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:Fig6 Argento EMBOReports2020 21-3.jpg|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:Institutional ELN-LIMS deployment: Highly customizable ELN-LIMS platform as a cornerstone of digital transformation for life sciences research institutes|Institutional ELN-LIMS deployment: Highly customizable ELN-LIMS platform as a cornerstone of digital transformation for life sciences research institutes]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The systematic recording and management of experimental data in academic life science research remains an open problem. École Polytechnique Fédérale de Lausanne (EPFL) engaged in a program of deploying both an [[electronic laboratory notebook]] (ELN) and a [[laboratory information management system]] (LIMS) six years ago, encountering a host of fundamental questions at the institutional level and within each [[laboratory]]. Here, based on our experience, we aim to share with research institute managers, principal investigators (PIs), and any scientists involved in a combined ELN-LIMS deployment helpful tips and tools, with a focus on surrounding yourself with the right people and the right software at the right time. In this article we describe the resources used, the challenges encountered, key success factors, and the results obtained at each phase of our project. Finally, we discuss the current and next challenges we face, as well as how our experience leads us to support the creation of a new position in the research group: the laboratory data manager. ('''[[Journal:Institutional ELN-LIMS deployment: Highly customizable ELN-LIMS platform as a cornerstone of digital transformation for life sciences research institutes|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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