## 2 Challenges with Sharing LCA Data  
Sharing LCA expertise between agencies through the publication, exchange, and reuse of LCI data is the primary driver for the LCA Commons. Currently, agencies can develop LCA models with deep specificity in areas of mission alignment. For example, the National Energy Technology Lab has experience developing fossil fuel pathways and carbon dioxide capture, storage, and utilization technologies. Argonne National Lab specializes in transportation, and US EPA collects emissions data and has expertise in impact modeling. Connecting LCA models developed in a distributed way into an integrated dataset brings the government's broad expertise together into a form usable for decision support. However, combining distributed datasets created for different applications into a single framework requires aligning basic LCA concepts and definitions, as well as assuring specific data goal and scope requirements are compatible. Firstly, no consensus definitions currently exist for the terms 'data', 'LCA data', or 'LCI data' within the LCA community. Practitioners commonly use the term 'LCA data' for complex information products representing quantitative information and a wide range of metadata describing the preparation of the data, and the goal and scope of the study the data were developed to support. For this discussion paper, we define 'LCI data' as: the information necessary to reproduce an LCI. This definition comes from the interest of the LCA Commons community to correctly reuse the models that produce LCI results as a source of secondary information for LCA. An LCI typically represents industrial or economic activities at the process or firm level. They include information products that take measured data from facilities and create a materials balance at a sufficient level of aggregation for an LCA study. This aggregation involves the application of engineering principles, LCA-specific value choices and assumptions, and direct input from subject matter experts. No specification exists for the necessary and sufficient information to reproduce an LCI, or method to determine an LCI data's fitness-for-purpose for new applications beyond its original intention. Increasing demand of reusing LCA results for new studies, and for the emergence of open data as a best practice for the scientific and information communities shines a light on this lack of specification. The LCA community made several efforts to formalize the information necessary to support data sharing and reuse. These efforts include the EcoSpold format developed by EcoInvent [9], the ILCD format developed by the European Commission Joint Research Council [10], and the openLCA JSON-LD data model developed by GreenDelta [11]. These three formalisms all start with the ISO 14048 standard for documenting LCA process data, and then develop their own approach to capture the additional information necessary for sharing LCI data models. The ISO 14000 series of standards underpins LCA, and ISO 14048 describes the minimum necessary meta-information for reporting individual process data. ISO14048's stated intention is to facilitate the reporting and review of LCA information and supports information exchange, with the expectation that LCA quality and reliability review depends on process information. ISO 14048 was not expressly intended to support the interpretation of LCI information for reuse for new applications. ISO 14048 requires documentation for a general description of the process, the sources of quantitative inputs into an LCI model, information on data collection, aggregation, and cut-off criteria, and how well a particular model supports the study's stated goal. ISO also requires other types of reporting metadata, such as identifying third party reviewers and sources of project funding. Although the LCA community considers the unit process as the foundational component of LCA data, the ISO specification does not require enough information to make it reusable.