DATASHEET.md

# Datasheet: German Commons

This is a datasheet compliant with the recommendations of [Gebru et al. (2018)](https://arxiv.org/abs/1803.09010v8), describing the properties of the **German Commons** dataset.

## Motivation

### Why was the dataset created?

German Commons addresses the critical gap in large-scale open German
text for language model training. Existing German corpora either lack
explicit licensing, contain web-scraped content of uncertain provenance,
or provide insufficient scale.

### Has the dataset been used already?

This represents the initial release of German Commons. No external usage
has occurred prior to publication. Constituent dataset may have already been used prior.

### What (other) tasks could the dataset be used for?

Beyond language model pretraining, German Commons supports all German
NLP research requiring clean, license-compliant text, multilingual model
development, or linguistic analysis of German text across domains. The
diverse domain coverage (legal, cultural, scientific, etc.) further
enables domain-specific model development and cross-domain evaluation
studies.

### Who funded the creation of the dataset?

Dataset compilation was supported by German and European research
grants: German Federal Ministry of Research, Technology, and Space
(BMFTR) under Grants  `01IS24077A`,  `01IS24077B`, and  `01IS24077D`, by
the ScaDS.AI Center for Scalable Data Analytics and Artificial
Intelligence, funded by the BMFTR and by the Sächsische
Staatsministerium für Wissenschaft, Kultur und Tourismus under Grant
 `ScaDS.AI`, and by the OpenWeb-Search.eu project, funded by the
European Union under Grant  `GA 101070014`. Constituent datasets
originate primarily from state-funded institutions across Germany and
Austria.

## Dataset Composition

### What are the instances?

Each instance represents a single German-language document with
associated metadata and licensing information.

### How many instances are there in total?

The dataset contains 35,778,211 documents comprising 154,558,196,961
GPT-2 tokens.

### What data does each instance consist of?

Each instance includes: a unique identifier for source
cross-referencing, source dataset name, quality-filtered and
paragraph-deduplicated raw text, canonical SPDX license URL, thematic
domain key, GPT-2 token count, a perplexity score calculated using a 
KenLM model trained on German Wikipedia text, and a OCR quality score 
calculated using [OCRoscope](https://github.com/Pleias/OCRoscope).

### Is there a label or target associated with each instance?

No supervised labels exist. However, each instance contains metadata
labels for thematic domain classification, licensing information, and
document length statistics.

### Is any information missing from individual instances?

Paragraph-level deduplication may alter texts from their original form.
Personally identifiable information has been systematically removed.

### Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set?

The dataset represents a filtered subset of source collections.
Filtering removes OCR errors, extraction artifacts, and low-quality or
duplicated content, creating a curated selection.

### Are there recommended data splits?

No predefined splits are provided. All data is intended for pretraining.

### Are there any errors, sources of noise, or redundancies in the dataset?

Despite quality filtering and deduplication, residual issues may remain:
cross-corpus text duplicates from overlapping sources, and extraction
artifacts from OCR and PDF-to-text processing.

### Is the dataset self-contained, or does it link to or otherwise rely on external resources?

The dataset is self-contained and centrally downloadable. The Source
dataset references provided enable reproducible reconstruction.

## Collection Process

### What mechanisms or procedures were used to collect the data?

Data collection employed multiple automated procedures: direct download
from institutional repositories and open platforms, programmatic
crawling via APIs where available, and automated text extraction from
PDF and other document formats using specialized libraries. Then, the
open source processing pipelines were applied for quality filtering and
deduplication all sources. Validation occurred through manual inspection
of sample outputs, cross-verification against source repositories, and
automated consistency checks.

### How was the data associated with each instance acquired?

All text data represents directly observable content from original
sources; no inference or derivation occurred. Metadata (licensing,
thematic classification, source attribution) was extracted directly from
source repository information or explicitly provided by institutional
datasets. Where PDF extraction was required, raw text underwent
validation against source documents to verify accuracy.

### If the dataset is a sample from a larger set, what was the sampling strategy?

Sampling was deterministic based on explicit criteria: German language
content as per automated classification explicit open licensing, quality
thresholds, and institutional source verification. No probabilistic
sampling occurred; all content meeting inclusion criteria was retained
after deduplication.

### Who was involved in the data collection process and how were they compensated?

Data collection was conducted by the author team using automated
systems. No crowdworkers, contractors, or external annotators were
employed. All processing occurred through programmatic methods without
manual content creation or labeling requiring compensation.

### Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances?

Collection occurred between January and August 2025, using source
dataset versions available through August 31st, 2025. The underlying
content creation spans multiple centuries, representing a temporal range
that significantly predates and extends beyond the collection timeframe.

## Data Preprocessing

### Was any preprocessing/cleaning/labeling of the data done?

Comprehensive preprocessing included: text extraction from PDFs and OCR
sources with encoding normalization, language detection and filtering
for German content, and quality filtering targeting digitization
artifacts and extraction errors, paragraph-level deduplication using
content hashing, systematic PII removal, format standardization across
all source types. Thematic domain classification was applied based on
source dataset.

### Was the raw data saved in addition to the preprocessed/cleaned/labeled data?

Raw data is not provided since all constituent source datasets remain
publicly accessible through their original repositories.

### Is the software used to preprocess/clean/label the instances available?

All preprocessing software is open source and available at
<https://github.com/coral-nlp/llmdata> , ensuring complete
reproducibility of the dataset.

### Does this dataset collection/processing procedure achieve the motivation for creating the dataset stated in the first section of this datasheet?

Yes. The procedure successfully addresses the identified gap by:
providing the largest collection to-date of openly licensed German text,
enabling open German language model development without licensing
uncertainties, and establishing reproducible methodology for future
dataset construction. This directly fulfills the stated motivation of
creating license-compliant, large-scale German training data.

### How will the dataset be distributed?

The dataset is distributed as Parquet files through multiple public
repositories for redundancy. Primary distribution occurs via Hugging
Face Hub at <https://huggingface.co/datasets/coral-nlp/german-commons>.

### When will the dataset be released/first distributed? What license (if any) is it distributed under?

Public release occurred on 2025/10/14. Dataset metadata and compilation
are licensed under ODC-BY 1.0 (<https://opendatacommons.org/licenses/by/1-0/>). Individual document texts retain
their original licenses as specified in each instance's SPDX URL field,
creating a heterogeneous but fully documented licensing structure.

### Are there any copyrights on the data?

Yes. Each document retains copyright under its original creator or
institutional provider, governed by the specific license indicated in
the instance metadata. The compilation itself does not claim additional
copyright over constituent texts.

### Are there any fees or access/export restrictions?

The dataset is freely accessible without fees or registration
requirements. However, users must comply with individual document
licenses, which may include attribution requirements or share-alike
provisions. Commercial use is permitted by all constituent licenses.

## Dataset Maintenance

### Who is supporting/hosting/maintaining the dataset?

The dataset is maintained by the authors of this report.

### Will the dataset be updated? If so, how often and by whom?

Updates may occur when significant new German open-source collections
become available. The original authors will coordinate updates, with
community contributions welcomed through the open-source pipeline.

### How will updates be communicated?

Updates will be announced through: versioned releases on hosting
platforms with detailed changelogs, academic publication updates when
substantial changes occur.

### If the dataset becomes obsolete how will this be communicated?

Obsolescence will be communicated through deprecation notices on all
hosting platforms.

### Is there a repository to link to any/all papers/systems that use this dataset?

No centralized usage repository will be maintained. Usage tracking
occurs through standard academic citation of the dataset paper. Users
are encouraged to cite the dataset publication when reporting results or
building derivative works.

### If others want to extend/augment/build on this dataset, is there a mechanism for them to do so?

The open-source `llmdata` pipeline enables community extensions through
standardized data ingestion protocols for new sources and automated
quality assessment and deduplication using established filtering
criteria. Community contributions undergo review by the maintenance
team.

## Ethical Considerations

### Were any ethical review processes conducted?

No formal institutional review board process was conducted. The dataset
relies exclusively on pre-existing, publicly available, and explicitly
licensed materials from established institutional sources. Data
processing incorporated ethical considerations including systematic PII
removal and exclusion of sources lacking clear licensing frameworks.

### Does the dataset contain data that might be considered confidential?

No. All included content derives from explicitly open-licensed
institutional sources.

### Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?

Potentially yes. The dataset spans centuries of German text documents,
which may include historical perspectives, political viewpoints, or
language that could be considered offensive by contemporary standards.
The scale and temporal range make comprehensive content moderation
infeasible. Users should exercise appropriate caution.

### Does the dataset relate to people?

The dataset may contain publicly available information relating to
individuals in various contexts including historical documents,
biographical information, academic citations, and government records.