The German Federal Courts Dataset 1950–2019: From Paper Archives to Linked Open Data

18 Pages Posted: 28 May 2020

See all articles by Hanjo Hamann

Hanjo Hamann

Max Planck Institute for Research on Collective Goods

Date Written: September 2019

Abstract

Various reasons explain why Europe lags behind the United States in empirical legal studies. One of them is a scarcity of available data on judicial decision making, even at the highest levels of adjudication. By institutional design, civil‐law judges have lower personal profiles than their common‐law counterparts. Hence very few empirical data are available on how courts are composed and how that composition changes over time. The present project remedies that by easing access to such data and lowering the threshold for empirical studies on judicial behavior. This paper introduces the German Federal Courts Dataset (GFCD) as a resource for empirical legal scholars, with the objective of inspiring more European lawyers to engage with empirical aspects of civil‐law adjudication. To that end, several thousand pages of German court documentation were digitized, transcribed into machine‐readable tables (ready to be imported into statistics software), and published online (www.richter-im-internet.de). To simultaneously explore innovative ways of sharing public‐domain datasets, the data were modeled as linked open data and imported into the Wikidata repository for use in any computational application.

Suggested Citation

Hamann, Hanjo, The German Federal Courts Dataset 1950–2019: From Paper Archives to Linked Open Data (September 2019). Journal of Empirical Legal Studies, Vol. 16, Issue 3, pp. 671-688, 2019, Available at SSRN: https://ssrn.com/abstract=3607366 or http://dx.doi.org/10.1111/jels.12230

Hanjo Hamann (Contact Author)

Max Planck Institute for Research on Collective Goods ( email )

Kurt-Schumacher-Str. 10
D-53113 Bonn, 53113
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
4
Abstract Views
130
PlumX Metrics