What is Research Data?

Last updated on 2026-02-05 | Edit this page

Overview

Questions

  • What is research data, and why is it important in academic and scientific research?
  • What are the different types of research data?
  • Where can research data come from?
  • What are the key components of research data management (RDM)?

Objectives

  • Data types
  • Sources of data
  • What is research data management (collection, storage, organisation, sharing etc)

Understanding data types

Alex is a researcher studying artworks in The Metropolitan Museum of Art. They have just received a dataset containing information about paintings, sculptures, textiles, drawings, and photographs from across the museum’s collection. Before Alex can analyse anything, they need to understand what kinds of data the dataset contains.

Even though the dataset looks like a simple spreadsheet, each column has an underlying data type, and knowing these types will help Alex (and you!) avoid errors, clean the dataset effectively, and choose the right kinds of visualisations or analyses.

Callout

Quick think

Before reading on, ask yourself:

  • What kinds of columns would you expect to see in a museum dataset?

Titles? Dates? Measurements? Artist names?

What is a data type?

A data type describes the kind of information a value represents. It tells the computer how to interpret the data:

  • Is it text?
  • A number?
  • A date?
  • A true/false flag?

When a dataset mixes formats (e.g., a date stored as text, or a number stored as a string), analysis becomes harder and mistakes are more likely. Alex will soon discover that the MET dataset contains a mix of clean values and some messy ones - for example, dates written as 1990, “ca. 1931”, and “07/06/2019 00:00”. Understanding data types helps Alex make sense of this variation.

Why data types matter

Data types are important because they affect how the computer reads, stores, and analyses information. If a column is stored in the wrong format, it can lead to errors, misleading results, or limitations in what you can do with the data.

In summary: knowing data types helps ensure the dataset is trustworthy, analysable, and ready for exploration

Common data types, examples from the MET Museum dataset

Here are some common data types you’ll encounter:

  • String: Text or characters, like "Claude Monet" or "Oil on canvas"
  • Integer: Whole numbers, like 1985, 42, or 0
  • Float: Decimal numbers, like 27.5 or 3.14
  • Boolean: True/False values, like TRUE, FALSE, Yes, No
  • Datetime: Calendar dates or timestamps, like "2020-01-01" or "12/11/2027"

Now that Alex has identified the main data types, let’s try classifying some values ourselves.

Challenge

Challenge

Challenge: What data type is it?

Alex found the following values in the MET dataset. For each one, decide what data type it currently is (it may not be what you think it should be!).

"Claude Monet"
1872
"ca. 1931"
"07/06/2019 00:00"
2021-07-14
27.5
"Oil on canvas"
TRUE

Write down the data type you would assign to each value.

"Claude Monet"           is a string
1872                     is an integer
"ca. 1931"               is a string (messy date)
"07/06/2019 00:00"       is a string (looks like a date but stored as text)
2021-07-14               is a date
27.5                     is a float
"Oil on canvas"          is a string
TRUE                     is a boolean

Notice how several values look like dates or numbers but are stored as text - this is common in real datasets and affects how we analyse them.

Identifying data types in your own dataset

So far, Alex has been looking at individual values. In practice, researchers usually work with whole datasets at once, often in spreadsheets, CSV files, or databases. Let’s think about how to identify data types when your data is laid out in columns.

Imagine Alex opens the MET Museum dataset in a spreadsheet. Each column represents a variable, and each row represents an artwork. The column headers might look something like this:

Object ID Title Artist Object Date Medium Is Public Domain Height (cm)
436121 Water Lilies Claude Monet 1906 Oil on canvas TRUE 200.5
459055 Untitled Unknown ca. 1931 Gelatin silver FALSE 27
12345 Portrait of a Man Rembrandt 07/06/2019 Oil on panel TRUE 98.0

Even without doing any analysis, Alex can already start identifying data types by asking a few simple questions about each column.

Look at the values, not just the column name

Column names are helpful, but they don’t always tell the full story. For each column, Alex checks:

  • Are the values mostly text, numbers, dates, or true/false?
  • Do all the values follow the same format?
  • Are there any “odd” entries that don’t match the rest?
Discussion

Challenge: Trust the name or the values?

Which of these columns would you inspect most carefully, and why?

  • ObjectID
  • Title
  • Artist
  • Object Date
  • Medium
  • Is Public Domain
  • Height_cm

Write down one reason based on the values you might expect to see.

Watch out for mixed data types in a single column

One of the most common problems in spreadsheets is mixing data types in the same column. Alex notices that Object Date contains:

  • 1906 (looks like an integer)
  • ca. 1931 (text)
  • 07/06/2019 (date-like text)

Even though these all describe dates, the computer will usually treat the entire column as text, which makes it hard to sort, filter, or calculate with.

Challenge

Challenge: Thinking about date formats

Look at the following values:

  • 1906

  • ca. 1931

  • 07/06/2019

  • Which values would be easy to convert?

  • Which values would be difficult or ambiguous?

  • What information is missing?

  • What assumptions might you need to make?

  • Some values only include a year, with no month or day.
  • Some values include uncertainty or approximation (e.g. “ca.”).
  • Some values depend on regional date conventions, making them ambiguous.
  • Converting dates may require assumptions, additional metadata, or decisions about how to represent uncertainty.

These are common issues in real datasets and will be addressed later in the course.

Use spreadsheet tools to check data types

Spreadsheets don’t just display data; they also interpret it. Most spreadsheet software gives visual and functional clues that indicate how values are stored, which can help you identify the underlying data type of a column.

Challenge

Challenge: What data type is this column?

Alex opens a different part of the MET dataset containing information about exhibitions and acquisitions.

Column name Values
Accession Number 1975.1, 2003.45a, 1988.12
Department European Paintings, Asian Art, Modern and Contemporary
Acquisition Year 1998, 2005, Unknown
Credit Line Gift of John Smith, Purchase, Bequest
On Display Yes, No
Gallery Number 802, 305, NA
Last Updated 2022-11-03, 03/07/2021, 15 Aug 2020

For each column:

  1. Decide what the data type currently is in the spreadsheet.
  2. Decide what the data type should ideally be for analysis.

Think about:

  • Mixed formats and missing values
  • Columns that look numeric but include text
  • Dates written in different ways

You do not need to clean the data, just identify the data types.

Column name Current data type Ideal data type
Accession Number String String
Department String String
Acquisition Year String (mixed) Integer or date
Credit Line String String
On Display String Boolean
Gallery Number String (mixed) Integer
Last Updated String (mixed dates) Date

Notes:

  • Accession Number looks numeric but contains letters and punctuation, so it must be text.
  • Acquisition Year mixes numbers with "Unknown", forcing the column to be stored as text.
  • On Display represents a yes/no value but is stored as strings.
  • Gallery Number includes numeric values and missing data (NA), which often results in text storage.
  • Last Updated represents dates, but inconsistent formats prevent it from being treated as a date automatically.

Where research data comes from

Research data can originate from many different sources. Understanding where data comes from helps researchers assess its reliability, limitations, and appropriate uses.

What is a data source?

A data source is the origin of the data - where it was collected, generated, or obtained. This could be a person, an instrument, a database, a sensor, or a computational process.

Discussion

Quick check

Which of the following could be considered a data source?

  • A spreadsheet downloaded from a website
  • A survey respondent
  • A microscope
  • A computer model

Answer: All of them.

Primary data

Primary data is data collected directly by the researcher for a specific research question. This might include surveys, interviews, experiments, field observations, or measurements. Primary data offers high relevance but often requires more time and resources to collect.

Discussion

Reflect

Have you ever collected primary data?

  • What made it valuable?
  • What made it challenging?

Secondary data

Secondary data is data that was originally collected by someone else for a different purpose and reused in a new study. Examples include government statistics, museum collections, published datasets, or previously published research data. Secondary data saves time but may not perfectly match the research question.

Generated or synthetic data

Generated or synthetic data is created through computational processes such as simulations, models, or algorithms. This includes data produced by climate models, agent-based simulations, or machine learning systems. Synthetic data is useful for testing hypotheses or protecting privacy, but depends heavily on the assumptions of the model.

Sensor and observational data

Sensor and observational data is collected automatically or systematically through observation, often over time. Examples include environmental sensors, satellite imagery, traffic counters, or wildlife cameras. This data can be large and continuous, requiring careful storage and management.

Data from instruments, tools, and experiments

Scientific instruments, laboratory equipment, or specialised tools produce this type of data. Examples include microscope images, sequencing data, spectrometer readings, or experimental measurements. Instrument data often requires calibration, metadata, and specialised software to interpret.

Discussion

Metadata moment

Why might metadata (for example, calibration settings or units) be especially important for this kind of data?

Examples of data sources in different disciplines

Different fields rely on different data sources. For example, historians may use archival documents, social scientists may use surveys or census data, natural scientists may collect experimental measurements, and digital humanities researchers may work with digitised texts or images.

Considering data quality and limitations

Every data source has limitations. Researchers should consider how the data was collected, potential biases, missing values, accuracy, and whether the data is appropriate for their research question. Understanding these limitations is essential for responsible analysis and interpretation.

Challenge

Classify it

You download a CSV file of air pollution measurements collected by a government agency.

Is this:

  • Primary data
  • Secondary data

Secondary data because you didn’t collect it yourself.

Challenge

Challenge: What kind of data source is this?

Below are several research scenarios. For each one, decide what type of data source is being described.

You may find that more than one category could apply; choose the best fit.

  1. A researcher records temperature and humidity every 10 minutes using a weather station on a university rooftop.

  2. A PhD student analyses digitised letters from a national archive that were scanned and published online by another institution.

  3. A social scientist designs and distributes a questionnaire to study students’ experiences of remote learning.

  4. A computer scientist creates a simulated dataset to test how an algorithm behaves under different conditions.

  5. A biologist collects gene expression data using a sequencing machine in a laboratory experiment.

  1. Sensor and observational data
    (Data collected automatically and repeatedly over time.)

  2. Secondary data
    (Data reused from an existing collection created by others.)

  3. Primary data
    (Data collected directly by the researcher for a specific study.)

  4. Generated or synthetic data
    (Data created through simulation or computational processes.)

  5. Data from instruments, tools, and experiments
    (Data produced by specialised scientific equipment.)

Introduction to research data management

What is Research Data Management (RDM)?

Why RDM matters

The research data lifecycle

RDM in practice: Alex and the MET Dataset

Figures

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Blue Carpentries hex person logo with no text.
You belong in The Carpentries!
Callout

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They are sometimes used to emphasise particularly important points but are also used in some lessons to present “asides”: content that is not central to the narrative of the lesson, e.g. by providing the answer to a commonly-asked question.

Key Points
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