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Workflow Paths


LINCS has developed a series of conversion workflows to cover the most common starting points for creating Linked Open Data (LOD).

All of the general information about contributing data to LINCS and the initial steps of expressing interest and completing the data intake interview process apply to all workflows.

The Four Conversion Workflows

Browse through the following four tabs for an overview of each workflow and to understand how to categorize your data. The rest of the pages in this conversion workflow documentation cover each individual conversion step in order. Each step contains these same four tabs so that you can tailor the instructions to your data.

Structured Data can take the form of spreadsheets (e.g., CSV, TSV, XSL, XSLX), relational databases, JSON files, RDF files, and XML files.

We count data as structured if:

  • The entities are all tagged individually (e.g., one entity per spreadsheet cell or XML element)

And the entities are connected, either:

  • In a hierarchical way (e.g., nested XML elements)
  • With relationships between entities expressed following some clearly-defined schema and data structure (e.g., spreadsheet headings relating columns of entities together)

Data Example

Here are data samples from two projects published with LINCS that began as structured data.

The Canadian Centre for Ethnomusicology data started as several spreadsheets with a row for each artifact.

CCEA-L1995.63Bamboo FluteEdmonton
CCEA1995.21Pair of Taiko DrumsShinano

The University of Saskatchewan Art Collection data began as an XML file with a parent element for each art object.

<?xml version="1.0" ?>
<rdf:RDF xmlns:rdf="">
<ObjectTitle>Portrait of Thomas Copland</ObjectTitle>
<ArtistName url="">Victor Albert Long</ArtistName>
<Medium url="">oil paint</Medium>
<Category url="">painting</Category>
<ArtistName url="">Lori Blondeau</ArtistName>
<Medium url="">inkjet print</Medium>
<Category url="">Photograph</Category>

Workflow Overview

This workflow is our most customizable and curatable because the entities and relationships are clearly defined in the source data. We typically create a custom conceptual mapping for each dataset, reusing past mappings where possible, and convert the data using the 3M mapping tool.