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

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'''"[[Journal:Persistent identification of instruments|Persistent identification of instruments]]"'''
'''"[[Journal:Data without software are just numbers|Data without software are just numbers]]"'''


Instruments play an essential role in creating research data. Given the importance of instruments and associated [[metadata]] to the assessment of [[data quality]] and data reuse, globally unique, persistent, and resolvable identification of instruments is crucial. The Research Data Alliance Working Group Persistent Identification of Instruments (PIDINST) developed a community-driven solution for persistent identification of instruments, which we present and discuss in this paper. Based on an analysis of 10 use cases, PIDINST developed a metadata schema and prototyped schema implementation with [[wikipedia:DataCite|DataCite]] and ePIC as representative [[wikipedia:Persistent identifier|persistent identifier]] infrastructures, and with HZB (Helmholtz-Zentrum Berlin für Materialien und Energie) and the BODC (British Oceanographic Data Centre) as representative institutional instrument providers. ('''[[Journal:Persistent identification of instruments|Full article...]]''')<br />
Great strides have been made to encourage researchers to [[Archival informatics|archive]] data created by research and provide the necessary systems to support their storage. Additionally, it is recognized that data are meaningless unless their provenance is preserved, through appropriate [[metadata]]. Alongside this is a pressing need to ensure the [[Software quality|quality]] and archiving of the software that generates data, through simulation and control of experiment or data collection, and that which [[Data analysis|analyzes]], modifies, and draws value from raw data. In order to meet the aims of reproducibility, we argue that [[Information management|data management]] alone is insufficient: it must be accompanied by [[Systems development life cycle|good software practices]], the training to facilitate it, and the support of stakeholders, including appropriate recognition for software as a research output. ('''[[Journal:Data without software are just numbers|Full article...]]''')<br />
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''Recently featured'':
''Recently featured'':
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Revision as of 15:44, 4 October 2021

"Data without software are just numbers"

Great strides have been made to encourage researchers to archive data created by research and provide the necessary systems to support their storage. Additionally, it is recognized that data are meaningless unless their provenance is preserved, through appropriate metadata. Alongside this is a pressing need to ensure the quality and archiving of the software that generates data, through simulation and control of experiment or data collection, and that which analyzes, modifies, and draws value from raw data. In order to meet the aims of reproducibility, we argue that data management alone is insufficient: it must be accompanied by good software practices, the training to facilitate it, and the support of stakeholders, including appropriate recognition for software as a research output. (Full article...)

Recently featured:

Persistent identification of instruments
Cannabis contaminants limit pharmacological use of cannabidiol
Development of an informatics system for accelerating biomedical research