Related papers: Provenance tools for Astronomy
Despite centuries of close association, statistics and astronomy are surprisingly distant today. Most observational astronomical research relies on an inadequate toolbox of methodological tools. Yet the needs are substantial: astronomy…
The increase of astronomical data produced by a new generation of observational tools poses the need to distribute data and to bring computation close to the data. Trying to answer this need, we set up a federated data and computing…
Modern AI systems are complex workflows containing multiple components and data sources. Data provenance provides the ability to interrogate and potentially explain the outputs of these systems. However, provenance is often too detailed and…
Successful data-driven science requires complex data engineering pipelines to clean, transform, and alter data in preparation for machine learning, and robust results can only be achieved when each step in the pipeline can be justified, and…
We explore how astronomers take observational data from telescopes, process them into usable scientific data products, curate them for later use, and reuse data for further inquiry. Astronomers have invested heavily in knowledge…
Data access and interoperability module connects the observation proposals, data, virtual machines and software. According to the unique identifier of PI (principal investigator), an email address or an internal ID, data can be collected by…
The widespread dissemination of machine learning tools in science, particularly in astronomy, has revealed the limitation of working with simple single-task scenarios in which any task in need of a predictive model is looked in isolation,…
Observational astronomy has shown significant growth over the last decade and has made important contributions to cosmology. A major paradigm shift in cosmology was brought about by observations of Type Ia supernovae. The notion that the…
Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic…
In the era of big data astronomy, next generation telescopes and large sky surveys produce data sets at the TB or even PB level. Due to their large data volumes, these astronomical data sets are extremely difficult to transfer and analyze…
The time domain has been identified as one of the most important areas of astronomical research for the next decade. The Virtual Observatory is in the vanguard with dedicated tools and services that enable and facilitate the discovery,…
A standard model for exposing structured provenance metadata of scientific assertions on the Semantic Web would increase interoperability, discoverability, reliability, as well as reproducibility for scientific discourse and evidence-based…
Standardizing and incentivizing the use of digital object identifiers (DOIs) to aggregate and identify both data analyzed and data generated by a research project will advance the field of astronomy to match best practices in other research…
The knowledge discovery potential of the new large astronomical databases is vast. When these are used in conjunction with the rich legacy data archives, the opportunities for scientific discovery multiply rapidly. A Virtual Observatory…
ESA Gaia mission is producing the more accurate source catalogue in astronomy up to now. That represents a challenge on the archiving area to make accessible this information to the astronomers in an efficient way. Also, new astronomical…
Astronomical datasets are growing in size and diversity, posing severe technical problems. At the same time scientific goals increasingly require the analysis of very large amounts of data, and data from multiple archives. The Virtual…
The Virtual Observatory (VO) will revolutionise the way we do Astronomy, by allowing easy access to all astronomical data and by making the handling and analysis of datasets at various locations across the globe much simpler and faster. I…
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…
With the ubiquity of computer vision in industry, the importance of image provenance is becoming more apparent. Provenance provides information about the origin and derivation of some resource, e.g., an image dataset, enabling users to…
This paper explores the integration of provenance tracking systems within the context of Semantic Web technologies to enhance data integrity in diverse operational environments. SURROUND Australia Pty Ltd demonstrates innovative…