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:Fig2 Lazarjani BMCJournCannaRes2020 2.png|240px]]</div>
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'''"[[Journal:Methods for quantification of cannabinoids: A narrative review|Methods for quantification of cannabinoids: A narrative review]]"'''
'''"[[Journal:Risk assessment for scientific data|Risk assessment for scientific data]]"'''


Around 144 [[Wikipedia:Cannabinoid|cannabinoids]] have been identified in the ''[[Wikipedia:Cannabis|Cannabis]]'' plant; among them [[Wikipedia:Tetrahydrocannabinol|tetrahydrocannabinol]] (THC) and [[Wikipedia:Cannabidiol|cannabidiol]] (CBD) are the most prominent ones. Because of the legal restrictions on cannabis in many countries, it is difficult to obtain standards to use in research; nonetheless, it is important to develop a cannabinoid quantification technique, with practical pharmaceutical applications for quality control of future therapeutic cannabinoids. To find relevant articles for this narrative review paper, a combination of keywords such as "[[Wikipedia:Cannabis (drug)|medicinal cannabis]]," "analytical," "quantification," and "cannabinoids" were searched for in PubMed, EMBASE, MEDLINE, Google Scholar, and Cochrane Library (Wiley) databases. ('''[[Journal:Methods for quantification of cannabinoids: A narrative review|Full article...]]''')<br />
Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use. Identifying such risk factors to data use is a key step in preventing or minimizing data loss. This paper presents an analysis of data risk factors that scientific data collections may face, and a data risk assessment matrix to support data risk assessments to help ameliorate those risks. The goals of this work are to inform and enable effective data risk assessment by: a) individuals and organizations who manage data collections, and b) individuals and organizations who want to help to reduce the risks associated with data preservation and stewardship. The data risk assessment framework presented in this paper provides a platform from which risk assessments can begin, and a reference point for discussions of data stewardship resource allocations and priorities. ('''[[Journal:Risk assessment for scientific data|Full article...]]''')<br />
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{{flowlist |
{{flowlist |
* [[Journal:Methods for quantification of cannabinoids: A narrative review|Methods for quantification of cannabinoids: A narrative review]]
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Revision as of 16:09, 8 November 2021

"Risk assessment for scientific data"

Ongoing stewardship is required to keep data collections and archives in existence. Scientific data collections may face a range of risk factors that could hinder, constrain, or limit current or future data use. Identifying such risk factors to data use is a key step in preventing or minimizing data loss. This paper presents an analysis of data risk factors that scientific data collections may face, and a data risk assessment matrix to support data risk assessments to help ameliorate those risks. The goals of this work are to inform and enable effective data risk assessment by: a) individuals and organizations who manage data collections, and b) individuals and organizations who want to help to reduce the risks associated with data preservation and stewardship. The data risk assessment framework presented in this paper provides a platform from which risk assessments can begin, and a reference point for discussions of data stewardship resource allocations and priorities. (Full article...)

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