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Merge pull request #6767 from galaxyproject/biont_news
[News Item] Add BioNT project announcement for translated tutorials
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news/_posts/2026-04-01-biont.md

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---
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layout: news
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title: "BioNT Brings Galaxy Training to German, Spanish, and Italian Learners"
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tags:
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- new tutorial
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- translation
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- community
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- contributing
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contributions:
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authorship:
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- hvelab
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- lisanna
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- ocaisa
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- Sch-Da
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- shiltemann
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- silviadg87
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- teresa-m
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- Tillsa
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- unode
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funding:
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- biont
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cover: "assets/images/BioNT_Logo.png"
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coveralt: "BioNT project logo"
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---
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We are thrilled to announce that the [BioNT project](https://biont-training.eu/) (Bio Network for Training)
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has contributed a significant set of human-curated translated Galaxy training materials to the GTN,
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now live and available to learners worldwide!
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## What is BioNT?
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BioNT is an international consortium of academic institutions and SMEs, co-funded by the EU Digital Europe
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programme (grant agreement 101100604), running from January 2023 to June 2026. Its mission is to build a high-quality
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training programme in digital skills for the biotechnology and biomedical sectors, particularly for
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learners with limited prior experience in biological data handling and computational tools.
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The University of Freiburg's Freiburg Galaxy Team is an active BioNT partner, contributing expertise in
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bioinformatics training and the Galaxy Training Network.
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## Human-curated translated Tutorials Now Available
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One of BioNT's core goals is to make training materials accessible to non-English-speaking communities.
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To that end, the BioNT team has produced careful, human-quality translations of key GTN tutorials into
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**German (DE)**, **Spanish (ES)**, and **Italian (IT)**.
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The following tutorials are now available in four languages:
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| English | German | Spanish | Italian |
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| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [Galaxy Intro: From Peaks to Genes]({% link topics/introduction/tutorials/galaxy-intro-peaks2genes/tutorial.md %}) | [Von Peaks zu Genen]({% link topics/introduction/tutorials/galaxy-intro-peaks2genes/tutorial_DE.md %}) | [De picos a genes]({% link topics/introduction/tutorials/galaxy-intro-peaks2genes/tutorial_ES.md %}) | [Dai picchi ai geni]({% link topics/introduction/tutorials/galaxy-intro-peaks2genes/tutorial_IT.md %}) |
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| [Quality Control]({% link topics/sequence-analysis/tutorials/quality-control/tutorial.md %}) | [Qualitätskontrolle]{% link topics/sequence-analysis/tutorials/quality-control/tutorial_DE.md %}l) | [Control de calidad]{% link topics/sequence-analysis/tutorials/quality-control/tutorial_ES.md %}l) | [Controllo qualità]({% link topics/sequence-analysis/tutorials/quality-control/tutorial_IT.md %}) |
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| [Mapping]({% link topics/sequence-analysis/tutorials/mapping/tutorial.md %}) | [Mapping]({% link topics/sequence-analysis/tutorials/mapping/tutorial_DE.md %}) | [Mapeo]({% link topics/sequence-analysis/tutorials/mapping/tutorial_ES.md %}) | [Mappatura]({% link topics/sequence-analysis/tutorials/mapping/tutorial_IT.md %}) |
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| [Reference-based RNA-Seq]({% link topics/transcriptomics/tutorials/ref-based/tutorial.md %}) | [Referenzbasierte RNA-Seq-Datenanalyse]({% link topics/transcriptomics/tutorials/ref-based/tutorial_DE.md %}) | [Análisis de datos RNA-Seq basados en referencias]{% link topics/transcriptomics/tutorials/ref-based/tutorial_ES.md %}l) | [Analisi dei dati RNA-Seq basata su riferimenti]({% link topics/transcriptomics/tutorials/ref-based/tutorial_IT.md %}) |
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| [Learning about one gene across biological resources and formats]({% link topics/data-science/tutorials/online-resources-gene/tutorial.md %}) | [Lernen über ein Gen]{% link topics/data-science/tutorials/online-resources-gene/tutorial_DE.md %}l) | [Aprendizaje sobre un gen]{% link topics/data-science/tutorials/online-resources-gene/tutorial_ES.md %}l) | [Imparare a conoscere un gene]({% link topics/data-science/tutorials/online-resources-gene/tutorial_IT.md %}) |
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| [One protein along UniProt]({% link topics/data-science/tutorials/online-resources-protein/tutorial.md %}) | [Ein Protein entlang der UniProt-Seite]({% link topics/data-science/tutorials/online-resources-protein/tutorial_DE.md %}) | [Una proteína a lo largo de la página UniProt]({%link topics/data-science/tutorials/online-resources-protein/tutorial_ES.md %}) | [Una proteina lungo la pagina UniProt]({% link topics/data-science/tutorials/online-resources-protein/tutorial_IT.md %}) |
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In addition to the tutorials, the BioNT team has translated a comprehensive set of **Galaxy FAQs** into
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all three languages, covering common actions such as importing data, creating collections, renaming
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datasets, using tutorial mode, and more. These translated FAQs are now embedded throughout the
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corresponding tutorials, giving learners a fully localised experience from start to finish.
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## Why This Matters
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Machine translation can provide a rough guide, but learners, especially those new to bioinformatics,
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benefit enormously from materials that read naturally in their own language. The BioNT team's
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human-translated content ensures that terminology is accurate, explanations are clear, and the learning
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experience is as smooth as possible for German, Spanish, and Italian-speaking communities across Europe
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and beyond.
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This work is a wonderful example of the GTN community's commitment to open, FAIR, and inclusive
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training for everyone.
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## Thank You!
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A huge thank you to all the BioNT contributors who made this possible — translators, reviewers, and
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everyone involved in getting these materials across the finish line:
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[@gtn:hvelab]({% link hall-of-fame/hvelab/ %}),
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[@gtn:lisanna]({% link hall-of-fame/lisanna/ %}),
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[@gtn:ocaisa]({% link hall-of-fame/ocaisa/ %})
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[@gtn:Sch-Da]({% link hall-of-fame/Sch-Da/ %}),
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[@gtn:shiltemann]({% link hall-of-fame/shiltemann/ %}).
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[@gtn:silviadg87]({% link hall-of-fame/silviadg87/ %}).
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[@gtn:teresa-m]({% link hall-of-fame/teresa-m/ %}),
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[@gtn:Tillsa]({% link hall-of-fame/Tillsa/ %}),
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[@gtn:unode]({% link hall-of-fame/unode/ %}),
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We also thank the BioNT consortium partners — including EMBL, ZB MED, EPFL, University of Barcelona,
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University of Freiburg, University of Oslo, biobyte solutions, RIcapacity, and HPCNow! — for making
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this international effort possible.
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The BioNT project is co-funded by the EU Digital Europe programme, grant agreement number 101100604.

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