Notes on my work with (cultural heritage) FAIR data. Topics cover Programming, Open Science, Digital Humanities, Knowledge Graphs, Linked Data, Metadata, data catalogs, Wikidata, RDF, SPARQL and the Semantic Web as well as personal interests.

Royal Library of Belgium

Sven Lieber

Hi everyone,  today’s summer-break edition is for anyone who likes free and open knowledge. There is a short video that explains the intriguing new
<strong>
 Wikifunctions
</strong>
project. Besides that I will talk about my experiences of creating a Wikidata page about a company, hopefully it inspires you to also contribute to free and open knowledge.

FAIR Data Digest #11

Hi everyone,  last week I told you about
<em>
 European Research Infrastructures Consortia
</em>
or ERIC in short. Today we gonna look into one of these ERICs that is active in the Cultural Heritage field. I also have to celebrate a tiny anniversary related to Wikidata and I will share a resource about FAIR Jupyter notebooks.

FAIR Data Digest #7

Dear subscriber,  Welcome to the first edition of my new weekly newsletter! Within different recurring categories I will provide updates of my work, my personal journey, and topics such as Knowledge Graphs, Linked Data, FAIR data, Open Science and many more. It is conference season!

FAIR Data Digest #1

Hi everyone,  today’s edition is about FAIR data that is not necessarily related to cultural heritage, but related to cars 🚗. We will have a look into an Open Data dataset of the Dutch government that provides data which hopefully helps me to answer some questions about
<strong>
 child safety seats
</strong>
. Statistics shown in this post come from this GitHub repository.

FAIR Data Digest #23

Hi everyone,  I hope you had a great summer. The academic year is about to start and I hope you are hungry for some knowledge :-) In today’s edition I want to introduce you to the European Open Science Cloud (EOSC).  ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest!

FAIR Data Digest #14

Hi everyone,  this last summer-break edition ties in with the previous edition. Last week I’ve wrote over research to make
<strong>
 publication metadata
</strong>
from the free publishing service CEUR-WS
<strong>
 available in a FAIR fashion
</strong>
, such that the data can be explored.

FAIR Data Digest #13

Hi everyone,  welcome to the 3rd edition of my newsletter! Besides the fact that we have suffered the first official heat wave in Belgium this year, I have other hot information to share.

FAIR Data Digest #3

Dear subscriber,  welcome to the second edition of the newsletter and also a warm welcome to all new subscribers. It has been an interesting week. In this edition I will talk about some work updates, a one-day workshop I’ve attended last week and I have a video recommendation.

FAIR Data Digest #2

Hi everyone,  welcome to today’s edition on the the
<em>
 European Day of Languages
</em>
(Q496423).  ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest! Subscribe for free to receive new posts and support my work.

FAIR Data Digest #17

Hi everyone,  in today’s slightly more technical edition I will focus on my recent encounters with the Linked Data query language SPARQL. Many of you may know it as a language to retrieve data (possibly from Wikidata). I mainly use it for a project where I recently found some performance issues that could be resolved by either rewriting the query or changing the approach.

FAIR Data Digest #22

Hello everyone,  welcome to today’s Halloween edition about
<em>
 data anti-patterns
</em>
that hopefully will scare you so much you don’t do them :-) Things you better avoid when aiming for FAIR data. Anti patterns?

FAIR Data Digest #20

Hi everyone,  greetings from Karlsruhe in Germany where I am attending a conference about research data infrastructure at the moment.

FAIR Data Digest #15

Hi everyone,  I’m on vacation now, but thanks to pre-scheduling you won’t miss out FAIR data updates! Today I want to share with you a research paper in which the authors report on the creation of large amounts of FAIR data which they host on Wikidata.

FAIR Data Digest #12

Hi everyone,  it has been a while since we talked about research infrastructures. Many of them might sound abstract or just like super specialized app stores. But today we have a brief look into something very tangible: heritage objects that are analyzed by instruments or are digitized in other forms. There is lots of diverse data in Heritage Science. ❓ FAIR Buzzword Bingo: what is E-RIHS?

FAIR Data Digest #21

Hi everyone,  welcome back to today’s edition in which I share a work update related to bibliographic data and provide information to an upcoming interesting talk. ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest! Subscribe for free to receive new posts and support my work.

FAIR Data Digest #19

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.

FAIR Data Digest #18

Hi everyone,  welcome to another summer-break edition. This time with a webinar recording about persistent identifiers for FAIR data and news about data cataloging with DCAT both in Europe and the US.  ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest!

FAIR Data Digest #10

Hi everyone,  greetings to one of our summer editions! Whether you're on vacation, just back, or about to head off, I've got some interesting content to share with you.

FAIR Data Digest #9

Hi everyone,  in today’s edition I will try to explain what DARIAH / CLARIAH is and what they do in the context of FAIR data. I am also sharing a video of some recent presentations about Wikipedia from the Wikimedia Research Showcase. ::: {.subscription-widget-wrap attrs=“{"url":"https://fairdata.substack.com/subscribe?","text":"Subscribe","language":"en"}” component-name=“SubscribeWidgetToDOM”}  Thanks for reading FAIR Data Digest!

FAIR Data Digest #8

Hi everyone,  if you are a researcher and you want to follow the FAIR data principles you are not left alone. In today’s edition I will try to explain what ERIC is and what they have to do with FAIR data. As for work updates: I was on a small camping vacation in France, so there are no work updates, but I came back with many ideas for the future!

FAIR Data Digest #6

Hi everyone,  did you ever wonder how FAIR data is put into practice? This week I went down the rabbit hole and what I brought back is the start of a new series to
<strong>
 unravel the many FAIR data initiatives
</strong>
. There are also some good news for (Belgian) FAIR data in my work update!

FAIR Data Digest #5

Hi everyone,  this week it’s all about hands-on! I will talk about a webinar on trying to fix quality issues in Wikidata by using its ontology. Additionally I will share a few resources around basics and advanced uses of the query language SPARQL that is used to query Linked Data, for example from Wikidata.

FAIR Data Digest #4

Hi everyone,  as promised, today you will hear more about the research data infrastructure conference where I and around
<strong>
 680 other people
</strong>
attended last week in Karlsruhe, Germany. Grab a cup of coffee of tea and enjoy today’s edition!

FAIR Data Digest #16

FAIR Data Digest

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.

<div>

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</div>

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

<div>

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</div>

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}

<div>

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</div>

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

<div>

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</div>

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,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"
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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</div>

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

::: {.captioned-button-wrap attrs="{\"url\":\"https://fairdata.substack.com/p/fair-data-digest-18?utm_source=substack&utm_medium=email&utm_content=share&action=share\",\"text\":\"Share\"}" component-name="CaptionedButtonToDOM"}
::: {.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
.primary}
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