HERO? TRAITOR? VICTIM? AMBIGUITY OF DATA IN WAR BIOGRAPHIES

Project WARLUX - Soldiers and their Communities in WWII: The Impact and Legacy of War Experiences in Luxembourg", at the Luxembourg Centre for Digital and Contemporary History (C²DH) of the University of Luxembourg researches the personal side of the history of Luxembourgish youth born between 1920 and 1927 who were enrolled into German services under the Nazi occupation of Luxembourg during World War II. The research focuses on personal testimonies and their individual war experience to uncover these men, women, and families' individual experiences. The Methodology includes a biographical approach to offer a micro-historical perspective on single actors to link individual life stories to home communities. WARLUX will analyse the individuals from their social environments, social background, or trajectories during the war and their life in the post-war period based on their biographies. But this is almost where the most significant challenge lies in avoiding pre-existing terminologies, e.g. Nazi terms. When analysing the dataset of contemporary Nazi documents and post-war documents, the sources describe the same objects and elements but require a different interpretation. When building a dataset for independent and objective research, it is crucial to distinguish between the various sources and make the data entries comprehensible. While for the Nazis, volunteers in the Waffen-SS were "exemplary fighters", in post-war Luxembourg, they were "traitors to the fatherland". The young men who deserted from the Wehrmacht during the war, "cowards" and "Wehrkraftzersetzer" for the Nazis, in peacetime "heroes" of the Grand Duchy of Luxembourg. 

The project has worked with a relational database (nodegoat) so far but is reaching its limits. This database's strength lies in linking objects while assigning strict categories and dates but fails when it comes to flexible and uncertain or ambiguous data. In many sources, there are different variations of dates and circumstances of the targets under study. For example, the Nazi authorities introduced the conscription law in Luxembourg on 30 August 1942. Every man born between 1920 and 1927 was called to register on the conscription lists. Therefore, men who enlisted in the Wehrmacht before this date are considered volunteers, which completely changed their status as mentioned during the war and after returning home. 

Mapping this scheme in a data model seems "easy" - date of enlistment XX, and then building the category of volunteer yes/no. But as the data repeatedly shows, it is more complex than expected. Volunteers who joined the German forces before August 1942 tried to avoid their "real" enlistment and changed it to an earlier date (reporting to the authorities) because they were aware of the severe consequences they and their families faced after the surrender of the Nazi forces. There are now two dates in the dataset. The relational structure only allows one date for the volunteer "tab". If I had chosen only one date, this case would have been overlooked.

The analysis requires a flexible and changeable dataset where the fixed points can be changed depending on the research question. It is always a challenge in historical research. Still, due to the mass data (10,000 objects/persons) that WARLUX uses, it needs a flexible and smooth data structure and, on the other hand, one that is as stringent and reliable as possible.