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

Institute of Physics, University of Belgrade, PO Box 57, 11000 Belgrade, Serbia
Website | E-Mail
Interests: solar and stellar astrophysics; high energy astrophysics; atomic and ionic collisions; atomic processes in white dwarfs and solar type stars; space weather studies of upper atmosphere; astrogeoInformatics; astroinformatics; databases
Guest Editor
Dr. Aleksandra Nina

The Astrophysics and Ionospheric Laboratory, Institute of Physics, Pregrevica 118, 11080 Zemun, Belgrade, Serbia
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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 (3 papers)

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Research

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
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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
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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
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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
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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
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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
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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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