> For the complete documentation index, see [llms.txt](https://docs.tracenable.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tracenable.com/platform/key-concepts.md). # Key Concepts ## Company Universe A **company universe** is the set of companies you select for analysis or export. * It can be built interactively in the [Company Screener](/platform/overview.md#company-screener), or defined through a reference list you share with us. That list can be an existing index (e.g., MSCI World, S\&P 500, DAX 40) or any custom list containing company- or security-level identifiers (e.g., ISINs, SEDOLs, FIGIs, tickers). * Once defined, the same universe can be reused across datasets and exports. * Universes give you control over *which companies* you are analyzing, whether it’s a small peer group or the entire Tracenable coverage. *** ## Dimensions and Metrics Tracenable datasets follow a dimensional model. * **Dimensions** are the ways you can analyze or slice the data. Each dataset is defined by one or more dimensions. * **Metrics** are the most granular layer: each one represents a unique combination of dimension values that defines a specific data point. **Example: GHG Emissions Dataset** The GHG dataset is structured around three dimensions: * **Level:** Total / Categories * **Scope:** Scope 1 / Scope 2 / Scope 3 * **Type:** Absolute / Revenue Intensity A metric is created by setting one value for each dimension. For example: * **Total Scope 1 (Absolute):** Level = Total • Scope = Scope 1 • Type = Absolute * **Scope 3 by Category (Absolute):** Level = Categories • Scope = Scope 3 • Type = Absolute (this yields values across the 15 Scope 3 categories reported by the company). The dimensional model is a transparent way to structure data: it removes ambiguity in naming and makes metric definitions predictable. --- # 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 dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.tracenable.com/platform/key-concepts.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. 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.