what is the data in research

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• Data Management

Defining Research Data

One definition of research data is: "the recorded factual material commonly accepted in the scientific community as necessary to validate research findings." ( OMB Circular 110 ).

Research data covers a broad range of types of information (see examples below), and digital data can be structured and stored in a variety of file formats.

Note that properly managing data (and records) does not necessarily equate to sharing or publishing that data.

Examples of Research Data

Some examples of research data:

• Documents (text, Word), spreadsheets
• Laboratory notebooks, field notebooks, diaries
• Questionnaires, transcripts, codebooks
• Audiotapes, videotapes
• Photographs, films
• Protein or genetic sequences
• Test responses
• Slides, artifacts, specimens, samples
• Collection of digital objects acquired and generated during the process of research
• Database contents (video, audio, text, images)
• Models, algorithms, scripts
• Contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
• Methodologies and workflows
• Standard operating procedures and protocols

Exclusions from Sharing

In addition to the other records to manage (below), some kinds of data may not be sharable due to the nature of the records themselves, or to ethical and privacy concerns. As defined by the OMB , this refers to:

• preliminary analyses,
• drafts of scientific papers,
• plans for future research,
• peer reviews, or
• communications with colleagues

Research data also do not include:

• Trade secrets, commercial information, materials necessary to be held confidential by a researcher until they are published, or similar information which is protected under law; and
• Personnel and medical information and similar information the disclosure of which would constitute a clearly unwarranted invasion of personal privacy, such as information that could be used to identify a particular person in a research study.

Some types of data, particularly software, may require special license to share.  In those cases, contact the Office of Technology Transfer to review considerations for software generated in your research.

Other Records to Manage

Although they might not be addressed in an NSF data management plan, the following research records may also be important to manage during and beyond the life of a project.

• Correspondence (electronic mail and paper-based correspondence)
• Project files
• Grant applications
• Ethics applications
• Technical reports
• Research reports
• Signed consent forms

Adapted from Defining Research Data by the University of Oregon Libraries.

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Research data management explained

What is research data.

Research data is any information that has been collected, observed, generated or created to validate original research findings.

Although usually digital, research data also includes non-digital formats such as laboratory notebooks and diaries.

Types of research data

Research data can take many forms. It might be:

• documents, spreadsheets
• laboratory notebooks, field notebooks, diaries
• questionnaires, transcripts, codebooks
• audiotapes, videotapes
• photographs, films
• test responses
• slides, artefacts, specimens, samples
• collections of digital outputs
• database contents (video, audio, text, images)
• models, algorithms, scripts
• contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
• methodologies and workflows
• standard operating procedures and protocols

Non-digital data

Non-digital data such as laboratory notebooks, ice-core samples and sketchbooks is often unique. You should assess the long-term value of any non-digital data and plan how you will describe and retain them.

You could digitise the materials, but this may not be possible for all types of data.

The University of Leeds research data repository (Research Data Leeds) describes digital materials and can also be used to create records for physical artefacts.

Please contact the team if you would like to discuss requirements for non-digital data.

Sources of research data

Research data can be generated for different purposes and through different processes.

• Observational data is captured in real-time, and is usually irreplaceable, for example sensor data, survey data, sample data, and neuro-images.
• Experimental data is captured from lab equipment. It is often reproducible, but this can be expensive. Examples of experimental data are gene sequences, chromatograms, and toroid magnetic field data.
• Simulation data is generated from test models where model and metadata are more important than output data. For example, climate models and economic models.
• Derived or compiled data has been transformed from pre-existing data points. It is reproducible if lost, but this would be expensive. Examples are data mining, compiled databases, and 3D models.
• Reference or canonical data is a static or organic conglomeration or collection of smaller (peer-reviewed) datasets, most probably published and curated. For example, gene sequence databanks, chemical structures, or spatial data portals.

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Research data management.

• What are research data?

Research data are defined as the  evidence  on which academic researchers build their analytic or other work HEFCE 2008

Research data are collected, observed or created, for the purposes of analysis to produce and validate original research results

Data can take many forms

• Still images, video and audio
• Survey results and interview transcripts
• Experimental observations
• Text corpuses
• Notebooks and lab books
• Models and software
• Can be created in a digital form
• Can be analogue that is converted to a digital form

The data lifecycle

The data lifecycle can extend well beyond the boundaries of a research project. From early planning stages to the long term storage of data and its reuse by others in the research community, there are discrete phases in the research data management cycle.

External guidance on research data

The UK Data Archive provides a very useful explanation of research data management activities at each stage of the research lifecycle.

• Open University Research Data Management Policy
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• Organising your files
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Data Module #1: What is Research Data?

Defining research data.

• Qualitative vs. Quantitative
• Types of Research Data
• Data and Statistics
• Let's Review...

Data Module Quick Navigation

Data Modules Table of Contents

#1 - What is Research Data? #2 - Planning for Your Data Use #3 - Finding & Collecting Data #4 -  Keeping Your Data Organized #5 -  Intellectual Property & Ethics #6 -  Storage, Backup, & Security #7 - Documentation

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Module created by Aaron Albertson, Beth Hillemann, & Ron Joslin.

Many people think of data-driven research as something that primarily happens in the sciences. It is often thought of as involving a spreadsheet filled with numbers. Both of these beliefs are incorrect. Research data are collected and used in scholarship across all academic disciplines and, while it can consist of numbers in a spreadsheet, it also takes many different formats, including videos, images, artifacts, and diaries. Whether a psychologist collecting survey data to better understand human behavior, an artist using data to generate images and sounds, or an anthropologist using audio files to document observations about different cultures, scholarly research across all academic fields is increasingly data-driven.

In our Data Literacy Modules, we will demonstrate the ways in which research data are gathered and used across various academic disciplines by discussing it in a very broad sense. We define research data as: any information collected, stored, and processed to produce and validate original research results. Data might be used to prove or disprove a theory, bolster claims made in research, or to further the knowledge around a specific topic or problem.

Other Definitions of Research Data

There are many different definitions of research data available. Here are just a few examples of other definitions. We share these examples to illustrate there is not universal consensus on a definition, although many similarities are apparent.

• U.S. Office of Management & Budget

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

• University of Edinburgh

"...recorded factual material commonly accepted in the scientific community as necessary to validate research findings..."  

• National Endowment for the Humanities

"...materials generated or collected during the course of conducting research..."

Research Data Formats

Research data takes many different forms.  Data may be intangible as in measured numerical values found in a spreadsheet or an object as in physical research materials such samples of rocks, plants, or insects. Here are some examples of the formats that data can take:

• Next: Qualitative vs. Quantitative >>
• Last Updated: Oct 14, 2024 2:59 PM
• URL: https://libguides.macalester.edu/data1

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What is data?

• Create a plan
• Find (re-use) data
• Analyse data

What is research data?

Research data  is any information that has been collected, observed, generated or created to validate original research findings. Research data may be arranged or formatted in such a way as to make it suitable for communication, interpretation and processing. Data comes in many formats, both digital and physical.

More information:

• What is research data? A useful definition from the University of Leeds
• What is research data? - PDF (3422 KB) Guide prepared by the Australian Research Data Commons (ARDC)

Video length: 36 sec

Common formats include:

• documents, spreadsheets
• laboratory notebooks, field notebooks, diaries
• questionnaires, transcripts, codebooks
• audiotapes, videotapes
• photographs, films
• test responses
• slides, artefacts, specimens, samples
• models, algorithms, scripts
• contents of an application (input, output, logfiles for analysis software, simulation software, schemas)
• methodologies and workflows
• standard operating procedures and protocols

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• Last Updated: Dec 4, 2024 3:32 PM
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Home » Primary Data – Types, Methods and Examples

Primary Data – Types, Methods and Examples

Table of Contents

Primary data is original information collected directly from firsthand sources to address specific research questions or hypotheses. Unlike secondary data, which is already collected and available from previous studies or databases, primary data is unique to the research being conducted. Researchers rely on primary data to gather relevant, targeted, and accurate information for their studies.

Primary Data

Primary data is raw data gathered directly from sources through various methods such as surveys, interviews, observations, and experiments. This type of data is typically specific to the needs of a study and provides up-to-date and original insights.

Key Features of Primary Data :

• Collected directly from sources, making it original and specific.
• Enables researchers to design data collection based on their unique objectives.
• Can be more time-consuming and costly compared to using secondary data.

Types of Primary Data

Primary data can be classified into several types based on the method of collection and the nature of the data. The main types include qualitative data and quantitative data .

1. Qualitative Data

Definition : Qualitative data is non-numerical and descriptive, focusing on the quality and nature of responses. It helps researchers understand thoughts, motivations, experiences, and social dynamics.

Characteristics :

• Descriptive, providing detailed insights into experiences or behaviors.
• Subjective and often exploratory.
• Typically collected through methods like interviews, focus groups, and observations.

Examples of Qualitative Data :

• Responses from in-depth interviews about consumer perceptions of a brand.
• Observational notes from studying classroom interactions.
• Descriptions of participants’ feelings and motivations during a focus group discussion.

2. Quantitative Data

Definition : Quantitative data is numerical and can be measured and analyzed statistically. This type of data helps researchers quantify variables and examine relationships among them.

• Numerical, allowing for statistical analysis.
• Objective and usually collected through structured methods.
• Provides measurable, comparable, and repeatable results.

Examples of Quantitative Data :

• Responses from a survey measuring customer satisfaction on a scale of 1 to 10.
• Number of hours worked per week collected from employee time sheets.
• Test scores of students to analyze the effectiveness of a teaching method.

Methods of Collecting Primary Data

Researchers can collect primary data using various methods, each suited to different types of data and research objectives. Key methods include surveys , interviews , observations , and experiments .

1. Surveys and Questionnaires

Definition : Surveys and questionnaires are tools for gathering information from a large group of respondents through a series of structured questions.

When to Use :

• Ideal for collecting quantitative data on attitudes, preferences, behaviors, and opinions.
• Useful for studies requiring data from a large and diverse sample.
• Design questions that align with research objectives, which can include closed-ended (quantitative) or open-ended (qualitative) questions.
• Distribute the survey online, via mail, or in person.
• Analyze responses for patterns or correlations.

Example : A company conducts a customer satisfaction survey asking clients to rate their recent purchase experience on a scale from 1 to 5.

2. Interviews

Definition : Interviews involve direct communication with participants to obtain detailed information on their views, experiences, or behaviors. They can be structured, semi-structured, or unstructured.

• Effective for gathering in-depth qualitative data.
• Suitable for exploring topics in detail, such as personal experiences or perceptions.
• Prepare questions that guide the conversation but allow flexibility for in-depth responses.
• Conduct interviews face-to-face, over the phone, or virtually.
• Record and analyze responses to identify themes or patterns.

Example : A researcher conducts semi-structured interviews with healthcare professionals to understand challenges in patient care.

3. Observations

Definition : Observation is a method where the researcher watches participants in their natural environment to gather data on behavior, interactions, or social processes.

• Suitable for studies focusing on real-time behaviors and social interactions.
• Commonly used in fields such as anthropology, sociology, and education.
• Identify the setting and behavior to observe.
• Decide between participant observation (researcher actively engages) or non-participant observation (researcher is an observer only).
• Record observations systematically, noting details about interactions, settings, and behaviors.

Example : A researcher observes interactions in a workplace to understand communication patterns and team dynamics.

4. Experiments

Definition : Experiments involve manipulating variables in a controlled setting to observe their effect on other variables. This method is often used in scientific research to establish cause-and-effect relationships.

• Suitable for testing hypotheses in controlled environments.
• Common in fields like psychology, medicine, and environmental science.
• Identify independent and dependent variables.
• Assign participants to control and experimental groups.
• Collect and analyze data to determine the effect of the manipulated variable.

Example : A researcher conducts a lab experiment to test the impact of a new teaching method on student performance, with one group using the new method and a control group using traditional methods.

Examples of Primary Data Collection in Research

• Method : Survey.
• Data : Customer feedback on product satisfaction, brand loyalty, and preferences.
• Outcome : Companies can use the data to adjust marketing strategies and improve products.
• Method : Observation.
• Data : Notes on student-teacher interactions and classroom engagement.
• Outcome : Insights into effective teaching practices and ways to improve student participation.
• Method : Experiment.
• Data : Health outcomes, such as blood pressure readings or recovery times after treatment.
• Outcome : Determine the effectiveness of treatments and medical interventions.
• Method : Interviews.
• Data : Personal accounts from community members regarding local environmental issues.
• Outcome : Understand the community’s concerns and perspectives on environmental policy.

Advantages of Primary Data

• Relevance and Specificity : Primary data directly addresses the research question and is tailored to the study’s needs.
• Up-to-Date Information : Data collected during the study is current, ensuring relevance and accuracy.
• Higher Control : Researchers control the data collection process, which improves data reliability and validity.

Disadvantages of Primary Data

• Cost and Time : Collecting primary data can be expensive and time-consuming, requiring significant resources.
• Limited Sample Size : Due to resource constraints, primary data studies may have smaller sample sizes, which can limit generalizability.
• Potential for Bias : Researchers or participants may unintentionally introduce bias during data collection or interpretation.

Tips for Collecting Primary Data

• Define Objectives : Clearly define research objectives to ensure the data collected is relevant and focused.
• Choose the Right Method : Select a data collection method that aligns with the research question and type of data needed.
• Ensure Ethical Compliance : Obtain informed consent and ensure data privacy and confidentiality.
• Pilot Test Instruments : Pilot test surveys or questionnaires to refine questions and ensure clarity.
• Document the Process : Keep detailed records of the data collection process to enhance transparency and reproducibility.

Primary data is invaluable for research, providing original insights and tailored information for specific studies. With methods like surveys, interviews, observations, and experiments, researchers can gather both qualitative and quantitative data to answer targeted questions. By selecting the appropriate type and method of data collection, primary data enhances the validity and relevance of research findings, allowing for impactful contributions to various fields.

• Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . SAGE Publications.
• Kothari, C. R. (2004). Research Methodology: Methods and Techniques . New Age International.
• Punch, K. F. (2013). Introduction to Social Research: Quantitative and Qualitative Approaches . SAGE Publications.
• Yin, R. K. (2017). Case Study Research and Applications: Design and Methods . SAGE Publications.
• Trochim, W. M., & Donnelly, J. P. (2008). The Research Methods Knowledge Base . Cengage Learning.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Data Management

Define research data.

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Research Data

• Research data is defined by the U.S. Office of Management and Budget as "the recorded factual material commonly accepted in the scientific community as necessary to validate research findings."
• This definition was adopted by Texas A&M University and included in the Standard Administrative Procedure: Responsible Stewardship of Research Data . 

What is Research Data?

• Examples of Research Data
• Other Records to Manage

Research data often includes the examples below, but may vary by discipline:

Text and numeric information

• Documents (text, Word), spreadsheets
• Research notes
• Laboratory notebooks, field notebooks, diaries
• Questionnaires, transcripts, codebooks
• Numeric information

Digital and physical objects 

Photos, films, and videos

Test responses

Charts and graphs

Protein or genetic sequences

Slides, artifacts, specimens, samples

Materials and other recorded information 

• Models, algorithms, scripts
• Content of an application (input, output, log files for analysis software, simulation software, schemas)
• Methodologies, protocols, and workflows

Adapted from  The Responsible Stewardship of Research Data , and  Defining Research Data  by North Carolina State Universities Libraries.

The following  records may be important to manage during and after a research project, but may not be required to be included in a data management plan:

• Grant applications
• Ethics applications
• Technical reports
• Research reports
• Signed consent forms

Adapted from  Defining Research Data  by North Carolina State Universities Libraries and  Defining Research Data  by the University of Oregon Libraries.

What is not considered data?

Examples include:.

• Preliminary analyses
• Drafts of scientific papers
• Plans for future research
• Peer reviews
• Communications with colleagues

Examples of restricted data:

• Trade secrets, commercial information, materials necessary to be held confidential by a researcher until they are published, or similar information which is protected under law; and 

personnel and medical information and similar information the disclosure of which would constitute a clearly unwarranted invasion of personal privacy, such as information that could be used to identify a particular person in a research study. 

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• Last Updated: Nov 8, 2024 10:33 AM
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