Enriching accessibility of digital heritage collections: from searching to exploring

A lot of digital heritage collections are available online nowadays. Most of the time searching through these collections is fairly straight forward: a user searches on keywords through a user-interface, and a list of clickable results is returned. Although this kind of interface usually works fine, users are getting more and more interested in other ways of exploring digital heritage data. This had led to several initiatives, such as Datastories from the Dutch Digital Heritage Network in which a user can create their own search queries, or the GLAM Workbench from Tim Sherrat that allows the user to explore Australian heritage collections through Jupyter Notebooks. The KB National Library of the Netherlands is also exploring new ways to showcase their digital heritage collections. This had led to two projects in the past year. The first project is the ‘Medieval Meme Generator’. Creating so called ‘memes’ (an image with a funny text) is currently very popular in the online world, especially in the age group of 15 to 35 years. Memes are widely shared on various platforms on the internet. Within the Medieval Meme Generator, a user can search through 15 pre-selected medieval images and transform them into a meme by adding text through the meme generator. A short background story of the origin of the images is shown when an image is selected. The meme generator combines modern trends with heritage collections, thereby achieving the interests of a target audience that is usually not really interested in these collections. Another project is the website ‘Delpher demo: pandemics through time’, which offers another approach in searching through the digital heritage collection. This site dives into the Dutch historical newspaper archive Delpher, to give an insight in the reporting of pandemics and infectious diseases through time. Four related main categories can be selected, based on recent reports about the corona pandemic. A user can start by clicking one of these categories, after which a user is brought to a timeline to discover more about the selected category. For each year, there are visuals that show word clusters and connections between words. Also, a link to the original newspapers in the online newspaper archive is provided. For those who are interested in learning programming themselves, datasets and Jupyter Notebooks have been made available to give a quick start into learning to analyse textual data with Python