Life cycle assessment (LCA) has become a crucial technique for evaluating environmental impacts across various stages of emerging clean production technologies and sustainable processes. This assessment plays a key role in guiding decision-making and policy development. Recent years have seen a substantial increase in such LCA investigations in the peer-reviewed literature. However, only a small fraction of these studies has transparently reported the foreground life cycle inventory (LCI) data of the technology under investigation. The available data are often poorly organized and lack a standard structure and metadata making them ambiguous and non-reusable. This leads to a situation where LCA outcomes must be accepted at face value due to irreplicable results. Currently, there are no standardized methods for publishing and sharing foreground LCIs with the relevant contextual information. To address this gap, this work develops a novel, minimalistic, user-centric, machine-accessible, and extensible schema based on the Brightway LCA framework to share the foreground LCI of standard (non-temporal) LCA investigations. In this schema, an inventory is made up of five components: dataset, dataset properties, metadata, dependencies, and network. A Python-based open-source tool has also been developed to convert LCIs according to the specifications of this schema. The proposed schema and the tool are demonstrated for the exemplary use case of an aviation fuel supply chain. This schema can improve the reusability and reliability of LCI datasets, a first step towards transparent, replicable, and robust LCA studies.