<html><head><meta http-equiv="content-type" content="text/html; charset=utf-8"></head><body dir="auto"><div dir="ltr"><meta http-equiv="content-type" content="text/html; charset=utf-8">The “magnum opus” of dynamic data citation. <br><br><div dir="ltr">-m.<div>Sent from my iPhone</div></div><div dir="ltr"><br>Begin forwarded message:<br><br></div><blockquote type="cite"><div dir="ltr"><b>From:</b> Umunshi via Data Citation WG <rda-datacitation-wg@rda-groups.org><br><b>Date:</b> November 19, 2021 at 18:44:17 GMT+1<br><b>To:</b> rauber@ifs.tuwien.ac.at, Data Citation WG <rda-datacitation-wg@rda-groups.org><br><b>Subject:</b> <b>[External] Re: [rda-datacitation-wg] RDA WGDC Paper summarizing adoption stories and lessons...</b><br><b>Reply-To:</b> umunshi@gmail.com<br><br></div></blockquote><blockquote type="cite"><div dir="ltr"><div dir="auto">congratulations!</div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Fri, 19 Nov 2021, 20:32 rauber via Data Citation WG, <<a href="mailto:rda-datacitation-wg@rda-groups.org">rda-datacitation-wg@rda-groups.org</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Dear all,<br>
<br>
I am very happy to inform you that the paper jointly written by many of <br>
the adopters of the recommendations on dynamic data citation has been <br>
published in the Hardvard Data Science Reviews:<br>
<br>
<br>
Rauber, A., Gößwein, B., Zwölf, C. M., Schubert, C., Wörister, F., <br>
Duncan, J., … Parsons, M. A. (2021). Precisely and Persistently <br>
Identifying and Citing Arbitrary Subsets of Dynamic Data. Harvard Data <br>
Science Review, 3(4).<br>
<a href="https://doi.org/10.1162/99608f92.be565013" rel="noreferrer noreferrer" target="_blank">https://doi.org/10.1162/99608f92.be565013</a><br>
<br>
<a href="https://hdsr.mitpress.mit.edu/pub/si7wzxxa/release/2?readingCollection=1ccd159a" rel="noreferrer noreferrer" target="_blank">https://hdsr.mitpress.mit.edu/pub/si7wzxxa/release/2?readingCollection=1ccd159a</a><br>
<br>
(Abstract attached below)<br>
<br>
It provides a summary of the recommendations, describes the reference <br>
implementations for different types of data and, most important of all, <br>
presents a number of implementations that have been deployed in various <br>
infrastructures. It is a kind of "opus magnum" for this WG, collecting a <br>
lot of the work done in one single report and shows how far we have come <br>
from the time when we have published the recommendations with some <br>
initial demonstrators, via proper reference implementations to <br>
infrastructures that have actually deployed them in practice and put <br>
them into full operation.<br>
<br>
My thanks go, specifically, to all the WG members who have put trust <br>
into the recommendations and put up the effort to actually implement and <br>
deploy them - and contributed to this paper summarizing all the lessons <br>
learned so far - thank you so much!<br>
<br>
I hope this paper is useful for other institutions who want to embark on <br>
implementing the recommendations, providing templates and points of <br>
reference. We will, of course, continue to support such adoptions - and <br>
we would be happy to learn more about other adoptions taking place <br>
collecting them, and sharing information about them - initially e.g. via <br>
our webinar series, and then maybe by a sequel to this paper. We have <br>
been a bit quite during the last plenaries as thse on-line meetings are <br>
not th emost efficient mechanisms for discussion, especially when a lot <br>
of these sessions are squeezed into a tight week. But we would be very <br>
happy to pick up the webinar series again if you have any adoption <br>
stories to share.<br>
<br>
Best regards,<br>
Andreas Rauber<br>
<br>
----------------------<br>
Abstract:<br>
Precisely identifying arbitrary subsets of data so that these can be <br>
reproduced is a daunting challenge in data-driven science, the more so <br>
if the underlying data source is dynamically evolving. Yet an increasing <br>
number of settings exhibit exactly those characteristics. Larger amounts <br>
of data are being continuously ingested from a range of sources (be it <br>
sensor values, online questionnaires, documents, etc.), with error <br>
correction and quality improvement processes adding to the dynamics. <br>
Yet, for studies to be reproducible, for decision-making to be <br>
transparent, and for meta studies to be performed conveniently, having a <br>
precise identification mechanism to reference, retrieve, and work with <br>
such data is essential. The Research Data Alliance (RDA) Working Group <br>
on Dynamic Data Citation has published 14 recommendations that are <br>
centered around time-stamping and versioning evolving data sources and <br>
identifying subsets dynamically via persistent identifiers that are <br>
assigned to the queries selecting the respective subsets. These <br>
principles are generic and work for virtually any kind of data. In the <br>
past few years numerous repositories around the globe have implemented <br>
these recommendations and deployed solutions. We provide an overview of <br>
the recommendations, reference implementations, and pilot systems <br>
deployed and then analyze lessons learned from these implementations. <br>
This article provides a basis for institutions and data stewards <br>
considering adding this functionality to their data systems.<br>
<br>
--<br>
Full post: <br>
<a href="https://www.rd-alliance.org/group/data-citation-wg/post/rda-wgdc-paper-summarizing-adoption-stories-and-lessons-learned" rel="noreferrer noreferrer" target="_blank">https://www.rd-alliance.org/group/data-citation-wg/post/rda-wgdc-paper-summarizing-adoption-stories-and-lessons-learned</a><br>
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</blockquote></div>
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