openlegaldata_citations — German court decision citations (Open Legal Data, 2019, 2026)

Description

Directed networks of the citations in German court decisions, to other decisions and to the sections of laws, extracted by Open Legal Data (the Open Legal Data Platform, de.openlegaldata.io), a project that collects German court decisions and laws from the websites of the courts and of the federal and state governments, and extracts their references automatically (from the citations in the text of the decisions, such as file numbers of decisions and "§ 433 BGB" for laws).

Each node is a court decision (a case) or a section of a law, and a directed edge from A to B means that A cites B: a decision cites another decision or a law section, or a law section refers to another section of the same law. The edges are weighted by the number of times the citation was extracted (a decision citing the same law section in several places of its text gives as many rows in the data). There are two networks:

2019: the citation graph of the first dataset of court decisions of Open Legal Data (published in 2019, with about 100,000 decisions), with decisions and laws identified by the numeric ids of the database of Open Legal Data (decisions and laws numbered separately); it contains only citations from decisions (442,236 rows).
2026: the dump of 20 May 2026 (7,438,459 rows), with decisions identified by their slug in Open Legal Data (e.g. bgh-2018-09-05-2-str-45417, the page https://de.openlegaldata.io/case/<slug>/) and law sections by the slug of their law book and their own (e.g. bgb/433); it also contains the references between the sections of the same law (from the law texts).

The decisions are described by their court and the attributes of the decision when they cite something (those cited without citing anything only have the attributes of their court in 2026, and none in 2019); the law sections by their law book, section number and title (when they are cited). The extraction is automatic, and the references to decisions cover only those that could be matched to a decision in the database.

The networks have the following properties:

(Node) is_law: whether the node is a law section (otherwise it is a court decision).
(Node) oldp_id (2019): the id of the decision or the law section in Open Legal Data.
(Node) slug (2026): the slug of the decision, or <book slug>/<section slug> for a law section.
(Node) court, chamber, city, state, jurisdiction, level_of_appeal (decisions): the court (e.g. Bundesgerichtshof), its chamber or senate (e.g. 2. Strafsenat), its city, its state (or Bundesrepublik Deutschland for federal courts), its branch of jurisdiction (e.g. Ordentliche Gerichtsbarkeit, Arbeitsgerichtsbarkeit) and its level of appeal (e.g. Bundesgericht, Landgericht).
(Node) court_id (2019, decisions): the id of the court in Open Legal Data (-1 if unknown).
(Node) date, file_number, decision_type (decisions): the date of the decision (YYYY-MM-DD), its file number (Aktenzeichen, e.g. 2 StR 454/17) and its type (e.g. Urteil, Beschluss).
(Node) review_status (2026, decisions): the review status of the decision in Open Legal Data (e.g. accepted).
(Node) source, private (2019, decisions): the source from which the decision was collected, and whether it was marked as private (1, 0 or -1 if unknown).
(Node) book_code, section, law_title (law sections): the abbreviation of the law (e.g. BGB, ZPO), the section (e.g. § 433) and its title.
(Node) book_slug (2026, law sections): the slug of the law book (e.g. bgb).
(Edge) count: the number of times the citation was extracted.

Tags

Informational Citation Legal Weighted Metadata

Added

2026-10-31

Citation

M. Ostendorff, T. Blume, S. Ostendorff, "Towards an Open Platform for Legal Information." Proceedings of the ACM/IEEE Joint Conference on Digital Libraries in 2020 (JCDL '20), 385-388 (2020) 10.1145/3383583.3398616 Sci-Hub

Upstream URL OK

https://huggingface.co/datasets/openlegaldata/legal-citation-graph-germany
https://huggingface.co/datasets/openlegaldata/legal-citation-graph

Upstream license

Open Database License (ODbL) v1.0 (2026); CC BY 4.0 (2019), https://opendatacommons.org/licenses/odbl/1-0/

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
2019	46,445	220,493	4.75	56.69	85.07	19.00	-0.12	0.00	12	1.00	Directed	Unipartite	is_law oldp_id court chamber city state jurisdiction level_of_appeal date file_number decision_type source private court_id book_code section law_title	count	1.5 MiB	2.4 MiB	2.4 MiB	2.2 MiB
2026	497,040	3,833,034	7.71	304.73	397.59	1286.57	-0.11	0.00	22	0.93	Directed	Unipartite	is_law slug court chamber city state jurisdiction level_of_appeal date file_number decision_type review_status book_code book_slug section law_title	count	19.9 MiB	34.0 MiB	33.7 MiB	31.7 MiB

Ridiculograms^*

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.