EzGOV: A tool to find, develop, and manage data governance documents
Poster No:
1823
Submission Type:
Abstract Submission
Authors:
Anibal Heinsfeld1, Dheeraj Bhatia1, Nicholas Lee1, Melanie Collier1, Kimberly Ray1, Franco Pestilli1
Institutions:
1The University of Texas at Austin, Austin, TX
First Author:
Co-Author(s):
Introduction:
Data governance (DG) encompasses the frameworks, tools, personnel, and resources necessary to ensure trusted and ethical research practices. Effective DG is essential for adhering to ethical standards, legal requirements, and established best practices, and involves managing key documents such as Institutional Review Board (IRB) approvals, participant consent forms, data use agreements (DUAs), and data management plans (DMPs). These documents regulate processes related to data access, participant consent, and inter-institutional collaboration, making DG a cornerstone of compliant and rigorous scientific research.
Currently, DG workflows are fragmented, as researchers rely on general-purpose tools like Microsoft Word, Google Docs, and LaTeX to create and manage governance documents. These tools, while functional, are not tailored for data governance processes, leading to inconsistencies, increased researcher workload, and elevated risks of non-compliance and inefficiencies. Moreover, the absence of standardized templates and automated tools exacerbates the time-intensive and error-prone nature of DG document creation, further hampering compliance and collaboration.
Currently, DG workflows are fragmented, as researchers rely on general-purpose tools like Microsoft Word, Google Docs, and LaTeX to create and manage governance documents. These tools, while functional, are not tailored for data governance processes, leading to inconsistencies, increased researcher workload, and elevated risks of non-compliance and inefficiencies. Moreover, the absence of standardized templates and automated tools exacerbates the time-intensive and error-prone nature of DG document creation, further hampering compliance and collaboration.
Methods:
To address these systemic challenges, we developed ezGov, an innovative, web-based platform designed to streamline data governance workflows at all stages of a research project. ezGov serves as a centralized hub for creating, managing, and organizing DG documents, offering researchers access to pre-defined, customizable templates aligned with compliance standards from leading agencies such as the U.S. National Institutes of Health (NIH) and the National Science Foundation (NSF). By consolidating DG processes into a single, intuitive platform, ezGov ensures consistency, reduces administrative overhead, and minimizes risks of error or non-compliance.
Results:
To further promote efficiency, ezGov seamlessly integrates with research platforms such as Brainlife.io, enabling DG documents to be incorporated into broader scientific workflows. This integration ensures responsible and transparent data sharing, fostering collaboration across institutions, disciplines, and international research communities. By linking data governance with research workflows, ezGov strengthens compliance, facilitates inter-institutional partnerships, and supports the global movement toward open and FAIR (Findable, Accessible, Interoperable, and Reusable) science.
Conclusions:
With its scalable architecture and interdisciplinary applicability, ezGov is a transformative solution for managing data governance in today's dynamic research environment. By automating and standardizing DG processes, the platform reduces the administrative burden on researchers, enhances compliance, and accelerates scientific discovery. Ultimately, ezGov empowers researchers to focus on advancing ethical, impactful, and collaborative research, redefining how data governance documents are created, managed, and utilized to support modern science.
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Workflows
Informatics Other 2
Keywords:
Data Organization
Other - Data Governance; Open Science; IRB; consent forms;
1|2Indicates the priority used for review
Abstract Information
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