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

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig4 SprengholzQuantMethSci2018 14-2.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system|Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook system]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The reproduction of findings from psychological research has been proven difficult. Abstract description of the data analysis steps performed by researchers is one of the main reasons why reproducing or even understanding published findings is so difficult. With the introduction of [[Jupyter Notebook]], a new tool for the organization of both static and dynamic [[information]] became available. The software allows blending explanatory content like written text or images with code for preprocessing and analyzing scientific data. Thus, Jupyter helps document the whole research process from ideation over data analysis to the interpretation of results. This fosters both collaboration and scientific quality by helping researchers to organize their work. This tutorial is an introduction to Jupyter. It explains how to set up and use the notebook system. While introducing its key features, the advantages of using Jupyter Notebook for psychological research become obvious. ('''[[Journal:Welcome to Jupyter: Improving collaboration and reproduction in psychological research by using a notebook 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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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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