七年时间跟踪数据捕捉了协作和失败动态:Gryzzly数据集
Jacob Levy Abitbol1, Louis Arod2
1Gryzzly, Lyon, France. jacob@gryzzly.io.
Scientific data
|April 5, 2025
概括
格里兹利数据集提供了对用户交互和项目时间表的大规模视图. 这些数据有助于理解生产力,团队动态和项目失败模式.
科学领域:
- 数据科学数据科学数据科学
- 网络科学 网络科学
- 组织行为 组织行为
背景情况:
- 大规模的数据集对于理解工作环境中复杂的人类行为至关重要.
- 现有的数据集往往缺乏用于捕捉微妙的项目动态所需的纵向,高分辨率的细节.
- 格瑞兹利软件提供了一个独特的机会,可以收集有关用户活动和项目生命周期的细粒度数据.
研究的目的:
- 介绍和描述Gryzzly时间跟踪数据集,这是一个新的研究资源.
- 通过分析其固有的网络属性和观察到的动态来验证数据集的完整性.
- 突出数据集在调查生产力,团队协作和项目失败方面的潜力.
主要方法:
- 来自Gryzzly软件使用数据 (2017-2024) 的440万个用户-任务交互的汇编.
- 分析不同行业的项目数据,包括计划和实际成本.
- 通过时间协作网络分析进行验证,检查用户活动,度分布和声明间时间.
主要成果:
- 该数据集包括440万次交互,12447名用户,173323个任务和50759个项目.
- 网络分析证实了预期的模式:昼夜用户活动,权力规律分布和异质的相互声明时间.
- 观察到的失败动态包括重尾连续长度和成功与失败项目的不同性能趋势.
结论:
- 格里兹利数据集是一个有价值的,高分辨率的资源,用于研究生产力和团队动态.
- 数据的结构支持对导致项目成功或失败的复杂因素的研究.
- 进一步的研究可以利用这个数据集来开发项目结果的预测模型.
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