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Sustainable Catalyst Research Lab: Getting Started

Start using Sustainable Catalyst Research Lab with a controlled example.

ProductSustainable Catalyst Research LabVerified version0.36.1ComponentCalibration and Validation, Dataset Registry, Design of Experiments, Distributed Compute, Evidence and Provenance, Experiment Framework, External Discovery, Instrumentation, Method Review, Numerical Solvers, Offline Field Work, Parameter Studies, Persistent Queue, Projects and Workspaces, Publication Packages, Python Compute Core, Reproducible Runs, Scientific Calculators, Visualization, Workflow OrchestrationUpdatedJuly 17, 2026Reading time18 minutes
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Sustainable Catalyst Research Lab · Getting Started

Governed scientific calculators, computation, datasets, runs, provenance, methods, experiments, calibration, queues, instruments, and publication packages.

Audience: product users, administrators, reviewers, and integrators.

Last verified version: 0.36.1

Reading time: 18–30 minutes

Status: Published support documentation.

Purpose

This guide provides a safe first run of Sustainable Catalyst Research Lab. It explains the product role, the main feature surface, the synthetic demonstration dataset, how to recognize a valid result, and when human or expert review is required.

Capability overview

  • Scientific Calculators
  • Python Compute Core
  • Numerical Solvers
  • Visualization
  • Projects and Workspaces
  • Dataset Registry
  • Reproducible Runs
  • Evidence and Provenance
  • Method Review
  • External Discovery
  • Experiment Framework
  • Parameter Studies
  • Design of Experiments
  • Calibration and Validation
  • Distributed Compute
  • Persistent Queue
  • Workflow Orchestration
  • Instrumentation
  • Offline Field Work
  • Publication Packages

Prerequisites

  • A modern supported browser or runtime
  • The current installed product and service version
  • A new demonstration workspace
  • No confidential, personal, credential, or production information
  • A named reviewer for consequential output

Sample data

This article uses a fictional Resilient Community Retrofit Program. The files contain one demonstration row for every documented feature, so each capability can be tested without private or production data.

Download CSV feature examples · Download JSON workspace example

Preview

# Feature Sample action Expected result Classification
1 Scientific Calculators calculate a heat-transfer coefficient with units a method-labeled result synthetic-demo
2 Python Compute Core run summary statistics through the supported backend a typed compute response synthetic-demo
3 Numerical Solvers fit a simple energy model and inspect convergence solver output and diagnostics synthetic-demo
4 Visualization plot measured versus predicted energy use a figure tied to dataset and run IDs synthetic-demo
5 Projects and Workspaces create LAB-RETROFIT-001 a persistent research workspace synthetic-demo

The downloadable files include all 20 feature rows.

Procedure

  1. Open the product from its canonical Sustainable Catalyst page or administrative route.
  2. Confirm the product and backend version before creating data.
  3. Create a synthetic workspace named RESEARCH-LAB-RETROFIT-001.
  4. Download the CSV and JSON examples supplied below.
  5. Use the feature matrix to complete the first six feature checkpoints.
  6. Save, close, and reopen the workspace to verify persistence.
  7. Inspect warnings, sources, units, permissions, and lifecycle state.
  8. Export or hand off only after completing the verification checklist.

1. Scientific Calculators

Action: calculate a heat-transfer coefficient with units.

Expected result: a method-labeled result.

2. Python Compute Core

Action: run summary statistics through the supported backend.

Expected result: a typed compute response.

3. Numerical Solvers

Action: fit a simple energy model and inspect convergence.

Expected result: solver output and diagnostics.

4. Visualization

Action: plot measured versus predicted energy use.

Expected result: a figure tied to dataset and run IDs.

5. Projects and Workspaces

Action: create LAB-RETROFIT-001.

Expected result: a persistent research workspace.

6. Dataset Registry

Action: register monthly meter data with schema, source, license, and units.

Expected result: a versioned dataset record.

Expected output

  • a method-labeled result
  • a typed compute response
  • solver output and diagnostics
  • a figure tied to dataset and run IDs
  • a persistent research workspace
  • a versioned dataset record
  • A saved record that reopens without data loss
  • An export or handoff that identifies product, version, artifact, source, and review state

Verification

  • Every sample value remains attached to its identifier and classification.
  • Warnings remain visible and are not silently treated as approval.
  • The saved workspace reopens in a clean session.
  • The product and version shown in output match the installed release.
  • Another reviewer can understand how the output was produced.

Responsible use

The Lab supports research and teaching, not unsupported scientific, engineering, clinical, or regulatory claims.

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