Royal Library of Belgium

Sven Lieber

Hi everyone,

this edition starts with two announcements before it dives into the
topic of software engineering skills for data science.

This newsletter started as a **weekly newsletter** in which I shared
topics around FAIR data and updates about my work. Selecting one or more
topics each week is fun and helps myself to *summarize* and *understand*
topics better. However, depending on the topic it takes some to prepare
the content. Additionally I don't get much response while seeing
declining opening rates instead of growth. I therefore decided to only
post **every other week**. This helps me to prepare the content better
and keeps your inboxes from overflowing :-)

🔍 The **second announcement** relates to making this newsletter **even
more FAIR**! Up until now, metadata about the newsletter is available on
*Wikidata*: the newsletter itself
([Q119153950](https://www.wikidata.org/wiki/Q119153950)) and editions of
it such as the [last edition](https://fair-data-digest.org/archive/17)
([Q122839768](https://www.wikidata.org/wiki/Q122839768)) on the European
Day of Languages. As of today, **each edition of the newsletter gets its
own Digital Object Identifier (DOI)**. This is thanks to the *Rogue
Scholar* platform over which you can learn more in today's *insights*
section!

🖥️ It is very likely that you depend on certain software for your work
on a daily basis, e.g. Excel or chat programs. This is the same for
researchers in many fields. Yet, specialized research software often has
to be developed for specific purposes. Also often, this software is
developed by the researchers themselves, **whether they are formally
trained in software engineering or not**. Imagine the computer program
you depend on was created by a colleague of yours who will retire soon.
Not a very comforting idea! In today's *coding corner* I link you a very
interesting article from the *Harvard Data Science Review* that provides
an analysis and lessons learned about the needed professionalization of
software development in research.

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# 🔍 Insights: Findable and Preserved Scholarly Communication {#insights-findable-and-preserved-scholarly-communication}

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Web content in the form of text often does not live very long. Websites
and blogs are abandoned/offline or at least evolve and move such that
previous content is difficult to find. Especially for modern science
communication in the form of scholarly blogs this is less than ideal.

*The Rogue Scholar* platform
([Q122915746](https://www.wikidata.org/wiki/Q122915746)) offers
solutions! Its slogan is *Science blogging on steroids*. You can
register your science blog and the platform acts as a sort of data
aggregator. One of the only prerequisites is that the full length your
content is openly available under a *Creative Commons license* for
example as an RSS ([Q45432](https://www.wikidata.org/wiki/Q45432)) feed.

You get the following advantages

- Your blog posts **findable** via assigned DOIs

- You are linked to your blog posts via your *ORCID identifier*
  ([Q51044](https://www.wikidata.org/wiki/Q51044))

- Your content becomes **searchable** via the platform

- Besides findability, your content will be **preserved** for the
  long-term

- if needed you can have a Mastodon
  ([Q27986619](https://www.wikidata.org/wiki/Q27986619)) bot boosting
  ("tweeting") **updates** on your blog posts

And this also offers **advantages for the scientific community**. Not
only does your content become more visible and findable, but in case
your blog discontinues, the DOI can link to an archived version of the
content. So it stays citable.

Interested? Getting DOIs for the first **50 posts per year** are
completely **free**. Go and check it out and support the platform.

[Discover content on the platform](https://rogue-scholar.org/){.button
.primary}

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# 🖥️ Coding Corner: Better Research Software {#coding-corner-better-research-software}

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Current research in many disciplines heavily depends on software. But
**just knowing how to code is not sufficient** to enable high quality
data science or advance a field. Research is about collaboration,
software needs to be shared and understood, and its developers trained!

::: {.captioned-image-container}
<figure>
<a
href="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd5af736-4d79-4747-8c6d-2c426406e26c_800x600.jpeg"
class="image-link is-viewable-img image2" target="_blank"
data-component-name="Image2ToDOM"></a>
<div class="image2-inset">
<img
src="https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd5af736-4d79-4747-8c6d-2c426406e26c_800x600.jpeg"
title="File:Research (2243540719).jpg" class="sizing-normal"
data-attrs="{&quot;src&quot;:&quot;https://substack-post-media.s3.amazonaws.com/public/images/dd5af736-4d79-4747-8c6d-2c426406e26c_800x600.jpeg&quot;,&quot;srcNoWatermark&quot;:null,&quot;fullscreen&quot;:null,&quot;imageSize&quot;:null,&quot;height&quot;:600,&quot;width&quot;:800,&quot;resizeWidth&quot;:null,&quot;bytes&quot;:null,&quot;alt&quot;:&quot;File:Research (2243540719).jpg&quot;,&quot;title&quot;:null,&quot;type&quot;:null,&quot;href&quot;:null,&quot;belowTheFold&quot;:true,&quot;topImage&quot;:false,&quot;internalRedirect&quot;:null}"
srcset="https://substackcdn.com/image/fetch/w_424,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd5af736-4d79-4747-8c6d-2c426406e26c_800x600.jpeg 424w, https://substackcdn.com/image/fetch/w_848,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd5af736-4d79-4747-8c6d-2c426406e26c_800x600.jpeg 848w, https://substackcdn.com/image/fetch/w_1272,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd5af736-4d79-4747-8c6d-2c426406e26c_800x600.jpeg 1272w, https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fdd5af736-4d79-4747-8c6d-2c426406e26c_800x600.jpeg 1456w"
sizes="100vw" loading="lazy" width="800" height="600"
alt="File:Research (2243540719).jpg" />
<div class="image-link-expand">
<img
src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHdpZHRoPSIxNiIgaGVpZ2h0PSIxNiIgdmlld2JveD0iMCAwIDI0IDI0IiBmaWxsPSJub25lIiBzdHJva2U9IiNGRkZGRkYiIHN0cm9rZS13aWR0aD0iMiIgc3Ryb2tlLWxpbmVjYXA9InJvdW5kIiBzdHJva2UtbGluZWpvaW49InJvdW5kIiBjbGFzcz0ibHVjaWRlIGx1Y2lkZS1tYXhpbWl6ZTIgIj48cG9seWxpbmUgcG9pbnRzPSIxNSAzIDIxIDMgMjEgOSI+PC9wb2x5bGluZT48cG9seWxpbmUgcG9pbnRzPSI5IDIxIDMgMjEgMyAxNSI+PC9wb2x5bGluZT48bGluZSB4MT0iMjEiIHgyPSIxNCIgeTE9IjMiIHkyPSIxMCI+PC9saW5lPjxsaW5lIHgxPSIzIiB4Mj0iMTAiIHkxPSIyMSIgeTI9IjE0Ij48L2xpbmU+PC9zdmc+"
class="lucide lucide-maximize2" />
</div>
</div>
<figcaption>Using software for research. Anders Sandberg from Oxford,
UK, CC BY 2.0, via Wikimedia Commons</figcaption>
</figure>
:::

Last week I read an article about the experiences in Research Software
Engineering at the *eScience institute* of the University of Washington
([Q101097636](https://www.wikidata.org/wiki/Q101097636)) in the *Harvard
Data Science Review*
([Q65011533](https://www.wikidata.org/wiki/Q65011533)).

Based to practical experiences they identified three needs that can be
addressed to different degrees depending on the scope of the research
project. For example whether it is a solo project offered by a single
developer, projects of a whole lab, or projects meant to serve a
community. The three needs are:

- **Readability** of the code such that also others can understand and
  maintain it

- **Resilience** (or reliability) of software to give confidence that
  the software works as intended

- **Reusability** of software by others, without adapting the code

The article provides practical examples from different disciplines.
Further the article investigates current gaps in software engineering
curricula for non computer-science students. Alternatives are proposed
and lessons from a new **Research Software Engineering** team are
shared.

Interested in how you can improve and measure your software engineering
quality with the **3Rs** or why it is easier to recruit senior software
engineers for academia rather than junior engineers? Check out the full
open access article at the Harvard Data Science Review.

[Read the full
article](https://doi.org/10.1162/99608f92.018bf012){.button .primary}

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That's it for this week of the FAIR Data Digest. If you found the
content interesting, please share and subscribe. See you *in two weeks*!

Sven

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::: {.preamble}
Thank you for reading FAIR Data Digest. This post is public so feel free
to share it.
:::

[Share](https://fairdata.substack.com/p/fair-data-digest-18?utm_source=substack&utm_medium=email&utm_content=share&action=share){.button
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:::


Hi everyone,  this edition starts with two announcements before it dives into the topic of software engineering skills for data science. This newsletter started as a
<strong>
 weekly newsletter
</strong>
in which I shared topics around FAIR data and updates about my work. Selecting one or more topics each week is fun and helps myself to
<em>
 summarize
</em>
and
<em>
 understand
</em>
topics better.

Postagens de Rogue Scholar

FAIR Data Digest #18