Displays CCF recommended rank of conferences/journals in dblp, Google Scholar, Connected Papers and WoS.
What is CCFrank?
CCFrank is a user-friendly Chrome extension designed to facilitate academic research. This tool displays the categorization of academic articles according to the 'Chinese Computer Society's Recommended International Academic Conference and Journal Directory'. Its functionality spans across platforms such as dblp, Google Scholar, Connected Papers, Semantic Scholar, and Web of Science, providing an easy and efficient way for researchers to identify the ranking and credibility of journals and conferences.
Extension stats
By: Wenyan Liu
Users: 20,000+
Creation date: 2020-11-06
Risk impact: Low risk impact
Risk likelihood: Very low risk likelihood
Manifest version: 3
Host permissions:
Size: 1.91M
Email: f.*****@hotmail.com
URLs: Privacy policy
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Extension summary
此扩展程序会添加一个网页标记,以便用户可以从 dblp、Google 学术、Connected Papers、Semantic Scholar 和 Web of Science 轻松查询学术文章的类别(即其在《中国计算机学会推荐国际学术会议和期刊目录》2022年拟定版(2022年12月)中的类别)。CCFrank 可执行以下操作:
在 dblp 的 publication、person、conferences 和 journals 搜索结果中标记论文的类别;适配 dblp 各镜像站点。
在 Google 学术的 articles 和 author 搜索结果中标记论文的类别;适配 Google 学术各镜像站点;
在 Connected Papers 的搜索结果中标记论文的类别。
在 Web of Science 的搜索结果中标记论文的类别。
在 Semantic Scholar 的 search results、citations 和 references 中标记会议和期刊论文的类别。
您的 Star 是我更新的动力 <3:https://github.com/WenyanLiu/CCFrank4dblp
版本历史:https://github.com/WenyanLiu/CCFrank4dblp#whats-new
See moreUser reviews
Pros
- 非常好用,插件很棒!
- 对用户有帮助,增加了学术搜索效率
Cons
- CCF评级错误,显示为None
- 在Google Scholar上不起作用或显示不完整
- 存在多个bug,导致同一会议/期刊的评级不一致
Most mentioned
- CCF rankings sometimes show as None
- Issues with Google Scholar compatibility
- Requests for updates and bug fixes
Recent reviews
很多会议显示不出来ccf级别,都是none
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation是ccf-b会议也没有显示
IWQos这个ccf b类会议居然显示CCF none实在是。。。。建议作者更改下,bug太多了,应该更新为字义相似识别,更近一步的筛选等读者一搜论文名即可
Extension safety
Risk impact
CCFrank requires very minimum permissions.
Risk likelihood
CCFrank has earned a good reputation and can be trusted.
Upgrade to see risk analysis details
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