RDM Learning Resources
Research Data Management learning resources, including guides, workshops, and a glossary of terms
This page contains information on upcoming UCalgary Research Data Management (RDM) workshops and events, resources, and an RDM glossary of terms. This page will be updated regularly as new workshops and resources are created.
RDM Water Cooler
On August 15, 2022, gather around the virtual "water cooler" to learn more about Research Data Management at UCalgary.
At this informal session, we will share information about the Tri-Agency RDM Policy and UCalgary's response to its requirements.
LCR Research Guide
This LCR Research Guide for Research Data Management offers supports for RDM, learning materials, and best practices and tools to support management of your research data.
Tri-Agency RDM Policy FAQ
The Tri-Agency RDM Policy FAQ offers information on both the policy and foundational RDM principles.
Data are facts, measurements, recordings, records, or observations collected by researchers and others, with a minimum of contextual interpretation. Data may be in any format or medium taking the form of text, numbers, symbols, images, films, video, sound recordings, pictorial reproductions, drawings, designs or other graphical representations, procedural manuals, forms, diagrams, workflows, equipment descriptions, data files, data processing algorithms, software, programming languages, code, or statistical records.
Research data are data that are used as primary sources to support technical or scientific enquiry, research, scholarship, or creative practice, and that are used as evidence in the research process and/or are commonly accepted in the research community as necessary to validate research findings and results. Research data may be experimental data, observational data, operational data, third party data, public sector data, monitoring data, processed data, or repurposed data. What is considered relevant research data is often highly contextual, and determining what counts as such should be guided by disciplinary norms.
Institutional RDM strategy
An institutional RDM strategy describes how the institution will provide its researchers with an environment that enables and supports RDM practices. Developing these strategies will help research institutions identify and address gaps and challenges in infrastructure, resources and practices related to RDM.
Each strategy should reflect the institution’s particular circumstances, including the institution’s size and capacity, geography, and other contextual factors. The strategy would likely require input from various institutional units such as the administrative research office, the research ethics board, library services, IT services, and departments and faculties.
Data management plan
A data management plan (DMP) is a living document, typically associated with an individual research project or program that consists of the practices, processes and strategies that pertain to a set of specified topics related to data management and curation. DMPs should be modified throughout the course of a research project to reflect changes in project design, methods, or other considerations.
DMPs guide researchers in articulating their plans for managing data; they do not necessarily compel researchers to manage data differently.
“Data deposit” refers to when the research data collected as part of a research project are transferred to a research data repository. The repository should have easily accessible policies describing deposit and user licenses, access control, preservation procedures, storage and backup practices, and sustainability and succession plans. The deposit of research data into appropriate repositories supports ongoing data-retention and, where appropriate, access to the data.
Ideally, data deposits will include accompanying documentation, source code, software, metadata, and any supplementary materials that provide additional information about the data, including the context in which it was collected and used to inform the research project. This additional information facilitates curation, discoverability, accessibility and reuse of the data.