---
language:
- en
license: other
task_categories:
- text-generation
size_categories:
- 10K<n<100K
pretty_name: Distributed Ledger Technology (DLT) Scientific Literature
dataset_info:
  features:
  - name: lang
    dtype: string
  - name: s2FieldsOfStudy
    list:
    - name: category
      dtype: string
    - name: source
      dtype: string
  - name: url
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  - name: fieldsOfStudy
    sequence: string
  - name: lang_conf
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  - name: title
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  - name: paperId
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  - name: venue
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  - name: authors
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    - name: name
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    struct:
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    - name: alternate_names
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    - name: alternate_urls
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    - name: id
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    - name: issn
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    - name: name
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    - name: type
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    - name: url
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  - name: abstract
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  - name: text
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  - name: openAccessPdf
    struct:
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    - name: license
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    - name: status
      dtype: string
    - name: url
      dtype: string
  - name: year
    dtype: int64
  - name: publicationTypes
    sequence: string
  - name: isOpenAccess
    dtype: bool
  - name: publicationDate
    dtype: timestamp[us]
  - name: references
    list:
    - name: paperId
      dtype: string
    - name: title
      dtype: string
  - name: total_tokens
    dtype: int64
  splits:
  - name: train
    num_bytes: 2352430295.715864
    num_examples: 37440
  download_size: 1245157122
  dataset_size: 2352430295.715864
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# DLT-Scientific-Literature

[**Paper**](https://huggingface.co/papers/2602.22045) | [**GitHub**](https://github.com/dlt-science/DLT-Corpus)

## Dataset Description

### Dataset Summary

DLT-Scientific-Literature is a specialized corpus of academic publications focused on Distributed Ledger Technology (DLT). This dataset is part of the larger DLT-Corpus collection, designed to support NLP research, language model development, and innovation studies in the DLT domain.

The dataset contains **37,440 scientific documents** with **564 million tokens**, spanning publications from **1978 to 2025**. All documents are in English and have been filtered for domain relevance using a fine-tuned BERT model.

This dataset is part of the `DLT-Corpus` collection. For the complete corpus including patents and social media data, see: https://huggingface.co/collections/ExponentialScience/dlt-corpus-68e44e40d4e7a3bd7a224402

### Languages

English (en)

## Dataset Structure

### Data Fields

The dataset includes the following fields for each document:

- **paperId**: Unique identifier from Semantic Scholar
- **title**: Title of the scientific publication
- **authors**: List of authors
- **year**: Publication year
- **publicationDate**: Full publication date
- **venue**: Publication venue (journal, conference, etc.)
- **publicationVenue**: Detailed venue information
- **publicationTypes**: Type of publication (e.g., JournalArticle, Conference)
- **abstract**: Abstract of the publication
- **text**: Full text content in Markdown format
- **url**: URL to the source document
- **openAccessPdf**: Link to open access PDF if available
- **isOpenAccess**: Boolean indicating open access status
- **fieldsOfStudy**: Academic fields associated with the paper
- **s2FieldsOfStudy**: Semantic Scholar's field classifications
- **references**: List of referenced papers
- **lang**: Language code
- **lang_conf**: Confidence score for language detection
- **tok_len**: Token length of the document
- **total_tokens**: Total number of tokens


### Data Splits

This is a single corpus without predefined splits. Users should create their own train/validation/test splits based on their specific research needs.

## Dataset Creation

### Curation Rationale

DLT-Scientific-Literature was created to address the lack of large-scale, domain-specific text corpora for NLP and computational research in the Distributed Ledger Technology field. The dataset enables researchers to:

- Develop DLT-specific language models and embeddings
- Conduct innovation studies and trend analysis
- Perform text mining on cutting-edge DLT research
- Study the evolution of concepts and terminology in the field

### Source Data

#### Data Collection

Scientific literature was collected from the **Semantic Scholar API** using domain-specific queries related to blockchain, distributed ledgers, cryptocurrencies, smart contracts, and related technologies.

#### Data Processing

The collection process involved:

1. **Query-based retrieval**: Using targeted keywords to retrieve relevant publications
2. **PDF parsing**: Converting PDF documents to Markdown format
3. **Language detection**: Filtering for English-language documents
4. **Length filtering**: Removing documents that are too short or too long
5. **Domain relevance filtering**: Using a fine-tuned BERT model to ensure documents are relevant to DLT

### Personal and Sensitive Information

This dataset contains only publicly available scientific literature. No personal or confidential data is included. Author names and affiliations are retained as they appear in the original publications, as this is standard academic practice.

## Considerations for Using the Data

### Discussion of Biases

Potential biases include:

- **Geographic bias**: Publications may be skewed toward institutions in certain countries
- **Language bias**: Only English-language publications are included
- **Temporal bias**: More recent years may have disproportionately more publications
- **Venue bias**: Certain journals or conferences may be over-represented
- **Citation bias**: Highly-cited papers may be more likely to be included

### Other Known Limitations

- **Temporal coverage**: While the dataset spans 1978-2025, the distribution is uneven with more recent years heavily represented
- **Access limitations**: Some publications may be missing due to access restrictions or API limitations
- **Quality variation**: Academic writing quality and rigor vary across publications
- **Parsing errors**: PDF-to-Markdown conversion may introduce formatting issues in some documents

## Additional Information

### Dataset Curators

Walter Hernandez Cruz, Peter Devine, Nikhil Vadgama, Paolo Tasca, Jiahua Xu

### Licensing Information

Mixed open-access licenses including:
- Creative Commons Attribution (CC-BY)
- Creative Commons Attribution-ShareAlike (CC-BY-SA)
- Creative Commons Zero (CC0)
- Other permissive open-access licenses

Individual license information is included in the metadata for each document where available. Users should check the specific license for each document before use.

### Citation Information

```bibtex
@misc{hernandez2026dlt-corpus,
      title={DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain}, 
      author={Walter Hernandez Cruz and Peter Devine and Nikhil Vadgama and Paolo Tasca and Jiahua Xu},
      year={2026},
      eprint={2602.22045},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2602.22045}, 
}
```