> 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 Tracenable uses a few core concepts throughout the platform. Understanding them will help you unlock the platform’s full potential. ## 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` 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 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.