Seven years of time-tracking data capturing collaboration and failure dynamics: the Gryzzly dataset

Area of Science:

• Data Science
• Network Science
• Organizational Behavior

Background:

• Large-scale datasets are crucial for understanding complex human behaviors in work environments.
• Existing datasets often lack the longitudinal, high-resolution detail needed to capture nuanced project dynamics.
• The Gryzzly software provides a unique opportunity to collect granular data on user activities and project lifecycles.

Purpose of the Study:

• To introduce and describe the Gryzzly time-tracking dataset, a novel resource for research.
• To validate the dataset's integrity by analyzing its inherent network properties and observed dynamics.
• To highlight the dataset's potential for investigating productivity, team collaboration, and project failure.

Main Methods:

• Compilation of 4.4 million user-task interactions from Gryzzly software usage data (2017-2024).
• Analysis of project data across diverse industries, including planned vs. actual costs.
• Validation through temporal collaboration network analysis, examining user activity, degree distributions, and inter-declaration times.

Main Results:

• The dataset comprises 4.4 million interactions, 12,447 users, 173,323 tasks, and 50,759 projects.
• Network analysis confirmed expected patterns: circadian user activity, power-law distributions, and heterogeneous inter-declaration times.
• Observed failure dynamics include heavy-tailed streak lengths and diverging performance trends for successful vs. failed projects.

Conclusions:

• The Gryzzly dataset is a valuable, high-resolution resource for studying productivity and team dynamics.
• The data's structure supports research into the complex factors contributing to project success and failure.
• Further research can leverage this dataset to develop predictive models for project outcomes.