Datasets (NVivo 15 Mac)

NVivo datasets store structured survey data; codable fields analyze responses, classifying fields store metadata for cases/attributes.

Apr 1, 2026
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Datasets (NVivo 15 Mac)

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Analyzing surveys (NVivo 15 Mac)

Autocode Datasets (NVivo 15 Mac)

Datasets (NVivo 15 Mac)

A dataset contains structured data arranged in rows and columns. Datasets are created by importing data, and cannot be edited inside NVivo.

You can create datasets by importing:

Microsoft Excel spreadsheets
NCapture files containing data collected from Facebook and Twitter.

When you import NCapture files containing data from Facebook or Twitter, the fields in the dataset are predetermined—based on the type of social media data you are importing.

The table below displays an example of a dataset containing survey responses. Each record (row) represents a single survey respondent. The fields (columns) contain demographic information about the respondent or their responses to the survey questions.

Example - Survey results

Q1 Name	Q2 Age	Q3 College educated	Q4 How can we improve road safety?	Q5 Other suggestions for improvement?
Anna	29	Yes	I think there should be more car-free zones.	Electric buses and taxis would help reduce pollution in the inner city.
Jack	31	No	Pedestrians need to feel safe. There should be better lighting and more police.	We should create more green spaces.

If you autocode this dataset, you must select the columns (or values) that you want to use to create your codes.

Before you import

Consider how you want to use your data in NVivo

You cannot change the analysis type (codable or classifying) of a column after import, so you should decide how you want to use your data before you import a dataset.

Open-ended questions that contain data you want to analyze are created and coded to codes (codable)—for example, responses to open-ended survey questions such as How do you think we can reduce our carbon emissions?

Close-ended questions that describe your data (metadata) are created as attributes (classifying)—for example, the ID number, Age and Annual Income of your survey respondents. Values in classifying fields:

Provide context when you view coded dataset content in a code.
Can be used to build case structures that group your codable content—for example, by Age or Sex.
Can be used to create and classify cases that represent the subjects of your research. For example, if you create a 'person' case for a survey respondent, you can use the classifying field values Age or Sex as attribute values on the case.

If you want to create cases that represent the subjects in your research—you will need a unique identifier for each subject in your data. If your data does not contain a unique identifier that you can use as a code name, you could add a unique ID number for each subject. This identifier should be imported as a classifying field.

Gather your data

You cannot add additional rows or columns to a dataset after import, so it is important to gather your data before you start the import operation.

For each dataset that you want to create, your data must be gathered into a Microsoft Excel spreadsheet (.xlsx or .xls).

You cannot select multiple worksheets (within a spreadsheet), so the data you want to import must be gathered into a single worksheet.

The maximum amount of data that can be imported into a single dataset is 256 fields (columns) and 100,000 records (rows).

Import data from spreadsheets

Prepare an Excel spreadsheet for import

Open the spreadsheet file that contains your data in Microsoft Excel.
Ensure that the data is gathered into a single worksheet.

Review your data as described below:

Element	Description
Unsupported characters	If the cells contain any of the following characters, you should remove these characters before importing: `& @ * $ \| % ~ - + / \`
Blank rows	Remove any blank rows within the data.
Extraneous content	Your file should contain only the rows you want to import, and (optionally) a 1st row containing field labels. Any other content should be removed.
Data types	Make sure every value in a field contains the same type of data. During the import operation, NVivo scans the first 25 rows of your file to determine what data types are appropriate for each field. If you choose to import a field using the date, time, date/time, Boolean, integer or decimal data types, all the rows in your text file must contain valid data for that data type. If a row after the 25th line, contains invalid data, the import operation will terminate with errors.
Merged cells	Merged cells can cause errors during the import operation. We recommend that you do not import spreadsheets containing merged cells.
Calculated values	If a cell displays a calculated value, the displayed value (not the formula) is imported into the dataset.
Error values	If any of the cells in your worksheet display error values such as #NUM or #DIV, you should correct them before you start the import operation. Columns containing error values can only be imported as text fields.
Buttons and checkboxes	If any cells contain controls such as buttons or checkboxes the spreadsheet cannot be imported. You should remove these controls before import.

Close the spreadsheet. Keeping the file open may cause errors during the import operation.

Import data and create a new dataset

The Import Dataset Assistant guides you through the import process, helping ensure that data is imported the way you want.

On the Import tab, click Dataset.
Select the spreadsheet file that contains the data you want to import.
Follow the instructions in the Import Dataset Assistant.

Import Dataset Assistant steps

Assistant step	Description
Select the worksheet to import	This step is only displayed if your spreadsheet contains multiple worksheets—select the worksheet that contains the data you want to import. Check the preview to make sure you have selected the right worksheet.
Specify how you want the data to be interpreted	Check the preview to make sure that the dates, times and number in your data are correctly interpreted. You can use the options on this step of the Wizard to change the way the data is interpreted, and to indicate whether the first row of your data contains column headings (field names). Time Delimiter If the hours, minutes and seconds in your times are separated by a delimiter character—for example, a colon (:)—enter the delimiter. Date Delimiter If the days, months and years in your dates are separated by a delimiter character—for example, a slash (/)—enter the delimiter. If the days, months and years are not separated by a delimiter, clear this field. Date Format The order of day, month and year in your dates—for example, if your file contains the date 10/05/2005 and you select MDY, NVivo interprets the date as 'October 05, 2005'. If you select DMY, NVivo interprets the same date as 'May 10, 2005'. When the data is imported into NVivo, it will be displayed in the date format that is specified in your system preferences (Language and Text). Four digit years Select this option, if the years in your dates include the century—for example '1999' or '2010'. If the years in your dates do not include the century, clear this check box. Decimal Symbol The decimal separator that is used in your numbers. When the data is imported into NVivo, it will be displayed with the decimal separator that is specified in your system preferences (Language and Text). First row contains field names Select this check box when the first row of your spreadsheet contains the names of your columns.
Specify information about each of the fields you are importing	On this screen you can choose which fields contain values that you want to code (codable fields) and which fields contain values that describe your data (classifying fields). By default, all fields (columns) are imported, and the analysis type (codable or classifying) is preselected based on the content in the first 25 rows of the data. The preview area shows the values in the first 25 rows and indicates how each field will be imported. Field Selection You can use these buttons to select or deselect all fields for import. For example, if you want to import only a few fields from a spreadsheet with many columns, you can deselect all fields, and then individually select the fields you want to import. Check the Field Options for each column because you cannot change this after the data has been imported. To view or change the field options for a column: Select the column by clicking the header in the preview area. Review the Field Options and make any changes. Field Name The field name that will be used for the currently selected field (column) when the new dataset is created. If the first row of your data does not contain column headings, the field names are set to Column A, Column B, Column C etc. You can change these field names to reflect the information in the column. Import Field Select this check box to import the field. If you clear this check box, the field is not imported. Analysis Type Choose whether this is a codable or classifying field. Codable Field Contains text you want to analyze—for example, responses to open-ended survey questions. Classifying Field Contains information about the data—for example, the ID number, Sex. Age or Location of survey respondents. You cannot code, query or annotate content in classifying fields. You cannot change the analysis type after the data has been imported, so you should consider how you want to use your data before you choose the analysis type. Data Type For codable fields, the data type is always 'Text' and cannot be changed. For classifying fields, the data type is preselected based on the values in the first 25 rows. If more than one data type can be used to store the values in the column, you can change the data type. Decimal Places When the decimal data type is selected, you can choose the number of decimal places—if you reduce the number of decimal places, the data will be truncated.
Enter a name, description for your new dataset, and enable/disable Autocode sentiment	You can change the name and optionally add a description. Autocode Sentiment You can choose to automatically detect and code sentiments in open-ended survey response. NOTE The Autocode Sentiment option is available only in NVivo 15.4.0 and later versions.

Work with datasets

Open and navigate dataset files

You can double-click a dataset in List View to open it in Detail View.

When you open a dataset, it opens in Table View (below)—the records and fields are displayed in a grid:

View a dataset as a table with rows and columns.

1 Classifying fields—contain information about your data—for example, the age and sex of survey respondents. Classifying fields have a gray background.

2Codable fields—contain the information you want to analyze—for example, responses to open-ended survey questions. Codable fields have a white background.

Click the expand icon (in the top right of the Detail View) to expand the Detail View and create more space to work. See Customize the workspace (NVivo 15 Mac)

Each row in a dataset has a unique record ID, based on the order in which it is imported. The ID is the first column in the Table pane.

Datasets of more than 100 records are divided into pages. Each page can contain up to 100 records. You can navigate from page to page by using the navigation buttons—you can move to the first, previous, next or last page.

Navigation buttons for a dataset

To navigate within a page, you can use scroll bars to move up and down, or left and right

What can I do in a dataset ?

When working in a dataset file you can:

Code or query the text in codable columns
Autocode to organize the data—for example, you could gather the survey responses for each participant in a case

Analyzing datasets

After importing a dataset youcan use a number of techniques to analyze the data—for example, you could:

Code the content—select the text in a codable field and code to new or existing codes.
Run a Word Frequency query to get a feel for what respondents are saying.
Autocode the dataset to make a code for each survey question and gather all the answers—a useful starting point for further exploration. Then autocode the dataset to make a case for each respondent and gather all of a respondent's answers.

Understand codable and classifying fields

When you import data from surveys, spreadsheets or text files, you choose identify closed-ended and open-ended questions. NVivo uses this information to identify the responses as 'codable' or 'classifying'.

You cannot change the analysis type (codable or classifying) of a response after import, so you should decide how you want to use your data before you import a dataset.

Fields that contain data that you intend to code and analyze should be stored as codable fields—for example, responses to open-ended survey questions, such as How do you think we can reduce our carbon emissions?

Fields that describe your data (metadata) should be stored as classifying fields—for example, the ID number, Age, Sex and Annual Income of your survey respondents. Values in classifying fields:

Provide context when you view coded dataset content in a code.
Can be used to build case structures that group your codable content—for example, by Age or Sex
Can be used to create and classify cases that represent the subjects of your research. For example, if you create a 'person' case for a survey respondent, you can use the classifying field values Age or Sex as attribute values on the case.

Comparison	Codable fields	Classifying fields
Type of content	Textual content that you want to analyze—for example, survey responses to open-ended questions	Values that describe the data—for example, in a set of survey responses, you may have classifying columns which contain the name, age or sex of the survey participants. Scaled responses—for example, answers on a scale from Strongly Disagree to Strongly Agree.
Data types	Text or file shortcut	Text, integer, decimal, date, time, date/time, or boolean
Background color	White	Gray
Edit content	No	No
Code content	Yes	No
Use values to build case and code hierarchies	No	Yes
Use values to populate case classification attribute values	No	Yes
Annotate & link	No	No
Query	Yes	No
Sort & filter	No	Yes

URL Name

NV15Mac-Content-files-datasets