JOURNAL ARTICLE
Redrawing and publishing the graphics in Wittgenstein's Nachlass.
Published In: Wittgenstein Studien. Neue Folge, 2025, v. 16, n. 1. P. 245 1 of 3
Database: Humanities Source Ultimate 2 of 3
Authored By: Lavazza, Michele 3 of 3
Abstract
In 2022 – 2024, on the initiative of the Wittgenstein Archives at the University of Bergen, a project was undertaken to digitally remake all the drawings in the Nachlass. The objective was to "normalise" them in a way similar to that in which text is normalised when transcribing and editing a manuscript for publication. Thanks to a cooperation between the directors of the WAB and two graphic artists, with further input from many experts, the approximately 1000 drawings were copied from facsimiles of Wittgenstein's manuscripts and typescripts as high-quality vector images. Before and during this process, discussions were had about technical choices as well as the philosophical role of individual items, the interpretation of which affected the way they were redrawn. The resulting visuals were embedded in the WAB's transcriptions of the Nachlass; moreover, a dedicated Wittgenstein Nachlass Graphics website was built to make the new, freely-licenced drawings available to browse, download and search by taxonomical tags. This paper aims to give an account of the project's history and editorial approach and to provide technical guidance on how to use the graphic files and the website through which they are made available. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Wittgenstein Studien. Neue Folge. 2025/01, Vol. 16, Issue 1, p245
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2025
- ISSN:18687431
- DOI:10.1515/witt-2025-0011
- Accession Number:189343190
- Copyright Statement:Copyright of Wittgenstein Studien. Neue Folge is the property of De Gruyter and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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