Brightcon 2026, hackathon and courses

Many life cycle inventories are technically public but practically unusable: they are buried in PDFs, supplementary spreadsheets, or tables with weak metadata, unclear licensing, and no stable version history. Sharing data with interested parties is often hindered by differences in LCA software and data formats, background database versions, and other incompatibilities. CLIC (Curated Life cycle Inventory Collection) is a human-powered platform built to close that gap by treating LCI data as a managed lifecycle, from original data to a usable, published version, rather than a one-off file upload.

In CLIC, contributors create dataset drafts by uploading inventories exported from various LCA software (Brightway, Simapro, and OpenLCA). Before review, submissions are first turned into a harmonised format, and then machine checked for required metadata, file validity, rights and license declarations, and controlled classifications. Community reviewers and editors then curate datasets through defined states from draft to submitted, under review, accepted, and published. Published versions are immutable, semantically versioned, and exposed through stable public pages, metadata search with a classification tree, a read-only API, and generating exports tailored for specific LCA software (i.e., Brightway, OpenLCA, and SimaPro) and background database (e.g. ecoinvent) version.

This talk presents CLIC as a proof-of-concept case study for building pragmatic digital infrastructure for an open environmental modelling data-sharing platform using a lightweight Django/PostgreSQL architecture. The core argument is that accessibility and interoperability do not come solely from releasing files; they require validation, version-aware publication, reviewable curation, keeping datasets up to date and relevant, and explicit export pathways usable in common LCA software. By structuring inventories this way, the platform creates a reliable foundation for downstream LCA automation, including background-database relinking, duplicate detection, and automated data cleaning and harmonisation. The aim is not only to host open inventories, but to make them discoverable, trustworthy, and genuinely reusable in research and practice.