Difference between revisions of "Main Page/Featured article of the week/2024"

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<h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 15–21:</h2>
<h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 22–28:</h2>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Ghiringhelli SciData23 10.png|240px]]</div>
'''"[[Journal:Shared metadata for data-centric materials science|Shared metadata for data-centric materials science]]"'''
 
The expansive production of data in [[materials science]], as well as their widespread [[Data sharing|sharing]] and repurposing, requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the [[Journal:The FAIR Guiding Principles for scientific data management and stewardship|FAIR data principles]] (that ask for data and information to be findable, accessible, interoperable, and reusable) must not be too narrow. At the same time, the wider materials science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science.” ... ('''[[Journal:Shared metadata for data-centric materials science|Full article...]]''')<br />
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|<br /><h2 style="font-size:105%; font-weight:bold; text-align:left; color:#000; padding:0.2em 0.4em; width:50%;">Featured article of the week: January 15–21:</h2>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Jadhav IntJofMolSci23 24-9.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Jadhav IntJofMolSci23 24-9.png|240px]]</div>
'''"[[Journal:A metabolomics and big data approach to cannabis authenticity (authentomics)|A metabolomics and big data approach to cannabis authenticity (authentomics)]]"'''  
'''"[[Journal:A metabolomics and big data approach to cannabis authenticity (authentomics)|A metabolomics and big data approach to cannabis authenticity (authentomics)]]"'''  

Revision as of 18:36, 29 January 2024

Featured article of the week archive - 2024

Welcome to the LIMSwiki 2024 archive for the Featured Article of the Week.

Featured article of the week: January 22–28:

Fig1 Ghiringhelli SciData23 10.png

"Shared metadata for data-centric materials science"

The expansive production of data in materials science, as well as their widespread sharing and repurposing, requires educated support and stewardship. In order to ensure that this need helps rather than hinders scientific work, the implementation of the FAIR data principles (that ask for data and information to be findable, accessible, interoperable, and reusable) must not be too narrow. At the same time, the wider materials science community ought to agree on the strategies to tackle the challenges that are specific to its data, both from computations and experiments. In this paper, we present the result of the discussions held at the workshop on “Shared Metadata and Data Formats for Big-Data Driven Materials Science.” ... (Full article...)


Featured article of the week: January 15–21:

Fig2 Jadhav IntJofMolSci23 24-9.png

"A metabolomics and big data approach to cannabis authenticity (authentomics)"

With the increasing accessibility of cannabis (Cannabis sativa L., also known as marijuana and hemp), its products are being developed as extracts for both recreational and therapeutic use. This has led to increased scrutiny by regulatory bodies, who aim to understand and regulate the complex chemistry of these products to ensure their safety and efficacy. Regulators use targeted analyses to track the concentration of key bioactive metabolites and potentially harmful contaminants, such as heavy metals and other impurities. However, the complexity of cannabis' metabolic pathways requires a more comprehensive approach. A non-targeted metabolomic analysis of cannabis products is necessary to generate data that can be used to determine their authenticity and efficacy ... (Full article...)


Featured article of the week: January 08–14:

GA Ishii SciTechAdvMatMeth2023 3-1.jpg

"Integration of X-ray absorption fine structure databases for data-driven materials science"

With the aim of introducing data-driven science and establishing an infrastructure for making X-ray absorption fine structure (XAFS) spectra findable and reusable, we have integrated XAFS databases in Japan. This integrated database (MDR XAFS DB) enables cross searching of spectra from more than 2,000 samples and more than 700 unique materials with machine-readable metadata. The introduction of a materials dictionary with approximately 6,000 synonyms has improved the search performance, and links with large external databases have been established. In order to compare spectra in the database, the energy calibration policies of each institution were compiled, and the energy calibration methods across institutions were shown ... (Full article...)


Featured article of the week: January 01–07:

Fig1 Heavey ForSciIntSyn2023 7.jpg

"Management and disclosure of quality issues in forensic science: A survey of current practice in Australia and New Zealand"

The investigation of quality issues detected within the forensic process is a critical feature in robust quality management systems (QMSs) to provide assurance of the validity of reported laboratory results and inform strategies for continuous improvement and innovation. A survey was conducted to gain insight into the current state of practice in the management and handling of quality issues amongst the government service provider agencies of Australia and New Zealand. The results demonstrate the value of standardized quality system structures for the recording and management of quality issues, but also areas where inconsistent reporting increases the risk of overlooking important data to inform continuous improvement ... (Full article...)