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AbstractPrinting technology has evolved through the past centuries due to technological progress. Within Digital Humanities, images are playing a more prominent role in research. For mass analysis of digitized historical images, bias can be introduced in various ways. One of them is the printing technology originally used. The classification of images to their printing technology e.g. woodcut, copper engraving, or lithography requires highly skilled experts. We have developed a deep learning classification system that achieves very good results. This paper explains the challenges of digitized collections for this task. To overcome them and to achieve good performance, shallow networks and appropriate sampling strategies needed to be combined. We also show how class activation maps (CAM) can be used to analyze the results.

Viola Parente-Čapková: Digitaaliset ihmistieteet kirjallisuudentutkimuksessa

Il progetto Texts on the Move mira a mappare la ricezione delle scrittrici russe in Finlandia e la ricezione delle autrici finlandesi (di lingua finlandese e svedese) in Russia. Il concetto di ���ricezione��� �� inteso in senso ampio, includendo non solo le traduzioni, ma anche altri modi in cui i testi si sono mossi e hanno reagito alla transnazionalit��, mettendo in evidenza un���ampia gamma di reti letterarie, culturali e sociali. Il progetto offre un nuovo sguardo sulla storia letteraria e sugli scambi culturali tra Finlandia e Russia, visti in un contesto europeo pi�� ampio. Si collega ai metodi digitali e alla ricerca sui media digitali, metodi e tematiche che si sono sviluppati nel campo degli studi di genere negli ultimi decenni. Introduce numerosi metodi misti e la mesoanalysis. �� il primo progetto di ricerca su ampia scala sulla storia delle relazioni letterarie tra la Finlandia e la Russia dal punto di vista di genere. Il progetto �� finanziato dalla Fondazione Emil Aaltonen, Finlandia. Studi Finno-Ugrici, n.s., V. 1 (2021): SFU ��� Studi Finno-Ugrici, n.s.

No abstract available.

In the domain of Galleries, Libraries, Archives and Museums (GLAM) institutions, creative and innovative tools and methodologies for content delivery and user engagement have recently gained international attention. New methods have been proposed to publish digital collections as datasets amenable to computational use. Standardised benchmarks can be useful to broaden the scope of machine-actionable collections and to promote cultural and linguistic diversity. In this article, we propose a methodology to select datasets for computationally driven research applied to Spanish text corpora. This work seeks to encourage Spanish and Latin American institutions to publish machine-actionable collections based on best practices and avoiding common mistakes. This research has been funded by the AETHER-UA (PID2020-112540RB-C43) Project from the Spanish Ministry of Science and Innovation.

Over the last two decades, a growing number of databases have been published online that record historical information on the production, distribution and reception of performing arts. The aim of this contribution is to present a starting inventory of European performing arts databases that are available online, since no such overview exists to date.

International audience; We present an OCR ground truth data set for historical prints and show improvement of recognition results over baselines with training on this data. We reflect on reusability of the ground truth data set based on two experiments that look into the legal basis for reuse of digitized document images in the case of 19th century English and German books. We propose a framework for publishing ground truth data even when digitized document images cannot be easily redistributed.

AbstractIn research and policies, the identification of trends as well as emerging topics and topics in decline is an important source of information for both academic and innovation management. Since at present policy analysis mostly employs qualitative research methods, the following article presents and assesses different approaches – trend analysis based on questionnaires, quantitative bibliometric surveys, the use of computer-linguistic approaches and machine learning and qualitative investigations. Against this backdrop, this article examines digital applications in cultural heritage and, in particular, built heritage via various investigative frameworks to identify topics of relevance and trendlines, mainly for European Union (EU)-based research and policies. Furthermore, this article exemplifies and assesses the specific opportunities and limitations of the different methodical approaches against the backdrop of data-driven vs. data-guided analytical frameworks. As its major findings, our study shows that both research and policies related to digital applications for cultural heritage are mainly driven by the availability of new technologies. Since policies focus on meta-topics such as digitisation, openness or automation, the research descriptors are more granular. In general, data-driven approaches are promising for identifying topics and trendlines and even predicting the development of near future trends. Conversely, qualitative approaches are able to answer “why” questions with regard to whether topics are emerging due to disruptive innovations or due to new terminologies or whether topics are becoming obsolete because they are common knowledge, as is the case for the term “internet”.