Related papers: Lessons Learned from Sloan Digital Sky Survey Oper…
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data…
In this work, we identify elements of effective machine learning datasets in astronomy and present suggestions for their design and creation. Machine learning has become an increasingly important tool for analyzing and understanding the…
In this paper we present a number of metrics for usage of the SAO/NASA Astrophysics Data System (ADS). Since the ADS is used by the entire astronomical community, these are indicative of how the astronomical literature is used. We will show…
Modern scientific data mainly consist of huge datasets gathered by a very large number of techniques and stored in very diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as…
The primary observational goals of the Sloan Digital Sky Survey are to obtain CCD imaging of 10,000 deg^2 of the north Galactic cap in five passbands, with a limiting magnitude in the r-band of 22.5, to obtain spectroscopic redshifts of…
The Sloan Digital Sky Survey is the largest redshift survey conducted to date, and the principal survey observations have all been conducted on the dedicated SDSS 2.5m and 0.5m telescopes at Apache Point Observatory. While the whole survey…
Cosmological data in the next decade will be characterized by high-precision, multi-wavelength measurements of thousands of square degrees of the same patches of sky. By performing multi-survey analyses that harness the correlated nature of…
The nature of scientific and technological data collection is evolving rapidly: data volumes and rates grow exponentially, with increasing complexity and information content, and there has been a transition from static data sets to data…
In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular,…
The large surveys and sensitive instruments of modern astronomy are turning ever more examples of variable objects, many of which are extending the parameter space to testing theories of stellar evolution and accretion. Future projects such…
Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies…
Astronomy produces extremely large data sets from ground-based telescopes, space missions, and simulation. The volume and complexity of these rich data sets require new approaches and advanced tools to understand the information contained…
This paper provides a case study on satellite data processing, storage, and distribution in the space weather domain by introducing the Satellite Data Downloading System (SDDS). The approach proposed in this paper was evaluated through…
Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…
The era of data-intensive astronomy is being ushered in with the increasing size and complexity of observational data across wavelength and time domains, the development of algorithms to extract information from this complexity, and the…
Online citizen science projects involve recruitment of volunteers to assist researchers with the creation, curation, and analysis of large datasets. Enhancing the quality of these data products is a fundamental concern for teams running…
Simulation tools are commonly used in the development and testing of new protocols or new networks. However, as satellite networks start to grow to encompass thousands of nodes, and as companies and space agencies begin to realize the…
The potential of the Sloan Digital Sky Survey for wide-field variability studies is illustrated using multi-epoch observations for 3,000,000 point sources observed in 700 deg2 of sky, with time spans ranging from 3 hours to 3 years. These…
The 2020s will be the most data-rich decade of astronomy in history. As the scale and complexity of our surveys increase, the problem of scheduling becomes more critical. We must develop high-quality scheduling approaches, implement them as…
This article introduces a set of distance education astronomy laboratory exercises for use by college students and instructors and discuss first usage results. This General Astronomy Education Source (GEAS) exercise set contains eight…