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Research Data Management

Understand what RDM is, why it's important to your research process, and how RRU Library can support you.

What is Research Data Management?

Research Data Management (RDM) is the intentional process, throughout an entire research project, of keeping data organized. This extends through:

  • planning your data collection method(s)
  • to clearly and completely documenting how you collect, label, clean, and prepare your data for analysis
  • to ensuring secure data storage during the research project
  • to recording your decisions made during analysis
  • to sharing both the findings and the data itself (as appropriate, depending on the project)
  • to ensuring the data's long term preservation upon completion of the research project

Why is Research Data Management Important?

  Proper data helps:

  • ensure your data is complete, documented, and accessible to you and/or to future researchers
  • satisfy grant, journal, or funder requirements.
  • raise the profile of your research
  • meet the data sharing expectations of your research community

Following proper data management practices can ensure you are methodical as you gather and manage your data.

What Counts as Research Data?

Research Data are the recorded observations created or collected in the process of conducting an original research project.  Research data can be either either digital or analog, qualitative or quantitative, and may come in a wide range of formats, among them:

  • Qualitative Data (text; e.g. TXT, DOC, PDF, RTF, HTML, XML)
  • Quantitative Data (numerical data; e.g. CSV, MAT, XLS, SPSS)
  • Spatial Data (e.g. raster, vector, grid)
  • Multimedia – (e.g. JPEG, TIFF, MPEG, MP3, Quicktime, Bitmap)
  • Models – (e.g. 3D, statistical, similitude, macroeconomic, causal)
  • Software – (e.g. Scripts written in Java, C, Perl, Python, Ruby, PHP, R, GIS)
  • Instrument specific – (e.g. field surveying equipment)
  • Others (all other digital or non digital content have the potential to become Research Data)

 

RDM Terms Defined

Definitions:

Research Data: 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.” (Tri-Agency Research Data Management Policy, Frequently Asked Questions, Government of Canada 2022).

Research Data Management (RDM): Research data management (RDM) refers to the processes applied through the lifecycle of a research project to guide the collection, documentation, storage, sharing and preservation of research data. RDM is essential throughout the data lifecycle—from data creation, processing, analysis, preservation, storage and access, to sharing and reuse (where appropriate), at which point the cycle begins again. Data management should be practiced over the entire lifecycle of the data, including planning the investigation, conducting the research, backing up data as it is created and used, disseminating data, and preserving data for the long term after the research investigation has concluded. (Tri-Agency Research Data Management Policy, Frequently Asked Questions, Government of Canada 2022).

Data Management Plan (DMP): A data management plan 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” (Tri-Agency Research Data Management Policy, Frequently Asked Questions, Government of Canada 2022).

Indigenous Research: Research in any field or discipline that is conducted by, grounded in or engaged with First Nations, Inuit, Métis or other Indigenous nations, communities, societies or individuals, and their wisdom, cultures, experiences or knowledge systems, as expressed in their dynamic forms, past and present. Indigenous research can embrace the intellectual, physical, emotional and/or spiritual dimensions of knowledge in creative and interconnected relationships with people, places and the natural environment. (Social Sciences and Humanities Research Council, Definition of Terms, Government of Canada 2021).

Data Deposit: 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” (Tri-Agency Research Data Management Policy, Frequently Asked Questions, Government of Canada 2022).

Metadata: Metadata are data about data—data that define and describe the characteristics of other data. Accurate and relevant metadata are essential for making research data findable. A principle to help determine what information should be included in metadata is the open archival information system model criterion that the information be “independently understandable.” “Independently understandable” means enough information has been provided in the metadata for someone else to be able to understand the data set without needing its creator explain it. (Tri-Agency Research Data Management Policy, Frequently Asked Questions, Government of Canada 2022).

Sensitive data: information that must be safeguarded against unwarranted access or disclosure. Sensitive data may include: personal information; personal health information; educational records; customer records; financial information; criminal information; geographic information (e.g., detailed locations of endangered species); confidential personnel information; information that is deemed to be confidential; information entrusted to a person, organization or entity with the intent that it be kept private and access be controlled or restricted; or information that is protected by institutional policy from unauthorized access. Sensitive data includes any information relating to an identified or identifiable natural person, organization or entity. (Sensitive Data Toolkit for Researchers Part 1: Glossary of Terms for Sensitive Data used for Research Purposes, Portage/CARL 2020)

Institutional Research Data Management 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. (Tri-Agency Research Data Management Policy, Frequently Asked Questions, Government of Canada 2022).