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

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-04-05 DOI:10.1038/s41597-025-04903-2

Jacob Levy Abitbol, Louis Arod

{"title":"Seven years of time-tracking data capturing collaboration and failure dynamics: the Gryzzly dataset.","authors":"Jacob Levy Abitbol, Louis Arod","doi":"10.1038/s41597-025-04903-2","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"578"},"PeriodicalIF":5.8000,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04903-2","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文本刊更多论文

求助全文

约1分钟内获得全文求助全文

来源期刊

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.