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Identify Potential Data Sources - From spreadsheets to external data repositories, there are a variety of potential data sources that can feed into your VIVO instance. See VIVO Data - what and from where. You’ll need to identify which data types and data sources are best aligned with the overall goals for your implementation. See Policy and planning question questions for VIVO data. It can take some time to evaluate the data content and quality in order to forecast your ingest strategy. See Data source specifications for implementation and Design of PubmedFetch.
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Map Data To Ontologies -The value of the semantic web lies within the ability to define data using widely accepted ontologies. See How to plan data ingest for VIVO. Understanding your data in order to accurately map to the VIVO-ISF ontology is an important step. See VIVO-ISF Ontology. Mapping to an ontology begins as a conceptual design process on a piece of paper or using a diagramming tool such as Vue. Your final data mapping will occur using a tool such as Karma. For more details about using Karma to map your data and produce VIVO compliant semantic web data please visit the Using Karma data integration tool page. Your mapping strategy will not only affect the searchability of your data, but also how easily it can be aggregated and reused by other systems.
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Add New Data & Sources - Data curation is an ongoing task and including new data types or new data sources will most likely be an aspect of maintaining your VIVO instance. You may find that your initial implementation draws more interest from owners of data repositories you hadn’t considered. You may want to prioritize new data sources based on the data quality and volume, as well as the number of new ontology mappings required. As an example, see Activating the ORCID integration.