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Data Introduction: Introduction

Getting started to manage, find, share, understand ethics, create, and cite data.

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Use the tabs on the top of the page to help you get started to manage, find, share, understand ethics, create, and cite data.


What is Data?

Research data is any systematic collection of information that is used by researchers for analysis. Typical examples of data include: 

  • Observational data: data captured in real-time, usually irreplaceable

Examples: Sensor data, telemetry, survey data, sample data, neuroimages

  • Experimental data: data from lab equipment, often reproducible, but can be expensive

Examples: gene sequences, chromatograms, toroid magnetic field data

  • Simulation data: data generated from test models where model and metadata (inputs) are more important than output data. 

Examples: climate models, economic models

  • Derived or compiled data: data that is reproducible (but very expensive)

Examples: text and data mining, compiled database, 3D models, data gathered from public documents

Research data can also include video, sound, or text data, as long as it is used for systematic analysis. For example,  a collection of video interviews use to gather and identify gesture and facial expressions in a study of emotional responses to stimuli would be considered research data.

All research data must be appropriately structured and documented in order for it to be used effectively for analysis. Additionally, any unique programs or models needed to analyze the data should also be preserved.

(Retrieved from Kettering University Library)

Intro to Research Data

Types of Data 

Research data, unlike other types of information, "is collected, observed, or created, for purposes of analysis to produce original research results”.

(Retrieved from University of Edinburgh)

data types


Not Research Data is treated with caution and archived:

  • Trade secretes, commercial information
  • Sensitive data: identifying, personal, medical, political, GPS

Organizing Your Data

The following are tips to help you keep your data organized.

1. File Names

Use consistent file naming and appropriate descriptive text

Good Bad Reason

2016_01_29 or


29_1_2016 or


  • Opposite to Kazakhstan date format because the computer sorts numerically. You want your years grouped together instead of day of the month.
  • Always add a 0 (zero) because 01 is smaller than 1 so will sort more logically.
Survey_Answer_Percent Survey Answer %
  • Avoid spaces in your file name. Instead use the underscore _
  • Avoid special characters like ~ ! @ # $ % ^ & * ( ) ` ; < > ? , [ ] { } ' "

2. Version Control

Keep different versions and drafts series of a documents by adding _01 or V01 to the end or beginning of your files

3. File Format

Save your data in an "Open source", standard encoding formats to keep files accessible over time

  • Video: mov., mpeg.
  • Audio: wav., mp3
  • Data: csv., sas.
  • Images: tiff, jpeg 2000
  • Text: PDF/A

4. Logical folder structure

Metadata (descriptive information) about your data to make it searchable, discoverable, identifiable and usable in the future. Describe project and data with descriptive, structural, technical, administrative data to define what is this data, how it was collected, who, how, when, where can find and use it:

  • origin: experimental, observational, raw or derived, physical collections, models, images, etc.
  • type: integer, Boolean, character, floating point, etc.
  • file type
  • instrument, methods, software used
  • scripts or codes
  • license, reasons for an embargo, usage rights
  • name of the project and dataset title
  • abstract
  • investigators and collaborators with contact information
  • handle (DOI or URL)
  • citation
  • publication date
  • geographic description
  • subject/keywords
  • sponsors

5. Data Storage

  • Local hard drives (weak) (ex. personal or office desktop/laptop computer)
  • External storage devices (weak) (ex. USB drives; external hard drives)
  • Networked storage (okay) (ex. University servers)
  • Cloud storage services (okay) (ex. Microsoft, Google, Mendeley)
  • Archives and Repositories (ex. NURepository)

Best Practices

  • Back up all data - storing copies in more than one place
  • the Rule of Three: keep 3 copies of your data in at least 2 different locations with at least 1 offline
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