Supporting Sustainable Digital Data Workflows in the Art and Humanities

The Data and Service Center for the Humanities ( DaSCH ) operates as a platform for humanities research data and ensures access to this data and promotes the networking of data with other databases (linked open data), to add value for further research and the interested public. As a competence center for digital methods and long-term use of digital data, it supports the hermeneutically oriented humanities in the use of state-of-the-art digital research methods. The DaSCH focuses on qualitative data and associated digital objects (images, sound, video, etc.) in the cultural heritage field. Long-term archiving or access is a major topic after the digital turn in the humanities, as many funding agencies such as the Swiss National Science Foundation and the European Commission are now requiring that a data management plan (D MP ) be in place to receive research funding. This new imperative raises many questions in the scientific community. This paper points out the contributions of the DaSCH for digital humanities researchers and the advantages of interoperability

Digitale Erlebnistouren

In diesem Beitrag werden die Vorteile digitaler Archivierung von archäologischen Funden und andere Kulturgüter betrachtet. Eingangs wird, um die digitalen Aufnahmen technikgeschichtlich zu verorten, die analoge wissenschaftliche Fotografie als Methode erörtert. Gerade im Bereich der Fotografie lässt sich darauf basierend der Übergang vom Analogen zum Digitalen aufzeigen. Nach dieser Heranführung wird das Reflectance Transformation Imaging Verfahren (RTI) vorgestellt. Dieses Verfahren bietet neue Möglichkeiten für nicht statische digitale, auf Fotografie basierende Bilder, welche sozusagen die Schreibtischforschung erweitert. Auf dem digitalen Fototisch lässt sich das Objekt unterschiedlich beleuchten und kann somit interaktiv weiter beforscht werden, unabhängig von Ort und Zeit

Artificial Intelligence for content and context metadata retrieval in photographs and image groups

Digitization projects of analog photographic collections are still growing in number, and therefore such assets of images become bigger continuously. Also, there is a strong trend towards open data and interfaces to access and reuse the image resources (FAIR data). To be able to search and find images in a repository, metadata of a certain depth must be existing. Typically, indexing and valorization, done by experts that know the (photographic) collections, is necessary to achieve such meta-information. There are various metadata standards based on different concepts for the description of collections. Some, like ISAD(G), are more related to the physical structure of archives, others, like CIDOC-CRM, take into account the content of the images in detail. Enhancing the depth of indexing increases the time necessary drastically. It is also a task that is not easily scalable because specific content related knowledge is necessary. With the assistance of artificial intelligence, historic photographic collections could potentially be enhanced with metadata semi-automatically. For the successful application of machine learning, it is essential to have robust training sets. In the presented paper, we show our observations in monitoring participants indexing historic collections of photographs. In the observations of workshops of people working with photographic heritage, it was monitored how single photographs but also image groups are described. Based on that knowledge, machine learning components can be trained and optimized for that particular type of source material. The demonstrated approach has the potential to support the work of valorization substantially. In addition, the approach has, to some extent, the potential to preserve the fundamental structures of knowledge of contemporary witnesses