A million + academic papers are structured in data sets covering core fields such as titles, abstracts, keywords, DOIs, taxonomy numbers, citation relationships, etc. Supporting literature search analysis, knowledge mapping, academic AI model training to provide a high-quality data base for scientific intelligence.
A comprehensive understanding of the structure, field dimensions and core features of the scientific paper data sets.
| Dimension | Description | Details |
|---|---|---|
| Total Data Volume | 1 million + papers | Chinese/English-language paper, updated on an ongoing basis |
| 14 major disciplines | Computer science, medicine, engineering, physics, chemistry, biology, mathematics, materials, environment, social sciences, etc. | |
| Time Span | 2005-2025 Years | 20 years of academic literature |
| Language ratio | 70% English / 30% Chinese | Bilingual labels to support cross-language retrieval |
| Citation Relations | 5 million + quotes | Include Quoted Type, Reference Context |
| Data Format | JSON / CSV / Parquet | Support multiple data delivery formats |
| Update Frequency | Quarterly incremental updates | About 50,000 new papers per quarter |
Each paper contains 12+ core fields, which are highly structured to meet the needs of the academic analysis of the whole scene.
Title (C/E), author list (including institutions), year of publication, journal/conference name, volume page number
Abstract in Chinese, 5-10 keywords (including MeSH terminographic mapping), research field labels
DOI, ISSN/ISBN, classification number (Medical chart/UDC), arXiv ID, PubMed ID
Bibliography, Introduction List, Reference Type (Method/background/comparing), Reference Context Snippets
Number of quotes, downloads, influence factors in periodicals, h5-index, Altmetric scores
Fund funding information, open source links, data set links, conference/journal hierarchy (CF/SCI division)
JSON structured an example of a data structure that is fully documented in scientific papers.
The scientific thesis data sets provide high-quality training data for a variety of AI applications.
Semantic Paper Search Engine to support cross-English and Chinese search and precision documentation recommendations
Building a paper-author-agency-keyword knowledge network to support scientific trend analysis and front-line discovery
Modified on a large model to enable automatic abstract generation of academic papers and extraction of key information
Multi-dimensional reference analysis and Altmetric indicator modelling, predicting impact and forward direction of the papers
Covers the data asset keyword to facilitate the accurate discovery of this data set by the search engine and the AI generator engine.
Provides previews of data samples, quality reports and one-on-one consultations to support customized subsets by discipline, year, language.