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Ansätze aus der Praxis und Überlegungen für die Zukunft</a></h1> <div class="meta"> <span class="counter "><a href="#comments" title="Zu den Kommentaren">1</a></span> <span>Veröffentlicht von <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?author=222">Patrick Helling</a> am <time datetime="2020-05-08">08. Mai 2020</time></span> </div> </header> <p>Nachbericht zur Session 3.2 des Barcamp „<a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?p=12806" target="_blank" rel="noopener noreferrer">Vermittlung von Data Literacy in den Geisteswissenschaften</a>“ auf der DHd 2020 in Paderborn</p> <p>Übersichtsblogpost zum Barcamp: Ulrike Wuttke, Marina Lemaire: „<a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?p=13748" target="_blank" rel="noopener noreferrer">Offen, vielfältig und kreativ. Ein Bericht zum Barcamp Data Literacy #dhddatcamp20 bei der DHd 2020</a>“, 08.06.2020, DHd Blog.</p> <p>Autor*innen:<br/><a href="https://web.archive.org/web/20210125033729/mailto:patrick.helling@uni-koeln.de" target="_blank" rel="noopener noreferrer">Patrick Helling</a>, Data Center for the Humanities (DCH), Universität zu Köln<br/><a href="https://web.archive.org/web/20210125033729/mailto:jacqueline.klusik@fau.de" target="_blank" rel="noopener noreferrer">Jacqueline Klusik-Eckert</a>, IZdigital, Friedrich-Alexander-Universität Erlangen-Nürnberg<br/><a href="https://web.archive.org/web/20210125033729/mailto:julian.schulz@lmu.de" target="_blank" rel="noopener noreferrer">Julian Schulz</a>, IT-Gruppe Geisteswissenschaften, LMU München</p> <p>Im Fokus der Session stand die Auseinandersetzung mit der Rollen- und Kompetenzverteilung unterschiedlicher Akteure und Infrastrukturen, die im Kontext von Data Literacy innerhalb der Geisteswissenschaften aktiv sind. Im Rahmen der Diskussion wurden folgende Fragen behandelt: Welche (universitären) Institutionen spielen eine tragende Rolle bei der Vermittlung von Data Literacy? Wie ist das Verhältnis zwischen fachspezifischen Kompetenzzentren und generischen Serviceeinrichtungen? Welche Strukturen sind nötig, um (geisteswissenschaftliche) Data Literacy in einem so heterogenen Ökosystem wie der Universität koordiniert und zielorientiert zu vermitteln?</p> <h3>Ansätze aus der Praxis: Unterschiedliche Organisationsformen für die Vermittlung von Data Literacy</h3> <p>Ausgangspunkt der Diskussion waren die Angebote des Digital Humanities Virtual Laboratory (<a href="https://web.archive.org/web/20210125033729/https://dhvlab.gwi.uni-muenchen.de/" target="_blank" rel="noopener noreferrer">DHVLab</a>), das an der Ludwig-Maximilians-Universität (LMU) München entwickelt wurde und mittlerweile an verschiedenen Standorten zum Einsatz kommt sowie des Digital Humanities Lab (<a href="https://web.archive.org/web/20210125033729/https://ub.fau.de/forschen/digital-humanities-lab/" target="_blank" rel="noopener noreferrer">DHLab</a>) der Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Trotz der sehr ähnlich klingenden Akronyme handelt es sich hierbei um zwei unterschiedliche Strategien hinsichtlich der Vermittlung von Data Literacy:</p> <p>Das DHVLab ist eine virtuelle, modulare Infrastruktur, die Studierenden und Forschenden einen möglichst niederschwelligen Zugang zu zentralen Werkzeugen und Methoden der Digital Humanities bieten möchte. Es eignet sich in diesem Sinne für die praxisnahe Vermittlung von grundlegenden Kompetenzen im Bereich Data Literacy. Das DHLab versteht sich als Forum des offenen Austausches über die Digital Humanities und zentrale Anlaufstelle für DH-Angelegenheiten. Mit Schulungen, Vorträgen und Unterstützung bei DH-bezogenen Fragestellungen sowohl für Studierende als auch für Forschende trägt es zur Bildung von Data Literacy am Standort bei.</p> <p>In diesem Rahmen kommen jeweils unterschiedliche universitäre Institutionen zusammen: Während das DHLab aus einem Zusammenschluss von Universitätsbibliothek, dem Interdisziplinären Zentrum für Digitale Geistes- und Sozialwissenschaften (<a href="https://web.archive.org/web/20210125033729/https://www.izdigital.fau.de/" target="_blank" rel="noopener noreferrer">IZdigital</a>) und den Digital Humanities Studiengängen der FAU entstanden ist, wurde die Plattform DHVLab von der <a href="https://web.archive.org/web/20210125033729/https://www.itg.uni-muenchen.de/index.html" target="_blank" rel="noopener noreferrer">IT-Gruppe Geisteswissenschaften</a> der LMU in Kooperation mit den Fachbereichen Kunstgeschichte, Geschichte, Sprachwissenschaften sowie der Statistik und Informatik entwickelt.</p> <p>Die Gründung von Labs im Sinne der beiden vorgestellten Initiativen stellt eine Struktur- und Organisationsform zur Vermittlung von Data Literacy dar, die eine zentrale, gemeinsame Entität aus unterschiedlichen Einrichtungen und Institutionen bildet und somit die Kooperation in den Vordergrund stellt. Ein solcher Zusammenschluss ist jedoch stark vom individuellen Engagement der einzelnen Mitglieder abhängig. Aktuell zeigt sich auch, dass durch das Ansiedeln an neutralen, außerfakultären Einrichtungen die Inhalte nicht automatisch in die Curricula der Studiengänge eingebunden werden. Es handelt sich im Fall des DHLabs um ein ergänzendes Angebot auf freiwilliger Basis.</p> <p>Im Rahmen der Diskussion wurde darüber hinaus noch ein weiteres, kollaboratives Modell vorgestellt: An der Universität zu Köln gibt es mit dem <a href="https://web.archive.org/web/20210125033729/https://dh.phil-fak.uni-koeln.de/" target="_blank" rel="noopener noreferrer">Institut für Digital Humanities</a> (IDH) einen institutionellen Fokus auf DH-Lehre und -Forschung, mit dem <a href="https://web.archive.org/web/20210125033729/https://cceh.uni-koeln.de/" target="_blank" rel="noopener noreferrer">Cologne Center for eHumanities</a> (CCeH) ein zentrales DH-Forschungs- und Lehrzentrum und mit dem <a href="https://web.archive.org/web/20210125033729/http://dch.phil-fak.uni-koeln.de/" target="_blank" rel="noopener noreferrer">Data Center for the Humanities</a> (DCH) ein Forschungsdatenzentrum für die Geisteswissenschaften. Dieser Dreierbund, dessen Teilbereiche unterschiedlich konstituiert und institutionell verankert sind, vermittelt geisteswissenschaftliche Data Literacy individuell, aber abgestimmt auf den Ebenen der DH-Forschung und -Lehre sowie des Forschungsdatenmanagements (FDM) an unterschiedliche Zielgruppen.</p> <p>Ein anderes Modell, das in der Session identifiziert wurde, ist die Bildung zentraler, institutioneller Informationsschnittstellen mit Vermittlungsfunktion, bspw. an einer Bibliothek oder einem universitären Rechenzentrum, die verschiedene (Fach-)Zentren und -einrichtungen mit mehr oder weniger klar definierten Kompetenzen zu Aspekten von Data Literacy koordinieren. Es gibt allerdings auch Standorte, die nur über eine einzelne, zentrale Anlaufstelle für die Vermittlung generischer Data Literacy verfügen.</p> <h3>Überlegungen für die Zukunft: Bedingungen für zielorientierte Data Literacy Vermittlungskonzepte </h3> <p>Generalisierte One-Fits-All-Bestrebungen, so ein Ergebnis der Diskussionen, scheinen allein auf Grund der individuellen, fachspezifischen Komplexität von Data Literacy und der häufig ohnehin bereits vorhandenen Diversität von Strukturen an einzelnen Standorten, keine zielorientierte und umfängliche Lösung zu sein. Dennoch, so die Erfahrungen aus dem Plenum, existiert an Standorten trotz verteilter Servicestrukturen weiterhin häufig die Vorstellung einer zentralisierten Verantwortlichkeit und schließlich auch eines entsprechenden Kompetenzmonopols in Bezug auf Data Literacy. Dies erschwert es, entsprechende Inhalte für unterschiedliche Zielgruppen abgestimmt zu platzieren und auf diverse Strukturebenen hineinzutragen. Hinsichtlich einer langfristigen, nachhaltigen Gewährleistung von bedarfsorientierten Angeboten zur Vermittlung von Data Literacy bedarf es, den Teilgebenden folgend, einer grundsätzlichen Veränderung der Perspektive weg von zentralen Dienstleistungsstrukturen hin zu dezentralen, verteilten und vor allem kooperativen Lösungsansätzen.</p> <p>Dies setzt jedoch zunächst eine Festigung des Verständnisses für die Komplexität von Data Literacy voraus, sprich für den Bedarf an fachspezifischen und forschungsnahen Expertisen und die daraus resultierende Konsequenz, dass umfassende Data Literacy nicht von einzelnen Institutionen oder Einrichtungen allein vermittelt werden kann.</p> <p>Entsprechend stellen insbesondere die Definition und Verteilung von Verantwortlichkeiten und Kompetenzbereichen sowie die kooperative Zusammenarbeit zwischen verschiedenen (generischen und fachspezifischen) Einrichtungen große Herausforderungen für die nachhaltige Vermittlung von Data Literacy dar. Dies gilt umso mehr vor dem Hintergrund ungleicher Ausgangssituationen wie bspw. einerseits zeitlich begrenzter und andererseits dauerhafter personeller und finanzieller Ausstattungen und entsprechend auch individueller, institutioneller Verankerungen.</p> <p>Unter diesen Voraussetzungen und mit einem klaren Willen zur Kooperation ist die eindeutige Trennung zwischen der Vermittlung von generischen Basisinhalten und -kompetenzen auf der einen und fachspezifischen Anforderungen auf der anderen Seite möglich. Für eine effektive Zusammenarbeit zwischen generisch ausgerichteten, institutionellen Strukturen und fachspezifischen Kompetenzzentren oder Labs, muss es jedoch auch eine entsprechende inhaltliche und organisatorisch zuständige, klare Kompetenz- und Aufgabenverteilung geben, sowie eine offene und klare Kommunikations- und inhaltliche Abstimmungskultur darüber.</p> <p>Umsetzbar erscheint dieser Ansatz jedoch nur, wenn man sich auf allen Ebenen der universitären Lehre darauf verständigen kann, dass Data Literacy im Bereich der allgemeinen Fachkompetenzen verankert werden sollte. Damit würde es wie wissenschaftliches Arbeiten oder Quellenkunde zum Kanon guter wissenschaftlicher Praxis gehören und sollte entsprechend in allen Studiengängen Teil des Kerncurriculums sein. Allgemeine Einführungen könnten von zentralen Einrichtungen übernommen werden, während die facheigene Vertiefung in den Lehreinheiten selbst stattfinden müsste.</p> <p>Dies scheint, so das grundsätzliche Fazit der Gruppe, die Basis dafür zu sein, Data Literacy, sowohl in Bezug auf Inhalte als auch auf Zielgruppen, bedarfs- und zielorientiert und auf klar definierten Kompetenzverteilungen beruhend, koordiniert und effizient umzusetzen und langfristig sicherzustellen.</p> <div class="social-share"> <div class="addthis_toolbox addthis_default_style addthis_32x32_style"> <a class="addthis_button_preferred_1"></a> <a class="addthis_button_preferred_2"></a> <a class="addthis_button_preferred_3"></a> <a class="addthis_button_preferred_4"></a> <a class="addthis_button_compact"></a> <a class="addthis_counter addthis_bubble_style"></a> </div> <script type="text/javascript" src="https://web.archive.org/web/20210125033729js_/http://s7.addthis.com/js/250/addthis_widget.js#pubid=xa-502bae7409ca736f"></script> </div>  <div class="holder"> <i class="fa fa-folder"></i> <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?cat=103" rel="category">Community</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?cat=534" rel="category">Konferenz</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?cat=242" rel="category">Lehre und Ausbildung</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?cat=10" rel="category">Reflektion</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?cat=67" rel="category">Studium</a>    <i class="fa fa-tag"></i> <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?tag=ag-datenzentren" rel="tag">AG Datenzentren</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?tag=barcamp" rel="tag">Barcamp</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?tag=bibliothek" rel="tag">Bibliothek</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?tag=data-literacy" rel="tag">Data Literacy</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?tag=datenzentren" rel="tag">Datenzentren</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?tag=dhd2020" rel="tag">DHd2020</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?tag=forschungsdaten" rel="tag">Forschungsdaten</a>, <a href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?tag=forschungsdatenmanagement" rel="tag">Forschungsdatenmanagement</a> </div> </article> <span id="comments"></span> <section class="comment-holder"> <div class="c-counter"> <span>Ein Kommentar </span> <a href="#respond"><i class="fa fa-comments"></i>Kommentar schreiben</a> </div> <ul class="comments"> <li> <div class="profile-holder"> <div class="img-holder"> <img width="46" height="46" alt="no image" src="https://web.archive.org/web/20210125033729im_/https://dhd-blog.org/app/themes/dhdblog/images/bg-comment.png"> </div> <div class="txt"> <header class="heading-holder"> <h2>Offen, vielfältig und kreativ. Ein Bericht zum Barcamp Data Literacy #dhddatcamp20 bei der DHd 2020 | DHd-Blog <time class="date" datetime="2013-12-10">01. Juli 2020</time></h2> </span><span class="fa fa-reply"><a rel="nofollow" class="comment-reply-link" href="https://web.archive.org/web/20210125033729/https://dhd-blog.org/?p=13566&replytocom=156452#respond" data-commentid="156452" data-postid="13566" data-belowelement="comment-156452" data-respondelement="respond" aria-label="Antworte auf Offen, vielfältig und kreativ. Ein Bericht zum Barcamp Data Literacy #dhddatcamp20 bei der DHd 2020 | DHd-Blog">Antworten</a></span> </header> <p>[…] werden? 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