Datasets:

ExponentialScience
/

DLT-Scientific-Literature

dataset_info:
  features:
    - name: lang
      dtype: string
    - name: s2FieldsOfStudy
      list:
        - name: category
          dtype: string
        - name: source
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    - name: url
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    - name: fieldsOfStudy
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    - 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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    - name: publicationVenue
      struct:
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        - name: alternate_urls
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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:
        - name: disclaimer
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        - name: license
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        - name: status
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        - 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-*
language:
  - en
pretty_name: Distributed Ledger Technology (DLT) Scientific Literature
size_categories:
  - 10K<n<100K

DLT-Scientific-Literature

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:

Query-based retrieval: Using targeted keywords to retrieve relevant publications
PDF parsing: Converting PDF documents to Markdown format
Language detection: Filtering for English-language documents
Length filtering: Removing documents that are too short or too long
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

@article{hernandez2025dlt-corpus,
  title={DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain},
  author={Hernandez Cruz, Walter and Devine, Peter and Vadgama, Nikhil and Tasca, Paolo and Xu, Jiahua},
  year={2025}
}