Data Uploader

The Data Uploader application is developed as a KNIME Analytics Platform plug-in comprising of several workflow nodes. Its aim is to allow easy data mapping and import of non-IUCLID data into IUCLID in a controlled way, supporting all IUCLID data sections.

The Data Uploader supports the transformation of data on chemicals to the IUCLID format. It is especially useful for conversion of legacy toxicity data into harmonised templates which can be stored in a IUCLID database. This tool can be used by owners of chemical databases who would like to share their information in an internationally recognised format. The application is developed as a KNIME Analytics Platform plug-in, comprising of several workflow nodes.

The main Data Uploader workflow consists of 6 KNIME nodes:



  • Connection to a IUCLID database
  • Selection of relevant IUCLID documents and fields
  • Mapping with an external data source
  • Validation of the data against the IUCLID format
  • Generation of IUCLID files (.i6z)
  • Upload to IUCLID




The Data Uploader workflow with the Incremental update node supports a stepwise approach. Using this approach, you are able to upload a set of simple entities (for example Reference Substance, Legal entity, Literature Reference) that can be later used as references in SUBSTANCE or TEMPLATE documents. 




If you have questions about Data Uploader, or suggestions on how to improve it, please fill in the contact form on the ECHA Website here. Under Request type, select Data Uploader.

Data Uploader Knime plug-in (10th June 2022 - v1.0 - compatible with IUCLID 6.6)

You can download the Data Uploader Knime plug-in. The installation package contains the following information:

  • The installation instructions
  • The Data Uploader Knime plug-in
  • A use case for creating reference substances
  • A use case for creating a substance with an endpoint study record, including the use of the incremental update node (cf. stepwise approach towards building a substance dataset)

The use cases come with the relevant example data sets and Knime workflows.

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