MIT License

项目介绍

jldbc/coffee-quality-database 是一个托管在 GitHub 上的开源项目,主要使用 R 编写,采用 MIT License 许可证,目前获得 264 个 Star、165 次 Fork。

仓库全名
jldbc/coffee-quality-database
开源许可证
MIT License
Star 数
264
Fork 数
165
未关闭 Issue
1

README 摘要

coffee-quality-database Digitizing 1,340 coffee reviews Data These data contain reviews of 1312 arabica and 28 robusta coffee beans from the Coffee Quality Institute's trained reviewers. The features include: Quality Measures Aroma Flavor Aftertaste Acidity Body Balance Uniformity Cup Cleanliness Sweetness Moisture Defects Bean Metadata Processing Method Color Species (arabica / robusta) Farm Metadata Owner Country…

完整内容见 GitHub 原始 README 或本页 。

常见问题

coffee-quality-database 用什么语言开发?

主要使用 R 编写,采用 MIT License 许可证,目前有 264 个 Star。

coffee-quality-database 可以免费商用吗?

仓库采用 MIT License 许可证,可免费使用与修改,商业使用请遵守许可证条款(例如保留版权声明与许可声明)。

如何获取 coffee-quality-database 的源码?

可以直接克隆仓库:git clone https://github.com/jldbc/coffee-quality-database.git,默认分支为 master;安装与运行步骤见仓库 README。

coffee-quality-database 最近还在维护吗?

仓库最近一次更新为 2018年6月17日,当前有 1 个未关闭 Issue;Star 数为 264,可作为社区活跃度的参考。

关于

Building the Coffee Quality Institute Database

描述取自 GitHub 官方 API,中文内容由 GitRead 中文社区 编辑团队整理。

简要分析

热度分
617
价值分
210
活跃状态
活跃

语言占比

Python
R

贡献者

README

coffee-quality-database

Digitizing 1,340 coffee reviews

Data

These data contain reviews of 1312 arabica and 28 robusta coffee beans from the Coffee Quality Institute’s trained reviewers. The features include:

Quality Measures

  • Aroma
  • Flavor
  • Aftertaste
  • Acidity
  • Body
  • Balance
  • Uniformity
  • Cup Cleanliness
  • Sweetness
  • Moisture
  • Defects

Bean Metadata

  • Processing Method
  • Color
  • Species (arabica / robusta)

Farm Metadata

  • Owner
  • Country of Origin
  • Farm Name
  • Lot Number
  • Mill
  • Company
  • Altitude
  • Region

The data folder contains both raw and cleaned data. The raw data is exactly as it was found on the CQI site. Since these human-recorded data use a variety of different encodings, abbreviations, and units of measurement for their farm names, altitude, region, and other fields, I recommend using the cleaned data as a starting point.

The site was scraped using a Selenium headless browser and Beautiful Soup. To replicate this or collect updated data, create a login for the CQI site and enter your credentials in the scraper

Source

These data were collected from the Coffee Quality Institute’s review pages in January 2018.

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