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:Fig1 Poirier DataScienceJournal2019 18-1.png|240px]]</div>
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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Palmieri Molecules2019 24-19.png|240px]]</div>
'''"[[Journal:Data sharing at scale: A heuristic for affirming data cultures|Data sharing at scale: A heuristic for affirming data cultures]]"'''
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'''"[[Journal:Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids|Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids]]"'''
  
Addressing the most pressing contemporary social, environmental, and technological challenges will require integrating insights and sharing data across disciplines, geographies, and cultures. Strengthening international data sharing networks will not only demand advancing technical, legal, and logistical infrastructure for publishing data in open, accessible formats; it will also require recognizing, respecting, and learning to work across diverse data cultures. This essay introduces a heuristic for pursuing richer characterizations of the “data cultures” at play in international, interdisciplinary data sharing. The heuristic prompts cultural analysts to query the contexts of data sharing for a particular discipline, institution, geography, or project at seven scales: the meta, macro, meso, micro, techno, data, and nano. The essay articulates examples of the diverse cultural forces acting upon and interacting with researchers in different communities at each scale. The heuristic we introduce in this essay aims to elicit from researchers the beliefs, values, practices, incentives, and restrictions that impact how they think about and approach data sharing. Rather than represent an effort to iron out differences between disciplines, this essay instead intends to showcase and affirm the diversity of traditions and modes of analysis that have shaped how data gets collected, organized, and interpreted in diverse settings. ('''[[Journal:Data sharing at scale: A heuristic for affirming data cultures|Full article...]]''')<br />
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In this work, the concentration of nine [[wikipedia:Cannabinoid|cannabinoid]]s—six neutral cannabinoids (THC, CBD, CBC, CBG, CBN, and CBDV) and three acidic cannabinoids (THCA, CBGA, and CBDA)—was used to identify the Italian retailers of ''[[wikipedia:Cannabis sativa|Cannabis sativa]]'' L. ([[wikipedia:Hemp|hemp]]), reinforcing the idea that the practice of categorizing hemp samples only using THC and CBD is inadequate. A [[high-performance liquid chromatography]]–[[tandem mass spectrometry]] (HPLC-MS/MS) method was developed for screening and simultaneously analyzing the nine cannabinoids in 161 hemp samples sold by four retailers located in different Italian cities. The hemp samples dataset was analyzed by [[wikipedia:Univariate analysis|univariate]] and [[wikipedia:Multivariate analysis|multivariate analysis]], with the aim to identify the associated hemp retailers without using any other [[information]] on the hemp samples such as [[wikipedia:Cannabis strains|''Cannabis'' strains]], seeds, soil and cultivation characteristics, geographical origin, product storage, etc. The univariate analysis highlighted that the hemp samples could not be differentiated by using any of the nine cannabinoids analyzed. ('''[[Journal:Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids|Full article...]]''')<br />
 
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Revision as of 16:31, 25 November 2019

Fig2 Palmieri Molecules2019 24-19.png

"Identification of Cannabis sativa L. (hemp) retailers by means of multivariate analysis of cannabinoids"

In this work, the concentration of nine cannabinoids—six neutral cannabinoids (THC, CBD, CBC, CBG, CBN, and CBDV) and three acidic cannabinoids (THCA, CBGA, and CBDA)—was used to identify the Italian retailers of Cannabis sativa L. (hemp), reinforcing the idea that the practice of categorizing hemp samples only using THC and CBD is inadequate. A high-performance liquid chromatographytandem mass spectrometry (HPLC-MS/MS) method was developed for screening and simultaneously analyzing the nine cannabinoids in 161 hemp samples sold by four retailers located in different Italian cities. The hemp samples dataset was analyzed by univariate and multivariate analysis, with the aim to identify the associated hemp retailers without using any other information on the hemp samples such as Cannabis strains, seeds, soil and cultivation characteristics, geographical origin, product storage, etc. The univariate analysis highlighted that the hemp samples could not be differentiated by using any of the nine cannabinoids analyzed. (Full article...)

Recently featured:

Data sharing at scale: A heuristic for affirming data cultures
Design and evaluation of a LIS-based autoverification system for coagulation assays in a core clinical laboratory
CyberMaster: An expert system to guide the development of cybersecurity curricula