Skip to Main Content

Research Data Management: Research Data & Research Data Management

A data management plan (DMP) is a written document that describes the data you expect to acquire or generate during the course of a research project, how you will manage, describe, analyze, and store those data, and what mechanisms you will use at the end

Research Data & Research Data Management

Research Data

Research data may be described as "... data collected, observed, or created for analysis purposes to produce original research results." 

Research data may be generated for different purposes and through different processes and may be divided into the following categories.  Each category may require a different type of data management plan. 

Observational 

  • captured in real-time
  • usually irreplaceable
  • examples include: sensor readings, survey results, telemetry, sample data, neurological images

Experimental

  • data from lab equipment
  • often reproducible (this can be expensive)
  • examples include: gene sequences, magnetic field readings

Simulation

  • data generated from test models
  • models and metadata where the input is more important than the output data
  • examples include: climate models and economic models.

Derived or compiled

  • reproducible (expensive)
  • examples include: text and data mining, 3D models

Reference or Canonical

  • a (static or organic) conglomeration or collection of smaller (peer-reviewed) datasets
  • most probably published or curated. 
  • examples include: gene sequence databanks, chemical structures, and spatial data portals. 

These data can come in many forms: text, numerical, multimedia, models, software, discipline-specific (i.e., FITS in astronomy, CIF in chemistry), or instrument-specific.

Research Data Management

Research data management, also known as Data Management, is the process of controlling the data generated during a research project. The outcome is usually a publication in the form of an article, report, thesis, dissertation, or similar document. 


Cartoon credit – Auke Herrema

 

Any research project will require some level of data management.  Funding agencies increasingly require researchers and scholars to plan and execute good data management practices.

Managing data or data management is an integral part of the research process.

It can be challenging, mainly when studies involve several researchers and/or are conducted from multiple locations.

How data is managed depends on the types of data involved, how data is collected and stored, and how it is used - throughout the research lifecycle.

The outcome of a research project depends in part on how well the raw data is managed.

Managing data helps the researcher to organize research files and data for easier access and analysis. It helps ensure the quality of the research. It supports the published results of the work and, in the long term, helps ensure accountability in data analysis.

Effective data management practices include:

  • Designating the responsibilities of every individual involved in the study.
  • Determining how data will be stored and backed up.
  • Implementing the data management plan.
  • Deciding how data will be dealt with through each modification of the study.