Data Library Guide
This guide is designed to help users navigate the Carbon Arc dataset library—a curated catalog of data products powering the Platform. It outlines how to evaluate datasets using a standardized specification system that surfaces critical attributes such as panel size, geographic coverage, data frequency, and supported use cases.
Whether you're exploring data for investment research, demand forecasting, or campaign optimization, this guide will help you read the Data Library and determine the datasets best suited to your goals, timeline, and budget. The dataset library itself is built for speed and usability, with a streamlined interface, flexible filters, and information dense dataset pages that provide transparency into schema, metrics, and available delivery methods—ensuring you can move from discovery to decision with confidence.
Video Walkthrough
Dataset Display
Datasets are displayed with structured metadata fields to enable rapid comparison. Each entry contains key specifications:
- Name — Title that reflects the dataset’s type and insight focus
- Dataset ID — Unique identifier for API queries and support references
- Description — Overview of methodology, scope, and workflow positioning
- Price — Per-megabyte cost model for budget planning and cost control
- History — Time span of available data for trend analysis
- Frequency — How often data is updated (e.g., weekly, monthly, historic)
- Lag — Time delay between real-world event and dataset update
- Last Update — Timestamp of the most recent data refresh
Sorting the Library
Datasets can be sorted using arrows beside each column header:
- ↑ Ascending order (e.g., earliest to latest)
- ↓ Descending order (e.g., latest to earliest)
Use sorting to compare timelines, pricing, or refresh intervals. Example: Sort by History to see which datasets offer the longest backfill coverage.
Filtering the Library
Use the left sidebar to filter by key criteria:
- Delivery Method — Table or Graph
- Ecosystem — Thematic grouping of datasets
- Allocation — Intended workflow domain
- Type — E.g., transactional, behavioral, structural
- Subject — Topic of analysis (e.g., employment, spend, brand)
You can also use the search bar to find datasets by keyword.
Understanding Dataset Pages
Clicking on a dataset opens a detailed view with the following sections:
Overview
Provides the context, coverage, and primary questions the dataset answers. This anchors the dataset in your workflow.
Key Metrics
Lists the KPIs or fields you’ll extract. These define what you can measure and analyze.
Other Product Highlights
Explains the level of aggregation, granularity, and refresh cadence. Clarifies precision and use case alignment.
Additional Information
Identifies strong-performing categories, sectors, or brands where the dataset provides exceptional signal.
Example Use Cases
Shows real-world scenarios and decisions enabled by the dataset, helping you validate its value for your organization.
Schema
Detailed structure and field descriptions.
Dataset Specification Reference
These terms appear consistently in both table and detail views:
| Field | Definition |
|---|---|
| Dataset ID | Unique identifier for ordering or API access |
| Pricing | Cost per megabyte to access dataset |
| History | Timeframe covered by the data |
| Frequency | Refresh schedule (e.g., weekly, historic, monthly) |
| Delivery | File format or streaming method |
| Panel Size | Number of entities or observations in dataset sample |
| Coverage | Scope of brands, categories, or geographies included |
| Bias | Known geographic, demographic, or sample collection skew |
| Lag | Time delay between real-world activity and dataset refresh |
| Geographic Availability | Countries, regions, or zip codes covered |
Build with Data
Use the “Build with Data” icon to jump into Carbon Arc Builder with this dataset preloaded. Builder provides structured exploration, insight creation, and export tools tied to the dataset’s ontology.
Summary
Carbon Arc’s data library makes it easier to find the right dataset, compare options, and activate high-quality insights. Whether you’re filtering for coverage or evaluating cost vs. history, the goal is simple: help you choose with clarity.