> For the complete documentation index, see [llms.txt](https://gitbook-docs.coinmetrics.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://gitbook-docs.coinmetrics.io/reference-data/methodologies/datonomy-methodology.md). # datonomy Methodology datonomy is a digital asset classification system developed by Coin Metrics, Goldman Sachs, and MSCI. MSCI administers it, which means MSCI owns this methodology and is responsible for approving every classification and every change to the structure. Coin Metrics contributes the market data behind the eligibility screening and serves the resulting classifications through the API. This document is the authoritative statement of how the classification works. Where these documentation pages summarize a rule, the summary is a convenience and this document governs. ## What the methodology covers * **Philosophy and objectives.** The principles the classification is built on, including why assets are grouped by context of use rather than by technical architecture, and an explicit statement of what datonomy is and is not intended for. * **Structure overview.** The three level hierarchy of classes, sectors, and subsectors, and the positional six-digit coding system behind it. * **Asset eligibility.** The accessibility thresholds an asset must meet to be classified and the lower thresholds it must keep meeting to stay classified, plus the screening a venue must pass to count toward those thresholds. * **Classification guidelines.** The sources consulted when classifying an asset, the rule that observed usage takes precedence over stated intent where the two conflict, and the review process for new assets, reclassifications, and removals. * **Structure review and governance.** How often the structure itself is reviewed, the role of the advisory board, and the public consultation process that material changes go through. * **Definitions.** The definition of each hierarchy level and of every class, sector, and subsector. This is the section to consult for what a specific category means. ## The document {% embed url="" %} Guiding Principles and Methodology for Datonomy, November 2022 {% endembed %} ## Related * [datonomy Overview](/reference-data/datonomy-overview.md): the classification structure, the full subsector hierarchy, and the eligibility rules in summary. * [Asset Taxonomy](/reference-data/datonomy-overview/asset-taxonomy.md): the classification of each covered asset, served by the API. * [Asset Taxonomy Metadata](/reference-data/datonomy-overview/asset-taxonomy-metadata.md): the class, sector, and subsector structure for a given taxonomy version. * [Talos Exchange Scorecard](/market-data/methodologies/talos-exchange-scorecard.md): the exchange evaluation behind the venue screening this methodology relies on. --- # 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://gitbook-docs.coinmetrics.io/reference-data/methodologies/datonomy-methodology.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.