This dataset contains 4,828 full biomedical articles paired with non-technical lay summaries derived from the eLife scientific journal.

This dataset contains 27,525 full biomedical articles paired with non-technical lay summaries derived from various journals published by the Public Library of Science (PLOS).

Large-scale manually-annotated corpus for 1,000 scientific papers (on computational linguistics) for automatic summarization. Summaries for each paper are constructed from the papers that cite that paper and from that paper's abstract. Source: ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks

A scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata. The unarXive data set contains * One million papers in plain text * 63 million citation contexts * 39 million reference strings * A citation network of 16 million connections The data is generated from all LaTeX sources on arXiv from 1991–2020/07 and therefore of higher quality than data generated from PDF files. Furthermore, as all citing papers are available in full text, citation contexts of arbitrary size can be extracted. Typical uses of the data set are approaches in * Citation recommendation * Citation context analysis * Reference string parsing The code for generating the data set is publicly available.

SemOpenAlex is an extensive RDF knowledge graph that contains over 26 billion triples about scientific publications and their associated entities, such as authors, institutions, journals, and concepts. * SemOpenAlex is licensed under CC0, providing free and open access to the data. * We offer the data through multiple channels, including RDF dump files, a SPARQL endpoint, and as a data source in the Linked Open Data cloud, complete with resolvable URIs and links to other data sources (ISNI, DOI, ORCID, ROR, Scopus, DOAJ, Wikidata, * Moreover, we provide embeddings for knowledge graph entities using high-performance computing. SemOpenAlex enables a broad range of use-case scenarios, such as * exploratory semantic search via our website, * large-scale scientific impact quantification, * other forms of scholarly big data analytics within and across scientific disciplines. * enables academic recommender systems, such as recommending collaborators, publications, and venues, including explainability capabilities. * can serve for RDF query optimization benchmarks, * creating scholarly knowledge-guided language models, * as a hub for semantic scientific publishing.

The TalkSumm dataset contains 1705 automatically-generated summaries of scientific papers from ACL, NAACL, EMNLP, SIGDIAL (2015-2018), and ICML (2017-2018). The dataset is provided as a list of titles and URLs and the corresponding summaries. Source: GitHub

FacetSum is a faceted summarization dataset for scientific documents. FacetSum has been built on Emerald journal articles, covering a diverse range of domains. Different from traditional document-summary pairs, FacetSum provides multiple summaries, each targeted at specific sections of a long document, including the purpose, method, findings, and value.

MS^2 (Multi-Document Summarization of Medical Studies) is a dataset of over 470k documents and 20k summaries derived from the scientific literature. This dataset facilitates the development of systems that can assess and aggregate contradictory evidence across multiple studies, and is one of the first large-scale, publicly available multi-document summarization dataset in the biomedical domain.

Science Journal for Kids Data This repository contains a dataset of abstracts from the Science Journal for Kids website and the original academic papers. It includes metadata such as titles, URLs, reading levels, and links to the full academic papers. The dataset is designed to support research and analysis of educational content tailored for young learners. ## Data The dataset is a curated collection of 284 original scientific abstracts and their adapted abstracts for children. These summaries are sourced from the Science Journal for Kids (SJK), a platform dedicated to making scientific research accessible and engaging for younger audiences. The dataset covers a diverse range of subjects, including biology, chemistry, health, environmental science, and more. The CSV file is approximately 716 KB in size. ## Data Format The dataset includes the following columns: - Category: The category or categories to which the article belongs. This can include areas such as Biodiversity, Conservation, Biology, Chemistry, Health, and Environmental Science. - Title: The title of the article, providing a concise description of the main topic or finding of the research. - Kids Abstract: The abstract of the article aimed at a younger audience. - Abstract (Original academic paper): The abstract from the original academic paper. - URL (Original academic paper): The URL to access the original academic paper. - Reading Levels: The suggested reading levels for the lay summary, which may include Elementary school, Middle school, Lower high school, and Upper high school. ## Citing this Repository If you use this dataset in your research, please cite it using the following BibTeX entry: @inproceedings{BioLaySumm_2024, author = {Loukritia Stefanou and Tatiana Passali and Grigorios Tsoumakas}, title = {AUTH at BioLaySumm 2024: Bringing Scientific Content to Kids}, booktitle = {Proceedings of the ACL 2024 BioNLP Workshop}, year = {2024}, address = {Bangkok, Thailand}, note = {A paper presented at the BioLaySumm 2024 shared task on lay summarization of biomedical research articles.} }

Science Journal for Kids Data This repository contains a dataset of abstracts from the Science Journal for Kids website and the original academic papers. It includes metadata such as titles, URLs, reading levels, and links to the full academic papers. The dataset is designed to support research and analysis of educational content tailored for young learners. ## Data The dataset is a curated collection of 284 original scientific abstracts and their adapted abstracts for children. These summaries are sourced from the Science Journal for Kids (SJK), a platform dedicated to making scientific research accessible and engaging for younger audiences. The dataset covers a diverse range of subjects, including biology, chemistry, health, environmental science, and more. The CSV file is approximately 716 KB in size. ## Data Format The dataset includes the following columns: - Category: The category or categories to which the article belongs. This can include areas such as Biodiversity, Conservation, Biology, Chemistry, Health, and Environmental Science. - Title: The title of the article, providing a concise description of the main topic or finding of the research. - Kids Abstract: The abstract of the article aimed at a younger audience. - Abstract (Original academic paper): The abstract from the original academic paper. - URL (Original academic paper): The URL to access the original academic paper. - Reading Levels: The suggested reading levels for the lay summary, which may include Elementary school, Middle school, Lower high school, and Upper high school. ## Citing this Repository If you use this dataset in your research, please cite it using the following BibTeX entry: @inproceedings{BioLaySumm_2024, author = {Loukritia Stefanou and Tatiana Passali and Grigorios Tsoumakas}, title = {AUTH at BioLaySumm 2024: Bringing Scientific Content to Kids}, booktitle = {Proceedings of the ACL 2024 BioNLP Workshop}, year = {2024}, address = {Bangkok, Thailand}, note = {A paper presented at the BioLaySumm 2024 shared task on lay summarization of biomedical research articles.} }