Dataset Variables

Agile Effectiveness (measured on a Likert scale from 2 to 5): This variable captures how respondents perceive the effectiveness of Agile methodology in enhancing project management processes.

Risk Mitigation (Likert scale 2 to 5): This variable reflects respondents' views on how well Agile methodology supports the mitigation of risks throughout the project lifecycle.

Management Satisfaction (Likert scale 2 to 5): This variable measures how satisfied the management is with the outcomes of projects where Agile methodologies were implemented.

Supply Chain Improvement (Likert scale 2 to 5): This variable captures the perceived improvements in supply chain processes that result from using Agile methods.

Time Efficiency (Likert scale 2 to 5): This measures the impact of Agile methodology on improving the efficiency of time management within projects.

Cost Savings (percentage from 10% to 48%): This variable quantifies the percentage of cost savings achieved as a result of implementing Agile methods.

Project Success (binary: 0 = Failure, 1 = Success): This is the dependent variable and represents whether or not the project was considered successful.

The statistic shows the share of software developers worldwide that have adopted agile development methodology and continuous integration (CI) practices worldwide, from 2015 to 2018, based on a survey of development professionals. As of early 2018, ** percent of respondents indicated their organization had adopted an agile development methodology, while ** percent practiced continuous integration.

The statistic depicts the size of the agile application life cycle management tool market worldwide, from 2013 to 2017. In 2016, the agile application life cycle management tool market is expected to be worth *** million U.S. dollars.

This dataset comprises survey data collected from individuals involved in the software development industry to investigate the integration of artificial intelligence (AI) tools within Agile frameworks. The survey aimed to understand current practices, challenges, and perceptions regarding the adoption of AI technologies in Agile software development processes. The dataset includes information on respondents' current roles in software development, familiarity with Agile frameworks and AI tools, experience with using AI tools in software development projects, perceived benefits of integrating AI within Agile frameworks, challenges associated with this integration, and willingness to adopt AI tools.

Columns:

Current Role: The role of the respondent in software development projects (e.g., Developer, Project Manager, Quality Assurance Engineer). Familiarity with Agile Frameworks: Level of familiarity with Agile methodologies (e.g., Not Familiar, Somewhat Familiar, Very Familiar). Familiarity with AI Tools: Level of familiarity with AI tools (e.g., Not Familiar, Somewhat Familiar, Very Familiar). AI Tools Usage: Whether the respondent has used AI tools in software development projects before (Yes/No). AI Tools Types: Types of AI tools used, if applicable (e.g., Natural Language Processing (NLP), Machine Learning Algorithms, Chatbots). Perceived Benefits: Perceived benefits of integrating AI tools within Agile frameworks (e.g., Enhanced decision-making processes, Improved product quality). Challenges: Challenges foreseen in integrating AI tools within Agile frameworks (e.g., Integration complexities, Lack of expertise in AI technologies). Willingness to Adopt AI Tools: Respondents' willingness to adopt AI tools within their Agile development processes, rated on a scale of 1 to 5. Usage: This dataset can be used for various purposes, including data analysis, research, and insights generation in the fields of software development, Agile methodologies, and artificial intelligence. Researchers, practitioners, and enthusiasts interested in understanding the intersection of AI and Agile in software development can explore this dataset to uncover trends, patterns, and correlations relevant to their areas of interest.

Acknowledgements: The dataset was collected for research purposes and made available by [Your Name/Organization]. We would like to express our gratitude to all participants who contributed their insights to this survey.

License: This dataset is made available under the [license type, e.g., CC BY-SA 4.0] license. Users are encouraged to attribute the dataset to [Your Name/Organization] when utilizing it for their projects or analyses.

This bundle contains supplementary materials for an upcoming academic publication Do Agile Scaling Approaches Make A Difference? An Empirical Comparison of Team Effectiveness Across Popular Scaling Approaches?, by Christiaan Verwijs and Daniel Russo. Included in the bundle are the dataset and SPSS syntaxes. This replication package is made available by C. Verwijs under a "Creative Commons Attribution Non-Commercial Share-Alike 4.0 International"-license (CC-BY-NC-SA 4.0).

About the dataset

The dataset (SPSS) contains anonymized response data from 15,078 team members aggregated into 4,013 Agile teams that participated from scrumteamsurvey.org. Stakeholder evaluations of 1,841 stakeholders were also collected for 529 of those teams. Data was gathered between September 2021, and September 2023. We cleaned the individual response data from careless responses and removed all data that could potentially identify teams, individuals, or their parent organizations. Because we wanted to analyze our measures at the team level, we calculated a team-level mean for each item in the survey. Such aggregation is only justified when at least 10% of the variance exists at the team level (Hair, 2019), which was the case (ICC = 35-50%). No data was missing at the team level.

Question labels and option labels are provided separately in Questions.csv. To conform to the privacy statement of scrumteamsurvey.org, the bundle does not include response data from before the team-level aggregation.

About the SPSS syntaxes

The bundle includes the syntaxes we used to prepare the dataset from the raw import, as well as the syntax we used to generate descriptives. This is mostly there for other researchers to verify our procedure.

Financial overview and grant giving statistics of Agile Denver Inc

Dataset for the study "Open approach to agile change in organizations and communities developing open-source projects"

Umbrella review - First review

The objective of this umbrella review is to check that there are no reviews in the defined period from 2000 to 2024 that respond to the objective of this research

Roughly ** percent of the surveyed Italian companies claim to use Agile at the enterprise level to increase speed, flexibility, and adaptability. About ** percent of the respondents claim to either use the technology or expect to implement it in the following years.

Financial overview and grant giving statistics of Agile Learning Center of New Orleans

Financial overview and grant giving statistics of Agile Project Leadership Network Of Houston Inc

As Agile continues to grow in popularity, more organizations are experiencing the frustration associated with attempts to move from traditional waterfall to Agile practices. Agile requires more than simply knowing how to do Scrum; it involves organizational and cultural change. Leaders of successful Agile projects recognize and manage this cultural change and the impacts it has on all stakeholders.

Our webinar, "From Waterfall to Agile: Managing Cultural Change and Impacts Across Stakeholder Groups," presents the experience of several states in implementing the Agile methodology and the various impacts on stakeholder groups. Presenters include Kevin Burt, Assistant Director for the Eligibility Services Division, Utah Department of Workforce Services; Wade Owen, Technical Director, Utah Child Welfare Management System (SAFE); and Tom Kine, Minnesota Statewide Automated Child Welfare Information Systems Manager and SQL Server Integration Services (SSIS) Application and Development Supervisor.

Metadata-only record linking to the original dataset. Open original dataset below.

This bundle contains data collected from the Apache Point Observatory 3.5m Telescope AGILE instrument during observations of the impact of the LCROSS spacecraft on the moon on October 9, 2009. The bundle contains raw and calibration data products.

These files are 250bp Illumina MiSeq paired-end sequencing reads in fastq format. Libraries were prepared from DNA fragments amplified from tomato bulk (heterogenous) samples around the fruit weight locus.

This dataset serves the purpose of quantitatively verifying how the Agile Manifesto and the implementation of ASD methodology affect an organization's agility, enabling the determination of whether ASD (‘doing agile’) alone, without AM values (‘being agile’), is sufficient—sample size: 1842 knowledge workers from Poland and Finland.

The Agile Development Service market has witnessed remarkable growth as businesses increasingly adopt agile methodologies to enhance their software development processes. This market, which encompasses a range of services designed to facilitate agile practices, is driven by the need for organizations to improve coll

There are two datasets as follows, 1) This original dataset of ICT patent applications includes the date of application and addresses of applicants during 2007-2021.

2) District administrative Boundaries at Three Levels in China

The Agile Project Management Software market has gained significant traction as industries increasingly recognize the need for adaptable and efficient project management solutions. This software enables organizations to implement Agile methodologies, which prioritize flexibility, collaboration, and customer feedback

Delaware’s FOCUS project uses a hybrid Agile approach that incorporates Agile practices while keeping within the parameters of typical fixed-scope projects. The FOCUS project is driven by engaged stakeholders, a consistent emphasis on scope, and disciplined monitoring of plans and progress.

Join leadership staff of the FOCUS project as they discuss their successes and challenges. They will speak about their Agile approaches for software development and describe their best practices for project management as well as for monitoring impediments and risks. They will also answer questions from participants. Speakers include the following:

Metadata-only record linking to the original dataset. Open original dataset below.

Dataset for the study "Open approach to agile change in organizations and communities developing open-source projects"

Frist review

This dataset contains records of 200 agile IT developers working on different projects. Each row represents data for one developer the data created of Agile team oxford univeristy and bradford collaboration.

Specifications (Column Details) Developer_ID – Unique identifier for each developer.

Project_Assignment – The project the developer is working on (e.g., Retail IT, Healthcare IT).

Role – The role of the developer (e.g., DevOps Engineer, Frontend Developer, Backend Developer).

Experience_Years – Total years of experience of the developer.

Resource_Allocation_Hours – Number of hours allocated to the developer.

Risk_Assessment_Score – Risk score for project completion (0-1 scale, where 1 indicates high risk).

Completion_Percentage – Percentage of project completion.

Real_Time_Resource_Prediction – AI-based prediction of required resources.

Project_Overrun_Percentage – Probability of project overrunning (percentage).

AI_Optimization_Effectiveness – Effectiveness of AI-driven optimization (0-1 scale).