> ## Documentation Index > Fetch the complete documentation index at: https://docs.atomscale.ai/llms.txt > Use this file to discover all available pages before exploring further. # Analyze > Compare runs, track active growths, and extract process understanding from your data Once your data is connected to Atomscale, the next step is analyzing it for information. The Analyze step turns raw process data into actionable insights by answering two core questions: 1. **How is this run the same or different from previous runs?** Compare any run against your full process history using learned similarity embeddings, instead of parameter-by-parameter matching. 2. **How uniform is this run internally?** Assess consistency within a single run, from whole-recipe sequences down to individual atomic layers and segment-to-segment variation. Everything in this section feeds directly into the [Act](/platform/get-started/act) step, where the metrics, comparisons, and similarity scores here power real-time alerts, anomaly detection, and process control. ## Exploring Run Similarity Comparing successful and failed runs is one of the most challenging tasks in process engineering. ***Run-to-run similarity*** uses learned embeddings to compare runs across their full process signatures, surfacing relationships that individual parameters wouldn't reveal. ### How It Works In the Explore Similarity page, choose a metric, transformation, and time scale window for the similarity calculation. These settings control what aspects of the process signature are used for comparison. A 2D scatter plot shows your data items positioned by similarity. Runs that were similar cluster together, while runs that diverged are far apart. Points are colored by the selected metric value. Click any point and select View Similar Data to see ranked matches with similarity scores. Filter results by project, physical sample, or tags to focus your comparison. ### When to Use This Run-to-run similarity is useful when you need to diagnose an unexpected result. Instead of manually comparing numerous parameter logs, you can start from a problem run and immediately find its closest matches, both good and bad, to isolate what changed. It's also valuable for process transfer: when bringing up a recipe on a new tool, similarity analysis shows you how closely the new tool's behavior matches the original across the full process fingerprint rather than just a handful of setpoints. ## Real-Time Monitoring During a live run, the **Monitor** page lets you watch a growth unfold in real time. This makes analysis actionable by following the process as it happens instead of waiting for the run to finish. ### Starting a Session On the Monitor page, click Monitor new growth and pick an entry point: capture a screen or window from your browser, or stream programmatically via the Atomscale Python SDK. Once the stream starts, the live session view opens automatically. RHEED video, growth metrics, and tool state update continuously as data arrives. ### Reading the Session The live session view shows three synchronized panels: * **RHEED Video**: the live diffraction stream, with one panel per azimuth. * **Growth Metrics**: time series of derived RHEED metrics as the growth progresses. This shows you the specific metrics that are changing. * **Tool State**: instrument parameter logs during the growth, providing context for changes in the growth metrics. During active streaming, all panels refresh automatically. A latency indicator shows how current the displayed data is. See the [Monitoring reference](/platform/reference/monitoring/index) for a full walkthrough of the Monitor page. ## Assess Within-Run Uniformity **Explore Similarity** can be used with segments of a single run to assess how consistent it is internally, identifying variation between segments, layers, or across different regions of the process timeline. This catches issues that run-to-run analysis alone can miss: a growth that looks acceptable overall but has early-stage drift, mid-run excursions, or layer-to-layer inconsistency that will affect device performance. ## Visualize and Explore Whenever you open a data item, physical sample, or project, Atomscale displays interactive charts for the associated data. These charts show both raw tool parameters and Atomscale's derived metrics all synchronized on the same timeline. Open chart settings to choose which series to display. Series are organized by data stream (RHEED, optical, metrology, etc.), then by series type (e.g., specular intensity, lattice spacing), then by individual data item. Toggle series individually or in bulk. Right-click a chart to add or remove additional charts, each with independent axis configuration. Configure the x-axis type per data stream (e.g., timestamp, relative time, frame number). Tooltips and zoom synchronize across charts that share the same x-axis type, so you can correlate events across different data streams. Series from live data sources update automatically. During active streaming, charts refresh continuously to show incoming data. The same derived metrics powering real-time monitoring and alerting are visible here in their raw time-series form. ## Next Steps The analysis capabilities on this page (similarity scoring, uniformity assessment, and real-time monitoring) are the foundation for everything in the Act step. The same models that let you compare runs historically also power real-time anomaly detection and alerting. Set up alerts, respond to anomalies, and close the loop with process control. Technical details on how similarity, anomaly detection, and tool state workflows work under the hood.