German-PD

# 🇩🇪 German Public Domain 🇩🇪
**German-Public Domain** or **German-PD** is a large collection aiming to aggregate all German monographies and periodicals in the public domain. As of March 2024, it is the biggest German open corpus.

## Dataset summary
The collection contains 260,638 individual texts making up 37,650,706,611 words recovered from multiple sources, including Internet Archive and various European national libraries and cultural heritage institutions. Each parquet file has the full text of 2,000 books selected at random.

## Curation method
The composition of the dataset adheres to the criteria for public domain works in the EU and, consequently, all Berne-countries for EU authors: any publication whose author is dead for more than 70 years. Additionally, the initial consolidation of public domain status for cultural heritage operates in the EU under the 2019 Copyright Directive (art. 14).

## Uses
The collection aims to expand the availability of open works for the training of Large Language Models. The text can be used for model training and republished without restriction for reproducibility purposes.

The rationales for creation of this collection are multifold:

* **Scientific**: We observe that the closure of training corpora represents a major barrier to AI research. Large language models face a real crisis of reproducibility.
* **Legal**: With the adoption of the AI Act with its obligations in terms of copyright law compliance for the pretraining corpora, the European AI ecosystem will have to change its provenance practices.
* **Cultural**: The linguistic diversity of the European Union is currently underrepresented. Unlike web archives, open, heritage, administrative, or scientific texts are often of high quality: they are long, multilingual, and editorialized publications.
* **Economical**: Today, value capture is concentrated on players whose financial resources are already considerable, allowing them to collect or purchase data at a high price. Making a royalty-free corpus available to as many people as possible frees innovation in uses and minimizes economic dependencies on dominant actors.

## License
The entire collection is in the public domain in all regions. This means that the patrimonial rights of each individual or collective right holders have expired.

There has been a debate for years in Europe over the definition of public domain and the possibility to restrict its use. Since 2019, the EU Copyright Directive states that "Member States shall provide that, when the term of protection of a work of visual art has expired, any material resulting from an act of reproduction of that work is not subject to copyright or related rights, unless the material resulting from that act of reproduction is original in the sense that it is the author's own intellectual creation." (art. 14)

## Future work
This dataset is not a one-time work but will continue to evolve significantly in three directions:

* Expansion of the dataset to the late 19th and early 20th century works and its further enhancement with currently unexploited collections coming from European patrimonial data repositories.
* Correction of computer generated errors in the text. All the texts have been transcribed automatically through the use of Optical Character Recognition (OCR) software. The original files have been digitized over a long time period (since the mid-2000s) and some documents should be. Future versions will strive either to re-OCRize the original text or use experimental LLM models for partial OCR correction.
* Enhancement of the structure/editorial presentation of the original text. Some parts of the original documents are likely unwanted for large scale analysis or model training (header, page count…). Additionally, some advanced document structures like tables or multi-column layout are unlikely to be well-formatted.

## Acknowledgements
The corpus was stored and processed with the generous support of Scaleway. It was built up with the support and concerted efforts of the state start-up LANGU:IA (start-up d’Etat), supported by the French Ministry of Culture and DINUM, as part of the prefiguration of the service offering of the Alliance for Language technologies EDIC (ALT-EDIC).

Corpus collection has been largely facilitated thanks to the open science LLM community insights, cooperation and support (Occiglot, Eleuther AI, OpenLLM France, Allen AI).

<div style="text-align: center;">
<img src="https://github.com/mch-dd/datasetlogo/blob/main/scaleway.jpeg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/>
<img src="https://github.com/mch-dd/datasetlogo/blob/main/ministere.png?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/>
<img src="https://github.com/mch-dd/datasetlogo/blob/main/occiglot.jpg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/>
</div>

# 🇩🇪 德语公共领域数据集 🇩🇪
**德语公共领域数据集（German-Public Domain，简称German-PD）**是一个旨在聚合所有处于公共领域的德语专著与期刊的大型合集。截至2024年3月，它已是规模最大的德语开放语料库。

## 数据集概览
该合集包含260,638篇独立文本，总字数达37,650,706,611，数据源自多个渠道，包括互联网档案馆（Internet Archive）以及多家欧洲国家图书馆与文化遗产机构。每个Parquet文件（Parquet）随机收录2000部图书的完整文本。

## 数据遴选标准
该数据集的构成遵循欧盟以及所有伯尔尼公约成员国中欧盟作者的公共领域作品判定标准：作者去世超过70年的出版物。此外，欧盟范围内的公共领域版权认定依据2019年版权指令（第14条）执行。

## 应用场景
该合集旨在提升开放作品的可获取性，以供大语言模型（Large Language Model，LLM）的训练使用。文本可无限制地用于模型训练与再发布，以保障研究可复现性。

创建该合集的动因兼具多重维度：
* **学术层面**：我们注意到训练语料库的封闭化已成为人工智能研究的主要壁垒之一，大语言模型正面临严峻的可复现性危机。
* **法律层面**：随着《人工智能法案》的通过，其要求预训练语料库必须符合版权法规，欧洲人工智能生态必须改变其数据溯源实践。
* **文化层面**：当前欧盟的语言多样性在语料库中代表性不足。与网络档案不同，开放的遗产类、行政类或学术类文本往往具备较高质量：篇幅较长、多语言且经过编辑加工。
* **经济层面**：当下数据的价值捕获集中于少数财力雄厚的巨头玩家，它们能够以高价收集或采购数据。向尽可能多的群体提供免版税语料库，能够解放创新应用，并降低对主导企业的经济依赖。

## 授权协议
该合集在所有地区均属于公共领域。这意味着任何个人或集体权利持有者的财产性权利均已过期。

欧洲曾就公共领域的定义以及限制其使用的可能性展开过多年争论。自2019年起，欧盟版权指令规定："各成员国应当规定，当视觉艺术作品的保护期届满后，对该作品进行复制行为所产生的任何材料不受版权或相关权利约束，除非该复制行为产生的材料具有独创性，即属于作者的原创智力成果。"（第14条）

## 未来规划
本数据集并非一次性项目，将从三个方向持续大幅演进：
* 将数据集覆盖范围拓展至19世纪末与20世纪初的作品，并利用当前尚未开发的欧洲遗产数据仓库中的合集进一步扩充语料。
* 修正文本中的计算机生成错误。所有文本均通过光学字符识别（Optical Character Recognition，OCR）软件自动转录。原始文件自2000年代中期以来历经多年数字化，部分文档存在瑕疵。未来版本将致力于对原始文本重新进行OCR处理，或借助实验性大语言模型完成部分OCR错误修正。
* 优化原始文本的结构与编辑呈现形式。部分原始文档中的冗余内容（如页眉、页码等）可能不适用于大规模分析或模型训练。此外，部分复杂文档结构（如表格或多栏布局）往往格式不佳。

## 致谢
本语料库的存储与处理得到了Scaleway的慷慨支持。该数据集的构建得到了国家初创企业LANGU:IA（法国国家初创企业）的支持与协同努力，该企业由法国文化部与DINUM资助，属于语言技术联盟（ALT-EDIC）服务预筹备项目的一部分。

语料库的收集工作在很大程度上得益于开放科学大语言模型社区的见解、合作与支持（包括Occiglot、Eleuther AI、OpenLLM France、Allen AI）。

<div style="text-align: center;">
<img src="https://github.com/mch-dd/datasetlogo/blob/main/scaleway.jpeg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/>
<img src="https://github.com/mch-dd/datasetlogo/blob/main/ministere.png?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/>
<img src="https://github.com/mch-dd/datasetlogo/blob/main/occiglot.jpg?raw=true" style="width: 33%; margin: 0 auto; display: inline-block;"/>
</div>