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<p class="MsoNormal">Dear GeoForAll community members, I hope you are all keeping safe and well.<o:p></o:p></p>
<p class="MsoNormal">I’m happy to be able to share these newly-published guidelines for data quality documentation and sharing principles that can support open science and robust decision making. I hope you will find it interesting…<o:p></o:p></p>
<p class="MsoNormal"><a href="https://datascience.codata.org/articles/10.5334/dsj-2022-008/">https://datascience.codata.org/articles/10.5334/dsj-2022-008/</a><o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">The nature of input data, metadata, software, and workflows can all impact a dataset's quality and fitness-for purpose. The ability to easily find, understand, and integrate data quality information strongly influences our decisions to
use data and our capacity to efficiently apply knowledge generated from that data. What are the community-recommended approaches for consistently documenting and sharing machine- and human-understandable dataset quality information?<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">A group of international Earth Science domain experts formed a Community of Practice and address this challenge by developing guidelines for making dataset quality information Findable, Accessible, Interoperable, and (Re)usable, i.e., FAIR,
for both machine and human end-users.<o:p></o:p></p>
<p class="MsoNormal">Ge Peng, Carlo Lacagnina, Robert R. Downs, Anette Ganske, Hampapuram K. Ramapriyan, Ivana Ivánová, Lesley Wyborn, Dave Jones, Lucy Bastin, Chung-lin Shie and David F. Moroni<o:p></o:p></p>
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