---
license: cc-by-4.0
language:
- en
size_categories:
- 10B<n<100B
pretty_name: Scientific Openly-Licensed Publications - Text
---

# Scientific Openly-Licensed Publications
This repository contains companion material for the following [publication](https://openaccess.thecvf.com/content/WACV2024/papers/Tarsi_SciOL_and_MuLMS-Img_Introducing_a_Large-Scale_Multimodal_Scientific_Dataset_and_WACV_2024_paper.pdf):

> Tim Tarsi, Heike Adel, Jan Hendrik Metzen, Dan Zhang, Matteo Finco, Annemarie Friedrich. **SciOL and MuLMS-Img: Introducing A Large-Scale Multimodal Scientific Dataset and Models for Image-Text Tasks in the Scientific Domain.** WACV 2024.

Please cite this paper if using the dataset, and direct any questions regarding the dataset
to [Tim Tarsi](mailto:tim.tarsi@gmail.com)


## Summary
Scientific Openly-Licensed Publications (SciOL) is the largest openly-licensed pre-training corpus for multimodal models in the scientific domain, covering multiple sciences including materials science, physics, and computer science. It consists of over 2.7M scientific scientific publications converted into semi-structured data. SciOL contains over 14 Billion tokens of extracted and structured text.


**Note: This repository only contains the textual data of SciOL. For the figures and captions see:**  
[SciOL-CI](https://huggingface.co/datasets/Timbrt/SciOL-CI)

## Data Format
We provide the annotations of our dataset in the JSON format. Files are grouped and compressed as zip files. We provide a basic index to find annotations by DOI, PMID or DOAJ id and keywords.

## Annotation Schema

Annotations are structured as in the following schema:
```
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "properties": {
    "doi": {
      "type": "string"
    },
    "keywords": {
      "type": "array",
      "items": {
        "type": "string"
      }
    },
    "license": {
      "type": "string"
    },
    "article": {
      "type": "object",
      "properties": {
        "title": {
          "type": "string"
        },
        "authors": {
          "type": "array",
          "items": {
            "type": "string"
          }
        },
        "abstract": {
          "type": "string"
        },
        "body_text": {
          "type": "string"
        },
        "bibliography": {
          "type": "string"
        }
      }
    }
  }
}
```


## Citation
If you use our dataset in your scientific, please cite our paper:
```
@InProceedings{Tarsi_2024_WACV,
    author    = {Tarsi, Tim and Adel, Heike and Metzen, Jan Hendrik and Zhang, Dan and Finco, Matteo and Friedrich, Annemarie},
    title     = {SciOL and MuLMS-Img: Introducing a Large-Scale Multimodal Scientific Dataset and Models for Image-Text Tasks in the Scientific Domain},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {4560-4571}
}

```

## License

The SciOL corpus is released under the [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.