SB Labs seeks to find new ways to combine the library’s digital cultural heritage collections and research, with the latest state of the art methods within machine learning. The lab is an initiative taken by the IT department at the State and University Library in Aarhus in 2016. SB Labs consists of the library’s own IT professionals - in collaboration with researchers within the field digital humanities.
If you are interested in getting in touch with SB Labs please contact us at:
Image collections presented as collages, with seamless zooming from full collection to full-screen single images. Context-sensitive meta data with link-back to the originating sources makes it easy to explore large collections.
Smurf visualises how use of language in Danish newspapers has evolved since the 18th century.
DOTS visualizes Danish cities referenced in articles related to your search in the Royal Library's Danish newspaper archive.
A serendipitous presentation of 1 million newspaper pages from Mediestream. For your convenience, the 20 terapixels from the scanned papers are packed into a single image. Only thing needed is to zoom a bit.
Word2Vec is a high-dimensional word embedding based on an unsupervised machine learning algorithm using a simple neural network. It maps each unique word in a large text corpus to a vector. The vector representation of the words reflects interesting semantic properties of the words. Words that appear in the same context will be close in the vector-space (similar words). But distance between words can also be used to find analogies. The word2vec demo features several corpora and a very large one based on over 65.000 Gutenberg E-books.