> For the complete documentation index, see [llms.txt](https://docs.scicrunch.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.scicrunch.io/terminology-services/interlex.md). # InterLex ## Introduction InterLex () is a dynamic lexicon, initially built on the foundation of NeuroLex ([PMID: 24009581](https://pubmed.ncbi.nlm.nih.gov/24009581/)), of biomedical terms and common data elements designed to help improve the way that biomedical scientists communicate about their data, so that information systems like the Neuroscience Information Framework (neuinfo.org) and the the Stimulating Peripheral Activity to Relieve Conditions (SPARC) can find data more easily and provide more powerful means of integrating data across distributed resources and datasets. One of the challenges for data integration and FAIR data (Findable, Accessible, Interoperable and Reusable) is the inconsistent use of terminology and data elements. InterLex allows for the association of data fields and data values to common data elements and terminologies enabling the crowdsourcing of data-terminology mappings within and across communities. InterLex provides a stable layer on top of the many other existing terminologies, lexicons, ontologies, and common data element collections and provides a set of inter-lexical and inter-data-lexical mappings. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.scicrunch.io/terminology-services/interlex.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.