About dataset sources

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Understand dataset sources

A dataset contains structured data arranged in records (rows) and fields (columns). For example, a dataset could contain 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; as in the example below:

Respondent Age Sex Question 1 response Question 2 response
Anna 29 Female I think there should be more car-free zones. Electric buses and taxis would help reduce pollution in the inner city.
Jack 31 Male Pedestrians need to feel safe. There should be better lighting and more police. We should create more green spaces.


The data in a dataset source must be imported from a spreadsheet, database or text file—you cannot create or edit a dataset inside NVivo.

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


Form View (below) shows only one record at a time, laid out as a form:


1 Classifying fields—contain information about your data—for example, the age and sex of survey respondents. Classifying fields have a grey background. Refer to Learn about codable and classifying fields (columns) for more information.

2 Codable fields—contain the information you want to analyze—for example, responses to open-ended survey questions. Codable fields have a white background. Refer to Learn about codable and classifying fields (columns) for more information.

3 Table and Form View tabs—use these tabs to switch between Table View and Form View.

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Open and navigate dataset sources

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

When the dataset is open in Detail View, you can switch between Table View (view all records) and Form View (view one record at a time).

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 Table View, and the first field in Form View. If you sort the dataset by the values in the ID column, the dataset is displayed in the order that the records were imported into NVivo.

You can navigate from record to record on Table or Form View by using the navigation buttons—you can move to the first, previous, next or last record.


1 Move first

2 Move previous

3 Current position

4 Move next

5 Move last

When you click in the Current position box, you can type a record number and then press ENTER to navigate to that record. The record number is counted sequentially from the beginning of the records as currently visible in Table View. Hidden records are not counted—the Status bar indicates if any records are hidden (filtered) ui_status_bar_filtered_icon.gif  or if all records are visible (the dataset is unfiltered) ui_status_bar_unfiltered_icon.gif. The record number does not correspond to the ID value or any other field value.

You can also:

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What can I do in a dataset?

When working in a dataset source you can:

Refer to Approaches to analyzing datasets for more information on various methods you can use to analyze a dataset source.

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Preparing data for import

Before you import data to create a new dataset source, you should consider how you want use the data in NVivo.

You cannot change the data after you have imported it into NVivo, so before import, you should check that:

If your dataset contains survey responses, and you want to create a case node for each respondent, then the dataset must contain a unique identifier that identifies the responses of each individual. A unique identifier could be the respondent's name, however, in a large survey, names may not be unique. For uniqueness and to protect the identity of your respondents, you may prefer to assign each respondent a unique ID number. You can then gather all responses of an individual respondent to a single node—refer to Approaches to analyzing datasets for more information.

If you have a very large amount of data to import and analyze, it is a good idea to experiment with a subset of the data. If you import a small amount of data, you can experiment with the various approaches to analyzing a dataset.  Once you are confident that you have imported the data in a way that supports your analysis, then you can import the entire dataset, and commence coding in earnest. Make sure you delete the sample dataset that you used for experimental purposes.

Refer to Import datasets for more information on preparing to import a dataset.

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Learn about codable and classifying fields

When you import data to create a new dataset, you can choose the 'analysis type' for each field (column)—you can select 'codable' or 'classifying'. Refer to Import datasets (Consider how you want to use your data in NVivo) for more information.

The following table compares codable and classifying fields:

Comparison Codable fields Classifying fields
Type of content Textual content that you want to analyze—for example, survey responses to open-ended questions such as What do you think is the most important environmental issue in your local area? 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, your survey might include questions that are answered by choosing a point on a 'strength of agreement scale' containing points ranging from Strongly Disagree to Strongly Agree.

Data types

Text or source shortcut

Text, integer, decimal, date, time, date/time, or boolean

Background color



Edit content No No
Code content Yes No
Use values to build node hierarchies No Yes
Use values to populate node classification attribute values No Yes
Annotate & link Yes No
Query Yes No (see note below)
Sort & filter No Yes

NOTE You cannot directly query the values of classifying fields, but you can use these values to create nodes or attribute values, and use the nodes or attribute values when you run queries, generate charts or other visualizations. Refer to Approaches to analyzing datasets for more information.

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Learn about datasets that contain source shortcuts

Databases can store 'binary objects', including images, documents, audio or video files. If you import a database table that includes 'binary objects', any image, document, audio or video files in supported formats (except plain text files) will be imported into NVivo.  

Binary objects are imported into NVivo as separate source files, and stored in a folder at the same location as the dataset. The dataset displays an icon representing the source:

Source type Icon
Document ui_icon_document.gif
Picture ui_icon_picture.gif
Audio ui_icon_audio.gif
Video ui_icon_video.gif

The icon is a shortcut to the source—if you click the icon, the source opens in Detail View. If you code the source shortcut, the entire source is coded at the node.

If you move the source to a new project folder location, the source shortcut in the dataset is updated.  If you delete the source, the source shortcut is deleted from the dataset.

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