, creating a repository that adheres to these principles…
Data is the most powerful decision-making tool at our disposal. However, despite the exponentially growing volumes of data generated in the world, putting it to effective use still presents many challenges. Relevant data seems to be never…
Data provenance (the process of determining the origin and derivation of data outputs) has applications across multiple domains including explaining database query results and auditing scientific workflows. Despite decades of research,…
This specification defines a protocol for retrieving image data from a variety of astronomical image repositories through a uniform interface. The interface is meant to be reasonably simple to implement by service providers. A query…
Astronomy has a distinguished tradition of using technology to accelerate the quality and effectiveness of science, and data-intensive initiatives such as the Virtual Observatory lead the way amongst other fields of science. However,…
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 recent years, digital object management practices to support findability, accessibility, interoperability, and reusability (FAIR) have begun to be adopted across a number of data-intensive scientific disciplines. These digital objects…
Organizations of all kinds, whether public or private, profit-driven or non-profit, and across various industries and sectors, rely on dashboards for effective data visualization. However, the reliability and efficacy of these dashboards…
Sustainable urban environments based on Internet of Things (IoT) technologies require appropriate policy management. However, such policies are established as a result of underlying, potentially complex and long-term policy making…
Accessing research data at any time is what FAIR (Findable Accessible Interoperable Reusable) data sharing aims to achieve at scale. Yet, we argue that it is not sustainable to keep accumulating and maintaining all datasets for rapid…
Data provenance describes how data came to be in its present form. It includes data sources and the transformations that have been applied to them. Data provenance has many uses, from forensics and security to aiding the reproducibility of…
Genetic Algorithm (GA) is a popular meta-heuristic evolutionary algorithm that uses stochastic operators to find optimal solution and has proved its effectiveness in solving many complex optimization problems (such as classification,…
This paper presents the CRISTAL-iSE project as a framework for the management of provenance information in industry. The project itself is a research collaboration between academia and industry. A key factor in the project is the use of a…
Materials Cloud is a platform designed to enable open and seamless sharing of resources for computational science, driven by applications in materials modelling. It hosts 1) archival and dissemination services for raw and curated data,…
Peer review is a cornerstone of modern scientific endeavor. However, there is growing consensus that several limitations of the current peer review system, from lack of incentives to reviewers to lack of transparency, risks to undermine its…
The findable, accessible, interoperable, and reusable (FAIR) data principles provide a framework for examining, evaluating, and improving how data is shared to facilitate scientific discovery. Generalizing these principles to research…