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Data Export 

Data export allows you to extract interview data from Interviewer or Fresco in standardized formats suitable for analysis in external tools.

Overview

After conducting interviews using Interviewer or Fresco, you need to export the data for analysis. Network Canvas provides flexible export options that allow you to export data in formats compatible with statistical software, network analysis programs, and spreadsheet applications.

The export process transforms your interview data—including ego attributes, alter attributes, and edge data—into structured files that you can import into analysis tools such as R, Python, SPSS, UCINET, Gephi, and many others.

Export locations

The export process differs slightly depending on which Network Canvas application you're using:

Interviewer (desktop applications):

Fresco (web-based):

File types

Network Canvas supports two file formats for data export. Choose the format that best suits your analysis needs.

CSV format

What it is: Comma-Separated Values (CSV) files are plain text files that represent tabular data. The CSV export creates multiple files, one for each type of entity in your network data.

Best for:

What you get:

The CSV export creates separate files for each entity type in your protocol:

Ego attribute list

Example structure:

ego_id,networkCanvasEgoUUID,networkCanvasCaseID,age,gender,sessionStart,sessionFinish
1,abc123-def456,case_001,34,Male,2025-01-15 10:30,2025-01-15 11:15
2,ghi789-jkl012,case_002,28,Female,2025-01-15 14:00,2025-01-15 14:45

Alter attribute list

Example structure:

ego_id,alter_id,networkCanvasUUID,networkCanvasEgoUUID,name,age,close_friend,contact_frequency
1,1,xyz789,abc123-def456,John,32,true,Daily
1,2,uvw456,abc123-def456,Sarah,29,true,Weekly
2,1,rst123,ghi789-jkl012,Michael,31,false,Monthly

Important notes:

Edge attribute list

Example structure:

ego_id,edge_id,edge_type,from,to,networkCanvasSourceUUID,networkCanvasTargetUUID,relationship_strength,frequency
1,1,friendship,1,2,xyz789,uvw456,strong,high

Important notes:

GraphML format

What it is: GraphML is an XML-based open standard for representing graph data. It stores the complete network structure in a single file.

Best for:

What you get:

Example structure:

<?xml version="1.0" encoding="UTF-8"?>
<graphml xmlns="http://graphml.graphdrawing.org/xmlns">
  <graph id="G" edgedefault="undirected">
    <node id="alter_1">
      <data key="name">John</data>
      <data key="age">32</data>
    </node>
    <node id="alter_2">
      <data key="name">Sarah</data>
      <data key="age">29</data>
    </node>
    <edge source="alter_1" target="alter_2">
      <data key="relationship_strength">strong</data>
    </edge>
  </graph>
</graphml>

Choosing between CSV and GraphML

ConsiderationCSVGraphML
Analysis focusStatistical analysis, ego network metricsNetwork visualization, graph analysis
File countMultiple files (one per entity type)Single file per session
ReadabilityHuman-readable, opens in ExcelXML format, needs specialized software
R compatibilityNative support, works with egoRRequires graph packages (igraph, network)
Data structureRelational (separate tables)Graph (nodes and edges)
Best for beginners✓ More familiar formatMay require learning curve

Our recommendation: Export data in both formats when first working with Network Canvas data. This allows you to experiment and determine which format works best with your analysis workflow.

Export options

Network Canvas provides several specialized export options that affect how your data is formatted. You can usually leave these at their default values unless you have specific requirements.

Merge sessions by protocol

This option combines all interview sessions into a single file (or set of files for CSV).

When enabled, this creates one combined file containing all interviews. For CSV exports, you get a single ego file, a single alter file per type, and a single edge file per type. For GraphML, this option is not applicable, as GraphML is always exported per-session. When disabled, the export creates separate files for each interview session.

Enable this option when you want all your data in one place for analysis, when you're importing into statistical software that works better with combined datasets, or when you're using the egoR package in R. Disable it when you want to analyze individual interviews separately, when you need to track which data came from which interview session, or when you're doing quality checks on individual interviews.

Even when merged, ego networks remain separate—the data is combined into shared files, but network boundaries are preserved through the ego identifiers.

Use screen layout coordinates

This option converts normalized node positions from Sociogram interfaces into screen-specific pixel coordinates.

Network Canvas stores node positions from Sociogram layout variables as normalized x/y coordinates (values between 0 and 1) with the origin at the top-left. This normalization allows layouts to be displayed on screens of different sizes while maintaining relative positions.

When enabled, this converts normalized coordinates to pixel coordinates based on screen size. In Interviewer, it uses the actual screen dimensions from the interview device. In Fresco, it uses a hardcoded size of 1920 x 1080 pixels. When disabled (the default), it exports normalized coordinates in the 0-1 range.

Enable this option when you want to recreate the exact visual layout as it appeared during the interview, when you're importing into visualization software that expects pixel coordinates, or when you need coordinates that match specific screen dimensions. Disable it when you want device-independent coordinates that work on any canvas size, when you're comparing layouts created on different screen sizes, or when you want maximum flexibility for visualization.

Example:

Normalized (default):

alter_id,name,x,y
1,John,0.25,0.33
2,Sarah,0.75,0.67

Screen coordinates (1920x1080):

alter_id,name,x,y
1,John,480,356
2,Sarah,1440,724

Additional export considerations

Unique identifiers: Network Canvas generates unique identifiers (UUIDs) for all entities:

These identifiers allow you to:

Session metadata: Ego files include valuable metadata:

Export workflows

Exporting from Interviewer

  1. Open Interviewer
  2. On the start screen, click the Manage or Export Sessions card
  3. Select the interview sessions you want to export
  4. Click the export button
  5. Choose your file type (CSV or GraphML)
  6. Configure export options if needed
  7. Choose a save location:
    • Windows/macOS/Linux: Standard file browser appears
    • iOS/Android: Share dialog appears—choose your storage location

Tips:

Exporting from Fresco

  1. Log into your Fresco dashboard
  2. Navigate to the interviews page
  3. Select interviews to export (or use Export all option)
  4. Click the Export selected button
  5. Choose your file type (CSV or GraphML)
  6. Configure export options if needed
  7. Click Start export process
  8. Download the exported file when ready

Tips:

Data structure details

Understanding the ego-alter-edge structure

Network Canvas organizes personal network data using a standard ego network structure:

Ego (participant)
├── Alter 1 (network member)
├── Alter 2 (network member)
├── Alter 3 (network member)
└── Edges (relationships between alters)
    ├── Alter 1 → Alter 2
    └── Alter 2 → Alter 3

Ego level: One row per participant, containing:

Alter level: Multiple rows per participant (one per nominated alter), containing:

Edge level: Relationships between alters within each ego network, containing:

Working with multiple entity types

If your protocol defines multiple node types (e.g., "Person" and "Place"), the export structure reflects this:

CSV export:

GraphML export:

Handling missing data

Network Canvas handles missing data as follows:

CSV format:

GraphML format:

Best practice: Document your protocol's required vs. optional fields so you know which missing values are expected.

Using exported data

In R (statistical analysis)

Network Canvas CSV exports are designed to work seamlessly with R, particularly the egoR package:

library(egoR)

# Read Network Canvas data
ego_data <- read_egonet(
  egos = "path/to/ego.csv",
  alters = "path/to/alters_Person.csv",
  edges = "path/to/edges_friendship.csv"
)

# Or use ideanet package (see tutorial)
library(ideanet)
nc_data <- nc_read(
  path = "path/to/export/folder",
  protocol = "path/to/protocol.netcanvas"
)

See also: Working with Network Canvas Data in R

In Python

import pandas as pd
import networkx as nx

# CSV approach
egos = pd.read_csv('ego.csv')
alters = pd.read_csv('alters_Person.csv')
edges = pd.read_csv('edges_friendship.csv')

# GraphML approach
G = nx.read_graphml('case_001_session_123.graphml')

In network visualization software

Gephi:

  1. Open Gephi
  2. File → Open → Select GraphML file
  3. Graph displays with all attributes available

UCINET:

  1. Import GraphML or CSV
  2. Convert to UCINET format if needed
  3. Run network analysis procedures

In spreadsheet software

Excel, Google Sheets, LibreOffice Calc:

  1. Open CSV files directly
  2. Each file opens as a separate spreadsheet
  3. Use ego_id to link across files
  4. Pivot tables and formulas work with the data

Best practices

During data collection

  1. Export frequently: Don't wait until all interviews are complete
  2. Test exports early: Export test data to verify format meets your needs
  3. Verify data structure: Check that all expected variables appear in exports
  4. Document your protocol: Keep notes on variable names and their meanings

File organization

  1. Create dedicated folders: Keep exports organized by study, date, or wave
  2. Use consistent naming: Develop a naming convention for export folders
  3. Version control: Keep track of which protocol version produced which exports
  4. Backup regularly: Store exports in multiple locations (local + cloud)

Data integrity

  1. Verify counts: Check that the number of participants matches expectations
  2. Check for duplicates: Ensure case IDs are unique if intended
  3. Validate ranges: Spot-check that variable values are within expected ranges
  4. Review metadata: Session times can help identify problematic interviews

Preparing for analysis

  1. Export both formats initially: Determine which works best for your workflow
  2. Document transformations: Keep notes on any data cleaning or recoding
  3. Preserve raw exports: Keep original exports before any modifications
  4. Create codebooks: Document variable meanings, especially categorical values

Troubleshooting

Common export issues

Problem: Export button is grayed out

Problem: Exported files are empty

Problem: Can't find exported files

Problem: CSV files won't open in Excel

Problem: Missing variables in export

Problem: Alter IDs don't match between files

Data quality checks

After exporting, verify:

  1. Record counts match expectations

    • Number of ego records = number of completed interviews
    • Alter counts seem reasonable
    • Edge counts make sense given network size

  2. Variable completeness

    • Required fields are populated
    • Optional fields show expected missingness
    • No unexpected missing data patterns

  3. Value ranges

    • Categorical variables use expected categories
    • Numerical values within realistic ranges
    • Dates are reasonable

  4. Identifier integrity

    • Case IDs are unique (unless repeat interviews intended)
    • UUIDs are present for all entities
    • Ego-alter-edge linkages are valid

Related concepts

Next steps

Now that you understand data export, you can:

  1. Export test data from a sample interview to verify format
  2. Practice importing exported data into your analysis software
  3. Review the Working with Data tutorial for R analysis workflows
  4. Plan your data organization strategy for your study