Special Issue "Data in Astrophysics & Geophysics: Research and Applications"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: closed (12 October 2018)

Special Issue Editors

Guest Editor
Dr. Vladimir A. Sreckovic

The Astrophysics and Ionospheric Laboratory, Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia
Website | E-Mail
Interests: solar and stellar astrophysics; space weather studies of upper atmosphere; astrogeoinformatics; astroinformatics; databases; data-mining; natural hazards
Guest Editor
Dr. Aleksandra Nina

The Astrophysics and Ionospheric Laboratory, Institute of Physics Belgrade, University of Belgrade, Belgrade, Serbia
Website | E-Mail
Interests: terrestrial ionosphere; VLF radio waves; databases; astro- and geophysical phenomena; ionospheric plasma parameters; data-mining; meteors, earthquakes, and tropical cyclones

Special Issue Information

Dear Colleagues,

The space and Earth’s layers are mediums permanently exposed to influences of numerous perturbations characterized with time- and space-dependent intensity. For this reason, detection of the astrophysical and terrestrial events and their influences, as well as the development and application of various models, must be based on observation data.

The challenges related to data volume, variety and data flow are similar in astro- and geo-observations. This Special Issue aims to encourage the communication among the disciplines by identifying and grouping relevant research solutions. Its goals are to engage a broad community of researchers, both users and contributors, to make new discoveries enabled by the growth of data and technology and to continue interdisciplinary exchanges of ideas and methodologies with other fields.

We would like to invite you to submit articles addressing the data collection in astrophysics and geophysics, its acquisition, processing, and management, so that these results will be used by other scientists and that the compilation of such data sets will be useful to data producers as well. Potential topics include, but are not limited to:

  • big data in astrophysics and geophysics
  • data processing, visualization and acquisition
  • line profiles data
  • interstellar spectra data
  • atomic and molecular data in astrophysics
  • Earth observation data
  • climate data records
  • natural hazards and disasters
  • remote sensing
Dr. Vladimir Sreckovic
Dr. Aleksandra Nina
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

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Open AccessArticle Russian–German Astroparticle Data Life Cycle Initiative
Received: 12 October 2018 / Revised: 12 November 2018 / Accepted: 24 November 2018 / Published: 28 November 2018
Cited by 1 | PDF Full-text (764 KB) | HTML Full-text | XML Full-text
Abstract
Modern large-scale astroparticle setups measure high-energy particles, gamma rays, neutrinos, radio waves, and the recently discovered gravitational waves. Ongoing and future experiments are located worldwide. The data acquired have different formats, storage concepts, and publication policies. Such differences are a crucial point in [...] Read more.
Modern large-scale astroparticle setups measure high-energy particles, gamma rays, neutrinos, radio waves, and the recently discovered gravitational waves. Ongoing and future experiments are located worldwide. The data acquired have different formats, storage concepts, and publication policies. Such differences are a crucial point in the era of Big Data and of multi-messenger analysis in astroparticle physics. We propose an open science web platform called ASTROPARTICLE.ONLINE which enables us to publish, store, search, select, and analyze astroparticle data. In the first stage of the project, the following components of a full data life cycle concept are under development: describing, storing, and reusing astroparticle data; software to perform multi-messenger analysis using deep learning; and outreach for students, post-graduate students, and others who are interested in astroparticle physics. Here we describe the concepts of the web platform and the first obtained results, including the meta data structure for astroparticle data, data analysis by using convolution neural networks, description of the binary data, and the outreach platform for those interested in astroparticle physics. The KASCADE-Grande and TAIGA cosmic-ray experiments were chosen as pilot examples. Full article
(This article belongs to the Special Issue Data in Astrophysics & Geophysics: Research and Applications)
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Open AccessArticle Binary Star Database (BDB): New Developments and Applications
Received: 16 September 2018 / Revised: 30 September 2018 / Accepted: 1 October 2018 / Published: 3 October 2018
Cited by 1 | PDF Full-text (273 KB) | HTML Full-text | XML Full-text
Abstract
Binary star DataBase (BDB) is the database of binary/multiple systems of various observational types. BDB contains data on physical and positional parameters of 260,000 components of 120,000 stellar systems of multiplicity 2 to more than 20, taken from a large variety of published [...] Read more.
Binary star DataBase (BDB) is the database of binary/multiple systems of various observational types. BDB contains data on physical and positional parameters of 260,000 components of 120,000 stellar systems of multiplicity 2 to more than 20, taken from a large variety of published catalogues and databases. We describe the new features in organization of the database, integration of new catalogues and implementation of new possibilities available to users. The development of the BDB index-catalogue, Identification List of Binaries (ILB), is discussed. This star catalogue provides cross-referencing between most popular catalogues of binary stars. Full article
(This article belongs to the Special Issue Data in Astrophysics & Geophysics: Research and Applications)
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Open AccessArticle A System for Acquisition, Processing and Visualization of Image Time Series from Multiple Camera Networks
Received: 13 May 2018 / Revised: 12 June 2018 / Accepted: 20 June 2018 / Published: 24 June 2018
Cited by 2 | PDF Full-text (11069 KB) | HTML Full-text | XML Full-text
Abstract
A system for multiple camera networks is proposed for continuous monitoring of ecosystems by processing image time series. The system is built around the Finnish Meteorological Image PROcessing Toolbox (FMIPROT), which includes data acquisition, processing and visualization from multiple camera networks. The toolbox [...] Read more.
A system for multiple camera networks is proposed for continuous monitoring of ecosystems by processing image time series. The system is built around the Finnish Meteorological Image PROcessing Toolbox (FMIPROT), which includes data acquisition, processing and visualization from multiple camera networks. The toolbox has a user-friendly graphical user interface (GUI) for which only minimal computer knowledge and skills are required to use it. Images from camera networks are acquired and handled automatically according to the common communication protocols, e.g., File Transfer Protocol (FTP). Processing features include GUI based selection of the region of interest (ROI), automatic analysis chain, extraction of ROI based indices such as the green fraction index (GF), red fraction index (RF), blue fraction index (BF), green-red vegetation index (GRVI), and green excess (GEI) index, as well as a custom index defined by a user-provided mathematical formula. Analysis results are visualized on interactive plots both on the GUI and hypertext markup language (HTML) reports. The users can implement their own developed algorithms to extract information from digital image series for any purpose. The toolbox can also be run in non-GUI mode, which allows running series of analyses in servers unattended and scheduled. The system is demonstrated using an environmental camera network in Finland. Full article
(This article belongs to the Special Issue Data in Astrophysics & Geophysics: Research and Applications)
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Open AccessArticle Improving the Efficiency of the ERS Data Analysis Techniques by Taking into Account the Neighborhood Descriptors
Received: 20 April 2018 / Revised: 28 May 2018 / Accepted: 29 May 2018 / Published: 30 May 2018
Cited by 2 | PDF Full-text (1557 KB) | HTML Full-text | XML Full-text
Abstract
Planning based on reliable information about the Earth’s surface is an important approach to minimize economic expenses conditioned by natural factors. Data collected by Earth remote sensing (ERS), as well as the analysis of such data using automated classification methods, are becoming more [...] Read more.
Planning based on reliable information about the Earth’s surface is an important approach to minimize economic expenses conditioned by natural factors. Data collected by Earth remote sensing (ERS), as well as the analysis of such data using automated classification methods, are becoming more and more important for research and practice activities related to assessing the spatio-temporal structure and sustainability of the Earth’s surface. The analysis of the authenticity of the surrounding areas enables a more objective classification of land plots on the basis of spatial patterns. Combined use of various environmental descriptors enables high-quality handling of neighborhood properties, as each descriptor provides its own specific information about a geospatial system. Experiments have shown that the diagnostics of the emergent properties of such internal structure by analyzing the diversity of dynamic characteristics allows reducing exposure to noise, obtaining a generalized result, and improving the classification accuracy. Full article
(This article belongs to the Special Issue Data in Astrophysics & Geophysics: Research and Applications)
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Other

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Open AccessData Descriptor Short Baseline Observations at Geodetic Observatory Wettzell
Received: 15 November 2018 / Revised: 23 November 2018 / Accepted: 25 November 2018 / Published: 10 December 2018
Cited by 1 | PDF Full-text (5506 KB) | HTML Full-text | XML Full-text
Abstract
The Geodetic Observatory Wettzell (GOW), jointly operated by the Federal Agency for Cartography and Geodesy (BKG), Germany and the Technical University of Munich, Germany is equipped with three radio telescopes for Very Long Baseline Interferometry (VLBI). Correlation capability is primarily designed for relative [...] Read more.
The Geodetic Observatory Wettzell (GOW), jointly operated by the Federal Agency for Cartography and Geodesy (BKG), Germany and the Technical University of Munich, Germany is equipped with three radio telescopes for Very Long Baseline Interferometry (VLBI). Correlation capability is primarily designed for relative positioning of the three Wettzell radio telescopes i.e., to derive the local ties between the three telescopes from VLBI raw data in addition to the conventional terrestrial surveys. A computing cluster forming the GO Wettzell Local Correlator (GOWL) was installed in 2017 as well as the Distributed FX (DiFX) software correlation package and the Haystack Observatory Postprocessing System (HOPS) for fringe fitting and postprocessing of the output. Data pre-processing includes ambiguity resolution (if necessary) as well as the generation of the geodetic database and NGS card files with υ Solve. The final analysis is either carried out with local processing software (LEVIKA short baseline analysis) or with the Vienna VLBI and Satellite (VieVS) software. We will present an overview of the scheduling, correlation and analysis capabilities at GOW and results obtained so. The dataset includes auxiliary files (schedule and log files) which contain information about the participating antenna, observed sources, clock offset between formatter and GPS time, cable delay, meteorological parameters (temperature, barometric pressure, and relative humidity) and ASCII files created after fringe fitting and final analysis. The published dataset can be used by the researchers and scientists to further explore short baseline interferometry. Full article
(This article belongs to the Special Issue Data in Astrophysics & Geophysics: Research and Applications)
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