## 4 Discussion  
Several lessons from developing a community of practice around sharing data and information, and building a workflow to support the community, informs the LCA Commons approach. First, when preparing LCI data for reuse, the data developer must directly contribute to documenting and formatting the data. A person not intimately familiar with how and why the data were developed cannot perform the work to a sufficient level. Second, the LCA modeling tools used to perform the original assessments must be integrated into whatever tools and workflows are necessary to prepare data for access and reuse. Third, data curation for sharing and reuse is an expensive investment. Having a clear use case and business model to support the effort is important. NAL has performed some 'third party' data curation work, transforming LCI information from spreadsheets and reports into openLCA ready LCI datasets. This curation required working intimately with the developers of the data to supply NAL with sufficient information to conform to the openLCA data structure. The amount of interpersonal communication and the opportunity for introducing errors made it clear that involving an external documentary to generate metadata and complete reporting formats proved highly inefficient and expensive. The researcher developing the models must document process-specific information for an LCA. The contexts for the data development result in specific documentation and metainformation for LCI information. Typically, only the data developer knows the information regarding the sources for underlying observational data, how the observations were processed and aggregated, and information on assumptions and cut-offs. The data developer or the project manager knows why this data was developed, what scope of study it was developed for, and how the final data products were combined and applied to a specific research question. A person not involved in the development of the data and the LCA for which the data was developed cannot produce sufficient metadata and information due to this high level of detail for data verification and reporting. In fact, the researchers themselves may not even be able to provide enough information to engender re-use if they do not make data publication a priority during goal and scope definition. The researcher must see data publication and re-use as worthy enough an objective to ensure the models are well documented from the beginning. The LCA information model presents complexities, with important information contained in the relationships between data objects, as well as in the documentation. Data reuse and interoperability depend on the references between processes and datasets in addition to the scope and representation of individual datasets. Stand-alone data curation and documentation tools make re-creating and verifying this relational information difficult. NAL engaged in several efforts to produce data exchange files in ILCD xml, EcoSpold V2, and JSON-LD using spreadsheets and separate desktop applications. The learning curve for using these separate tools added inefficiency without significant benefit, and more critically, using these tools divorced the data from their intended application as datasets in LCA models. Integrating data curation and preparation into openLCA gave researchers a way to prepare data for publication without learning a new tool, and allowed important insight into how the data would appear to others reusing the information. Furthermore, using webservices to transfer the data between the researcher and the remote repository increased efficiency, reduced data loss, and allowed NAL to incorporate other valuable tools, such as version control and publication workflows, directly into the modeling software [12]. Often, the best time to prepare data for reuse is when the data is being originally developed. This is when the primary engineers or scientists are actively engaged in preparing the data for the LCA study.