Creating Guidance for Canadian Dataverse Curators: Portage Network’s Dataverse Curation Guide

Alexandra Cooper,Erin Clary,Ève Paquette-Bigras,Erin Macpherson,Louise Gillis,Michael Steeleworthy,Jason Brodeur

doi:10.7191/jeslib.2021.1201

Abstract

Purpose: This paper introduces the Portage Network’s Dataverse Curation Guide and the new bilingual curation framework developed to support it. Brief Description: Canadian academic institutions and national organizations have been building infrastructure, staffing, and programming to support research data management. Amidst this work, a notable gap emerged between requirements for data curation in general repositories like Dataverse and the requisite workflows and guidance materials needed by curators to meet them. In response, Portage, a national network of data experts, organized a working group to develop a Dataverse curation guide built upon the Data Curation Network’s CURATED workflow. To create a bilingual resource, the original CURATE(D) acronym was modified to CURATION—which has the same meaning in both French and English—and steps were augmented with Dataverse-specific guidance and mapped to three conceptualized levels of curation to assist curators in prioritizing curation actions. Methods: An environmental scan of relevant deposit and curation guidance materials from Canadian and international institutions identified the need for a comprehensive Dataverse Curation Guide, as most existing resources were either depositor-focused or contained only partial workflows. The resulting Guide synthesized these guidance materials into the CURATION steps and mapped actions to various theoretical levels of data repository services and levels of curation. Resources: The following documents are supplemental to the Dataverse Curation Guide: the Portage Dataverse North Metadata Best Practices Guide, the Scholars Portal Dataverse Guide, and the Data Curation Network CURATED Workflow and Data Curation Primers.

Highlights

Data curation—the active management of research data as it is created, maintained, used, archived, shared, and reused—is a core component within the assemblage of infrastructure, processes, schemas, and curator expertise that supports best practices in Research Data Management (RDM).1 The execution of a well-articulated data curation workflow can make good data better by expertly describing its contents, creating a coherent structure, providing meaningful documentation, enabling automation through code and syntax, and linking to other data and outputs.Over the past half decade, Canadian academic institutions and programs have developed national RDM infrastructure and services
Resources: The following documents are supplemental to the Dataverse Curation Guide: the Portage Dataverse North Metadata Best Practices Guide, the Scholars Portal Dataverse Guide, and the Data Curation Network CURATED Workflow and Data Curation Primers
This Guide introduces the CURATION framework, a set of steps for curating new datasets deposited in Dataverse

Summary

Methods

An environmental scan of relevant deposit and curation guidance materials from Canadian and international institutions identified the need for a comprehensive Dataverse Curation Guide, as most existing resources were either depositor-focused or contained only partial workflows. The resulting Guide synthesized these guidance materials into the CURATION steps and mapped actions to various theoretical levels of data repository services and levels of curation. Resources: The following documents are supplemental to the Dataverse Curation Guide: the Portage Dataverse North Metadata Best Practices Guide, the Scholars Portal Dataverse Guide, and the Data Curation Network CURATED Workflow and Data Curation Primers

Introduction

Conclusion