目录
开放数据获取开放数据获取
创建: Jul 09, 2019 | 16:04
介绍
本页面主要收集各类科学学研究用数据
Open Academic Graph
link: https://www.openacademic.ai/oag/
Open Academic Graph (OAG) is a large knowledge graph unifying two billion-scale academic graphs: Microsoft Academic Graph (MAG) and AMiner. In mid 2017, we published OAG v1, which contains 166,192,182 papers from MAG and 154,771,162 papers from AMiner (see below) and generated 64,639,608 linking (matching) relations between the two graphs. This time, in OAG v2, author, venue and newer publication data and the corresponding matchings are available.
APS期刊引用数据
link: https://journals.aps.org/datasets
The corpus of Physical Review Letters, Physical Review, and Reviews of Modern Physics is comprised of over 450,000 articles and dates back to 1893. We are making available two data sets based on this corpus:
1) Citing article pairs: This data set consists of pairs of APS articles that cite each other. For instance, if article A cites article B, there will be an entry in the data set consisting of the pair of DOIs for A and B. This data set will be formatted as a comma-separated values (CSV) file consisting of the DOI pairs, one pair per line.
2) Article metadata: This data set consists of the basic metadata of all APS journal articles. The metadata provided includes the following fields: DOI, journal, volume, issue, first page and last page OR article id and number of pages, title, authors, affiliations, publication history, PACS codes, table of contents heading, article type, and copyright information.
Microsoft Academic Graph
link: https://docs.microsoft.com/en-us/academic-services/graph/
The Microsoft Academic Graph is a heterogeneous graph containing scientific publication records, citation relationships between those publications, as well as authors, institutions, journals, conferences, and fields of study. This graph is used to power experiences in Bing, Cortana, Word, and in Microsoft Academic. The graph is currently being updated on a weekly basis.
中国科学文献服务系统
link: http://sciencechina.cn/
中国科学引文数据库（Chinese Science Citation Database，简称CSCD）创建于1989年，收录我国数学、物理、化学、天文学、地学、生物学、农林科学、医药卫生、工程技术和环境科学等领域出版的中英文科技核心期刊和优秀期刊千余种，目前已积累从 1989 年到现在的论文记录 5234065条，引文记录70868360 条。中国科学引文数据库内容丰富、结构科学、数据准确。系统除具备一般的检索功能外，还提供新型的索引关系——引文索引，使用该功能，用户可迅速从数百万条引文中查询到某篇科技文献被引用的详细情况，还可以从一篇早期的重要文献或著者姓名入手，检索到一批近期发表的相关文献，对交叉学科和新学科的发展研究具有十分重要的参考价值。中国科学引文数据库还提供了数据链接机制，支持用户获取全文。
中国引文数据库
link: https://ref.cnki.net/ref
《中国引文库》主要功能包括引文检索、检索结果分析、作者引证报告、文献导出、数据分析器及高被引排序等模块。
