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2022•Practice and Experience in Advanced Research ComputingRequires access

Data Management Workflows in Interdisciplinary Highly Collaborative Research

Esen Tuna, Katie Chapman, Inna Kouper

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Abstract

Data curation is an important aspect in research projects. Effective data management is critical for data curation, and it not only contributes to the success of projects but makes research outputs findable, accessible, interoperable and reusable. We have examined interdisciplinary highly collaborative research (IHCR) practices in selected projects to propose data management workflows. This synopsis of work in progress discusses one of these workflows that helps locate information when there are multiple collaborators and the digital assets are spread across multiple storage systems and institutions.

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What this paper is about

Data curation is an important aspect in research projects. Effective data management is critical for data curation, and it not only contributes to the success of projects but makes research outputs findable, accessible, interoperable and reusable. We have examined interdisciplinary highly collaborative research (IHCR) practices in selected projects to propose data management workflows. This synopsis of work in progress discusses one of these workflows that helps locate information when there are multiple collaborators and the digital assets are spread across multiple storage systems and institutions.

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Available abstract

Data curation is an important aspect in research projects. Effective data management is critical for data curation, and it not only contributes to the success of projects but makes research outputs findable, accessible, interoperable and reusable. We have examined interdisciplinary highly collaborative research (IHCR) practices in selected projects to propose data management workflows. This synopsis of work in progress discusses one of these workflows that helps locate information when there are multiple collaborators and the digital assets are spread across multiple storage systems and institutions.

Key concepts: Workflow, Data curation, Interoperability, Computer science, Data management, Data science, Work (physics), Research data

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