The Database Preservation Toolkit is a software that automates the migration of a relational database to the second version of the Software Independent Archiving of Relational Databases format. This flexible tool supports the currently most popular Relational Database Management Systems and can also convert a preserved database back to a Database Management System, allowing for some specific usage scenarios in an archival context. The conversion of databases between different formats, whilst retaining the databases’ significant properties, poses a number of interesting implementation issues, which are described along with their current solutions. To complement the conversion software, the Database Visualization Toolkit is introduced as a software that allows access to preserved databases, enabling a consumer to quickly search and explore a database without knowing any query language. The viewer is capable of handling big databases and promptly present search and filter results on millions of records.
This paper describes the features of both tools and the methods used to pilot them in the context of the European Archival Records and Knowledge Preservation project on several European national archives.

Based on the example of Friedrich Kittler’s digital papers at the Deutsches Literaturarchiv Marbach (DLA), this paper explores digital estates and their challenges on different logical levels within the pre-archival analysis, documentation and indexing process. As opposed to long-term digital preservation procedures, which are set about afterwards when relevant digital objects have already been identified, this process starts shortly after physical material (computers, hard drives, disks…) is delivered to the archive and has been ingested and safeguarded into volume image files. In this situation, it is important to get an overview of the “current state”: Which data was delivered (amount, formats, duplicates, versions)? What is the legal status of the stored data? Which digital objects are relevant and should be accessible for which types of users/researchers etc.? What kind of contextual knowledge needs to be preserved for the future? In order to address these questions and to assign meaning to both technological and documentation needs, the digital analysis tool “Indexer” was developed. It combines automated, information retrieval routines with human interaction features, thereby completing the necessary toolset for processing unstructured digital estates. It turns out however, that intellectual work and deep knowledge of the collection context still play an important role and must work hand in hand with the new automation efforts.