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SUMMARY:Data Management for Reproducible Research (RDM Jumpstart)
DESCRIPTION:Dates: May 12-16\, 2025 \n\n\n\nThis week-long workshop will introduce attendees to best practices in Research Data Management (RDM) using common tools to support research transparency and reproducibility. Robust implementation of RDM principles enables researchers to address bias and reproducibility\, effectively share their research\, and ensure long term access to their research inputs and outputs. From research question development to findings dissemination\, RDM underpins a fruitful and successful academic career. \n\n\n\nSessions will address the importance and underlying principles of RDM; we’ll explore issues related to RDM and the growing landscape of RDM-related requirements stemming from funders and publishers. Using the R programming language\, the Open Science Framework (OSF)\, and Borealis (Dataverse)\, we’ll explore solutions to address these issues and enable compliance with funder and publisher requirements. \n\n\n\nAll attendees will work with a common dataset to explore how to ask questions of data using common computational tools. Throughout\, attendees will be introduced to: the documentation and metadata requirements to ensure accessibility: considerations to address different aspects of reproducibility; practices to maintain their data’s integrity; and ways to ensure their final data deposit is adherent to FAIR (Findable\, Accessible\, Interoperable\, Reusable) principles. \n\n\n\nDraft program and schedule\n\n\n\nSubject to change. \n\n\n\nDay 1 \n\n\n\n\nWhat is Research Data Management\n\n\n\nPrinciples of RDM and FAIR (Findable\, Accessible\, Interoperable\, Reusable)\n\n\n\nIntegrating RDM in the research data lifecycle\n\n\n\nDMPs (Data Management Plans)\n\n\n\nSetting up a project in OSF\n\n\n\n\nDay 2 \n\n\n\n\nLicensing concerns as they relate to data\n\n\n\nIntroduction to R and RStudio\n\n\n\n\nDay 3 \n\n\n\n\nFile organization\, naming\, and version control\n\n\n\nData types in R\n\n\n\nProprietary and non-proprietary data storage formats\n\n\n\nPreparing data for analysis: recoding\, creating new variables\, and filters\n\n\n\n\nDay 4 \n\n\n\n\nBest practices for code management\n\n\n\nData visualization using R\n\n\n\nStructured practice time and Q&A\n\n\n\n\nDay 5 \n\n\n\n\nPreparing data\, code\, documentation\, and other materials for deposit\n\n\n\nDepositing data to Borealis\n\n\n\nGuest speakers and wrap-up\n\n\n\n\nLink to slides \n\n\n\nWho is creating and delivering this workshop?\n\n\n\nIn alphabetic order… \n\n\n\n\nErin McCoy\, Research Assistant\, Carleton University\n\n\n\nGia-Huy Hoang\, Research Assistant\, University of Calgary\n\n\n\nJane Fry\, Data Services Librarian\, Carleton University\n\n\n\nJennifer Abel\, Research Data Management Librarian\, University of Calgary\n\n\n\nMaria Amoros\, Research Assistant\, University of Victoria\n\n\n\nMathew Vis-Dunbar\, Data Librarian\, UBCO\n\n\n\nNick Rochlin\, Data Science Librarian\, University of Victoria
URL:https://ccdhhn.ca/workshop/data-management-for-reproducible-research/
CATEGORIES:20+ hour workshop
ATTACH;FMTTYPE=image/png:https://ccdhhn.ca/wp-content/uploads/2024/01/Screenshot-2024-01-05-at-4.13.12 PM.png
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