Use the Data Tools Directly¶

Upload your CSV. Get FAIR-compliant data back. No CKAN account required.

The Mat-O-Lab data tools run as public web services. You can submit a CSV file — say, a tensile test table from your lab — and receive a structured, machine-readable output within seconds. This guide walks you through each tool and a complete worked example using real steel measurement data.

What you need before you start¶

Your CSV file, or a public link to it (e.g. a GitHub raw URL or a direct download link from your lab system)
A mapping file (.yaml) — either one your data curator has prepared for you, or one generated by MapToMethod (see below)

No installation. No account. No command-line knowledge needed.

The three tools¶

Tool	What it does	Web UI	API explorer
CSVToCSVW	Reads your CSV and adds structured column descriptions	csvtocsvw.matolab.org	/api/docs
MapToMethod	Generates the mapping file that links your columns to standard concepts	maptomethod.matolab.org	/api/docs
RDFConverter	Applies the mapping and produces FAIR-compliant output	rdfconverter.matolab.org	/api/docs

Run them in order — CSVToCSVW first, RDFConverter last. MapToMethod is needed only when setting up a new measurement type.

Step 1 — Annotate your CSV (CSVToCSVW)¶

Go to csvtocsvw.matolab.org.

CSVToCSVW web UI — URL input, file upload, and Start Conversion button

You will see a form with a field labelled URL Data File.

Paste the URL of your CSV file into that field, then click Execute.

The service reads your CSV columns (names, units, separators) and returns an annotated description file in JSON-LD format — a standard way of attaching meaning to your data. Save this file; you will need it in Step 3.

Worked example — INSTRON tensile test data

This real CSV from the IOFMaterialsTutorial repository contains three columns: Zeit [s], Maschine [mm], and Kraft [kN] — time, displacement, and force from a steel tensile test.

Try it yourself — Step 1: Annotate a CSV

Web UIcurl

Open csvtocsvw.matolab.org
Find the annotate form

Fill data_url:

https://github.com/Mat-O-Lab/CSVToCSVW/raw/main/examples/example2.csv

Leave encoding as auto
Click Execute

The response is a JSON-LD file — your CSVW. Save it or copy the URL from the response header; you need it in Step 3.

Local file? Use the annotate_upload form instead — upload the file directly. Same result, no public URL needed.

curl -X POST "https://csvtocsvw.matolab.org/api/annotate?return_type=json-ld" \
  -H "Content-Type: application/json" \
  -d '{"data_url": "https://github.com/Mat-O-Lab/CSVToCSVW/raw/main/examples/example2.csv",
       "encoding": "auto"}' \
  --output example2-metadata.json

For a local file:

curl -X POST "https://csvtocsvw.matolab.org/api/annotate_upload?return_type=json-ld" \
  -F "file=@/path/to/your/sample.csv" \
  --output sample.csvw.json

✓ Expected: a JSON-LD file with @type: csvw#TableGroup and columns each carrying a qudt:unit annotation.

Step 2 — Explore column types (MapToMethod)¶

When to use this step

This step is used when setting up a new type of measurement — to understand what standard concepts your columns correspond to. If your data curator has already prepared a mapping file for your measurement type, you can skip to Step 3.

Go to maptomethod.matolab.org.

MapToMethod web UI — metadata URL, template URL, type selector panels, and Start Mapping button

The types form shows you which categories of scientific concepts appear in your annotated file. The entities form lists your individual columns with their internal names — the identifiers you will need when building a mapping.

Try it yourself — Step 2: Explore column types

Web UIcurl

Open maptomethod.matolab.org
Find the types form

Fill url:

https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json

Click Execute

The result lists RDF type IRIs found in the file — e.g. csvw#Column, qudt/DerivedUnit. This tells you what the service can map.

To generate a mapping, use the mapping form. You need the CSVW URL, a template .ttl URL (from your data curator), and a column-to-slot assignment. See Author a Mapping for the full walkthrough.

Explore types:

curl "https://maptomethod.matolab.org/api/types?url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json"

✓ Expected:

["http://qudt.org/schema/qudt/DerivedUnit", "http://www.w3.org/ns/csvw#Column",
 "http://www.w3.org/ns/csvw#TableGroup", "http://www.w3.org/ns/prov#Activity",
 "http://www.w3.org/ns/prov#SoftwareAgent"]

Generate a mapping (IOFMaterialsTutorial example):

curl -X POST "https://maptomethod.matolab.org/api/mapping" \
  -H "Content-Type: application/json" \
  -d '{
    "data_url":     "https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json",
    "template_url": "https://github.com/Mat-O-Lab/IOFMaterialsTutorial/raw/main/LengthMeasurement.ttl",
    "predicate":    "http://purl.obolibrary.org/obo/RO_0010002",
    "map": {
      "table-1-LengthMm": "https://github.com/Mat-O-Lab/IOFMaterialsTutorial/raw/main/LengthMeasurement.ttl/LengthData"
    }
  }' \
  --output my-mapping.yaml

✓ Expected: a YARRRML .yaml file ready for use in Step 3.

See Author a Mapping for a full walkthrough of building the mapping from scratch.

Step 3 — Convert to FAIR data (RDFConverter)¶

Go to rdfconverter.matolab.org.

RDFConverter web UI — YARRRML mapping URL, optional data source URL, and Start Conversion button

First, validate your mapping (recommended):

Use the checkmapping form. Paste the URL of your CSVW file and the URL of your mapping file into the two fields, then click Execute.

The result shows two numbers:

rules_applicable — how many mapping rules matched your data
rules_skipped — how many rules were skipped because they did not match

If rules_skipped is 0, everything matched. If it is greater than 0, check Author a Mapping — Troubleshooting before continuing.

Then, produce the output:

Use the createrdf form. Paste the mapping URL and choose turtle as the return type, then click Execute.

Try an existing mapping on your own data

The Optional: Data Source URL field lets you override the data URL that is hardcoded inside the mapping file. This means you can take any published mapping (e.g. from the real datasets table below), point it at your own similarly-formatted CSVW, and test whether the mapping works for your data — without modifying the mapping file at all. Leave the field empty to use the data URL embedded in the mapping.

This is also exactly how the CKAN integration works. For CSV uploads, CKAN generates the CSVW and then calls RDFConverter with the mapping URL and the CSVW URL as the data source override. For Catena-X JSON payloads, CKAN passes the actual JSON file URL as the override — this is what lets a single SAMM mapping file apply to every incoming payload and automatically identify which SAMM aspect model it belongs to.

The service applies the mapping and returns a .ttl file — a FAIR knowledge graph. This file contains your measurement data enriched with standardised scientific concepts, ready for sharing, archiving, or querying.

Try it yourself — Step 3: Validate and convert

Web UIcurl

Validate first (recommended):

Open rdfconverter.matolab.org
Find the checkmapping form

Fill mapping_url:

https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-map.yaml

Fill data_url:

https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json

Click Execute

✓ Expected: rules_applicable: 1, rules_skipped: 0

Then produce the knowledge graph:

Find the createrdf form
Fill the same mapping_url and data_url as above
Set return_type to turtle
Click Execute

The result is your .ttl knowledge graph — download or copy the content.

Validate:

curl -X POST "https://rdfconverter.matolab.org/api/checkmapping" \
  -G \
  --data-urlencode "mapping_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-map.yaml" \
  --data-urlencode "data_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json"

✓ Expected: {"rules_applicable": 1, "rules_skipped": 0}

Convert:

curl -X POST "https://rdfconverter.matolab.org/api/createrdf?return_type=turtle" \
  -G \
  --data-urlencode "mapping_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-map.yaml" \
  --data-urlencode "data_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json" \
  --output measurements-joined.ttl

✓ Expected: a Turtle file with PMDco process nodes and QUDT quantity values.

Real datasets to explore¶

These are complete, published examples you can use immediately in any of the forms above:

Dataset	CSV file	Mapping	FAIR output
IOFMaterialsTutorial — steel length measurements	measurements.csv	measurements-map.yaml	measurements-joined.ttl
BAMresearch DF-TEM-PAW — particle detection runs	detection_runs.csv	detection_runs-map.yaml	detection_runs-joined.ttl

For developers and scripted pipelines¶

The web forms above call the same HTTP API endpoints that can be called from scripts or CI/CD pipelines. If you are comfortable with command-line tools, the sections below show how to run the same steps using curl.

CSVToCSVW — annotate from URL¶

curl -X POST "https://csvtocsvw.matolab.org/api/annotate?return_type=json-ld" \
  -H "Content-Type: application/json" \
  -d '{"data_url": "https://github.com/Mat-O-Lab/CSVToCSVW/raw/main/examples/example2.csv",
       "encoding": "auto"}' \
  --output example2-metadata.json

For private (local) files, use the upload endpoint:

curl -X POST "https://csvtocsvw.matolab.org/api/annotate_upload?return_type=json-ld" \
  -F "file=@/path/to/local/sample.csv" \
  --output sample.csvw.json

MapToMethod — explore types and generate a mapping¶

curl "https://maptomethod.matolab.org/api/types?url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json"

["http://qudt.org/schema/qudt/DerivedUnit", "http://www.w3.org/ns/csvw#Column",
 "http://www.w3.org/ns/csvw#TableGroup", "http://www.w3.org/ns/prov#Activity",
 "http://www.w3.org/ns/prov#SoftwareAgent"]

curl -X POST "https://maptomethod.matolab.org/api/mapping" \
  -H "Content-Type: application/json" \
  -d '{
    "data_url":     "https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json",
    "template_url": "https://github.com/Mat-O-Lab/IOFMaterialsTutorial/raw/main/LengthMeasurement.ttl",
    "predicate":    "http://purl.obolibrary.org/obo/RO_0010002",
    "map": {
      "table-1-LengthMm": "https://github.com/Mat-O-Lab/IOFMaterialsTutorial/raw/main/LengthMeasurement.ttl/LengthData"
    }
  }' \
  --output my-mapping.yaml

RDFConverter — validate and convert¶

curl -X POST "https://rdfconverter.matolab.org/api/checkmapping" \
  -G \
  --data-urlencode "mapping_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-map.yaml" \
  --data-urlencode "data_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json"

{"rules_applicable": 1, "rules_skipped": 0}

curl -X POST "https://rdfconverter.matolab.org/api/createrdf?return_type=turtle" \
  -G \
  --data-urlencode "mapping_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-map.yaml" \
  --data-urlencode "data_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json"

{
  "filename": "measurements-metadata-joined.ttl",
  "graph": "@prefix iof: ...\n<.../table-1-LengthMm> ...",
  "num_mappings_applied": 1,
  "num_mappings_skipped": 0
}

Full pipeline shell script¶

CSV → CSVW → validate → RDF in one script, using real IOFMaterialsTutorial data:

#!/usr/bin/env bash
set -e

CSV_URL="https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements.csv"
MAPPING_URL="https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-map.yaml"

# Step 1: Annotate CSV → CSVW JSON-LD
curl -s -X POST "https://csvtocsvw.matolab.org/api/annotate?return_type=json-ld" \
  -H "Content-Type: application/json" \
  -d "{\"data_url\": \"${CSV_URL}\", \"encoding\": \"auto\"}" \
  --output measurements-metadata.json
echo "✓ CSVW: measurements-metadata.json"

# Step 2: Validate mapping (dry-run)
CHECK=$(curl -s -X POST "https://rdfconverter.matolab.org/api/checkmapping" \
  -G \
  --data-urlencode "mapping_url=${MAPPING_URL}" \
  --data-urlencode "data_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json")
echo "✓ Check: ${CHECK}"

# Step 3: Apply mapping → joined RDF
curl -s -X POST "https://rdfconverter.matolab.org/api/createrdf?return_type=turtle" \
  -G \
  --data-urlencode "mapping_url=${MAPPING_URL}" \
  --data-urlencode "data_url=https://raw.githubusercontent.com/Mat-O-Lab/IOFMaterialsTutorial/main/measurements-metadata.json" \
  --output measurements-joined.ttl
echo "✓ Knowledge graph: measurements-joined.ttl"

Next: Full API Reference — all endpoints with parameters and response schemas.