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<p>*Apologies for cross-posting*<br>
</p>
<p>Dear colleagues,</p>
<p>Data quality assurance is a fundamental issue for repositories
that seek to ensure trust in their services. On October 5, 2022,
this topic was addressed in a workshop, hosted by the project
re3data COREF [1]. The objective of the workshop was to approach
the pervasive, yet elusive topic of data quality management from
several viewpoints.</p>
<p>The recordings and presentation slides are now published and
linked via the re3data COREF blog: <a
class="moz-txt-link-freetext"
href="https://coref.project.re3data.org/blog/workshop_materials">https://coref.project.re3data.org/blog/workshop_materials</a>
</p>
<p>In the first part of the workshop, results from a comprehensive
survey on data quality assurance at research data repositories
that was conducted in re3data COREF were presented, followed by
reflections on quality management from the perspective of the
certification organization CoreTrustSeal. In the second part,
repositories from earth and environmental sciences (PANGAEA [2]),
humanities (ARCHE [3]) and social sciences (UK Data Archive [4])
shared their approaches on data quality assurance.</p>
<p>The findings of the survey on the status quo of data quality
assurance practices at research data repositories have been
published in the Data Science Journal: <br>
</p>
<blockquote>
<p>Kindling, M., & Strecker, D. (2022). Data Quality Assurance
at Research Data Repositories. Data Science Journal, 21(1), 18.
<a class="moz-txt-link-freetext"
href="https://doi.org/10.5334/dsj-2022-018">https://doi.org/10.5334/dsj-2022-018</a>
<br>
</p>
</blockquote>
<p>The main findings reported in the paper are:<br>
</p>
<ul>
<li>Quality assurance at research data repositories is
multifaceted and nonlinear. Although there are some common
patterns, individual approaches to ensuring data quality are
diverse.</li>
<li>In the context of research data, data quality and metadata
quality are enmeshed and can not be clearly separated.</li>
<li>Research data repositories significantly contribute to data
quality. However, data quality assurance sets high expectations
for repositories and requires a lot of resources. This is in
part due to a path dependence of data review on review processes
for text publications.</li>
<li>Information on results of the formal assessment and review of
individual datasets are not yet widely available.</li>
<li>The association between the certification status of a
repository and its data quality assurance practices is weak.</li>
</ul>
With best regards from the re3data COREF team
<p>Nina Weisweiler</p>
[1] <a class="moz-txt-link-freetext"
href="https://coref.project.re3data.org/project">https://coref.project.re3data.org/project</a><br>
[2] <a class="moz-txt-link-freetext" href="https://pangaea.de/">https://pangaea.de/</a>
<br>
[3] <a class="moz-txt-link-freetext"
href="https://arche.acdh.oeaw.ac.at/browser/">https://arche.acdh.oeaw.ac.at/browser/</a><br>
[4] <a class="moz-txt-link-freetext"
href="https://www.data-archive.ac.uk/">https://www.data-archive.ac.uk/</a>
<p></p>
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