IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Jacob Levy Abitbol, Louis Arod
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引用次数: 0

摘要

我们介绍 Gryzzly 时间跟踪数据集:这是一个纵向、高分辨率的数据集,记录了 12447 个用户与 50759 个项目中 173323 个任务之间的 440 万次交互,时间跨度从 2017 年到 2024 年。该数据集由 Gryzzly 软件的实际使用数据编制而成,涵盖了营销、金融和银行等不同行业的项目。它提供了有助于项目完成的日常活动的详细视图,包括有关参与用户、他们所从事的任务以及每个项目的计划成本与实际成本的信息。为了验证已发布的数据,我们分析了底层的时间协作网络,发现了一些预期的模式,如昼夜节律用户活动、度分布的幂律特征以及异质分布的声明间时间。此外,我们还观察到了有据可查的失败动态,包括失败链条长度的重尾分布,以及成功项目和失败项目之间性能改进趋势的分化。这些特点使 Gryzzly 数据集成为研究生产率、团队动态和项目失败的关键资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Seven years of time-tracking data capturing collaboration and failure dynamics: the Gryzzly dataset.

We introduce the Gryzzly time-tracking dataset: a longitudinal, high-resolution collection of 4.4 million interactions recorded between 12,447 users and 173,323 tasks across 50,759 projects, spanning from 2017 to 2024. Compiled from real-world usage data of the Gryzzly software, the dataset encompasses projects from diverse industries such as marketing, finance, and banking. It provides a detailed view of daily activities contributing to project completion, including information about the users involved, the tasks they worked on, and the planned versus actual costs of each project. To validate the published data, we analyzed the underlying temporal collaboration network, revealing expected patterns such as circadian user activity, power-law characteristics in degree distributions, and heterogeneously distributed inter-declaration times. Additionally, we observed well-documented failure dynamics, including a heavy-tailed distribution of failure streak lengths and diverging performance improvement trends between successful and failed projects. These features make the Gryzzly dataset a key resource for studying productivity, team dynamics, and project failure.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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