Difference between revisions of "Template:Article of the week"

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A '''[[scientific data management system]]''' (SDMS) is a piece or package of software that acts as a document management system (DMS), capturing, cataloging, and archiving data generated by [[laboratory]] instruments ([[HPLC]], [[mass spectrometry]]) and applications ([[LIMS]], analytical applications, [[electronic laboratory notebook]]s) in a compliant manner. The SDMS also acts as a gatekeeper, serving platform-independent data to informatics applications and/or other consumers.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


As with many other [[laboratory informatics]] tools, the lines between a [[LIMS]], [[ELN]], and an SDMS are at times blurred. However, there are some essential qualities that an SDMS owns that distinguishes it from other informatics systems. It's built to handle unstructured, mostly heterogeneous data; it typically acts as a seamless "wrapper" for other data systems like LIMS and ELN in the laboratory; and it is designed primarily for data consolidation, knowledge management, and knowledge asset realization. An SDMS also must be focused on increasing research productivity without sacrificing data sharing and collaboration efforts. ('''[[Scientific data management system|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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Latest revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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