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"data uncertainty evaluation"

The article examines the peculiarities of creation (updating) digital topographic maps of the scale 1:50 000 for the Main State Topographic Map of Ukraine for creation and maintaining the seamless topographic database for national needs, which is located on the Geoportal to ensure the development of the National Spatial Data Infrastructure (NSDI) in Ukraine. Several special problems were identified along...


This article aims to provide a glance about the current and future applications of Big Data Analytics (BDA) within the Oil&Gas industry, which is searching for an improvement in data managing equal to the improvement in techniques and technologies required by current and future hydrocarbon production. The connection and compatibility of BDA with the hydrocarbon sector is analyzed, highlighting its...


The residual presence of critical components (e.g. Asbestos Containing Materials – ACMs) still represents one of the main criticalities for the Occupational Safety and Health – OS&H in many large public facilities (Lee and Van Orden, 2007).

Since...


This article focuses on the evaluation of the oil and gas potential and the investigation of reservoir properties within the terrigenous horizons of the Middle and Lower Jurassic deposits located in the Kandym uplift of the Bukhara-Khiva region. The assessment methodology presented in this study is based on the analysis of geophysical well logging data curves. The authors conducted an analysis using real...

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ISSN 1121-9041

CiteScore:
2020: 3.8
CiteScore measures the average citations received per peer-reviewed document published in this title.
CiteScore values are based on citation counts in a range of four years (e.g. 2016-2019) to peer-reviewed documents (articles, reviews, conference papers, data papers and book chapters) published in the same four calendar years, divided by the number of these documents in these same four years (e.g. 2016 —19).
Source Normalized Impact per Paper (SNIP):
2019: 1.307
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR)
2019: o.657
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal's impact.
Journal Metrics: CiteScore: 1.0 , Source Normalized Impact per Paper (SNIP): 0.381 SCImago Journal Rank (SJR): 0.163

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