Royal Library of Belgium

Sven Lieber

Hi everyone,

last week I told you about *European Research Infrastructures Consortia*
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.

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❓ Language is a huge part of our lives, thus naturally there is lots of
research around it, and data. For example, recorded language in form of
**audio** or written language in the form of **text documents**. In this
week's *FAIR Buzzword Bingo* series I will introduce the **Common
Language Resources and Technology Infrastructures (CLARIN)**. This
research infrastructure provides data, tools and services related to
language material.

🌐 I guess I don't have to introduce
*[Wikidata](https://www.wikidata.org/)* to you. It was already featured
in this newsletter a few times. Just a few days ago I made my **1000th
Wikidata edit.** A reason to celebrate! I will briefly mention what my
1000th edit was and how you can contribute to Wikidata yourself. This
will also mark the start of the new category *Wikidata Spotlight* where
from time to time I will share what I have edited and why.

🖥️ Whether you are a programmer or not, chances are high that you have
heard the term *Jupyter Notebooks* at least once before. If not also no
problem, I will briefly explain it and share a recent resource that
explains **how you can make your Jupyter notebooks FAIR**. Thanks for
this tip to my colleague [Sally
Chambers](https://twitter.com/schambers3).

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# ❓ FAIR Buzzword Bingo: CLARIN {#fair-buzzword-bingo-clarin}

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There are already countless intertwined research and infrastructure
initiatives in the cultural heritage field, even more beyond it. Every
week I will focus on one of the many FAIR data-related initiatives,
**this week: the Common Language Resources and Technology Infrastructure
(CLARIN)**

Imagine that you are researching something that relates to languages and
text processing. You may need **voice recordings in a specific
language** from a specific time or **digitized documents** such as
newspapers or legal texts. Where do you get data and tools to do this?
And more importantly, **how do search for it in in a uniform way?**

CLARIN ([Q2986825](https://www.wikidata.org/wiki/Q2986825)) provides a
research infrastructure offering data, tools and services to support
research based on language resources. Via their platform you can find
relevant **data at one of the many participating institutions**. One
example is the **Virtual Language Observatory** where you can search in
nearly a million metadata records. For example, when searching "Flemish"
you can find [The Dutch Song
Database](https://vlo.clarin.eu/record/https_58__47__47_hdl.handle.net_47_11372_47_LRT-608_64_format_61_cmdi?4&count=95&index=7&q=flemish),
a [Flemish parliamentary
debate](https://vlo.clarin.eu/record/https_58__47__47_hdl.handle.net_47_1839_47_00-0000-0000-0002-E59B-9?2&count=95&index=1&q=flemish)
or a [video
collection](https://vlo.clarin.eu/record/https_58__47__47_hdl.handle.net_47_11372_47_LRT-1511_64_format_61_cmdi?3&count=95&index=8&q=flemish)
about Teaching Dutch in primary and secondary education.

[Visit the Virtual Language
Observatory](https://vlo.clarin.eu/?0){.button .primary}

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<figcaption>Logo of the CLARIN-ERIC: https://www.clarin.eu</figcaption>
</figure>
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## Background

CLARIN, or more precisely CLARIN-ERIC is one of the European Research
Infrastructure Consortiums I talked about in the [last
edition](https://fair-data-digest.org/archive/6). It was built in 2012
with nine founding members after the initial CLARIN project
([Q120669114](https://www.wikidata.org/wiki/Q120669114)) between 2008
and 2011. In the meantime there are **member consortia from 22 European
states**, observer consortia from **South Africa**, **Switzerland** and
the **UK**, as well as a third-party consortium from the **United
States**.

Within the CLARIN-Plus
([Q100319394](https://www.wikidata.org/wiki/Q100319394)) project between
2015 and 2017 was dedicated to enhance CLARIN. The initiative became
even more mature.

> *"The project aim was to consolidate and strengthen the CLARIN
> infrastructure in the following areas: the central technical hub and
> central office, partnerships with other infrastructures, outreach and
> governance"* - [CLARIN-Plus
> outcomes](https://web.archive.org/web/20230604145731/https://www.clarin.eu/content/clarin-stronger-ever-clarin-plus-project-outcomes)

The partnership with other infrastructures relates for example to
*Europeana* (that I covered in a [previous
edition](https://fair-data-digest.org/archive/5)) and *DARIAH*, an
initiative that I will cover in a future edition.

Depending on your needs you can find relevant CLARIN member institutions
for your use case. CLARIN member institutions are organized in **three
types of centres**: *Technical Centres (B)* , *Metadata providing
Centres (C)* and *Knowledge Centres (K)*. Knowing this helps you to
estimate what kind of data and services you can expect from a specific
member institution.

[Information about CLARIN
centres](https://www.clarin.eu/content/clarin-centres){.button .primary}

## CLARIN and FAIR data

The most important question for this newsletter: **how about FAIR
data?** The CLARIN network is the combined effort of many different
institutions. They agreed on certain standards and make **searching**
***for*** **data easy**. In the book *The Enterprise Data Catalog*, [Ole
Olesen-Bagneux](https://www.linkedin.com/in/ole-olesen-bagneux/)
specifies two types of data search: searching ***for*** data and
searching ***in*** data. The former is important to identify relevant
data in the first place. And among others, this is where the CLARIN
infrastructure provides added value: by providing standardized metadata
catalogues!

Additionally, according to the CLARIN information video (that you will
find below), they explicitly contribute to easy access by making all
resources FAIR, in their case:

- **Findable** through the CLARIN discovery boards

- **Accessible** through single-sign on

- **Interoperable** thanks to CLARIN's promotion of open science
  standards

- **Reusable** because users know what they can and cannot do with any
  dataset

**What do you think about CLARIN? Is there something I have forgotten?**
Below you will find a short YouTube video that introduces CLARIN high
level. If you are interested in more details here are some links for
further reading

- The *Tour De CLARIN* series that highlights different centers:
  <https://www.clarin.eu/Tour-de-CLARIN>

- An open bibliography related to CLARIN:
  <https://www.zotero.org/groups/562080/clarin/collections/DNV2LZAN>

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# 🌐 Wikidata Spotlight {#wikidata-spotlight}

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Anyone can edit Wikidata! You can do it either manually via their
website or automatically for data in batch via tools such as
[QuickStatements](https://www.wikidata.org/wiki/Help:QuickStatements).
Just a few days ago I started to use QuickStatements for the first time.
This makes my tiny anniversary short before even more astonishing for
me: I already have performed **1000th edits on Wikidata, manually!**

**How can you edit Wikidata?** Just visit a Wikidata item in the browser
and click one of the edit buttons to add statements or change the
description. It is that easy! Actually you don't even have to login. If
you do not have an account, your IP address will be shown in the editing
log. And yes, there is an editing log! On every item you can click *View
history* to see *who* did *when* contribute *what* to the item. Only for
certain protected pages or when you want to use QuickStatements you need
an account and at least 50 edits.

You may wonder what my 1000th edit was. While curating information about
CLARIN (see section above), I had to indicate somewhere specific
**Technology Readiness Levels (TRLs)**. In case you haven't heard of it
before, there are nine such TRLs. They were **defined by NASA in the
1970s** to **estimate the maturity of technologies**. Since a decade or
so, they are also used in funding programmes of the European Union.

Wikidata only knew the single concept TRL that represents the scale as
such ([Q1478071](https://www.wikidata.org/wiki/Q1478071)). I created one
concept for each of the levels such that they can be used independently
to annotate data. **And TRL-6 was my 1000th edit**:
[Q120669350](https://www.wikidata.org/wiki/Q120669350). I still have to
investigate which property should be used best for the TRL levels. So
far I have used *compliesWith*
([P5009](https://www.wikidata.org/wiki/Property:P5009)). What do you
think? How would you use these TRL levels on Wikidata?

[Start curating data yourself!](https://www.wikidata.org/){.button
.primary}

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# 🖥️ Coding corner {#coding-corner}

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**What do you do if you have (FAIR) data?** I guess you want to **use
it** to reach some sort of a goal. This means you have to process the
data, aggregate it or visualize it. These kinds of workflows and the
specific steps you perform (and why you perform them) are crucial.
Ideally these **workflows should be FAIR too!**

Especially for data science, the **context and the why** of data
processing are **important**. You can use Jupyter notebooks as an
interactive environment in which you can mix a documentation of your
steps and pieces of code.

> *"The Jupyter Notebook is an open-source, browser-based tool for
> creating virtual lab notebooks that document research workflows, code,
> data and visualisations. It is ideal for interactive data science and
> scientific computing across disciplines, supporting programming
> languages including Python, R, Julia and a few others"* - [DOI:
> 10.1109/MSR.2019.00077](https://doi.org/10.1109/MSR.2019.00077)

Just a few days ago, *Australian Research Data Commons (ARDC)* published
a practical guide on how to make Jupyter notebooks FAIR. One of their
tips is to use code versioning systems such as
*[git](https://en.wikipedia.org/wiki/Git)*
[](https://en.wikipedia.org/wiki/Git) and to obtain DOI identifiers for
code, for example via an existing [GitHub
feature](https://docs.github.com/en/repositories/archiving-a-github-repository/referencing-and-citing-content).
You can check out their complete guide for more information.

[Visit the practical
guide](https://ardc.edu.au/resource/fair-for-jupyter-notebooks-a-practical-guide/){.button
.primary}

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That's it for this week of the FAIR Data Digest. I hope you found the
content interesting. Don't forget to share or subscribe. See you next
week!

Sven

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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.

Messages de Rogue Scholar

FAIR Data Digest #7